NEural Engine for Discovering Luminous Events (NEEDLE): identifying rare transient candidates in real time from host galaxy images
Bibliographic record
Abstract
Known for their efficiency in analyzing large data sets, machine learning classifiers are widely used in wide-field sky surveys. The upcoming Vera C. Rubin Observatory Legacy of Time and Space Survey (LSST) will generate millions of alerts every night, enabling the discovery of large samples of rare events. Identifying such objects soon after explosion will be essential to study their evolution. This requires a machine learning framework that makes use of all available transient and contextual information. Using $\sim5400$ transients from the ZTF Bright Transient Survey as input data, we develop NEEDLE, a novel hybrid classifier to select for two rare classes with strong environmental preferences: superluminous supernovae (SLSNe) preferring dwarf galaxies, and tidal disruption events (TDEs) occurring in the centres of nucleated galaxies. The input data includes detection and reference images, photometric information from the alert packets, and host galaxy magnitudes from Pan-STARRS. Despite having only a few tens of examples of the rare classes, our average (best) completeness on an unseen test set reaches 77% (93%) for SLSNe and 72% (87%) for TDEs. This may still result in a large fraction of false positives for the rare transients, given the large class imbalance in real surveys. However, the goal of NEEDLE is to find good candidates for spectroscopic classification, rather than to select pure photometric samples. Our network is designed with LSST in mind and we expect performance to improve further with the higher resolution images and more accurate transient and host photometry that will be available from Rubin. Our system will be deployed as an annotator on the UK alert broker, Lasair, to provide predictions to the community in real time.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".