The SuperMACHO Microlensing Survey
Bibliographic record
Abstract
We present the first results from our next-generation microlensing survey, the SuperMACHO project. We are using the CTIO 4m Blanco telescope and the MOSAIC imager to carry out a search for microlensing toward the Large Magellanic Cloud (LMC). We plan to ascertain the nature of the population responsible for the excess microlensing rate seen by the MACHO project. Our observing strategy is optimized to measure the differential microlensing rate across the face of the LMC. We find this derivative to be relatively insensitive to the details of the LMC's internal structure but a strong discriminant between Galactic halo and LMC self lensing. In December 2003 we completed our third year of survey operations. 2003 also marked the first year of real-time microlensing alerts and photometric and spectroscopic followup. We have extracted several dozen microlensing candidates, and we present some preliminary light curves and related information. Similar to the MACHO project, we find SNe behind the LMC to be a significant contaminant - this background has not been completely removed from our current single-color candidate sample. Our follow-up strategy is optimized to discriminate between SNe and true microlensing.To search for other articles by the author(s) go to: http://adsabs.harvard.edu/abstract_service.html
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".