modified: TrackRecon.C

new folder:   cross_section, this folder allows for cross section calculations for the ANASEN data, a readme and some example files are provided
	modified:   run_tr.sh

TrackRecon.C

@@ -24,6 +24,8 @@ Int_t colors[40] = {
 #include <TRandom3.h>
 #include <TSpline.h>
 #include <TSystem.h> // gSystem->mkdir for the pc_calib_raw/ output directory
+#include <TCutG.h>   // p/alpha graphical cuts (classifySiPc)
+#include <TFile.h>
 
 #include <cmath> // std::floor/fabs/lround for vertexZSliceTag()
 #include <fstream>
@@ -102,29 +104,39 @@ inline bool siPcCoincident(double t_si, double t_pc)
   return dt > kSiPcDtMin && dt < kSiPcDtMax;
 }
 
-// PC anode dE gate, gas-region proton/alpha separation for the p(a,a)p elastic
-// branches (protonAlphaElastic_core, formerly protonMiscHistograms /
-// protonMiscHistograms_sx3). Rough,
-// energy-independent threshold read off Calib_dE_AnodeE_vs_QQQE (calibrated
-// anode dE, MeV, vs QQQ/SX3 E): proton and alpha loci are well separated below
-// ~5-6 MeV Si energy but converge at high Si energy/high dE (the bright
-// corner near the top of the plot) -- events there are NOT reliably
-// classified by a flat cut. Revisit with a TCutG/polygon if that corner
-// matters. anodeE_MeV < 0 means no valid per-wire calibration was available
-// for this hit, not "low dE" -- kUnknown must be handled as its own case,
-// never silently folded into kProton.
 constexpr double kProtonAlphaAnodeDeGate_MeV = 0.045;
+// |Ex| window that counts a p + 4He hit as elastic, for the _exgate vertex-z / theta_cm histograms.
+constexpr double kElasticExGate_MeV = 1.0;
 enum class SiPcPid
 {
   kUnknown = -1,
   kProton = 0,
-  kAlpha = 1
+  kAlpha = 1,
+  kMaybeProton = 2 // graphical cuts only: the proton band below E_Si 5 MeV
 };
-inline SiPcPid classifyByAnodeDe(double anodeE_MeV)
+struct PidCuts
+{
+  TCutG *alpha = nullptr;
+  TCutG *proton = nullptr;
+  TCutG *maybep = nullptr;
+};
+PidCuts pidCutsQQQ, pidCutsSX3;
+bool pidUseCuts = false; // true once Begin() has found all six pid_* cuts in CUTLIST
+
+inline SiPcPid classifySiPc(double siE_MeV, double anodeE_MeV, bool isQQQ)
 {
   if (anodeE_MeV < 0.0)
     return SiPcPid::kUnknown;
-  return (anodeE_MeV < kProtonAlphaAnodeDeGate_MeV) ? SiPcPid::kProton : SiPcPid::kAlpha;
+  if (!pidUseCuts)
+    return (anodeE_MeV < kProtonAlphaAnodeDeGate_MeV) ? SiPcPid::kProton : SiPcPid::kAlpha;
+  const PidCuts &cuts = isQQQ ? pidCutsQQQ : pidCutsSX3;
+  if (cuts.alpha->IsInside(siE_MeV, anodeE_MeV))
+    return SiPcPid::kAlpha;
+  if (cuts.proton->IsInside(siE_MeV, anodeE_MeV))
+    return SiPcPid::kProton;
+  if (cuts.maybep->IsInside(siE_MeV, anodeE_MeV))
+    return SiPcPid::kMaybeProton;
+  return SiPcPid::kUnknown;
 }
 
 inline TVector3 beamAxisOrigin()
@@ -588,7 +600,39 @@ void TrackRecon::Begin(TTree * /*tree*/)
       std::cerr << "CUTLIST=" << getenv("CUTLIST")
                 << " set but no cut named 'simcprfgate2' found in it -- Gate2 Si-MCP/RF gating disabled" << std::endl;
     }
+
+    const char *pidNames[6] = {"pid_alpha_qqq", "pid_proton_qqq", "pid_maybep_qqq",
+                               "pid_alpha_sx3", "pid_proton_sx3", "pid_maybep_sx3"};
+    TCutG *pidFound[6] = {};
+    int pidListed = 0;
+    for (int i = 0; i < 6; ++i)
+    {
+      try
+      {
+        pidFound[i] = plotter->FindCut(pidNames[i]);
+        ++pidListed;
+      }
+      catch (const std::out_of_range &)
+      {
+      }
+    }
+    if (pidListed > 0)
+    {
+      if (pidListed < 6 || std::find(pidFound, pidFound + 6, nullptr) != pidFound + 6)
+      {
+        std::cerr << "CUTLIST=" << getenv("CUTLIST") << ": needs all six pid_{alpha,proton,maybep}_{qqq,sx3}, each a "
+                  << "file with a TCutG named CUTG -- stopping" << std::endl;
+        gSystem->Exit(1);
+      }
+      pidCutsQQQ = {pidFound[0], pidFound[1], pidFound[2]};
+      pidCutsSX3 = {pidFound[3], pidFound[4], pidFound[5]};
+      pidUseCuts = true;
+    }
   }
+  if (pidUseCuts)
+    std::cout << "p/alpha PID: graphical cuts pid_{alpha,proton,maybep}_{qqq,sx3} from CUTLIST=" << getenv("CUTLIST") << std::endl;
+  else
+    std::cout << "p/alpha PID: flat anode gate " << kProtonAlphaAnodeDeGate_MeV << " MeV (no pid_* cuts in CUTLIST)" << std::endl;
 
   if (getenv("reactiondata"))
   {
@@ -1084,9 +1128,8 @@ inline void pcEnergyCalibrationAccumulateProton(const std::vector<Event> &PC_Eve
     if (!(pcevent.multi1 >= 1 && pcevent.multi2 >= 1))
       return;
     if (!(pcevent.Energy2 > 1400)) // cathode-tagged alpha; intentionally NOT anode-dE
-      return;                      // PID (classifyByAnodeDe) -- this function derives the
+      return;
-                                   // anode calibration those thresholds depend on, so it
+
-                                   // needs a selection that doesn't presuppose it.
     if (TMath::Abs(sievent.pos.DeltaPhi(pcevent.pos)) > phi_win)
       return;
 
@@ -4158,7 +4201,9 @@ void protonAlphaElastic_core(HistPlotter *plotter, const std::vector<Event> &Si_
       {
         // const std::string ejtag = (alphaHyp && sievent.Energy1 < 5) ? "_a" : "_p";
         std::string ejtag;
-        if (alphaHyp && sievent.Energy1 < 5)
+        if (pidUseCuts)
+          ejtag = alphaHyp ? "_a" : (pid == SiPcPid::kMaybeProton ? "_maybep" : "_p");
+        else if (alphaHyp && sievent.Energy1 < 5)
           ejtag = "_a";
         else if (sievent.Energy1 >= 5.0 && sievent.Energy1 < 7.0)
           ejtag = "_p";
@@ -4185,6 +4230,25 @@ void protonAlphaElastic_core(HistPlotter *plotter, const std::vector<Event> &Si_
         plotter->Fill2D(rx + "_Ex_vs_X" + ejtag + sfx, 100, -100, 120, 800, -6, 15, sievent.pos.X(), Ex, pmlabel);
         plotter->Fill2D(rx + "_Ex_vs_Y" + ejtag + sfx, 100, -100, 120, 800, -6, 15, sievent.pos.Y(), Ex, pmlabel);
 
+        {
+          const double thetaCM = alphaHyp ? 180.0 - 2.0 * theta * 180 / M_PI
+                                          : (theta + TMath::ASin(TMath::Sin(theta) * mass_1H / mass_4He)) * 180 / M_PI;
+          const bool exGate = TMath::Abs(Ex) < kElasticExGate_MeV;
+          auto fillZTheta = [&](const std::string &variant)
+          {
+            plotter->Fill2D(rx + "_VertexZ_vs_ThetaCM" + variant + ejtag + sfx, 800, -400, 400, 180, 0, 180, vertex_z, thetaCM, pmlabel);
+          };
+          fillZTheta("");
+          if (exGate)
+            fillZTheta("_exgate");
+          if (hasCathode)
+          {
+            fillZTheta("_cath");
+            if (exGate)
+              fillZTheta("_cath_exgate");
+          }
+        }
+
         if (isQQQ)
         {
           const std::string qtag = "_q" + std::to_string(sievent.ch1 / 16);
@@ -4276,10 +4340,23 @@ void protonAlphaElastic_core(HistPlotter *plotter, const std::vector<Event> &Si_
         }
       };
 
-      if (pid != SiPcPid::kAlpha)
+      if (pidUseCuts)
-        fillHypothesis(false); // proton, or PID unavailable (legacy default)
+      {
-      if (pid == SiPcPid::kAlpha && sievent.Energy1 < 6.2)
+        // Graphical cuts: each hit as what the cuts call it (p and maybe-p both with proton kinematics, filed apart);
-        fillHypothesis(true);
+        // kUnknown (in no gate, or no anode calibration) is filled as nothing -- counted in elastic_pidUnknown.
+        if (pid == SiPcPid::kProton || pid == SiPcPid::kMaybeProton)
+          fillHypothesis(false);
+        else if (pid == SiPcPid::kAlpha)
+          fillHypothesis(true);
+      }
+      else
+      {
+        // Old flat gate, kept as it was: kUnknown is filled as a proton (legacy default).
+        if (pid != SiPcPid::kAlpha)
+          fillHypothesis(false);
+        if (pid == SiPcPid::kAlpha && sievent.Energy1 < 6.2)
+          fillHypothesis(true);
+      }
     };
 
     // --- a1c1/a1c2 (multi1==1) and a2c1/a2c2 (multi1==2), from PC_Events. The
@@ -4306,7 +4383,7 @@ void protonAlphaElastic_core(HistPlotter *plotter, const std::vector<Event> &Si_
         plotter->Fill1D(rx + "_dt_Cathode" + sfx, 600, -2000, 2000, pcevent.Time2 - sievent.Time1, misclabel);
 
       double anodeE_MeV = (pcevent.Anodech >= 0 && pcevent.Anodech < 24) ? pcevent.Energy1MeV : -1.0;
-      SiPcPid pid = classifyByAnodeDe(anodeE_MeV);
+      SiPcPid pid = classifySiPc(sievent.Energy1, anodeE_MeV, isQQQ);
       if (pid == SiPcPid::kUnknown)
         plotter->Fill1D(rx + "_pidUnknown" + sfx, 2, 0, 2, 1.0, misclabel);
 
@@ -4448,7 +4525,7 @@ void protonAlphaElastic_core(HistPlotter *plotter, const std::vector<Event> &Si_
         if (!anyValidWire)
           anodeE_MeV_a0 = -1.0;
 
-        SiPcPid pid = classifyByAnodeDe(anodeE_MeV_a0);
+        SiPcPid pid = classifySiPc(sievent.Energy1, anodeE_MeV_a0, isQQQ);
         if (pid == SiPcPid::kUnknown)
           plotter->Fill1D(rx + "_pidUnknown" + sfx, 2, 0, 2, 1.0, misclabel);
 

