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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 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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.899
Threshold uncertainty score0.155

Codex and Gemma teacher scores by category

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

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 head0.015
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 teacher head, not a consensus.

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