Search for gamma rays from SNe with a variable-size sliding-time-window analysis of the <i>Fermi</i>-LAT data
Bibliographic record
Abstract
ABSTRACT We present a systematic search for γ-ray emission from supernovae (SNe) in the Fermi Large Area Telescope (LAT) Pass 8 data. The sample of targets consists of 55,880 candidates from the Open Supernova Catalogue. We searched for γ rays from SNe by means of a variable-size sliding-time-window analysis. Our results confirm the presence of transient γ-ray emission from the sources of non-AGN classes, including transitional pulsars, solar flares, γ-ray bursts, novae, and the Crab Nebula, which are projected near some of these SN’s positions, and also strengthen support to the variable signal in the direction of SN iPTF14hls. The analysis is successful in finding both short (e.g. solar flares) and long (e.g. transitional pulsars) high flux states. Our search reveals two new γ-ray transient signals occurred in 2019 in the directions of optical transients that are SN candidates, AT2019bvr and AT2018iwp, with their flux increases within 6 months after the dates of SN’s discoveries. These signals are bright and their variability is at a higher statistical level than that of iPTF14hls. An exploration of archival multiwavelength observations towards their positions is necessary to establish their association with SNe or other classes of sources. Our analysis, in addition, shows a bright transient γ-ray signal at low Galactic latitudes in the direction of PSR J0205+6449. In addition, we report the results of an all-sky search for γ-ray transient sources. This provided two additional candidates to gamma-ray transient sources.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| 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) | 0.001 | 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 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".