Intermediate-Mass Stripped Stars in the Magellanic Clouds: Forward Modeling the Observed Population Discovered Via UV Excess
Bibliographic record
Abstract
Stripped stars are hot, helium-rich stars formed when binary interactions remove a star's hydrogen envelope. While low-mass ($\lesssim 1 M_\odot$) and high-mass ($\gtrsim 8 M_\odot$) stripped stars are well studied as hot subdwarfs and Wolf-Rayet stars, their intermediate-mass counterparts ($1-8 M_\odot$) have only recently been discovered. The Stripped-Star Ultraviolet Magellanic Cloud Survey (SUMS) identified UV-excess sources (i.e., sources lying blueward of the main sequence) in the Magellanic Clouds using Swift-UVOT photometry and selected 820 photometric stripped-star candidates. However, the completeness and purity of this sample remain poorly understood. We forward model the population of stripped stars in the Magellanic Clouds using a binary population synthesis model combined with spatially resolved star formation histories and simulated UV photometry. To assess survey sensitivity, we inject simulated sources into real Swift-UVOT images and reproduce the SUMS selection process, including crowding, extinction, and photometric quality cuts. For the SMC and LMC, we respectively recover 31\% and 15\% of sources with intrinsic UV excess, and $11\%$ and $7\%$ of all stripped stars. The rest are missed due to dilution by luminous companions, crowding, high extinction, and limited survey coverage. The observed population is biased toward systems with low-mass companions formed by common envelope evolution and systems with compact object companions. We predict contamination of the observed stripped-star candidates by main-sequence stars with spurious UV excess due to crowding and provide guidelines for selecting higher-purity subsamples. Our population synthesis model matches the number and properties of observed stripped star candidates with masses of $(2-5) M_\odot$ well. It underpredicts the number of lower- and higher-mass candidates, perhaps due to contamination.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".