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Record W4399784579 · doi:10.25518/0037-9565.11899

Astrometric and Photometric Calibrators for the 4-m International Liquid Mirror Telescope

2024· article· en· W4399784579 on OpenAlexfundno aff
Naveen Dukiya, Bhavya Ailawadhi, Talat Akhunov, E. F. Borra, Monalisa Dubey, Jiuyang Fu, Baldeep Grewal, Paul Hickson, Brajesh Kumar, Kuntal Misra, Vibhore Negi, Kumar Pranshu, Ethen Sun, Jean Surdej

Bibliographic record

VenueBulletin de la Société Royale des Sciences de Liège · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdaptive optics and wavefront sensing
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaService Public de WallonieUniversité de LiègeBelgian Federal Science Policy OfficeFonds De La Recherche Scientifique - FNRSDepartment of Science and Technology, Ministry of Science and Technology, IndiaYork University
KeywordsTelescopePhysicsAstronomyOptical telescopePhotometry (optics)OpticsStars

Abstract

fetched live from OpenAlex

The International Liquid Mirror Telescope (ILMT) is a 4-meter class survey telescope. It achieved its first light on 29th April 2022 and is now undergoing the commissioning phase. It scans the sky in a fixed wide strip centred at the declination of and works in Time Delay Integration (TDI) mode. We present a full catalog of sources in the ILMT strip derived by crossmatching Gaia DR3 with SDSS DR17 and PanSTARRS-1 (PS1) to supplement the catalog with apparent magnitudes of these sources in g, r, and i filters. These sources can serve as astrometric calibrators. The release of Gaia DR3 provides synthetic photometry in popular broadband photometric systems, including the SDSS g, r, and i bands for ∼220 million sources across the sky. We have used this synthetic photometry to verify our crossmatching performance and, in turn, create a subset of the catalog with accurate photometric measurements from two reliable sources.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.699
Threshold uncertainty score0.604

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.030
GPT teacher head0.323
Teacher spread0.293 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2024
Admission routes1
Has abstractyes

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