cross_section/README.md

@@ -0,0 +1,197 @@
+# Cross-section pipeline (ANASEN active target)
+
+This folder turns data into a cross section for any reaction in the ANASEN active target. There are three steps,
+one script each, and one config file per study shared by all three:
+
+```
+root -l -b -q 'extract_yields.C+("examples/27Al_ap_runA.toml")'           # 1. ROOT -> yields_<det>.csv
+~/myenv/bin/python -B geometric_efficiency.py examples/27Al_ap_runA.toml   # 2. detectors.py -> eps_<det>.csv  ~10 s
+~/myenv/bin/python -B cross_section.py        examples/27Al_ap_runA.toml   # 3. -> xs_<det>.csv, xs.png      ~3 s
+```
+
+Run them in WSL from this folder (`~/ANASEN_analysis/cross_section`). Step 1 needs ROOT; steps 2 and 3 need
+`~/myenv/bin/python`, not ROOT.
+
+| file | what it does |
+|---|---|
+| `extract_yields.C` | **Step 1, the only step that reads the data.** It reads a TH1/TH2, or fills a histogram from a TTree with a cut, and writes a plain CSV histogram. |
+| `geometric_efficiency.py` | Step 2: geometric efficiency per bin, a toy Monte Carlo of the reaction, with every hit rule taken from `detectors.py`. |
+| `detectors.py` | **The detectors as they are now** (Run A): QQQ3, the four analysed SX3s, the counter, the beam tube and the PCB hole, plus the hit rules. To change the geometry, edit this file. |
+| `cross_section.py` | Step 3: reads only CSVs (the yields and an efficiency) and computes σ(E_cm) or dσ/dΩ(θ_cm). |
+| `common.py` | What the three scripts share: the config, the beam track E(z), the bins. |
+| `examples/` | One commented config per study: `27Al_ap_runA.toml` and `paap_ta_runs_hist.toml`. |
+| `examples/reference/` | Barnard 1964 (EXFOR A1113) for p + ⁴He. |
+| `inputs/` | The beam track (`beam_tracks.csv`) and the TALYS states (`talys_27Al_ap.csv`). The p + ⁴He track is read from `../eloss_calculations/`. |
+| `out/<study>/` | Each study's outputs: `yields_<det>.csv`, `eps_<det>.csv`, `xs_<det>.csv`, `xs.png`. |
+
+## Starting a new study
+
+1. Copy the closest example in `examples/` (excitation function: `27Al_ap_runA.toml`; angular distribution:
+   `paap_ta_runs_hist.toml`) and give it its own `out_dir`.
+2. Fill in `[extract]` for your histogram or tree, then run step 1 and look at `yields_<det>.csv`.
+3. Fill in `[reaction]`, `[beam]` and `[bins]`, then run step 2. Check that `eps_<det>.csv` is non-zero where you
+   expect events.
+4. Fill in `[target]`, `[acceptance]` and any `[[reference]]`, then run step 3.
+
+If the detectors have changed, edit `detectors.py`, not the configs.
+
+![27Al(a,p)](out/27Al_ap/xs.png)
+
+## The formula
+
+| mode | result | formula |
+|---|---|---|
+| `excitation` | σ(E_cm), mb | Y / (N_b · n · dz · ε) |
+| `angular` | dσ/dΩ(θ_cm) in one E_cm (or z) window, mb/sr | Y / (N_b · n · dz · ε · ΔΩ) |
+
+- **Y:** the yield in the bin.
+- **N_b:** the number of beam particles.
+- **n:** target atoms per cm² per mm of path.
+- **dz:** the length of beam path over which the beam's E_cm lies in the bin, read off the beam track E(z).
+- **ε:** the efficiency of the bin.
+
+## Step 1: `extract_yields.C`, data to CSV
+
+`[extract] source` says where the events are:
+- `"hist"`: a TH1 or TH2 in a ROOT file. A TH2 is projected on the axis that holds `variable`, or kept 2D with
+  `angle_axis` for the angular mode.
+- `"tree"`: a ROOT TTree (`file` may be a glob or a list of runs), filled into `bins` after `cut`.
+
+Cuts use `and` / `or` (the macro turns them into `&&` / `||`) and ROOT's TTree::Draw syntax.
+
+**Histogram paths** are `"folder/name"`.
+- **They are case-sensitive.** Production files of different vintages spell the detector differently: the 27Al
+  outputs use `_m27Alax+misc_qqq/...`, the proton runs' `Output_p/Al_protons.root` uses `elastic+misc_QQQ_a/...`.
+  Put the spelling in `[det_vars]`:
+  ```toml
+  dets = ["qqq", "sx3"]                # the names detectors.py knows
+  [det_vars.qqq]
+  DET = "QQQ"
+  [det_vars.sx3]
+  DET = "SX3"
+  [extract]
+  hist = "elastic+misc_{DET}_a/elastic_VertexReconZ_a_{DET}"
+  ```
+- **A name that itself contains `/`** (e.g. `dE3_E_AnodeQQQR_A1C2_(vertex_fix_z/100)=0.000000`) is found too.
+- **If the path doesn't exist,** the macro stops and names the closest histogram paths in the file.
+
+The CSV it writes, `<out_dir>/yields_<det>.csv`, is the hand-off between the steps. `cross_section.py` reads nothing
+else, so a yield made any other way works too as long as it is in this format:
+
+```
+1D:  <var>_lo, <var>_hi, counts, variance
+2D:  <var>_lo, <var>_hi, theta_cm_lo, theta_cm_hi, counts, variance     (angular mode)
+```
+
+`<var>` is `z` (vertex z, mm), `e_beam` (MeV) or `ecm` (MeV). The bins may be finer than the analysis bins and need
+not line up with them. Step 3 shares each one out by overlap.
+
+It is run like this:
+
+```
+root -l -b -q 'extract_yields.C+("examples/27Al_ap_runA.toml")'
+root -l -b -q 'extract_yields.C+("examples/27Al_ap_runA.toml", "out/elsewhere")'   # optional output folder
+```
+
+- **Trees:** it fills the histogram with `TTree::Draw` on a `TChain`, so `file` may be a glob or a list of runs.
+- **Cuts:** write them with `and` / `or` (e.g. `"det == 1 and e_si > 0.5"`). The macro turns them into `&&` / `||`.
+- **TOML:** the macro has a small reader that handles only what these configs use: strings, numbers, one-line lists
+  and `{start, stop, step}` tables. It stops with an error on anything else.
+- **Errors:** a missing file or tree, or a tree with no entries, stops it with exit code 1 rather than writing zeros.
+- **One edge case:** an entry exactly on a tree histogram's last upper edge goes to ROOT's overflow.
+
+## Step 2: `geometric_efficiency.py`, ε from the detector geometry
+
+The toy runs once per bin:
+- vertices uniform over the bin's path (angular mode: over the window);
+- beam energy at each vertex from the nominal track;
+- the residual state drawn from fixed weights, or from TALYS state cross sections at the bin's E_cm;
+- isotropic CM emission (angular mode: inside the θ_cm bin);
+- relativistic two-body kinematics and straight tracks.
+
+The particle the silicon sees can be the ejectile or the recoil (`detect`). θ_cm is always the ejectile's, so with
+`detect = "recoil"` the bins are still in the ejectile's CM angle. For p + ⁴He that is the proton's angle, Barnard's
+convention, which TrackRecon's `elastic_VertexZ_vs_ThetaCM` histograms also use. The output has one column per
+condition, each times the azimuthal coverage:
+
+| column | the particle... |
+|---|---|
+| `eps_si` | reaches the analysed silicon (solid angle) |
+| `eps_si_thr` | ...and has a vertex energy above the Si threshold. This is the ch5 "solid angle" convention: a kinematic cut, not geometry. |
+| `eps_counter` | `eps_si`, and it crosses the guard, anode and cathode radii inside the counter |
+| **`eps_geometric`** | `eps_counter`, and it clears the beam tube and the PCB hole. **This is the geometric efficiency.** |
+
+Each column has a Monte Carlo error, `<column>_rel_err`. Gas energy loss, Si thresholds after the gas and the counter's
+deposit threshold are **not** included, because they are not geometry. That is why the adopted `eps_tube` of
+`../scratch/xs_27Al_lowE_2026-10-15` is lower than `eps_geometric`; to use it instead, see the commented
+`[acceptance]` block in the example. The angular distribution is isotropic.
+
+## Step 3: `cross_section.py`
+
+`[acceptance] source` picks ε:
+- `"geometric"`: step 2's file; `column` chooses which efficiency.
+- `"csv"`: any file, matched on a bin edge or interpolated.
+- `"value"`: one number for every bin.
+- `"none"`: ε = 1, which gives σ_app.
+
+`[[acceptance.extra]]` multiplies in further factors, such as an intrinsic efficiency. The output `xs_<det>.csv` keeps
+every intermediate number: the bins, z ranges, dz, counts, σ_app, ε, σ and its errors (stat, ε, total), and the
+references. The header lines record every input. The N_b and n uncertainties are a common scale, so the header gives
+them as a percentage and they stay out of the error columns.
+
+## The config (TOML)
+
+Paths are relative to the config file, and `~` is the home folder. `{det}` is replaced by each entry of `dets`, and
+so is every key of `[det_vars.<det>]`. Commented templates in `examples/`:
+- `27Al_ap_runA.toml`: a TH2 from the merged Run A production file, all three steps.
+- `paap_ta_runs_hist.toml`: p + ⁴He elastic **straight from a TrackRecon.C production file**, as for 27Al. It reads
+  the TH2 `elastic_VertexZ_vs_ThetaCM_cath_exgate_a_<DET>` (vertex z × proton θ_cm). `protonAlphaElastic_core` fills
+  that histogram since 2026-10-16 in every species folder, in four variants: all, `_cath`, `_exgate`, `_cath_exgate`.
+  - Input: `~/ANASEN_analysis/Output_p/results_run022.root` (run 22 alone, run_tr.sh block 5 with CUTLIST),
+    N_b = 1.35 pA × 672.65 s = 5.67 × 10⁹, vertex window z = −170 to −53 mm.
+  - On 2026-10-16 the merged runs 18–22 agreed with a per-hit route to 2–4% in QQQ3 counts and about 3% in dσ/dΩ.
+
+| section | used by | keys |
+|---|---|---|
+| top | all | `title`, `mode` (`excitation` / `angular`), `dets` (names in `detectors.py`: `qqq`, `sx3`), `out_dir` |
+| `[extract]` histogram | 1 | `source = "hist"`, `file`, `hist`, `variable`, `axis` (the axis holding `variable`), `angle_axis` (keep it 2D), `other_range` |
+| `[extract]` tree | 1 | `source = "tree"`, `file` (glob or list), `tree`, `variable`, `branch`, `bins`, `cut`, `weight_branch`, `angle_branch` + `angle_bins` |
+| `[reaction]` | 2, 3 | `beam_mass_u`, `target_mass_u`; for step 2 also `ejectile_mass_u`, `recoil_mass_u`, `detect`, and `states` *or* `states_csv`; optional `ecm_per_ebeam` |
+| `[beam]` | 2, 3 | track: `track_csv` + `track_e_col` (+ `nominal_e_col`, `track_z_col`), *or* `eloss_table` + `e_entrance_mev` + `z_entrance_mm`. N_b: `n_beam` *or* `charge_c` *or* `current_a` + `time_s`; `charge_state`, `n_beam_rel_err`. For Run A runs, `../runlog/beam_exposure.py` gives `n_beam` for any set of runs from the elog's cup readings |
+| `[bins]` | 2, 3 | `ecm_edges` (a list or `{start, stop, step}`); angular: `theta_edges_deg` and `window_ecm` *or* `window_z_mm` |
+| `[efficiency]` | 2 | `n_per_bin` (default 400 000), `seed` |
+| `[target]` | 3 | `pressure_torr`, `temperature_k`, `mole_fraction`, `atoms_per_molecule`, `rel_err`; *or* `n_per_cm2_per_mm` |
+| `[yield]` | 3 | `csv`: only to read yields from somewhere other than step 1's output |
+| `[acceptance]` | 3 | `source` and its keys (see above), `[[acceptance.extra]]` |
+| `[[reference]]` | 3 | `csv`, `x_col`, `y_col`, `label`, `scale`, `dets` |
+| `[plot]` | 3 | `yscale`, `xlim`, `ylim`, `xlabel`, `ylabel` |
+
+## Example: p + ⁴He elastic scattering, Ta run 22 (`examples/paap_ta_runs_hist.toml`)
+
+```
+root -l -b -q 'extract_yields.C+("examples/paap_ta_runs_hist.toml")'
+~/myenv/bin/python -B geometric_efficiency.py examples/paap_ta_runs_hist.toml
+~/myenv/bin/python -B cross_section.py        examples/paap_ta_runs_hist.toml
+```
+
+dσ/dΩ(θ_cm) from the α recoils (`detect = "recoil"`, bins in the proton's θ_cm, Barnard's convention), against
+Barnard, Jones & Weil (1964). Histogram: `elastic_VertexZ_vs_ThetaCM_cath_exgate_a_<DET>` (cathode topologies,
+graphical α cut, |E_x| < 1 MeV). The merged runs 18–22 gave QQQ3 0.44–0.54 × Barnard at 132–156°; SX3 is at 1–3%.
+Not in `detectors.py`: the Ta holder rod (~28% of the azimuth downstream of each foil) and Si thresholds after the gas.
+
+## Checks (run in the Windows repo, `/mnt/c/Users/sengi/ANASEN_analysis/cross_section`; the scripts are not in this copy)
+
+- 27Al(α,p), Run A: yields, dz and nominal E_cm equal the earlier study's in every bin; ε_si_thr agrees with its
+  independent toy within the MC errors.
+- Synthetic events with a known σ, ε and dσ/dΩ are returned with unit-Gaussian pulls.
+- `extract_yields.C` and the Python version gave byte-identical yields on every example.
+
+**Not verified:** the physics inputs are whatever the config and `detectors.py` say. The checks show the arithmetic and
+the bookkeeping are right, and that `detectors.py` reproduces the geometry of `anasen_detectability.py`. They cannot
+show that the geometry, a track or an N_b is right. In particular:
+- The dE/dx scale of a `.dat` table is not written in the file. The live 250 Torr / 3% tables were regenerated on
+  2026-10-16 and match the 0.962 track to 3 keV.
+- `detectors.py` gives QQQ3 0.967 of the azimuth, but TrackRecon drops QQQ pixels without a calibration, which leaves
+  about 0.675. QQQ3 ε is therefore about 1.4× too high, and its cross sections about 1.4× too low.
+- Where only a sliver of solid angle is open, ε is tiny and σ explodes. Example: QQQ3 at E_cm 5.4 MeV, ε = 1.5 × 10⁻⁵,
+  behind the beam tube. The error bar shows it; no ε cut is applied.

