FINDING, CHARACTERIZING, AND CLASSIFYING VARIABLE SOURCES IN MULTI-EPOCH SKY SURVEYS: QSOs AND RR LYRAE IN PS1 3π DATA
Bibliographic record
Abstract
ABSTRACT In area and depth, the Pan-STARRS1 (PS1) 3π survey is unique among many-epoch, multi-band surveys and has enormous potential for the all-sky identification of variable sources. PS1 has observed the sky typically seven times in each of its five bands (grizy) over 3.5 years, but unlike SDSS, not simultaneously across the bands. Here we develop a new approach for quantifying statistical properties of non-simultaneous, sparse, multi-color light curves through light curve structure functions, effectively turning PS1 into a ∼35-epoch survey. We use this approach to estimate variability amplitudes and timescales (ω r , τ) for all point sources brighter than r P1 = 21.5 mag in the survey. With PS1 data on SDSS Stripe 82 as “ground truth,” we use a Random Forest Classifier to identify QSOs and RR Lyrae based on their variability and their mean PS1 and WISE colors. We find that, aside from the Galactic plane, QSO and RR Lyrae samples of purity ∼75% and completeness ∼92% can be selected. On this basis we have identified a sample of ∼1,000,000 QSO candidates, as well as an unprecedentedly large and deep sample of ∼150,000 RR Lyrae candidates with distances from ∼10 to ∼120 kpc. Within the Draco dwarf spheroidal, we demonstrate a distance precision of 6% for RR Lyrae candidates. We provide a catalog of all likely variable point sources and likely QSOs in PS1, a total of 25.8 × 106 sources.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 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".