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Record W2946714617

Exoplanet Detection Limits of the Athabasca University Robotic Telescope (AURT)

2017· article· en· W2946714617 on OpenAlexaff
Jared Fairbanks

Bibliographic record

VenueStudent Research Proceedings · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstronomy and Astrophysical Research
Canadian institutionsMacEwan University
Fundersnot available
KeywordsExoplanetTelescopePhysicsAstronomyStarlightPlanetTransit (satellite)Spitzer Space TelescopeSatelliteAstrobiologyStarsEngineering
DOInot available

Abstract

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The discovery and study of exoplanets is an important area of inquiry in the field of astronomy. The transit method is a powerful technique for discovering and studying exoplanets. When the orbital plane of an exoplanet is aligned with the Earth a transit may be observed. As the exoplanet transits, it blocks some of the starlight causing the total flux from the star to drop. With the proper equipment this decrease in the brightness of the star can be measured. The result is a light curve that shows the brightness of the star decreasing as the exoplanet travels in front of it and then returns to its original level when the exoplanet is no longer eclipsing the star. The objectives of this research project are to demonstrate the sensitivity with which exoplanet orbital parameters can be derived using the Athabasca University Robotic Telescope (AURT), and to determine the exoplanet detection limit of this telescope. Satellite survey missions are an important source for finding new exoplanets. But launching satellites is difficult and expensive. Time on space based satellite is also in limited supply. As such there is a need for more Earth based surveys using small telescopes. It is hoped that the work of this research project will be the foundation on which a search for new exoplanets using the AURT can begin. Discipline: Earth and Planetary Sciences Faculty Mentor: Dr. Stefan Cartledge

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.093
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.001
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 head0.067
GPT teacher head0.365
Teacher spread0.298 · 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.

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

Citations0
Published2017
Admission routes1
Has abstractyes

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