cross_section/common.py

@@ -0,0 +1,183 @@
+"""Shared by extract_yields.py, geometric_efficiency.py and cross_section.py: the config, the beam track, the bins.
+
+The cross section in this active target:
+    sigma(E_cm)        = Y / (N_b * n * dz * eps)                  mode = "excitation"
+    dsigma/dOmega(th)  = Y / (N_b * n * dz * eps * dOmega)         mode = "angular"
+The beam loses energy along z, so a vertex at z means the reaction happened at the beam energy E(z) there.
+An E_cm bin is the stretch of beam path dz over which the beam's E_cm lies in the bin.
+"""
+import os
+import tomllib
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+
+# ============================================================ config
+
+def read_config(config_file):
+    """Read the TOML config. Returns the config and a function that turns paths in it into absolute paths
+    (relative paths are relative to the config file's folder)."""
+    config_file = Path(config_file).resolve()
+    with open(config_file, "rb") as f:
+        config = tomllib.load(f)
+
+    def full_path(p):
+        p = os.path.expanduser(str(p))           # "~/..." is the home folder, as in a shell
+        if p.startswith("/") or Path(p).is_absolute():
+            return p
+        return str((config_file.parent / p).resolve())
+
+    return config, full_path
+
+
+def config_for_detector(config, det):
+    """Copy of the config with "{det}" (and any key of [det_vars.<det>]) replaced in every string.
+    This lets one config serve several detectors, e.g. hist = "..._{det}"."""
+    replacements = {"det": det}
+    replacements.update(config.get("det_vars", {}).get(det, {}))
+
+    def replace(value):
+        if isinstance(value, str):
+            for key, new in replacements.items():
+                value = value.replace("{" + key + "}", str(new))
+            return value
+        if isinstance(value, dict):
+            return {k: replace(v) for k, v in value.items()}
+        if isinstance(value, list):
+            return [replace(v) for v in value]
+        return value
+
+    return replace(config)
+
+
+def output_file(config, full_path, kind, det):
+    """Where each step writes for one detector: <out_dir>/<kind>_<det>.csv, kind = yields, eps or xs."""
+    out_dir = Path(full_path(config.get("out_dir", "out")))
+    out_dir.mkdir(parents=True, exist_ok=True)
+    return out_dir / (f"{kind}_{det}.csv" if det else f"{kind}.csv")
+
+
+def make_edges(spec):
+    """Bin edges from a list [e0, e1, ...] or from {start = .., stop = .., step = ..} (stop included)."""
+    if isinstance(spec, dict):
+        n_bins = int(round((spec["stop"] - spec["start"]) / spec["step"]))
+        return spec["start"] + spec["step"] * np.arange(n_bins + 1)
+    return np.array(spec, dtype=float)
+
+
+def write_csv(table, header_lines, out_file):
+    with open(out_file, "w", newline="\n") as f:
+        for line in header_lines:
+            f.write(f"# {line}\n")
+        table.to_csv(f, index=False, float_format="%.6g")
+
+
+# ============================================================ beam track
+
+class BeamTrack:
+    """Beam energy (MeV, lab) along z (mm). The energy falls with z and is 0 past the stopping point.
+
+    `energy` is the track used to define the bins (the one the data were reconstructed with). `nominal` is an
+    optional second track (e.g. another stopping-power scale): it gives each bin's nominal E_cm and the beam
+    energy in the efficiency toy. It defaults to the same track."""
+
+    def __init__(self, z, energy, nominal=None, description=""):
+        self.z = np.asarray(z, dtype=float)
+        self.energy = np.asarray(energy, dtype=float)
+        self.nominal = self.energy if nominal is None else np.asarray(nominal, dtype=float)
+        self.description = description
+
+    def energy_at(self, z, nominal=False):
+        return np.interp(z, self.z, self.nominal if nominal else self.energy)
+
+    def z_where_energy_is(self, e):
+        """The z where the beam has energy e.
+        e <= 0 gives the stopping point; e above the entrance energy gives the entrance z."""
+        if e <= 0:
+            stopped = np.flatnonzero(self.energy <= 0)
+            return float(self.z[stopped[0]]) if len(stopped) else float(self.z[-1])
+        # np.interp needs increasing x: energy decreases along z, so interpolate in -energy
+        return float(np.interp(-e, -self.energy, self.z))
+
+
+def load_beam_track(beam, full_path):
+    """Either a CSV with columns z and E (e.g. beam_tracks.csv), or an energy-loss table from
+    eloss_calculations/ plus the beam energy at the entrance."""
+    if "track_csv" in beam:
+        table = pd.read_csv(full_path(beam["track_csv"]), comment="#")
+        z = table[beam.get("track_z_col", "z_mm")]
+        energy = table[beam["track_e_col"]]
+        nominal = table[beam["nominal_e_col"]] if "nominal_e_col" in beam else None
+        text = f"{beam['track_csv']}, column {beam['track_e_col']}"
+        if nominal is not None:
+            text += f" (nominal E_cm from {beam['nominal_e_col']})"
+        return BeamTrack(z, energy, nominal, text)
+
+    if "eloss_table" in beam:
+        # Table columns: energy (MeV), thickness (mg/cm2), distance along the path (cm)
+        table = np.loadtxt(full_path(beam["eloss_table"]), comments="#")
+        table_energy, table_cm = table[:, 0], table[:, 2]
+        e_entrance, z_entrance = float(beam["e_entrance_mev"]), float(beam["z_entrance_mm"])
+        if e_entrance > table_energy[0]:
+            raise ValueError(f"e_entrance_mev = {e_entrance} is above the table's start energy {table_energy[0]}")
+        # Where in the table the beam starts, then walk along it in 0.1 mm steps
+        start_cm = np.interp(-e_entrance, -table_energy, table_cm)
+        z = np.arange(z_entrance, beam.get("z_max_mm", 400.0) + 0.05, 0.1)
+        path_cm = start_cm + (z - z_entrance) / 10.0
+        energy = np.interp(path_cm, table_cm, table_energy, right=0.0)
+        return BeamTrack(z, energy, None, f"{beam['eloss_table']}, {e_entrance} MeV at z = {z_entrance} mm")
+
+    raise KeyError("[beam] needs either track_csv or eloss_table")
+
+
+def ecm_per_beam_energy(reaction):
+    """E_cm = E_beam x m_target / (m_beam + m_target)   (non-relativistic; or give ecm_per_ebeam directly)."""
+    if "ecm_per_ebeam" in reaction:
+        return float(reaction["ecm_per_ebeam"])
+    return reaction["target_mass_u"] / (reaction["beam_mass_u"] + reaction["target_mass_u"])
+
+
+# ============================================================ bins
+
+def energy_bins(bins, track, k):
+    """E_cm bins and, for each, the stretch of beam path [z_up, z_down] where the beam's E_cm is inside it.
+    The higher energy is upstream, so z_up belongs to the bin's top edge."""
+    ecm_edges = make_edges(bins["ecm_edges"])
+    t = pd.DataFrame({"ecm_lo_mev": ecm_edges[:-1], "ecm_hi_mev": ecm_edges[1:]})
+    t["ecm_c_mev"] = 0.5 * (t["ecm_lo_mev"] + t["ecm_hi_mev"])
+    t["z_up_mm"] = [track.z_where_energy_is(e / k) for e in t["ecm_hi_mev"]]
+    t["z_c_mm"] = [track.z_where_energy_is(e / k) for e in t["ecm_c_mev"]]
+    t["z_down_mm"] = [track.z_where_energy_is(e / k) for e in t["ecm_lo_mev"]]
+    t["dz_mm"] = t["z_down_mm"] - t["z_up_mm"]
+    t["ecm_nominal_mev"] = k * track.energy_at(t["z_c_mm"], nominal=True)
+    # True if the beam enters the chamber already inside this bin (dz then starts at the entrance)
+    t["partial_top"] = t["ecm_hi_mev"] / k > track.energy[0]
+    t["d_omega_sr"] = 1.0                    # not used in excitation mode
+    return t
+
+
+def angle_bins(bins, track, k):
+    """theta_cm bins inside one window, given in E_cm (window_ecm) or in vertex z (window_z_mm).
+    Returns the bins and the window as (ecm_lo, ecm_hi, z_up, z_down)."""
+    if "window_ecm" in bins:
+        ecm_lo, ecm_hi = bins["window_ecm"]
+        z_up, z_down = track.z_where_energy_is(ecm_hi / k), track.z_where_energy_is(ecm_lo / k)
+    else:
+        z_up, z_down = bins["window_z_mm"]
+        ecm_lo, ecm_hi = k * track.energy_at(z_down), k * track.energy_at(z_up)
+    theta_edges = make_edges(bins["theta_edges_deg"])
+    t = pd.DataFrame({"theta_lo_deg": theta_edges[:-1], "theta_hi_deg": theta_edges[1:]})
+    t["theta_c_deg"] = 0.5 * (t["theta_lo_deg"] + t["theta_hi_deg"])
+    t["d_omega_sr"] = 2 * np.pi * (np.cos(np.radians(t["theta_lo_deg"])) - np.cos(np.radians(t["theta_hi_deg"])))
+    t["z_up_mm"], t["z_down_mm"] = z_up, z_down
+    t["dz_mm"] = z_down - z_up
+    return t, (ecm_lo, ecm_hi, z_up, z_down)
+
+
+def make_bins(config, track, k):
+    """The bins of the config's mode. Returns (table, window or None)."""
+    if config.get("mode", "excitation") == "excitation":
+        return energy_bins(config["bins"], track, k), None
+    return angle_bins(config["bins"], track, k)

