Creating A Fitting Algorithm for Exoplanet Detection
Bibliographic record
Abstract
The purpose of this project is to create an accurate fitting function for transiting stars. Data from these transiting stars will ultimately be used to identify new exoplanets. It is very apparent that a fitting function that addresses multiple environmental parameters affecting brightness measurements is needed as exoplanet identification can be a cumbersome and time consuming process. Utilising computer code will streamline the process of exoplanet identification and can then allow researchers to focus their energies solely on data collection. It should be duly noted that in order to collect data from transiting stars; one can use amateur equipment but, for the sake of maximum accuracy; a high powered telescope would be more suitable (“Exoplanet Transit Parameters”, 2008). Previous students at MacEwan that collected transiting star data utilised the Athabasca University Robotic Telescope (AURT) (“Defining the Transit Method”, 2017). This telescope requires booking periods thus, in order to maximize utility and efficiency; more energy should be spent on data collection and result analysis. The current state of exoplanet discovery is both innovative and exciting. In fact in February of 2017, a team of astronomers at the University of Liege in Belgium discovered four more Earth-sized exoplanets (bringing the total up to seven) orbiting Trappist-1 which is a star that is classified as an ultracool dwarf (“Astronomers Discover”, 2017). Another discovery would be that of Proxima b, the exoplanet that is closest to our solar system (“An Earth-like Atmosphere”, 2017). Astronomers are currently debating whether or not Proxima b will be able to sustain life. Thus, it can be shown that exoplanet discovery and analysis yields results and is vital for our understanding of the universe. Works Cited Garner, R. (2017, July 31). An Earth-like Atmosphere May Not Survive Proxima b’s Orbit. Retrieved January 21, 2018, from https://www.nasa.gov/feature/goddard/2017/an-earth-like- atmosphere-may-not-survive-proxima-b-s-orbit Kopchuk, T. (2017, September 22). Defining the Transit Method Observation Limits of the Athabasca University Robotic Telescope. Retrieved January 8, 2018. Pejcha, O. (2008, September 7). Exoplanet transit parameters from amateur-astronomers observations. Retrieved January 16, 2018. Scharf, C. A. (2012, January 20). An Abundance of Exoplanets Changes our Universe. Retrieved January 21, 2018, from https://blogs.scientificamerican.com/life-unbounded/an- abundance-of-exoplanets-changes-our-universe/ *Indicates supervisor
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.015 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".