Towards Quantifying Veto Power with the LDMX HCal prototype
Bibliographic record
Abstract
According to astronomical observations of galaxies and other large scale dynamics, the universe contains more mass than what is visible. This missing mass is called "dark matter" and have been a subject of study for physicists for decades. The Light Dark Matter eXperiment (LDMX) is a proposed experiment with the purpose to search for dark matter in a low mass range that not many experiments have probed to date. LDMX is a fixed target experiment utilizing electrons scattering in a tungsten target to produce dark matter. The creation of dark matter is then inferred through missing momentum and energy in the final state using high precision measurements. One of the tools that are vital to LDMX is the hadronic calorimeter (HCal), which is responsible for detecting and vetoing neutral hadrons in the final state. A prototype of this detector was tested at CERN in 2022. This thesis uses data taken during the test beam and builds on previous analysis to investigate potential limitations to the HCal's veto power. Monte Carlo simulation data is produced and used as an ideal baseline for for comparison with real data. The HCal's veto power is probed partly through producing particle detection efficiencies in comparison with the simulated data. Limitations to the veto power caused by signal- and error correlations in the prototype are then investigated. The HCal prototype is found to behave well in terms of detection efficiency when compared to Monte Carlo data and the effect of correlated signals are found to not significantly impact the detectors veto power. The effect of readout errors and correlations between them are, however, argued to potentially pose issues to veto power.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".