cross_section/cross_section.py

@@ -0,0 +1,332 @@
+"""Step 3: the cross section, from the yields CSV (step 1) and an efficiency (step 2, or any other).
+
+    ~/myenv/bin/python -B cross_section.py examples/27Al_ap_runA.toml       # no ROOT needed; any python with pandas
+
+    sigma(E_cm)        = Y / (N_b * n * dz * eps)                  mode = "excitation"
+    dsigma/dOmega(th)  = Y / (N_b * n * dz * eps * dOmega)         mode = "angular"
+
+    Y       yield in the bin, from <out_dir>/yields_<det>.csv (or [yield] csv); see extract_yields.py for the format
+    N_b     number of beam particles                              [beam]
+    n       target atoms per cm^2 per mm of beam path             [target]
+    dz      length of beam path (mm) over which the beam's E_cm lies in the bin, from the beam track E(z)
+    eps     efficiency of the bin                                 [acceptance]
+    dOmega  solid angle of the theta_cm bin (angular mode)
+
+Writes <out_dir>/xs_<det>.csv (every intermediate number; the header records every input) and <out_dir>/xs.png.
+
+WHAT IT DOES, STEP BY STEP (see analyse_detector())
+    1. beam track:     beam energy E at every z
+    2. normalisation:  N_b and n
+    3. bins:           E_cm bins -> their z ranges and dz  (or theta_cm bins inside one energy window)
+    4. yields:         share the yield histogram's bins into those bins
+    5. efficiency:     eps for each bin
+    6. cross section:  the formula, with statistical and efficiency errors
+"""
+import argparse
+import datetime
+import re
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+import common
+
+BOLTZMANN = 1.380649e-23             # J/K
+PASCAL_PER_TORR = 133.322368
+ELEMENTARY_CHARGE = 1.602176634e-19  # C
+CM2_PER_MB = 1e-27
+YIELD_VARIABLES = ("z", "e_beam", "ecm")
+
+
+# ============================================================ normalisation
+
+def number_of_beam_particles(beam):
+    """N_b from: n_beam directly, or integrated charge, or current x time. Returns (N_b, relative error, text)."""
+    charge_state = beam.get("charge_state", 1)
+    if "n_beam" in beam:
+        n_b, text = beam["n_beam"], "given"
+    elif "charge_c" in beam:
+        n_b = beam["charge_c"] / (charge_state * ELEMENTARY_CHARGE)
+        text = f"{beam['charge_c']} C / ({charge_state} e)"
+    elif "current_a" in beam:
+        n_b = beam["current_a"] * beam["time_s"] / (charge_state * ELEMENTARY_CHARGE)
+        text = f"{beam['current_a']} A x {beam['time_s']} s / ({charge_state} e)"
+    else:
+        raise KeyError("[beam] needs n_beam, charge_c, or current_a and time_s")
+    return float(n_b), float(beam.get("n_beam_rel_err", 0.0)), text
+
+
+def target_atoms_per_cm2_per_mm(target):
+    """n = (gas molecules per cm^3) x (mole fraction of the target molecule) x (target atoms per molecule) x 0.1 cm/mm.
+    E.g. He in 250 Torr He + 3% CO2 at 293.15 K: mole_fraction 0.97, atoms_per_molecule 1 -> 7.988e17.
+    Returns (n, relative error, text)."""
+    if "n_per_cm2_per_mm" in target:
+        return float(target["n_per_cm2_per_mm"]), float(target.get("rel_err", 0.0)), "given"
+    pressure_pa = target["pressure_torr"] * PASCAL_PER_TORR
+    molecules_per_cm3 = pressure_pa / (BOLTZMANN * target["temperature_k"]) * 1e-6
+    fraction = target.get("mole_fraction", 1.0)
+    atoms = target.get("atoms_per_molecule", 1)
+    n = molecules_per_cm3 * fraction * atoms * 0.1
+    text = f"{target['pressure_torr']} Torr, {target['temperature_k']} K, mole fraction {fraction}, {atoms} atom(s)/molecule"
+    return n, float(target.get("rel_err", 0.0)), text
+
+
+# ============================================================ yields
+
+def read_yields(file):
+    """The yields CSV of extract_yields.py. Returns (table, variable), variable = z, e_beam or ecm."""
+    table = pd.read_csv(file, comment="#")
+    for variable in YIELD_VARIABLES:
+        if f"{variable}_lo" in table.columns:
+            return table, variable
+    raise ValueError(f"{file}: no column z_lo, e_beam_lo or ecm_lo (see extract_yields.py for the format)")
+
+
+def in_yield_units(variable, k, ecm_range, z_range):
+    """Convert a bin, given both as an E_cm range and a z range, into the units of the yield's variable."""
+    if variable == "ecm":
+        return ecm_range
+    if variable == "e_beam":
+        return ecm_range[0] / k, ecm_range[1] / k
+    return z_range
+
+
+def fraction_inside(src_lo, src_hi, lo, hi):
+    """Fraction of each yield bin [src_lo, src_hi) that lies inside [lo, hi). Used to share the yield's bins
+    between the analysis bins when their edges do not line up."""
+    return np.clip(np.minimum(src_hi, hi) - np.maximum(src_lo, lo), 0, None) / (src_hi - src_lo)
+
+
+def yields_excitation(table, variable, t, k):
+    """Yield and its error in each E_cm bin."""
+    if "theta_cm_lo" in table.columns:          # a 2D yield: add up the theta bins first
+        table = table.groupby([f"{variable}_lo", f"{variable}_hi"], as_index=False)[["counts", "variance"]].sum()
+    src_lo, src_hi = table[f"{variable}_lo"].to_numpy(), table[f"{variable}_hi"].to_numpy()
+    lo, hi = in_yield_units(variable, k, (t["ecm_lo_mev"], t["ecm_hi_mev"]), (t["z_up_mm"], t["z_down_mm"]))
+    counts, errors = [], []
+    for a, b in zip(lo, hi):
+        f = fraction_inside(src_lo, src_hi, a, b)
+        counts.append(np.sum(f * table["counts"]))
+        errors.append(np.sqrt(np.sum(f ** 2 * table["variance"])))
+    return np.array(counts), np.array(errors)
+
+
+def yields_angular(table, variable, t, window, k):
+    """Yield and its error in each theta_cm bin, for the part of the yield inside the energy (or z) window."""
+    if "theta_cm_lo" not in table.columns:
+        raise ValueError("angular mode needs a 2D yield (theta_cm_lo/hi columns): set angle_axis or angle_branch in [extract]")
+    ecm_lo, ecm_hi, z_up, z_down = window
+    w_lo, w_hi = in_yield_units(variable, k, (ecm_lo, ecm_hi), (z_up, z_down))
+    in_window = fraction_inside(table[f"{variable}_lo"].to_numpy(), table[f"{variable}_hi"].to_numpy(), w_lo, w_hi)
+    th_lo, th_hi = table["theta_cm_lo"].to_numpy(), table["theta_cm_hi"].to_numpy()
+    counts, errors = [], []
+    for a, b in zip(t["theta_lo_deg"], t["theta_hi_deg"]):
+        f = in_window * fraction_inside(th_lo, th_hi, a, b)
+        counts.append(np.sum(f * table["counts"]))
+        errors.append(np.sqrt(np.sum(f ** 2 * table["variance"])))
+    return np.array(counts), np.array(errors)
+
+
+# ============================================================ efficiency
+
+def efficiency(config, det, t, full_path):
+    """eps and its relative error for every bin. [acceptance] source =
+         "geometric": <out_dir>/eps_<det>.csv from geometric_efficiency.py; `column` picks the efficiency
+                      (default eps_geometric), its Monte Carlo error comes with it
+         "csv":       any file: csv, x_col, eps_col, match = "bin" (same bin edge: our column `key` = their x_col)
+                      or "interp" (interpolate at our column `key`; NaN outside the file's range), rel_err_col or rel_err
+         "value":     one number for every bin (value, rel_err)
+         "none":      eps = 1, the apparent cross section (the default)
+       [[acceptance.extra]] name, value, rel_err: further factors, multiplied in (e.g. an intrinsic efficiency)."""
+    eff = config.get("acceptance", {})
+    mode = config.get("mode", "excitation")
+    source = eff.get("source", "none")
+    n_bins = len(t)
+    eps, rel_err = np.ones(n_bins), np.zeros(n_bins)
+    text = "none (eps = 1: apparent cross section)"
+
+    if source == "geometric":
+        column = eff.get("column", "eps_geometric")
+        file = common.output_file(config, full_path, "eps", det)
+        eff = {"csv": str(file), "match": "bin", "eps_col": column, "rel_err_col": f"{column}_rel_err",
+               "x_col": "ecm_lo_mev" if mode == "excitation" else "theta_lo_deg", "extra": eff.get("extra", [])}
+        source = "csv"
+    if source == "csv":
+        table = pd.read_csv(full_path(eff["csv"]), comment="#")
+        match = eff.get("match", "interp")
+        default_key = {("excitation", "bin"): "ecm_lo_mev", ("excitation", "interp"): "ecm_nominal_mev",
+                       ("angular", "bin"): "theta_lo_deg", ("angular", "interp"): "theta_c_deg"}[(mode, match)]
+        key = eff.get("key", default_key)
+        their_x = table[eff["x_col"]].to_numpy(float)
+        their_eps = table[eff["eps_col"]].to_numpy(float)
+        if "rel_err_col" in eff:
+            their_err = table[eff["rel_err_col"]].to_numpy(float)
+        else:
+            their_err = np.full(len(table), eff.get("rel_err", 0.0))
+
+        ours = t[key].to_numpy(float)
+        if match == "bin":
+            for i, x in enumerate(ours):
+                same = np.flatnonzero(np.isclose(their_x, x, rtol=1e-6, atol=1e-9))
+                eps[i] = their_eps[same[0]] if len(same) else np.nan
+                rel_err[i] = their_err[same[0]] if len(same) else np.nan
+        else:
+            order = np.argsort(their_x)
+            eps = np.interp(ours, their_x[order], their_eps[order], left=np.nan, right=np.nan)
+            rel_err = np.interp(ours, their_x[order], their_err[order], left=np.nan, right=np.nan)
+        text = f"{eff['csv']}, column {eff['eps_col']} ({match} on {eff['x_col']} = our {key})"
+    elif source == "value":
+        eps = np.full(n_bins, float(eff["value"]))
+        rel_err = np.full(n_bins, float(eff.get("rel_err", 0.0)))
+        text = f"constant {eff['value']}"
+    elif source != "none":
+        raise ValueError(f"[acceptance] source must be geometric, csv, value or none, not {source!r}")
+
+    for extra in eff.get("extra", []):
+        eps = eps * extra["value"]
+        rel_err = np.hypot(rel_err, extra.get("rel_err", 0.0))
+        text += f" x {extra.get('name', 'extra factor')} {extra['value']}"
+    return eps, rel_err, text
+
+
+# ============================================================ cross section
+
+def cross_section(t, n_b, n, eps, eps_rel_err, name):
+    """The formula. `name` is "sigma_mb" (excitation) or "dsdo_mb_sr" (angular). Adds columns to t:
+         <name>_app, <name>_app_err      apparent cross section (eps = 1), statistical error
+         <name>, <name>_err_stat         divided by eps
+         <name>_err_eps, <name>_err      error from eps; total (stat and eps in quadrature)
+       The N_b and n uncertainties are a common scale for all bins and are NOT in these columns."""
+    exposure = n_b * n * t["dz_mm"] * t["d_omega_sr"] * CM2_PER_MB      # events per mb (per mb/sr)
+    exposure = exposure.where(exposure > 0)                             # dz = 0 (past the stopping point) -> NaN
+    eps = pd.Series(eps, index=t.index).where(lambda e: e > 0)          # eps = 0 -> NaN
+    t[f"{name}_app"] = t["counts"] / exposure
+    t[f"{name}_app_err"] = t["stat_err"] / exposure
+    t["eps"] = eps
+    t["eps_rel_err"] = eps_rel_err
+    t[name] = t[f"{name}_app"] / eps
+    t[f"{name}_err_stat"] = t[f"{name}_app_err"] / eps
+    t[f"{name}_err_eps"] = t[name] * t["eps_rel_err"]
+    t[f"{name}_err"] = np.hypot(t[f"{name}_err_stat"], t[f"{name}_err_eps"])
+    return t
+
+
+def add_references(t, references, det, x_col, full_path):
+    """Reference curves ([[reference]]: csv, x_col, y_col, label, optional scale and dets) interpolated at our x
+    (NaN outside their range). Returns the curves for the plot."""
+    curves = []
+    for ref in references or []:
+        if "dets" in ref and det not in ref["dets"]:
+            continue
+        table = pd.read_csv(full_path(ref["csv"]), comment="#").sort_values(ref["x_col"])
+        x = table[ref["x_col"]].to_numpy(float)
+        y = ref.get("scale", 1.0) * table[ref["y_col"]].to_numpy(float)
+        label = ref.get("label", Path(ref["csv"]).stem)
+        column = "ref_" + re.sub(r"\W+", "_", label).strip("_").lower()
+        t[column] = np.interp(t[x_col], x, y, left=np.nan, right=np.nan)
+        curves.append((label, x, y))
+    return curves
+
+
+def analyse_detector(config, det, full_path):
+    """Steps 1-6 for one detector. Returns the result table, its header lines, the x column, the cross-section
+    column name and the reference curves."""
+    mode = config.get("mode", "excitation")
+    k = common.ecm_per_beam_energy(config["reaction"])
+
+    # 1. beam track
+    track = common.load_beam_track(config["beam"], full_path)
+
+    # 2. normalisation
+    n_b, n_b_rel_err, n_b_text = number_of_beam_particles(config["beam"])
+    n, n_rel_err, n_text = target_atoms_per_cm2_per_mm(config["target"])
+
+    # 3. bins and 4. yields
+    yield_file = full_path(config["yield"]["csv"]) if "csv" in config.get("yield", {}) \
+        else common.output_file(config, full_path, "yields", det)
+    yields, variable = read_yields(yield_file)
+    t, window = common.make_bins(config, track, k)
+    if mode == "excitation":
+        t["counts"], t["stat_err"] = yields_excitation(yields, variable, t, k)
+        name, x_col, window_text = "sigma_mb", "ecm_nominal_mev", ""
+    else:
+        t["counts"], t["stat_err"] = yields_angular(yields, variable, t, window, k)
+        name, x_col = "dsdo_mb_sr", "theta_c_deg"
+        ecm_lo, ecm_hi, z_up, z_down = window
+        window_text = f"; window E_cm {ecm_lo:.4g} to {ecm_hi:.4g} MeV, z {z_up:.2f} to {z_down:.2f} mm"
+
+    # 5. efficiency and 6. cross section
+    eps, eps_rel_err, eps_text = efficiency(config, det, t, full_path)
+    t = cross_section(t, n_b, n, eps, eps_rel_err, name)
+    curves = add_references(t, config.get("reference"), det, x_col, full_path)
+
+    header = [
+        f"{config.get('title', '')} {det.upper()}: {mode} mode{window_text}",
+        f"yield: {yield_file} (binned in {variable})",
+        f"beam track: {track.description}; E_cm = {k:.6g} x E_beam",
+        f"N_b = {n_b:.6g} ({n_b_text}); n = {n:.6g} target atoms per cm2 per mm ({n_text})",
+        f"eps: {eps_text}",
+        f"common scale uncertainty from N_b and n: {100 * np.hypot(n_b_rel_err, n_rel_err):.3g} % (not in the error columns)",
+        f"written by cross_section.py on {datetime.date.today().isoformat()}",
+    ]
+    return t, header, x_col, name, curves
+
+
+def make_plot(results, config, out_file):
+    import matplotlib
+    matplotlib.use("Agg")
+    import matplotlib.pyplot as plt
+
+    opts = config.get("plot", {})
+    excitation = config.get("mode", "excitation") == "excitation"
+    fig, ax = plt.subplots(figsize=opts.get("figsize", (9, 6)))
+    plotted_refs = set()
+    for det, (t, x_col, name, curves) in results.items():
+        ok = np.isfinite(t[name]) & (t[name] > 0)
+        ax.errorbar(t.loc[ok, x_col], t.loc[ok, name], yerr=t.loc[ok, f"{name}_err"],
+                    fmt="o", ms=4, capsize=2, label=det.upper() or "data")
+        for label, x, y in curves:
+            if label not in plotted_refs:
+                ax.plot(x, y, "-", lw=1.2, label=label)
+                plotted_refs.add(label)
+    ax.set_xlabel(opts.get("xlabel", "E$_\\mathrm{cm}$ (MeV)" if excitation else "$\\theta_\\mathrm{cm}$ (deg)"))
+    ax.set_ylabel(opts.get("ylabel", "$\\sigma$ (mb)" if excitation else "d$\\sigma$/d$\\Omega$ (mb/sr)"))
+    ax.set_yscale(opts.get("yscale", "log"))
+    if "xlim" in opts:
+        ax.set_xlim(*opts["xlim"])
+    if "ylim" in opts:
+        ax.set_ylim(*opts["ylim"])
+    ax.set_title(config.get("title", ""))
+    ax.legend(fontsize=8)
+    fig.tight_layout()
+    fig.savefig(out_file, dpi=110)
+    plt.close(fig)
+
+
+def main(argv=None):
+    parser = argparse.ArgumentParser(description="Step 3: cross section from the yields CSV and an efficiency.")
+    parser.add_argument("config", help="TOML config file")
+    args = parser.parse_args(argv)
+    config, full_path = common.read_config(args.config)
+
+    results = {}
+    for det in config.get("dets", [""]):
+        print(f"--- {det or 'data'}")
+        det_config = common.config_for_detector(config, det)
+        t, header, x_col, name, curves = analyse_detector(det_config, det, full_path)
+        common.write_csv(t, header, common.output_file(config, full_path, "xs", det))
+        results[det] = (t, x_col, name, curves)
+        print("\n".join(header[:-1]))
+        show = [x_col, "dz_mm" if name == "sigma_mb" else "d_omega_sr", "counts", f"{name}_app", "eps", name,
+                f"{name}_err"] + [c for c in t.columns if c.startswith("ref_")]
+        print(t[show].to_string(index=False, float_format=lambda v: f"{v:.4g}"), "\n")
+
+    out_png = common.output_file(config, full_path, "xs", "").with_suffix(".png")
+    make_plot(results, config, out_png)
+    print(f"written to {out_png.parent}")
+    return results
+
+
+if __name__ == "__main__":
+    main()

