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Record W2088204237 · doi:10.1088/0004-6256/145/3/79

PRECISE HIGH-CADENCE TIME SERIES OBSERVATIONS OF FIVE VARIABLE YOUNG STARS IN AURIGA WITH<i>MOST</i>

2013· article· en· W2088204237 on OpenAlexaff
Ann Marie Cody, Jamie Tayar, Lynne A. Hillenbrand, J. M. Matthews, T. Kallinger

Bibliographic record

VenueThe Astronomical Journal · 2013
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Star Formation Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPhysicsAstrophysicsT Tauri starLight curveStarsPhotometry (optics)AstronomyBrightnessVariable starPleiadesStarspot

Abstract

fetched live from OpenAlex

To explore young star variability on a large range of timescales, we have used the MOST satellite to obtain 24 days of continuous, sub-minute cadence, high-precision optical photometry on a field of classical and weak-lined T Tauri stars (TTSs) in the Taurus-Auriga star formation complex. Observations of AB Aurigae, SU Aurigae, V396 Aurigae, V397 Aurigae, and HD 31305 reveal brightness fluctuations at the 1%-10% level on timescales of hours to weeks. We have further assessed the variability properties with Fourier, wavelet, and autocorrelation techniques, identifying one significant period per star. We present spot models in an attempt to fit the periodicities, but find that we cannot fully account for the observed variability. Rather, all stars exhibit a mixture of periodic and aperiodic behavior, with the latter dominating stochastically on timescales less than several days. After removal of the main periodicity, periodograms for each light curve display power-law trends consistent with those seen for other young accreting stars. Several of our targets exhibited unusual variability patterns not anticipated by prior studies, and we propose that this behavior originates with the circumstellar disks. The MOST observations underscore the need for investigation of TTS light variations on a wide range of timescales in order to elucidate the physical processes responsible; we provide guidelines for future time series observations.

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 score0.400
Threshold uncertainty score0.491

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 communication0.0000.001
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 head0.007
GPT teacher head0.192
Teacher spread0.185 · 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

Citations26
Published2013
Admission routes1
Has abstractyes

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