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Record W3201669646 · doi:10.1093/mnras/stab2850

Ultraluminous X-ray sources in seven edge-on spiral galaxies

2021· article· en· W3201669646 on OpenAlexafffund
Kristen C. Dage, Noah Vowell, Erica Thygesen, Arash Bahramian, Daryl Haggard, Konstantinos Kovlakas, Arunav Kundu, Thomas J. Maccarone, Jay Strader, Ryan Urquhart, Stephen E. Zepf

Bibliographic record

VenueMonthly Notices of the Royal Astronomical Society · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysical Phenomena and Observations
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaNational Aeronautics and Space AdministrationCanada Research ChairsSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungRegional Studies AssociationJet Propulsion Laboratory
KeywordsPhysicsAstrophysicsBulgeSpiral galaxyGalaxyHaloAstronomyPoint sourceLenticular galaxyElliptical galaxyOptics

Abstract

fetched live from OpenAlex

ABSTRACT We investigate a sample of seven edge-on spiral galaxies using Chandra observations. Edge-on spiral galaxies allow us to clearly separate source associated with their star-forming regions versus the outer edges of the system, offering a clear advantage over other systems. We uncover a number of X-ray point sources across these galaxies, and after eliminating contaminating foreground and background sources, we identify 12 candidate ultraluminous X-ray sources (ULXs). All of these sources are projected on to the central regions, implying that the majority of ULXs in this sample of spiral galaxies are disc/bulge, and thus not halo sources. This also includes two transient ULXs, which may be long-duration transients and low-mass X-ray binaries. This finding illustrates the need for further studies of transient ULXs.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.010
GPT teacher head0.205
Teacher spread0.195 · 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 designObservational
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

Citations5
Published2021
Admission routes2
Has abstractyes

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