cross_section/detectors.py

@@ -0,0 +1,80 @@
+"""The ANASEN detectors as they are now (Run A, 27Al campaign), for the geometric efficiency.
+
+geometric_efficiency.py reads everything about the detectors from here. Change a number here and every efficiency
+computed afterwards follows it.
+
+Sources:
+  - Si and counter: anasen_detectability.py of ~/TALYS/al027_a_scripts_stoichgas_dedx0962_2026-10-08
+    ("detector geometry as used by TrackRecon.C"; counter radii from ClassPW.h)
+  - beam tube and PCB hole: the author, 2026-10-15 (../scratch/reaction_efficiency_tube_2026-10-15)
+Geometry only: straight tracks from the vertex, no energy loss.
+"""
+import numpy as np
+
+# ---------------------------------------------------------------- silicon
+# QQQ3: the downstream annular detector
+QQQ_Z_MM = 105.0                       # TrackRecon.C qqq_z
+QQQ_R_MM = (50.0, 100.0)               # inner and outer active radius (16 rings: rho = 50 + 50/16 (ring + 0.5))
+QQQ_PHI_FRACTION = 4 * 87.0 / 360.0    # 4 quadrants x 87 deg of wedges
+
+# SX3: the barrel; only the four detectors TrackRecon.C builds events for, downstream half only
+SX3_R_MM = 88.0
+SX3_Z_MM = (-3.0, 72.0)                # z_local + 75/2 - 3
+SX3_IDS = (1, 3, 7, 9)
+SX3_HALF_ANGLE = np.arctan(4 * 40.30 / (8.0 * 88.0 * np.cos(np.radians(15.0))))   # outer strip edge, each side
+SX3_PHI_FRACTION = len(SX3_IDS) * 2 * SX3_HALF_ANGLE / (2 * np.pi)
+
+PHI_FRACTION = {"qqq": QQQ_PHI_FRACTION, "sx3": SX3_PHI_FRACTION}
+
+# Si energy thresholds (MeV), TrackRecon.C reaction_ax_core si_ecut. Used only for the eps_si_thr column.
+SI_THRESHOLD_MEV = {"qqq": 0.45, "sx3": 0.6}
+
+# ---------------------------------------------------------------- proportional counter
+COUNTER_HALF_LENGTH_MM = 174.3
+COUNTER_RADII_MM = (32.0, 37.0, 42.0)  # guard, anode, cathode (ClassPW.h)
+
+# ---------------------------------------------------------------- upstream apertures
+# (name, radius mm, z mm of the face that binds). A track from an on-axis vertex only moves away from the axis,
+# so an opening binds at its downstream end.
+APERTURES = (("beam tube", 16.0, -189.0),
+             ("PCB hole", 18.0, -174.3))
+
+
+# ---------------------------------------------------------------- hit rules
+# z_vertex in mm, theta = lab polar angle of the particle (radians). All return one True/False per track.
+
+def hits_silicon(det, z_vertex, theta):
+    """Does the straight track from z_vertex at angle theta reach the active silicon of `det` ("qqq" or "sx3")?
+    Polar angle only: multiply by PHI_FRACTION[det] for the azimuthal coverage.
+    A track that reaches the SX3 barrel's z range is counted as SX3, not QQQ."""
+    s, c = np.sin(theta), np.cos(theta)
+    with np.errstate(divide="ignore", invalid="ignore"):
+        z_at_sx3 = z_vertex + SX3_R_MM * c / s                         # z where the track reaches the barrel radius
+        on_sx3 = (z_at_sx3 >= SX3_Z_MM[0]) & (z_at_sx3 <= SX3_Z_MM[1])
+        if det == "sx3":
+            return on_sx3
+        r_at_qqq = np.where(c > 0, (QQQ_Z_MM - z_vertex) * s / c, -1.0)  # radius where it reaches the QQQ plane
+    if det == "qqq":
+        return (QQQ_Z_MM > z_vertex) & (r_at_qqq >= QQQ_R_MM[0]) & (r_at_qqq <= QQQ_R_MM[1]) & ~on_sx3
+    raise ValueError(f"unknown detector {det!r}: detectors.py defines 'qqq' and 'sx3'")
+
+
+def crosses_counter(z_vertex, theta):
+    """Does the track cross the guard, anode and cathode radii inside the counter's length?"""
+    with np.errstate(divide="ignore", invalid="ignore"):
+        cot = np.cos(theta) / np.sin(theta)
+        ok = np.ones(np.shape(z_vertex), bool)
+        for r in COUNTER_RADII_MM:
+            ok &= np.abs(z_vertex + r * cot) <= COUNTER_HALF_LENGTH_MM
+    return ok
+
+
+def clears_apertures(z_vertex, theta):
+    """Does the track pass the beam tube and the PCB hole? Only vertices upstream of an aperture can be blocked
+    by it: blocked when the track is farther from the axis than the opening's radius at the opening's z."""
+    s, c = np.sin(theta), np.cos(theta)
+    tan_theta = s / np.where(c > 0, c, 1e-9)        # backward tracks from upstream never reach the detectors
+    ok = np.ones(np.shape(z_vertex), bool)
+    for _name, radius, z_face in APERTURES:
+        ok &= ~((z_vertex < z_face) & ((z_face - z_vertex) * tan_theta > radius))
+    return ok

cross_section/examples/27Al_ap_runA.toml

@@ -0,0 +1,88 @@
+# 27Al(a,p)30Si excitation function, Run A, QQQ3 and SX3. Same inputs as ../../scratch/xs_27Al_lowE_2026-10-15.
+#
+#   ~/myenv/bin/python -B extract_yields.py       examples/27Al_ap_runA.toml   # 1. ROOT -> out/27Al_ap_runA/yields_<det>.csv
+#   ~/myenv/bin/python -B geometric_efficiency.py examples/27Al_ap_runA.toml   # 2. detectors.py -> eps_<det>.csv
+#   ~/myenv/bin/python -B cross_section.py        examples/27Al_ap_runA.toml   # 3. -> xs_<det>.csv, xs.png
+#
+# Each script reads the sections it needs. Paths are relative to this file's folder.
+# "{det}" is replaced by each entry of `dets`.
+
+title = "27Al(a,p)30Si, Run A"
+mode = "excitation"                 # "excitation": sigma(E_cm);  "angular": dsigma/dOmega(theta_cm)
+dets = ["sx3", "qqq"]               # names known to detectors.py
+out_dir = "../out/27Al_ap"
+
+# ------------------------------------------------------------- step 1: extract_yields.py
+[extract]
+# One fill per candidate: x = vertex z, y = tracked beam energy. Projected on y (all z, including overflow).
+source = "hist"
+file = "~/ANASEN_analysis/Output_27Al_X0_Y0/Output_27Al.root"
+hist = "_m27Alax+misc_{det}/m27Alax_BeamEnergy_vs_VertexZ_{det}"
+axis = "y"
+variable = "e_beam"
+
+# ------------------------------------------------------------- the reaction (steps 2 and 3)
+[reaction]
+beam_mass_u = 26.981538             # 27Al   (atomic masses, as TrackRecon.C)
+target_mass_u = 4.002603254         # 4He
+ejectile_mass_u = 1.007825032       # p      -> Q = 2.372 MeV for the ground state
+recoil_mass_u = 29.973770           # 30Si
+detect = "ejectile"
+ecm_per_ebeam = 0.12918             # the rounded factor of the established analysis (the masses give 0.129182)
+# 30Si states weighted by the TALYS (a,p) state cross sections at each bin's E_cm
+states_csv = "../inputs/talys_27Al_ap.csv"
+# or fixed weights:  states = [{ex_mev = 0.0, weight = 1.0}, {ex_mev = 2.235, weight = 0.5}]
+
+[beam]
+# Beam energy along z;
+# the nominal E_cm of each bin and the beam energy in the efficiency toy come from the 0.962 track.
+track_csv = "../inputs/beam_tracks.csv"
+track_z_col = "z_mm"
+track_e_col = "e_beam_mev_0962"
+# Alternative: build the track from an energy-loss table (check which dE/dx scale the table was made with):
+#   eloss_table = "../../eloss_calculations/aluminum_lookup_80MeV_250torr_3pc.dat"
+#   e_entrance_mev = 56.16
+#   z_entrance_mm = -454.0
+n_beam = 1.4405e12                     # Faraday cup, ch5 ssec:beam_exposure: the 45 runs (2.2096e12)
+# For another set of runs: ../../runlog/beam_exposure.py "<runs>" --data-dir ../../Output_27Al_fb
+n_beam_rel_err = 0.5
+
+[bins]
+ecm_edges = { start = 0.0, stop = 7.25, step = 0.25 }
+
+# ------------------------------------------------------------- step 2: geometric_efficiency.py
+[efficiency]
+n_per_bin = 400000
+seed = 20261016
+
+# ------------------------------------------------------------- step 3: cross_section.py
+[target]
+# 4He in 250 Torr He + 3% CO2 at 20 C  -> 7.988e17 He atoms per cm2 per mm
+pressure_torr = 250.0
+temperature_k = 293.15
+mole_fraction = 0.97
+atoms_per_molecule = 1
+
+# [yield] csv = "..."               # only to read yields from somewhere other than <out_dir>/yields_<det>.csv
+
+[acceptance]
+source = "geometric"                # <out_dir>/eps_<det>.csv from step 2
+column = "eps_geometric"            # or eps_si, eps_si_thr, eps_counter
+# To use the adopted efficiency of ../../scratch/xs_27Al_lowE_2026-10-15 instead (includes gas loss and thresholds):
+#   source = "csv"
+#   csv = "../../scratch/xs_27Al_lowE_2026-10-15/out/xs_{det}.csv"
+#   match = "bin"
+#   x_col = "ecm_lo_mev"
+#   eps_col = "eps_tube_adopted"
+#   rel_err_col = "eps_tube_rel_err"
+
+[[reference]]
+csv = "../inputs/talys_27Al_ap.csv"
+x_col = "ecm_mev"
+y_col = "sigma_total_mb"
+label = "TALYS (a,p), discrete states"
+
+[plot]
+yscale = "log"
+xlim = [0, 7.4]
+ylim = [1e-3, 3e4]

