The Time-domain Spectroscopic Survey: Target Selection for Repeat Spectroscopy
Bibliographic record
Abstract
Abstract As astronomers increasingly exploit the information available in the time domain, spectroscopic variability in particular opens broad new channels of investigation. Here we describe the selection algorithms for all targets intended for repeat spectroscopy in the Time Domain Spectroscopic Survey (TDSS), part of the extended Baryon Oscillation Spectroscopic Survey within the Sloan Digital Sky Survey (SDSS)-IV. Also discussed are the scientific rationale and technical constraints leading to these target selections. The TDSS includes a large “repeat quasar spectroscopy” (RQS) program delivering ∼13,000 repeat spectra of confirmed SDSS quasars, and several smaller “few-epoch spectroscopy” (FES) programs targeting specific classes of quasars as well as stars. The RQS program aims to provide a large and diverse quasar data set for studying variations in quasar spectra on timescales of years, a comparison sample for the FES quasar programs, and an opportunity for discovering rare, serendipitous events. The FES programs cover a wide variety of phenomena in both quasars and stars. Quasar FES programs target broad absorption line quasars, high signal-to-noise ratio normal broad line quasars, quasars with double-peaked or very asymmetric broad emission line profiles, binary supermassive black hole candidates, and the most photometrically variable quasars. Strongly variable stars are also targeted for repeat spectroscopy, encompassing many types of eclipsing binary systems, and classical pulsators like RR Lyrae. Other stellar FES programs allow spectroscopic variability studies of active ultracool dwarf stars, dwarf carbon stars, and white dwarf/M dwarf spectroscopic binaries. We present example TDSS spectra and describe anticipated sample sizes and results.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".