Searching For Transiting Planets Around One Million Stars
Open Access
- Author:
- Melton, Elizabeth
- Graduate Program:
- Astronomy and Astrophysics
- Degree:
- Doctor of Philosophy
- Document Type:
- Dissertation
- Date of Defense:
- July 06, 2022
- Committee Members:
- Ian Czekala, Major Field Member
Rebekah Dawson, Major Field Member
Jason Wright, Major Field Member
Joel Hartman, Special Member
Hyungsuk Tak, Outside Unit & Field Member
Eric Feigelson, Chair & Dissertation Advisor
Rebekah Dawson, Program Head/Chair - Keywords:
- exoplanets
machine learning - Abstract:
- This dissertation focused on identifying high-quality planet candidates from the Transiting Exoplanet Satellite Survey (TESS) in a careful and well-characterized manner that allows for analysis and evaluation of both the methodology and the resulting candidate list. Year 1 southern hemisphere TESS full frame image light curves extracted with the DIAmante pipeline are processed through the AutoRegressive Planet Search statistical procedure. This effort is called the DIAmante TESS AutoRegressive Planet Search (DTARPS). ARIMA models remove lingering autocorrelated noise, the Transit Comb Filter identifies the strongest periodic signal in the light curve, and a Random Forest machine learning classifier is trained and applied to identify the best potential candidates. Classifier training sets are based on injections of planetary transit signals, eclipsing binaries, and sinusoidal variable stars. The optimized classifier has a True Positive Rate of 92.5% and a False Positive Rate of 0.43% from the labeled training set. After the classifier is applied to the nearly 1 million DIAmante light curves, the resulting DTARPS Analysis list has 7,377 potential exoplanet candidates. The completeness map of the injected planetary signals shows high recall rates for planets with 8 − 30 R_⊕ radii and periods 0.6−13 days and poor completeness for planets with radii < 2 R_⊕ or periods < 1 day. A multistage vetting procedure is applied to the DTARPS Analysis list including: centroid motion and crowding metrics, False Alarm and False Positive reduction, photometric binary elimination, and ephemeris match removal. The resulting catalog of DTARPS Candidates consists of 777 stars of which 457 are newly identified. Candidates are flagged for possible blending from nearby stars based on Zwicky Transient Facility data and for possible radial velocity variations based on Gaia satellite data. Orbital periods and planetary radii are refined using astrophysical modeling. The DTARPS Catalog has mostly members with planet radii > 4 R_⊕ and periods 0.2 < P < 10 days. Four sub-populations of exoplanet candidates of high interest to the community are discussed: Neptune desert candidates, ultra short period candidates, atmospheric transmission follow-up candidates, and candidates around M dwarfs. The catalog dramatically increased the number of candidates in the Neptune Desert. Potential sources of contamination are examined and the results from previous surveys and preliminary reconnaissance spectroscopy are combined to set an approximate limit on contamination; the DTARPS Candidate Catalog likely suffers from 40-50% contamination by eclipsing binaries. The well-characterized methodology of DTARPS permits calculation of approximate planetary occurrence rates in reasonable agreement with the occurrence rates derived from the Kepler mission survey. Altogether, DTARPS provides perhaps the largest and most reliable catalog of TESS exoplanets independently derived of independently of the TESS Objects of Interest identified by the TESS Science Team.