cross_section/examples/paap_ta_runs_hist.toml

@@ -0,0 +1,87 @@
+# p + 4He elastic scattering, dsigma/dOmega(theta_cm), from the alpha recoils of the Run A Ta-foil runs 18-22, straight
+# from a TrackRecon.C production file -- the same route as 27Al_ap_runA.toml. The histogram is
+# elastic_VertexZ_vs_ThetaCM_<variant>_a_<DET>, filled by protonAlphaElastic_core since 2026-10-16.
+# paap_ta_runs.toml is the same measurement from the p-alpha study's per-hit list.
+#
+#   (once) process runs 18-22 with CUTLIST=pid_cuts/pid_cutlist.txt and merge them, e.g.
+#          bash scratch/paap_hist_2026-10-16/reprocess.sh          (or run_tr.sh block 5, then hadd runs 18-22 only)
+#   ~/myenv/bin/python -B extract_yields.py       examples/paap_ta_runs_hist.toml   # or, the same in ROOT:
+#   root -l -b -q 'extract_yields.C+("examples/paap_ta_runs_hist.toml")'
+#   ~/myenv/bin/python -B geometric_efficiency.py examples/paap_ta_runs_hist.toml
+#   ~/myenv/bin/python -B cross_section.py        examples/paap_ta_runs_hist.toml
+#
+# Paths are relative to this file's folder.
+
+title = "p + 4He elastic, alpha recoils, Ta runs 18-22 (TrackRecon histograms)"
+mode = "angular"
+dets = ["qqq", "sx3"]
+out_dir = "../out/paap_ta_run22"
+
+[det_vars.qqq]                       # TrackRecon.C spells the detector in upper case in these folders
+DET = "QQQ"
+[det_vars.sx3]
+DET = "SX3"
+
+# ------------------------------------------------------------- step 1: extract_yields.py / extract_yields.C
+[extract]
+# The merged output of runs 18-22 (not 17: its foil, at z = -135.7 mm, sits inside the vertex window below).
+# Point this at your own merged file to use your own processing.
+source = "hist"
+file = "~/ANASEN_analysis/Output_p/results_run022.root"
+# Folder _a: hits the p/alpha cut (pid_alpha_<det>, CUTLIST) calls alpha. Variant _cath_exgate: cathode topologies
+# (a1c1, a1c2, a2c1, a2c2) and |Ex| < 1 MeV under the alpha hypothesis, i.e. elastic. Others: "" (all topologies,
+# no Ex gate), "_cath", "_exgate".
+hist = "elastic+misc_{DET}_a/elastic_VertexZ_vs_ThetaCM_cath_exgate_a_{DET}"
+axis = "x"                           # x = vertex z (mm)
+angle_axis = "y"                     # y = proton theta_cm (deg)
+variable = "z"
+
+# ------------------------------------------------------------- the reaction (steps 2 and 3)
+[reaction]
+beam_mass_u = 1.007825032            # p
+target_mass_u = 4.002603254          # 4He
+ejectile_mass_u = 1.007825032        # p
+recoil_mass_u = 4.002603254          # 4He
+detect = "recoil"                    # the silicon sees the alpha; theta_cm is the proton's
+states = [{ ex_mev = 0.0, weight = 1.0 }]
+
+[beam]
+# As paap_ta_runs.toml: 6.89 MeV protons into the gas; N_b = run 17's Ta-foil Rutherford current (1.35 pA) times
+# the wall time of runs 18-22. The run log's 13 pA would give 2.6378e11 (../../runlog/beam_exposure.py "18-22").
+eloss_table = "../../eloss_calculations/proton_lookup_30MeV_250torr_3pc.dat"
+e_entrance_mev = 6.89
+z_entrance_mm = -454.0
+current_a = 1.35e-12
+time_s = 672.65
+charge_state = 1
+
+[bins]
+window_z_mm = [-170.0, -53.0]        # upstream of every run's foil by 25 mm for runs 18-22, inside the counter
+theta_edges_deg = { start = 88.0, stop = 168.0, step = 8.0 }
+
+# ------------------------------------------------------------- step 2: geometric_efficiency.py
+[efficiency]
+n_per_bin = 400000
+seed = 20261016
+
+# ------------------------------------------------------------- step 3: cross_section.py
+[target]
+pressure_torr = 250.0
+temperature_k = 293.15
+mole_fraction = 0.97
+atoms_per_molecule = 1
+
+[acceptance]
+source = "geometric"
+column = "eps_geometric"
+
+[[reference]]
+csv = "reference/barnard1964_A1113003.csv"
+x_col = "theta_cm_deg"
+y_col = "dsdo_window_mb_sr"
+label = "Barnard 1964, at the window's E_p"
+
+[plot]
+yscale = "linear"
+xlim = [80, 175]
+ylim = [0, 130]

cross_section/examples/reference/barnard1964_A1113003.csv

@@ -0,0 +1,144 @@
+# Barnard, Jones & Weil, NP 50, 604 (1964), EXFOR A1113003: p + 4He elastic, dsigma/dOmega_cm (mb/sr)
+# Legendre order 6 fits of the 6.016 and 6.977 MeV distributions; dsdo_window: log-linear in E_p at 6.7437 MeV,
+# the beam energy at the centre of the vertex window -170.0 to -53.0 mm of paap_ta_runs.toml
+# written by make_barnard_reference.py
+theta_cm_deg,dsdo_6p016_mb_sr,dsdo_6p977_mb_sr,dsdo_window_mb_sr
+26,514.406,470.065,480.465
+27,504.693,461.948,471.98
+28,494.922,453.754,463.423
+29,485.115,445.501,454.81
+30,475.295,437.203,446.161
+31,465.481,428.878,437.491
+32,455.692,420.54,428.816
+33,445.946,412.203,420.153
+34,436.261,403.88,411.513
+35,426.651,395.584,402.912
+36,417.132,387.326,394.36
+37,407.715,379.117,385.87
+38,398.413,370.966,377.45
+39,389.235,362.882,369.111
+40,380.191,354.874,360.861
+41,371.288,346.947,352.705
+42,362.532,339.108,344.652
+43,353.928,331.362,336.705
+44,345.481,323.714,328.869
+45,337.192,316.166,321.147
+46,329.064,308.722,313.542
+47,321.097,301.384,306.056
+48,313.29,294.152,298.688
+49,305.642,287.028,291.44
+50,298.152,280.012,284.312
+51,290.816,273.103,277.302
+52,283.631,266.301,270.408
+53,276.592,259.604,263.63
+54,269.697,253.011,256.964
+55,262.94,246.52,250.409
+56,256.315,240.129,243.962
+57,249.819,233.836,237.62
+58,243.445,227.639,231.379
+59,237.189,221.535,225.238
+60,231.045,215.523,219.192
+61,225.008,209.599,213.24
+62,219.074,203.763,207.379
+63,213.237,198.013,201.606
+64,207.495,192.345,195.918
+65,201.842,186.761,190.315
+66,196.276,181.257,184.794
+67,190.793,175.833,179.353
+68,185.391,170.488,173.993
+69,180.069,165.223,168.711
+70,174.824,160.037,163.508
+71,169.656,154.931,158.384
+72,164.564,149.905,153.339
+73,159.549,144.961,148.375
+74,154.61,140.099,143.492
+75,149.75,135.322,138.691
+76,144.969,130.63,133.975
+77,140.27,126.028,129.346
+78,135.655,121.516,124.807
+79,131.127,117.097,120.359
+80,126.688,112.775,116.005
+81,122.342,108.552,111.75
+82,118.093,104.431,107.595
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+84,109.9,96.5094,99.6024
+85,105.964,92.7147,95.7706
+86,102.14,89.0351,92.0532
+87,98.4316,85.4736,88.4535
+88,94.8431,82.0333,84.9746
+89,91.378,78.7172,81.6197
+90,88.0401,75.528,78.3918
+91,84.8326,72.4683,75.2935
+92,81.7586,69.5403,72.3274
+93,78.8209,66.7463,69.4959
+94,76.0221,64.0881,66.8009
+95,73.3644,61.5673,64.2442
+96,70.8494,59.1852,61.8272
+97,68.4787,56.9427,59.5509
+98,66.2533,54.8405,57.4162
+99,64.1738,52.8789,55.4235
+100,62.2404,51.058,53.5729
+101,60.4529,49.3774,51.864
+102,58.8107,47.8365,50.2963
+103,57.3127,46.4343,48.8688
+104,55.9576,45.1696,47.5803
+105,54.7435,44.0406,46.4292
+106,53.6684,43.0456,45.4134
+107,52.7296,42.1824,44.5309
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+109,51.25,40.8414,43.1556
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cross_section/extract_yields.C

