First microlensing candidates from the MEGA survey of M 31
Bibliographic record
Abstract
We present the first M 31 candidate microlensing events from the Microlensing Exploration of the Galaxy and Andromeda (MEGA) survey. MEGA uses several telescopes to detect microlensing towards the nearby Andromeda galaxy, M 31, in order to establish whether massive compact objects are a significant contribution to the mass budget of the dark halo of M 31. The results presented here are based on observations with the Isaac Newton Telescope on La Palma, during the 1999/00 and 2000/01 observing seasons. In this data set, 14 variable sources consistent with microlensing have been detected, 12 of which are new and 2 have been reported previously by the POINT-AGAPE group. A preliminary analysis of the spatial and timescale distributions of the candidate events support their microlensing nature. We compare the spatial distributions of the candidate events and of long-period variable stars, assuming the chances of finding a long-period variable and a microlensing event are comparable. The spatial distribution of our candidate microlensing events is more far/near side asymmetric than expected from the detected long-period variable distribution. The current analysis is preliminary and the asymmetry not highly significant, but the spatial distribution of candidate microlenses is suggestive of the presence of a microlensing halo.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 | 0.000 |
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".