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Record W4399658323 · doi:10.1117/12.3018958

Realizing the potential of the Dragonfly Spectral Line Mapper: calibration methods and on-sky performance

2024· article· en· W4399658323 on OpenAlexaff
Deborah Lokhorst, Seery Chen, Imad Pasha, Victoria Purcell, W. Paul Bowman, Qing Liu, Zili Shen, Aidan MacNichol, Evgeni I. Malakhov, Roberto Abraham, Pieter van Dokkum

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdaptive optics and wavefront sensing
Canadian institutionsUniversity of TorontoHerzberg Institute of Astrophysics
Fundersnot available
KeywordsCalibrationSkyDragonflyLine (geometry)Remote sensingComputer scienceGeologyAstronomyPhysicsMathematics

Abstract

fetched live from OpenAlex

The Dragonfly Spectral Line Mapper is an innovative all-refracting telescope designed to carry out ultra-low surface brightness wide-field mapping of visible wavelength line emission. Equipped with ultranarrowband (0.8nm bandwidth) filters mounted in Dragonfly Filter-Tilter instrumentation, the Dragonfly Spectral Line Mapper maps Hα, [NⅡ]λ6583, and [OⅢ]λ5007 line emission produced by structures with sizes ranging from ∼1 to 1000kpc in the local Universe. These spatial scales encompass that of the exceedingly diffuse and faintly radiating circumgalactic medium, which is singularly difficult to detect with conventional mirror-based telescope instrumentation. Extremely careful control of systematics is required to directly image these large scale structures, necessitating high fidelity sky background subtraction, wavelength calibration, and specialized flat-fielding methods. In this paper, we discuss the on-sky performance of the Dragonfly Spectral Line Mapper with these methods in place.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.015
GPT teacher head0.283
Teacher spread0.268 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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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