@@ -0,0 +1,378 @@
+// Step 1 in ROOT: yields out of ROOT, into a plain CSV histogram. The ROOT twin of extract_yields.py:
+// same config, same output (<out_dir>/yields_<det>.csv), same format. Use whichever is handier.
+//
+//   root -l -b -q 'extract_yields.C+("examples/27Al_ap_runA.toml")'
+//   root -l -b -q 'extract_yields.C+("examples/27Al_ap_runA.toml", "out/elsewhere")'   // another output folder
+//                                                                                       // (relative to where root runs)
+// THE CSV FORMAT (cross_section.py reads only this)
+//   1D:  <var>_lo, <var>_hi, counts, variance
+//   2D:  <var>_lo, <var>_hi, theta_cm_lo, theta_cm_hi, counts, variance        (angular mode)
+// <var> is z (mm), e_beam (MeV) or ecm (MeV). One row per bin; under/overflow are not written.
+//
+// CONFIG: the [extract] section, `dets`, `out_dir`, `title` and [det_vars.<det>] of the study's TOML, exactly as for
+// extract_yields.py (keys listed there). Two differences:
+//   - `cut` is handed to TTree::Draw. Write it with and / or (e.g. "det == 1 and e_si > 0.5"): they become && / ||
+//     here and stay valid pandas syntax for the Python version.
+//   - A tree histogram puts an entry exactly on the last upper edge in the overflow (numpy puts it in the last bin).
+// The TOML reader below handles only what these configs use: "strings", numbers, one-line [lists] and
+// { start = .., stop = .., step = .. } tables. Anything else stops with an error.
+
+#include <TChain.h>
+#include <TDirectory.h>
+#include <TFile.h>
+#include <TKey.h>
+#include <TH1.h>
+#include <TH2.h>
+#include <TROOT.h>
+#include <TString.h>
+#include <TSystem.h>
+
+#include <cmath>
+#include <cstdio>
+#include <fstream>
+#include <functional>
+#include <map>
+#include <stdexcept>
+#include <string>
+#include <vector>
+
+namespace xs {
+
+using Section = std::map<std::string, std::string>;     // key -> raw value text
+using Config = std::map<std::string, Section>;          // section name ("" = top level) -> keys
+
+// ============================================================ minimal TOML reading
+
+std::string Trim(const std::string& s)
+{
+   const auto a = s.find_first_not_of(" \t\r\n");
+   if (a == std::string::npos) return "";
+   const auto b = s.find_last_not_of(" \t\r\n");
+   return s.substr(a, b - a + 1);
+}
+
+std::string StripComment(const std::string& line)
+{
+   bool inString = false;
+   for (size_t i = 0; i < line.size(); ++i) {
+      if (line[i] == '"') inString = !inString;
+      if (line[i] == '#' && !inString) return line.substr(0, i);
+   }
+   return line;
+}
+
+Config ReadToml(const std::string& file)
+{
+   std::ifstream in(file);
+   if (!in) throw std::runtime_error("cannot open config " + file);
+   Config config;
+   std::string section, line;
+   while (std::getline(in, line)) {
+      line = Trim(StripComment(line));
+      if (line.empty()) continue;
+      if (line.rfind("[[", 0) == 0) { section = line; continue; }          // array tables (e.g. [[reference]]): not used here
+      if (line[0] == '[') { section = Trim(line.substr(1, line.size() - 2)); continue; }
+      const auto eq = line.find('=');
+      if (eq == std::string::npos) throw std::runtime_error("cannot read config line: " + line);
+      const std::string value = Trim(line.substr(eq + 1));
+      if ((value[0] == '[' && value.back() != ']') || (value[0] == '{' && value.back() != '}'))
+         throw std::runtime_error("lists and tables must be on one line: " + line);
+      config[section][Trim(line.substr(0, eq))] = value;
+   }
+   return config;
+}
+
+std::string Unquote(const std::string& v)
+{
+   if (v.size() >= 2 && v.front() == '"' && v.back() == '"') return v.substr(1, v.size() - 2);
+   return v;
+}
+
+// "[a, b]" or "{ k = v, ... }" -> its items, split on commas outside quotes
+std::vector<std::string> SplitItems(const std::string& v)
+{
+   std::vector<std::string> items;
+   std::string inner = Trim(v.substr(1, v.size() - 2)), item;
+   bool inString = false;
+   for (char c : inner) {
+      if (c == '"') inString = !inString;
+      if (c == ',' && !inString) { items.push_back(Trim(item)); item.clear(); }
+      else item += c;
+   }
+   if (!Trim(item).empty()) items.push_back(Trim(item));
+   return items;
+}
+
+// Bin edges from [e0, e1, ...] or { start = .., stop = .., step = .. } (stop included), computed as numpy does
+std::vector<double> Edges(const std::string& v)
+{
+   std::vector<double> edges;
+   if (v.front() == '[') {
+      for (const auto& item : SplitItems(v)) edges.push_back(std::stod(item));
+      return edges;
+   }
+   std::map<std::string, double> table;
+   for (const auto& item : SplitItems(v)) {
+      const auto eq = item.find('=');
+      table[Trim(item.substr(0, eq))] = std::stod(Trim(item.substr(eq + 1)));
+   }
+   const double start = table.at("start"), stop = table.at("stop"), step = table.at("step");
+   const long nBins = std::lround((stop - start) / step);
+   for (long i = 0; i <= nBins; ++i) edges.push_back(start + step * i);
+   return edges;
+}
+
+// The [extract] section of one detector: every string with {det} and the [det_vars.<det>] keys replaced
+Section ExtractSection(const Config& config, const std::string& det)
+{
+   std::map<std::string, std::string> replacements = {{"det", det}};
+   const auto vars = config.find("det_vars." + det);
+   if (vars != config.end())
+      for (const auto& kv : vars->second) replacements[kv.first] = Unquote(kv.second);
+   const auto ex = config.find("extract");
+   if (ex == config.end()) throw std::runtime_error("the config has no [extract] section");
+   Section out;
+   for (const auto& kv : ex->second) {
+      TString value = kv.second.c_str();
+      for (const auto& r : replacements) value.ReplaceAll(("{" + r.first + "}").c_str(), r.second.c_str());
+      out[kv.first] = value.Data();
+   }
+   return out;
+}
+
+std::string Get(const Section& s, const std::string& key, const std::string& fallback = "\x01")
+{
+   const auto it = s.find(key);
+   if (it != s.end()) return Unquote(it->second);
+   if (fallback == "\x01") throw std::runtime_error("[extract] needs the key " + key);
+   return fallback;
+}
+
+// ============================================================ the yields table
+
+struct Row {
+   double lo, hi, thetaLo, thetaHi, counts, variance;
+};
+
+// Sum of weights squared of a bin (global index); a histogram without Sumw2 counts each entry once
+double Variance(const TH1* h, int bin)
+{
+   return h->GetSumw2N() > 0 ? h->GetSumw2()->At(bin) : h->GetBinContent(bin);
+}
+
+// The histogram at "folder/name". A name that itself contains '/' (e.g. "dE_(z/100)=1") is not reachable with Get(),
+// so each split into an existing folder + a key of that folder is also tried. Not found: the error names the
+// histograms whose path matches when case is ignored (e.g. "elastic+misc_qqq_a" for "elastic+misc_QQQ_a").
+TH1* FindHistogram(TFile* file, const std::string& path)
+{
+   if (auto* h = dynamic_cast<TH1*>(file->Get(path.c_str()))) return h;
+   // every split into folder + name: at each '/', from the last one back, and finally no folder at all
+   std::vector<std::pair<std::string, std::string>> splits;
+   for (size_t i = path.size(); i-- > 0;)
+      if (path[i] == '/') splits.emplace_back(path.substr(0, i), path.substr(i + 1));
+   splits.emplace_back("", path);
+   for (const auto& [folder, name] : splits) {
+      TDirectory* dir = folder.empty() ? static_cast<TDirectory*>(file) : file->GetDirectory(folder.c_str());
+      if (!dir) continue;
+      if (TKey* key = dir->GetKey(name.c_str()))
+         if (auto* h = dynamic_cast<TH1*>(key->ReadObj())) return h;
+   }
+   std::string close;
+   TString lower = path.c_str();
+   lower.ToLower();
+   std::function<void(TDirectory*, const std::string&)> walk = [&](TDirectory* dir, const std::string& prefix) {
+      for (auto* obj : *dir->GetListOfKeys()) {
+         auto* key = static_cast<TKey*>(obj);
+         const std::string full = prefix + key->GetName();
+         if (TString(key->GetClassName()).BeginsWith("TDirectory"))
+            walk(dir->GetDirectory(key->GetName()), full + "/");
+         else if (TString(full.c_str()).EqualTo(lower, TString::kIgnoreCase))
+            close += " '" + full + "'";
+      }
+   };
+   walk(file, "");
+   throw std::runtime_error("no histogram " + path + " in " + file->GetName() + (close.empty() ? "" : "; same path ignoring case:" + close) +
+                            ". Paths are case-sensitive; for upper-case names per detector use [det_vars], e.g. DET = \"QQQ\"");
+}
+
+std::vector<Row> FromHistogram(const Section& ex, const std::string& path, bool& twoD)
+{
+   TFile* file = TFile::Open(path.c_str());
+   if (!file || file->IsZombie()) throw std::runtime_error("cannot open " + path);
+   const std::string name = Get(ex, "hist");
+   TH1* h = FindHistogram(file, name);
+
+   std::vector<Row> rows;
+   twoD = false;
+   if (h->GetDimension() == 1) {
+      for (int i = 1; i <= h->GetNbinsX(); ++i)
+         rows.push_back({h->GetXaxis()->GetBinLowEdge(i), h->GetXaxis()->GetBinUpEdge(i), 0, 0,
+                         h->GetBinContent(i), Variance(h, i)});
+      file->Close();
+      return rows;
+   }
+
+   // TH2: `axis` holds the variable; the other axis is theta_cm (angle_axis given) or is summed
+   const bool varOnX = Get(ex, "axis", "x") == "x";
+   TH2* h2 = static_cast<TH2*>(h);
+   TAxis* varAxis = varOnX ? h2->GetXaxis() : h2->GetYaxis();
+   TAxis* otherAxis = varOnX ? h2->GetYaxis() : h2->GetXaxis();
+   const int nVar = varAxis->GetNbins(), nOther = otherAxis->GetNbins();
+   auto global = [&](int iVar, int iOther) { return varOnX ? h2->GetBin(iVar, iOther) : h2->GetBin(iOther, iVar); };
+
+   if (ex.count("angle_axis")) {                       // keep it 2D: one row per (variable bin, theta bin)
+      twoD = true;
+      for (int i = 1; i <= nVar; ++i)
+         for (int j = 1; j <= nOther; ++j)
+            rows.push_back({varAxis->GetBinLowEdge(i), varAxis->GetBinUpEdge(i), otherAxis->GetBinLowEdge(j),
+                            otherAxis->GetBinUpEdge(j), h2->GetBinContent(global(i, j)), Variance(h2, global(i, j))});
+      file->Close();
+      return rows;
+   }
+
+   // project on the variable: the other axis summed whole (with under/overflow), or the fraction inside other_range
+   std::vector<double> weight(nOther + 2, 1.0);
+   if (ex.count("other_range")) {
+      const auto range = Edges(ex.at("other_range"));
+      weight.front() = weight.back() = 0.0;
+      for (int j = 1; j <= nOther; ++j) {
+         const double lo = otherAxis->GetBinLowEdge(j), hi = otherAxis->GetBinUpEdge(j);
+         weight[j] = std::max(0.0, std::min(hi, range[1]) - std::max(lo, range[0])) / (hi - lo);
+      }
+   }
+   for (int i = 1; i <= nVar; ++i) {
+      double counts = 0, variance = 0;
+      for (int j = 0; j <= nOther + 1; ++j) {
+         counts += weight[j] * h2->GetBinContent(global(i, j));
+         variance += weight[j] * weight[j] * Variance(h2, global(i, j));
+      }
+      rows.push_back({varAxis->GetBinLowEdge(i), varAxis->GetBinUpEdge(i), 0, 0, counts, variance});
+   }
+   file->Close();
+   return rows;
+}
+
+std::vector<Row> FromTree(const Section& ex, const std::vector<std::string>& files, bool& twoD)
+{
+   TChain chain(Get(ex, "tree").c_str());
+   for (const auto& f : files) chain.Add(f.c_str());        // TChain::Add takes wildcards in the file name
+   if (chain.GetEntries() <= 0)                              // a missing tree or file must not become a table of zeros
+      throw std::runtime_error("no entries in tree " + Get(ex, "tree") + " of " + files.front() + (files.size() > 1 ? " ..." : ""));
+
+   // The cut, with and / or as && / ||; a weight branch multiplies it
+   TString cut = Get(ex, "cut", "").c_str();
+   cut.ReplaceAll(" and ", " && ").ReplaceAll(" or ", " || ");
+   if (cut.IsNull()) cut = "1";
+   TString selection = ex.count("weight_branch") ? TString::Format("(%s)*(%s)", Get(ex, "weight_branch").c_str(), cut.Data())
+                                                 : cut;
+
+   const auto edges = Edges(ex.at("bins"));
+   const TString branch = Get(ex, "branch").c_str();
+   gROOT->cd();
+   std::vector<Row> rows;
+   twoD = ex.count("angle_branch") > 0;
+   if (!twoD) {
+      TH1D h("h_extract_yields", "", edges.size() - 1, edges.data());
+      h.Sumw2();
+      const Long64_t n = chain.Draw(branch + ">>h_extract_yields", selection, "goff");
+      printf("  %d file(s); %lld entries after the cut\n", chain.GetListOfFiles()->GetEntries(), n);
+      for (int i = 1; i <= h.GetNbinsX(); ++i)
+         rows.push_back({edges[i - 1], edges[i], 0, 0, h.GetBinContent(i), Variance(&h, i)});
+      return rows;
+   }
+   const auto angleEdges = Edges(ex.at("angle_bins"));
+   TH2D h("h_extract_yields2", "", edges.size() - 1, edges.data(), angleEdges.size() - 1, angleEdges.data());
+   h.Sumw2();
+   const Long64_t n = chain.Draw(TString(Get(ex, "angle_branch").c_str()) + ":" + branch + ">>h_extract_yields2",
+                                 selection, "goff");                 // "y:x" in TTree::Draw
+   printf("  %d file(s); %lld entries after the cut\n", chain.GetListOfFiles()->GetEntries(), n);
+   for (size_t i = 1; i < edges.size(); ++i)
+      for (size_t j = 1; j < angleEdges.size(); ++j)
+         rows.push_back({edges[i - 1], edges[i], angleEdges[j - 1], angleEdges[j], h.GetBinContent(h.GetBin(i, j)),
+                         Variance(&h, h.GetBin(i, j))});
+   return rows;
+}
+
+void WriteCsv(const std::vector<Row>& rows, bool twoD, const std::string& var, const std::vector<std::string>& header,
+              const std::string& outFile)
+{
+   FILE* f = fopen(outFile.c_str(), "w");
+   if (!f) throw std::runtime_error("cannot write " + outFile);
+   for (const auto& line : header) fprintf(f, "# %s\n", line.c_str());
+   fprintf(f, twoD ? "%s_lo,%s_hi,theta_cm_lo,theta_cm_hi,counts,variance\n" : "%s_lo,%s_hi,counts,variance\n",
+           var.c_str(), var.c_str());
+   for (const auto& r : rows) {
+      if (twoD) fprintf(f, "%.6g,%.6g,%.6g,%.6g,%.6g,%.6g\n", r.lo, r.hi, r.thetaLo, r.thetaHi, r.counts, r.variance);
+      else      fprintf(f, "%.6g,%.6g,%.6g,%.6g\n", r.lo, r.hi, r.counts, r.variance);
+   }
+   fclose(f);
+}
+
+} // namespace xs
+
+void extract_yields(const char* configFile, const char* outDirOverride = "")
+{
+   using namespace xs;
+   try {
+      const Config config = ReadToml(configFile);
+      const std::string configDir = gSystem->GetDirName(configFile).Data();
+      auto fullPath = [&](const std::string& p) -> std::string {
+         if (p.rfind("~/", 0) == 0 && getenv("HOME")) return std::string(getenv("HOME")) + p.substr(1);  // "~/..." = home
+         return p.empty() || p[0] == '/' ? p : configDir + "/" + p;
+      };
+
+      const Section& top = config.count("") ? config.at("") : Section();
+      std::vector<std::string> dets = {""};
+      if (top.count("dets")) {
+         dets.clear();
+         for (const auto& d : SplitItems(top.at("dets"))) dets.push_back(Unquote(d));
+      }
+      const std::string outDir = *outDirOverride ? std::string(outDirOverride)
+                                                 : fullPath(top.count("out_dir") ? Unquote(top.at("out_dir")) : "out");
+      gSystem->mkdir(outDir.c_str(), kTRUE);
+      const std::string title = top.count("title") ? Unquote(top.at("title")) : "";
+
+      for (const auto& det : dets) {
+         const Section ex = ExtractSection(config, det);
+         const std::string source = Get(ex, "source"), var = Get(ex, "variable");
+         if (source != "hist" && source != "tree")
+            throw std::runtime_error("[extract] source must be \"hist\" or \"tree\", not " + source +
+                                     (source == "csv" ? " (event-list CSVs: use extract_yields.py)" : ""));
+         std::vector<std::string> files;                      // `file` may be one path or a list
+         if (ex.at("file").front() == '[') for (const auto& f : SplitItems(ex.at("file"))) files.push_back(fullPath(Unquote(f)));
+         else files.push_back(fullPath(Get(ex, "file")));
+         const std::string fileText = ex.at("file").front() == '[' ? ex.at("file") : Get(ex, "file");
+         printf("--- %s: %s : %s\n", det.empty() ? "data" : det.c_str(), fileText.c_str(),
+                Get(ex, source == "hist" ? "hist" : "tree").c_str());
+
+         bool twoD = false;
+         std::vector<Row> rows;
+         std::string from;
+         if (source == "hist") {
+            rows = FromHistogram(ex, files.front(), twoD);
+            from = "histogram " + Get(ex, "hist");
+         } else if (source == "tree") {
+            rows = FromTree(ex, files, twoD);
+            from = "tree " + Get(ex, "tree") + ", branch " + Get(ex, "branch");
+            if (!Get(ex, "cut", "").empty()) from += ", cut: " + Get(ex, "cut");
+         } else {
+            throw std::runtime_error("[extract] source must be \"hist\" or \"tree\", not " + source +
+                                     (source == "csv" ? " (event-list CSVs: use extract_yields.py)" : ""));
+         }
+
+         TString detUpper = det.c_str();
+         detUpper.ToUpper();
+         const std::string outFile = outDir + "/" + (det.empty() ? "yields.csv" : "yields_" + det + ".csv");
+         WriteCsv(rows, twoD, var,
+                  {title + " " + detUpper.Data() + ": yields, binned in " + var, "from " + fileText + " : " + from,
+                   "written by extract_yields.C"},
+                  outFile);
+         double total = 0;
+         for (const auto& r : rows) total += r.counts;
+         printf("  %.0f counts in %zu bins -> %s\n", total, rows.size(), outFile.c_str());
+      }
+   } catch (const std::exception& e) {
+      fprintf(stderr, "extract_yields.C: %s\n", e.what());
+      gSystem->Exit(1);                                   // a failed extraction must not look like a finished one
+   }
+}

