Characterization of a Maximum Likelihood Gamma-Ray Reconstruction Algorithm for VERITAS
Bibliographic record
Abstract
We characterize the improved angular and energy resolution of a new likelihood gamma-ray reconstruction for VERITAS. The algorithm uses the average photoelectrons stored in templates that are based on simulations of large numbers of showers as a function of 5 gamma-ray parameters: energy, zenith angle, core location (x,y), and depth of first interaction in the atmosphere. Comparing the template predictions of the average photoelectrons in each pixel to observed photoelectrons allows us to calculate the likelihood. By maximizing the likelihood, we find the optimal gamma-ray parameters. The maximum likelihood reconstruction improves on the standard VERITAS analysis which relies on: 1. the weighted average of the axis of elongation in the images to determine the gamma-ray direction and 2. look-up tables that relate the observed energy deposition of Cherenkov photons to the true gamma-ray energy. Not only is the maximum likelihood method more accurate, but it is also not biased by missing pixel information due to the edge of the camera or pixel cleaning. The drawback is that it takes more CPU time (80 ms/event).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".