Astrometric and Photometric Standard Candidates for the Upcoming 4-m International Liquid Mirror Telescope Survey
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The International Liquid Mirror Telescope (ILMT) is a 4-m class survey telescope that has recently achieved first light and is expected to swing into full operations by January 1, 2023. It scans the sky in a fixed [Formula: see text] wide strip centered at the declination of [Formula: see text] and works in Time Delay Integration (TDI) mode. We present a full catalog of sources in the ILMT strip that can serve as astrometric calibrators. The characteristics of the sources for astrometric calibration are extracted from Gaia EDR3 as it provides a very precise measurement of astrometric properties such as RA ([Formula: see text]), Dec ([Formula: see text]), parallax ([Formula: see text]), and proper motions ([Formula: see text] & [Formula: see text]). We have crossmatched the Gaia EDR3 with SDSS DR17 and PanSTARRS-1 (PS1) and supplemented the catalog with apparent magnitudes of these sources in [Formula: see text], and i filters. We also present a catalog of spectroscopically confirmed white dwarfs with Sloan Digital Sky Survey (SDSS) magnitudes that may serve as photometric calibrators. The catalogs generated are stored in an SQLite database for query-based access. We also report the offsets in equatorial positions compared to Gaia for an astrometrically calibrated TDI frame observed with the ILMT.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it