A Systematic Search for Outbursting AM CVn Systems with the Zwicky Transient Facility
Bibliographic record
Abstract
Abstract AM CVn systems are a rare type of accreting binary that consists of a white dwarf and a helium-rich, degenerate donor star. Using the Zwicky Transient Facility (ZTF), we searched for new AM CVn systems by focusing on blue, outbursting stars. We first selected outbursting stars using the ZTF alerts. We cross matched the candidates with Gaia and Pan-STARRS catalogs. The initial selection of candidates based on the Gaia BP-RP contains 1751 unknown objects. We used the Pan-STARRS g-r and r-i color in combination with the Gaia color to identify 59 strong AM CVn candidates. We obtained identification spectra of 35 sources, of which 18 are high-priority candidates, and discovered nine new AM CVn systems and one magnetic CV that shows only He-ii lines. Using the outburst recurrence time, we estimate the orbital periods of the nine new AM CVn systems that are in the range of 29–50 minutes. We conclude that targeted follow up of blue, outbursting sources is an efficient method to find new AM CVn systems and we plan to follow up all candidates we identified to systematically study the population of outbursting AM CVn systems.
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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 | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 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".