The MACHO Project: Microlensing Results from 5.7 Years of Large Magellanic Cloud Observations
Bibliographic record
Abstract
We report on our search for microlensing toward the Large Magellanic Cloud (LMC). Analysis of 5.7 yr of photometry on 11.9 million stars in the LMC reveals 13-17 microlensing events. A detailed treatment of our detection efficiency shows that this is significantly more than the ~2-4 events expected from lensing by known stellar populations. The timescales ( ) of the events range from 34 to 230 days. We estimate the microlensing optical depth toward the LMC from events with 2 < < 400 days to be τ = 1.2 × 10 -7 , with an additional 20% to 30% of systematic error. The spatial distribution of events is mildly inconsistent with LMC/LMC disk self-lensing, but is consistent with an extended lens distribution such as a Milky Way or LMC halo. Interpreted in the context of a Galactic dark matter halo, consisting partially of compact objects, a maximum-likelihood analysis gives a MACHO halo fraction of 20% for a typical halo model with a 95% confidence interval of 8%-50%. A 100% MACHO halo is ruled out at the 95% confidence level for all except our most extreme halo model. Interpreted as a Galactic halo population, the most likely MACHO mass is between 0.15 and 0.9 M ☉ , depending on the halo model, and the total mass in MACHOs out to 50 kpc is found to be 9 × 10 10 M ☉ , independent of the halo model. These results are marginally consistent with our previous results, but are lower by about a factor of 2. This is mostly due to Poisson noise, because with 3.4 times more exposure and increased sensitivity to long-timescale events, we did not find the expected factor of ~4 more events. In addition to a larger data set, this work also includes an improved efficiency determination, improved likelihood analysis, and more thorough testing of systematic errors, especially with respect to the treatment of potential backgrounds to microlensing. We note that an important source of background are supernovae (SNe) in galaxies behind the LMC.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".