Microlensing Optical Depth toward the Galactic Bulge Using Clump Giants from the MACHO Survey
Bibliographic record
Abstract
Using 7 yr of MACHO survey data, we present a new determination of the optical depth to microlensing toward the Galactic bulge. We select the sample of 62 microlensing events (60 unique) on clump giant sources and perform a detailed efficiency analysis. We use only the clump giant sources because these are bright bulge stars and are not as strongly affected by blending as other events. Using a subsample of 42 clump events concentrated in an area of 4.5 deg 2 with 739,000 clump giant stars, we find τ = 2.17 × 10 -6 at ( l , b ) = (1 50, - 2 68), somewhat smaller than found in most previous MACHO studies but in excellent agreement with recent theoretical predictions. We also present the optical depth in each of the 19 fields in which we detected events and find limits on optical depth for fields with no events. The errors in optical depth in individual fields are dominated by Poisson noise. We measure optical depth gradients of (1.06 ± 0.71) × 10 -6 deg -1 and (0.29 ± 0.43) × 10 -6 deg -1 in the Galactic latitude b and longitude l directions, respectively. Finally, we discuss the possibility of anomalous duration distribution of events in the field 104 centered on ( l , b ) = (3 11, - 3 01), as well as investigate spatial clustering of events in all fields.
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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 | 0.002 | 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) | 0.001 | 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".