Hadronic vs. electromagnetic pulse shape discrimination in CsI(Tl) for high energy physics experiments
Bibliographic record
Abstract
Pulse shape discrimination using CsI(Tl) scintillators to perform neutral hadron particle identification is explored with emphasis towards application at high energy electron-positron collider experiments. Through the analysis of the pulse shape differences between scintillation pulses from photon and hadronic energy deposits using neutron and proton data collected at TRIUMF, it is shown that the pulse shape variations observed for hadrons can be modelled using a third scintillation component for CsI(Tl), in addition to the standard fast and slow components. Techniques for computing the hadronic pulse amplitudes and shape variations are developed and it is shown that the intensity of the additional scintillation component can be computed from the ionization energy loss of the interacting particles. These pulse modelling and simulation methods are integrated with GEANT4 simulation libraries and the predicted pulse shape for CsI(Tl) crystals in a $5 \times 5$ array of $5 \times 5 \times 30 \text{ cm}^3$ crystals is studied for hadronic showers from 0.5 and 1 GeV/c $K_L^0$ and neutron particles. Using a crystal level and cluster level approach for photon vs hadron cluster separation we demonstrate proof-of-concept for neutral hadron detection using CsI(Tl) pulse shape discrimination in high energy electron-positron collider experiments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".