Using LSST Microlensing to Constrain Dark Compact Objects in Spherical and Disk Configurations
Bibliographic record
Abstract
Abstract The Legacy Survey of Space and Time (LSST) with the Vera Rubin Observatory will provide strong microlensing constraints on dark compact objects (DCOs) in our Galaxy. However, most current forecasts limit their analysis to Primordial Black Holes (PBH). It is unclear how well LSST microlensing will be able to constrain alternative models of DCOs with different Galactic spatial profile distributions at a subdominant DM fraction. In this work, we investigate how well LSST microlensing will constrain spherical or disk-like Galactic spatial distributions of DCOs, taking into account extended observing times, baryonic microlensing background, and sky distribution of LSST sources. These extensions represent significant improvements over existing microlensing forecasts in terms of both accuracy and versatility. We demonstrate this power by deriving new LSST sensitivity projections for DCOs in spherical and disk-like distributions. We forecast that LSST will be able to constrain one-solar-mass PBHs to have a DM fraction under 4.1 × 10−4. One-solar-mass objects in a dark disk distribution with the same dimensions as the Galactic disk will be constrained below 3.1 × 10−4, while those with m = 105 M ⊙ will be constrained to below 3.4 × 10−5. We find that compressed dark disks can be constrained up to a factor of ∼10 better than ones with identical dimensions to the baryonic disk. We also find that dark disks become less tightly constrained when they are tilted with respect to our own disk. This forecasting software is a versatile tool, capable of constraining any model of DCOs in the Milky Way with microlensing, and is made publicly available .
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 | 0.001 | 0.002 |
| 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 | 0.002 | 0.001 |
| 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".