cross_section/geometric_efficiency.py

@@ -0,0 +1,164 @@
+"""Step 2: geometric efficiency of every bin, for the detectors as they are now (detectors.py).
+
+    ~/myenv/bin/python -B geometric_efficiency.py examples/27Al_ap_runA.toml      # ~1 min; numpy and pandas only
+
+A toy Monte Carlo per bin (same bins as the cross section):
+  - vertices uniform over the bin's stretch of beam path (angular mode: over the window);
+  - beam energy at each vertex from the nominal beam track;
+  - the residual nucleus' state drawn from [reaction] states: fixed weights, or state cross sections
+    from a table (e.g. TALYS) at the bin's nominal E_cm;
+  - emission isotropic in the CM (angular mode: cos theta_cm uniform inside the theta bin, theta_cm of the ejectile);
+  - relativistic two-body kinematics, straight tracks from the vertex.
+
+Writes <out_dir>/eps_<det>.csv, one row per bin, with these efficiencies (each times the azimuthal coverage):
+    eps_si          the particle reaches the analysed silicon                 (solid angle)
+    eps_si_thr      eps_si, and its energy at the vertex is above the Si threshold   (the ch5 "solid angle" convention;
+                    a kinematic cut, not geometry)
+    eps_counter     eps_si, and it crosses the guard, anode and cathode radii inside the counter
+    eps_geometric   eps_counter, and it clears the beam tube and the PCB hole    <- THE GEOMETRIC EFFICIENCY
+and the Monte Carlo relative error of each (<name>_rel_err).
+Not included, because they are not geometry: energy loss in the gas, the Si threshold after the gas, the counter's
+deposit threshold, intrinsic efficiencies. They are why the adopted eps_tube of ../scratch/xs_27Al_lowE_2026-10-15 is lower.
+
+CONFIG
+    [reaction]    beam_mass_u, target_mass_u, ejectile_mass_u, recoil_mass_u   (atomic masses; Q from these)
+                  detect = "ejectile" (default) or "recoil": the particle the silicon sees
+                  states = [{ex_mev = 0.0, weight = 1.0}, ...]           fixed state weights, or
+                  states_csv = file with ecm_mev and one column ex_<Ex in MeV> per state (state cross sections)
+    [efficiency]  n_per_bin (default 400000), seed
+    [beam], [bins], mode, dets as for the cross section.
+"""
+import argparse
+
+import numpy as np
+import pandas as pd
+
+import common
+import detectors
+
+U = 931.49410242          # MeV per u
+EFFICIENCIES = ("eps_si", "eps_si_thr", "eps_counter", "eps_geometric")
+
+
+def two_body(t_beam, reaction, ex, cos_cm):
+    """Relativistic two-body kinematics, beam on target at rest -> ejectile (at cos_cm in the CM) + recoil (state ex).
+    Returns the lab kinetic energy (MeV) and polar angle (rad) of the detected particle, and whether the channel is
+    open at this beam energy."""
+    m1, m2 = reaction["beam_mass_u"] * U, reaction["target_mass_u"] * U
+    m3, m4 = reaction["ejectile_mass_u"] * U, reaction["recoil_mass_u"] * U + ex
+    s = (m1 + m2) ** 2 + 2 * m2 * t_beam                      # invariant mass squared
+    sqrt_s = np.sqrt(s)
+    is_open = sqrt_s > m3 + m4
+    e_total = t_beam + m1 + m2
+    beta = np.sqrt(t_beam ** 2 + 2 * t_beam * m1) / e_total   # of the CM in the lab
+    gamma = e_total / sqrt_s
+    p_cm = np.sqrt(np.clip((s - (m3 + m4) ** 2) * (s - (m3 - m4) ** 2), 0, None)) / (2 * sqrt_s)
+    sin_cm = np.sqrt(1 - cos_cm ** 2)
+
+    # the recoil goes the opposite way in the CM
+    mass, sign = (m3, 1.0) if reaction.get("detect", "ejectile") == "ejectile" else (m4, -1.0)
+    e_cm = np.sqrt(p_cm ** 2 + mass ** 2)
+    p_z = gamma * (sign * p_cm * cos_cm + beta * e_cm)
+    energy = gamma * (e_cm + beta * sign * p_cm * cos_cm)
+    theta = np.arctan2(p_cm * sin_cm, p_z)
+    return np.where(is_open, energy - mass, 0.0), theta, is_open
+
+
+def q_value_mev(reaction):
+    return (reaction["beam_mass_u"] + reaction["target_mass_u"]
+            - reaction["ejectile_mass_u"] - reaction["recoil_mass_u"]) * U
+
+
+def state_weights(reaction, ecm, states_table):
+    """Excitation energies of the residual nucleus and their probabilities at this E_cm. States that are closed
+    (Ex >= Q + E_cm) get weight 0; if none is open, the ground state alone."""
+    if states_table is None:
+        states = reaction.get("states", [{"ex_mev": 0.0, "weight": 1.0}])
+        ex = np.array([s["ex_mev"] for s in states], float)
+        w = np.array([s["weight"] for s in states], float)
+    else:
+        columns = [c for c in states_table.columns if c.startswith("ex_")]
+        ex = np.array([float(c[3:]) for c in columns])
+        # the table row nearest this E_cm, among rows with any cross section (below the first such row, its weights)
+        rows = states_table[states_table[columns].sum(axis=1) > 0]
+        row = rows.iloc[np.argmin(np.abs(rows["ecm_mev"].to_numpy() - max(ecm, rows["ecm_mev"].min())))]
+        w = row[columns].to_numpy(float)
+    w = np.where(ex < q_value_mev(reaction) + ecm, w, 0.0)
+    if w.sum() <= 0:
+        w = (ex == 0).astype(float)
+    return ex, w / w.sum()
+
+
+def efficiency_of_bin(det, reaction, track, z_range, cos_range, ecm, states_table, n, rng):
+    """Toy for one bin: n reactions with vertices uniform in z_range and cos(theta_cm) uniform in cos_range."""
+    z = rng.uniform(z_range[0], z_range[1], n)
+    t_beam = track.energy_at(z, nominal=True)
+    ex_states, weights = state_weights(reaction, ecm, states_table)
+    ex = rng.choice(ex_states, size=n, p=weights)
+    cos_cm = rng.uniform(cos_range[0], cos_range[1], n)
+    energy, theta, is_open = two_body(t_beam, reaction, ex, cos_cm)
+    is_open &= t_beam > 0
+
+    on_si = is_open & detectors.hits_silicon(det, z, theta)
+    passed = {"eps_si": on_si,
+              "eps_si_thr": on_si & (energy > detectors.SI_THRESHOLD_MEV[det]),
+              "eps_counter": on_si & detectors.crosses_counter(z, theta)}
+    passed["eps_geometric"] = passed["eps_counter"] & detectors.clears_apertures(z, theta)
+
+    phi = detectors.PHI_FRACTION[det]
+    row = {"ground_state_weight": float(weights[ex_states == 0].sum()), "frac_open": float(is_open.mean())}
+    for name, ok in passed.items():
+        p = ok.mean()
+        row[name] = phi * p
+        row[f"{name}_rel_err"] = np.sqrt(p * (1 - p) / n) / p if p > 0 else np.nan     # binomial
+    return row
+
+
+def main(argv=None):
+    parser = argparse.ArgumentParser(description="Step 2: geometric efficiency per bin (detectors.py).")
+    parser.add_argument("config", help="TOML config file")
+    args = parser.parse_args(argv)
+    config, full_path = common.read_config(args.config)
+
+    for det in config.get("dets", [""]):
+        c = common.config_for_detector(config, det)
+        reaction, settings = c["reaction"], c.get("efficiency", {})
+        mode = c.get("mode", "excitation")
+        k = common.ecm_per_beam_energy(reaction)
+        track = common.load_beam_track(c["beam"], full_path)
+        bins, window = common.make_bins(c, track, k)
+        states_table = pd.read_csv(full_path(reaction["states_csv"]), comment="#") if "states_csv" in reaction else None
+        rng = np.random.default_rng(settings.get("seed", 20261016))
+        n = int(settings.get("n_per_bin", 400_000))
+
+        rows = []
+        for _, b in bins.iterrows():
+            if b["dz_mm"] <= 0:                                  # past the beam's stopping point
+                rows.append({name: np.nan for name in EFFICIENCIES})
+                continue
+            if mode == "excitation":
+                ecm = b["ecm_nominal_mev"]
+                cos_range = (-1.0, 1.0)
+            else:
+                ecm = k * track.energy_at(0.5 * (b["z_up_mm"] + b["z_down_mm"]), nominal=True)
+                cos_range = (np.cos(np.radians(b["theta_hi_deg"])), np.cos(np.radians(b["theta_lo_deg"])))
+            rows.append(efficiency_of_bin(det, reaction, track, (b["z_up_mm"], b["z_down_mm"]), cos_range,
+                                          ecm, states_table, n, rng))
+        table = pd.concat([bins.reset_index(drop=True), pd.DataFrame(rows)], axis=1)
+
+        states_text = reaction.get("states_csv", reaction.get("states", "ground state only"))
+        header = [f"{c.get('title', '')} {det.upper()}: geometric efficiency per bin ({mode} mode), detectors.py",
+                  f"beam track: {track.description}; Q = {q_value_mev(reaction):.4f} MeV; detected: "
+                  f"{reaction.get('detect', 'ejectile')}; states: {states_text}",
+                  f"isotropic CM, straight tracks, {n} reactions per bin; azimuthal coverage {detectors.PHI_FRACTION[det]:.4f}",
+                  "eps_geometric = silicon + counter + apertures; eps_si_thr adds the Si threshold at the vertex energy",
+                  "written by geometric_efficiency.py"]
+        out = common.output_file(config, full_path, "eps", det)
+        common.write_csv(table, header, out)
+        x_col = "ecm_nominal_mev" if mode == "excitation" else "theta_c_deg"
+        print(f"--- {det}: -> {out}")
+        print(table[[x_col, *EFFICIENCIES]].to_string(index=False, float_format=lambda v: f"{v:.4g}"))
+
+
+if __name__ == "__main__":
+    main()

cross_section/inputs/talys_27Al_ap.csv

@@ -0,0 +1,182 @@
+# 27Al(a,p)30Si; TALYS 2.2 run /home/vsitaraman/TALYS/runs/al027_a_v3
+# E_cm = E_a,lab * 0.870818 (xs_series.py LAB2CM); grid as calculated, no smoothing
+# sigma_total_mb = sum of the discrete-state columns ex_<Ex MeV> (fig2 'all levels (sum)')
+# group_<label>_mb = states in ch5 tab:level_windows groups; sigma_channel_mb = <prefix>.tot (discrete + continuum)
+# written by tools/xs/02_export_talys.py
+ecm_mev,e_alpha_lab_mev,sigma_total_mb,ex_0.000,ex_2.235,ex_3.498,ex_3.769,ex_3.788,ex_4.810,ex_4.831,ex_5.231,ex_5.279,ex_5.372,ex_5.487,ex_5.614,ex_5.951,ex_6.503,ex_6.537,ex_6.641,ex_6.693,ex_6.744,ex_6.865,ex_6.915,ex_6.999,ex_7.043,ex_7.079,ex_7.223,ex_7.256,ex_7.441,ex_7.508,ex_7.612,ex_7.624,ex_7.634,ex_7.667,ex_7.810,ex_7.912,ex_8.105,ex_8.156,ex_8.163,ex_8.191,ex_8.194,ex_8.290,ex_8.333,ex_8.441,group_3.498_mb,group_5.279_mb,group_5.951_mb,sigma_channel_mb
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run_tr.sh

@@ -99,6 +99,7 @@ if [[ 1 -eq 1 ]]; then
     export DATASET="27Al"
     export PREFIX="Run_"
     export OUT_DIR="Output_p"    
+    export CUTLIST=pid_cuts/pid_cutlist.txt   
     export CATHODE_GAIN=3.0
     rm -f ${OUT_DIR}/*.root
     export source_vertex=-200.0 # Source on the entrance window
@@ -109,6 +110,7 @@ if [[ 1 -eq 1 ]]; then
     parallel --bar -j 8 process_run :::  {17..22}
     hadd -j 4 -k ${OUT_DIR}/Al_protons.root ${OUT_DIR}/results_run0{15..22}.root
     unset CATHODE_GAIN
+    unset CUTLIST
     # exit
 fi