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Record W2032385062 · doi:10.1088/0004-6256/149/4/146

FIRST PRECISION LIGHT CURVE ANALYSIS OF THE NEGLECTED EXTREME MASS RATIO SOLAR-TYPE BINARY HR BOOTIS

2015· article· en· W2032385062 on OpenAlexaff
R. G. Samec, Barry Benkendorf, James B. Dignan, R. M. Robb, J. Kring, D. R. Faulkner

Bibliographic record

VenueThe Astronomical Journal · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstro and Planetary Science
Canadian institutionsDominion Astrophysical ObservatoryUniversity of Victoria
Fundersnot available
KeywordsPhysicsLight curveAstrophysicsMass ratioEphemerisContact binaryOrbital periodBinary numberRoche lobePolarBinary starAstronomyStars

Abstract

fetched live from OpenAlex

HR Bootis is a neglected binary that is found to be a solar-type (G2V) extreme mass ratio binary (EMRB). It was discovered by Hanley & Shapley in 1940. Surprisingly, little has been published in the intervening years. In 1999 it was characterized by a 0.31587 day orbital period. Since that time it has been observed by various observers who have determined ∼20 timings of minimum light over the past ∼15,000 orbits. Our observations in 2012 represent the first precision curves in the BVRcIc Johnson–Cousins wavelength bands. The light curves have rather low amplitudes, averaging some 0.5 magnitudes, yet they exhibit total eclipses, which is typical of the rare group of solar-type EMRBs. An improved linear ephemeris was computed along with a quadratic ephemeris showing a decaying orbit, which indicates magnetic breaking may be occurring. The light curve solution reveals that HR Boo is a contact system with a somewhat low 21% Roche-lobe fill-out but a mass ratio of q = 4.09 (0.2444), which defines it as an EMRB. Two spots, both hot, were allowed to iterate to fit the light curve asymmetries. Their radii are 32° and 16°. Both are high-latitude polar spots indicative of strong magnetic activity. The shallow contact yet nearly equal component temperatures makes it an unusual addition to this group.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score0.776

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.025
GPT teacher head0.226
Teacher spread0.201 · 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 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

Citations8
Published2015
Admission routes1
Has abstractyes

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