Following the Path of Individual Beam Electrons through LDMX
Bibliographic record
Abstract
The Light Dark Matter Experiment (LDMX) is a fixed-target missing-momentum experiment which hopes to detect dark matter in the MeV-GeV mass range. This is done by firing an 8 GeV electron beam at a thin tungsten target, where the electrons produce dark matter through a process called dark bremsstrahlung. Electrons which have gone through dark matter production can then be found by looking for missing energy in the detector. As the electron beam fires incredibly large amounts of particles at the target, many of which are not expected to do anything, a trigger system is set in place, which singles out events with no missing energy and discards the corresponding data. As of now this trigger works by looking at the full energy of all simultaneously incoming electrons. This leads to a decrease in trigger efficiency when several electrons enter the detector at once, as the energy resolution of the detector degrades at higher energies. In order to improve the performance of the trigger, a matching algorithm is developed, which should allow for the study of individual electron energy instead of the grouped energy of all electrons together, thereby removing pile-up electrons which have not lost any energy to dark matter production. This project focuses on the performance of this matching algorithm in the case when two electrons enter the detector at once. It finds that the algorithm works nominally for the single-electron case, but struggles in the when two electrons enter the detector at once, especially when their energy deposits are spatially close or overlap. In the case when all incoming electrons are matched, studies of the energy distributions point towards the matching algorithm being viable for use for pile-up removal. It is also shown to have a potential use in cases where only part of the incoming electrons are matched.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".