Follow-up strategy of ILMT discovered supernovae
Bibliographic record
Abstract
The 4m International Liquid Mirror Telescope (ILMT) facility continuously scans the same sky strip ($\sim$22$^\prime$ wide) on each night with a fixed pointing towards the zenith direction. It is possible to detect hundreds of supernovae (SNe) each year by implementing an optimal image subtraction technique on consecutive night images. Prompt monitoring of ILMT-detected SNe is planned under the secured target of opportunity mode using ARIES telescopes (1.3m DFOT and 3.6m DOT). Spectroscopy with the DOT facility will be useful for the classification and detailed investigation of SNe. During the commissioning phase of the ILMT, supernova (SN) 2023af was identified in the ILMT field of view. The SN was further monitored with the ILMT and DOT facilities. Preliminary results based on the light curve and spectral features of SN 2023af are presented.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".