Testing LMC Microlensing Scenarios: The Discrimination Power of the SuperMACHO Microlensing Survey
Bibliographic record
Abstract
Characterizing the nature and spatial distribution of the lensing objects that produce the previously measured microlensing optical depth toward the Large Magellanic Cloud (LMC) remains an open problem. We present an appraisal of the ability of the SuperMACHO Project, a next-generation microlensing survey directed toward the LMC, to discriminate between various proposed lensing populations. We consider two scenarios: lensing by a uniform foreground screen of objects and self-lensing by LMC stars. The optical depth for "screen lensing" is essentially constant across the face of the LMC, whereas the optical depth for self-lensing shows a strong spatial dependence. We have carried out extensive simulations, based on data obtained during the first year of the project, to assess the SuperMACHO survey's ability to discriminate between these two scenarios. In our simulations we predict the expected number of observed microlensing events for various LMC models for each of our fields by adding artificial stars to the images and estimating the spatial and temporal efficiency of detecting microlensing events using Monte Carlo methods. We find that the event rate itself shows significant sensitivity to the choice of the LMC luminosity function, limiting the conclusions that can be drawn from the absolute rate. If instead we determine the differential event rate across the LMC, we will decrease the impact of these systematic biases and render our conclusions more robust. With this approach the SuperMACHO Project should be able to distinguish between the two categories of lens populations. This will provide important constraints on the nature of the lensing objects and their contributions to the Galactic dark matter halo.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".