Nearly one million light curves from the TESS Year 1 southern hemisphere extracted from Full Frame Images with the DIAmante pipeline are processed through the AutoRegressive Planet Search statistical procedure. ARIMA models remove trends and 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 include injections of both planetary transit signals and contaminating eclipsing binaries. The optimized classifier has a True Positive Rate of 92.8% and a False Positive Rate of 0.37% from the labeled training set. The result of this DIAmante TESS autoregressive planet search (DTARPS) analysis is a list of 7,377 potential exoplanet candidates. The classifier has a False Positive Rate of 0.3%, a 64% recall rate for previously confirmed exoplanets, and a 78% negative recall rate for known False Positives. 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. The list has many False Alarms and False Positives that need to be culled with multifaceted vetting operations (Paper II).
The DIAmante TESS AutoRegressive Planet Search (DTARPS) project seeks to identify photometric transiting planets from 976,814 southern hemisphere stars observed in Year 1 of the TESS mission. This paper follows the methodology developed by Melton et al. (2022a, Paper I) using light curves extracted and pre-processed by the DIAmante project (Montalto et al. 2020). Paper I emerged with a list of 7,377 light curves with statistical properties characteristic of transiting planets but dominated by False Alarms and False Positives. Here a multistage vetting procedure is applied including: centroid motion and crowding metrics, False Alarm and False Positive reduction, photometric binary elimination, and ephemeris match removal. The vetting produces a catalog of 462 DTARPS Candidates across the southern ecliptic hemisphere and 310 objects in a spatially incomplete Galactic Plane list. Fifty-eight percent were not previously identified as transiting systems. 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 resulting parameters closely match published values for Confirmed Planets. Their properties are discussed in Paper III.
The DIAmante TESS AutoRegressive Planet Search (DTARPS) project, using novel statistical methods, has identified several hundred candidates for transiting planetary systems obtained from 0.9 million Full Frame Image light curves obtained in the TESS Year 1 southern hemisphere survey (Melton et al. 2022a and2022b). Several lines of evidence, including limited reconnaissance spectroscopy, indicate that at least half are true planets rather than False Positives. Here various aspects populational properties of these objects are examined. Half of the DTARPS candidates are hot Neptunes, populating the 'Neptune desert' found in Kepler planet samples. The DTARPS samples also identify dozens of Ultra Short Period planets with orbital periods down to 5 hours, high priority systems for atmospheric transimssion spectroscopy, and planets orbiting low-mass M stars. DTARPS methodology is sufficiently well-characterized at each step that preliminary planet occurrence rates can be estimated. Except for the increase in hot Neptunes, DTARPS planet occurrence rates are consistent with Kepler rates. Overall, DTARPS provides one of the largest and most reliable catalog of TESS exoplanet candidates that can be tapped to improve our understanding of various exoplanetary populations and astrophysical processes.
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