An algorithm to resolve <i>γ</i> -rays from charged cosmic rays with DAMPE
Bibliographic record
Abstract
Abstract The DArk Matter Particle Explorer (DAMPE), also known as Wukong in China, which was launched on 2015 December 17, is a new high energy cosmic ray and γ -ray satellite-borne observatory. One of the main scientific goals of DAMPE is to observe GeV-TeV high energy γ -rays with accurate energy, angular and time resolution, to indirectly search for dark matter particles and for the study of high energy astrophysics. Due to the comparatively higher fluxes of charged cosmic rays with respect to γ -rays, it is challenging to identify γ -rays with sufficiently high efficiency, minimizing the amount of charged cosmic ray contamination. In this work we present a method to identify γ -rays in DAMPE data based on Monte Carlo simulations, using the powerful electromagnetic/hadronic shower discrimination provided by the calorimeter and the veto detection of charged particles provided by the plastic scintillation detector. Monte Carlo simulations show that after this selection the number of electrons and protons that contaminate the selected γ -ray events at ∼ 10GeV amounts to less than 1% of the selected sample. Finally, we use flight data to verify the effectiveness of the method by highlighting known γ -ray sources in the sky and by reconstructing preliminary light curves of the Geminga pulsar.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".