Fermi Unassociated Sources in the MeerKAT Absorption Line Survey
Bibliographic record
Abstract
Abstract Over 2000 γ-ray sources identified by the Large Area Telescope on NASA's Fermi Gamma-ray Space Telescope are considered unassociated, meaning that they have no known counterparts in any other frequency regime. We have carried out an image-based search for steep spectrum radio sources, with in-band spectral index < −1.4, within the error regions of Fermi unassociated sources using 1–1.4 GHz radio data from the MeerKAT Absorption Line Survey (MALS) data release. The first MALS data release with a median rms noise of 22–25 μJy and 735,649 sources is a significant advance over past image-based searches with improvements in sensitivity, resolution, and bandwidth. Steep spectrum candidates were identified using a combination of in-band spectral indices from MALS and existing radio surveys. We developed an optical and infrared source classification scheme in order to distinguish between Galactic pulsars and radio galaxies. In total, we identify nine pulsar candidates toward six Fermi sources that are worthy of follow-up for pulsation searches. We also report 41 steep spectrum radio galaxy candidates that may be of interest in searches for high-redshift radio galaxies. We show that MALS, due to its excellent continuum sensitivity, can detect 80% of the known pulsar population. This exhibits the promise of identifying exotic pulsar candidates with future image-based surveys with the Square Kilometre Array and its precursors.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".