We report the Transiting Exoplanet Survey Satellite (TESS) discovery of a three-planet system around the bright Sun-like star HD 22946 (V≈8.3 mag), also known as TIC 100990000, located 63 parsecs away. The system was observed by TESS in Sectors 3, 4, 30 and 31 and two planet candidates, labelled TESS Objects of Interest (TOIs) 411.01 (planet c) and 411.02 (planet b), were identified on orbits of 9.57 and 4.04 days, respectively. In this work, we validate the two planets and recover an additional single transit-like signal in the light curve, which suggests the presence of a third transiting planet with a longer period of about 46 days. We assess the veracity of the TESS transit signals and use followup imaging and time series photometry to rule out false positive scenarios, including unresolved binary systems, nearby eclipsing binaries or background/foreground stars contaminating the light curves. Parallax measurements from Gaia Early Data Release 3, together with broad-band photometry and spectroscopic follow-up by TFOP allowed us to constrain the stellar parameters of TOI-411, including its radius of 1.157±0.025R . Adopting this value, we determined the radii for the three exoplanet candidates and found that planet b is a super-Earth, with a radius of 1.72 ± 0.10 R ⊕ , while planet c and d are sub-Neptunian planets, with radii of 2.74 ± 0.14 R ⊕ and 3.23 ± 0.19 R ⊕ respectively. By using dynamical simulations, we assessed the stability of the system and evaluated the possibility of the presence of other undetected, non-transiting planets by investigating its dynamical packing. We find that the system is dynamically stable and potentially unpacked, with enough space to host at least one more planet between c and d. Finally, given that the star is bright and nearby, we discuss possibilities for detailed mass characterization of its surrounding worlds and opportunities for the detection of their atmospheres with the James Webb Space Telescope.
In the last decade, exoplanets space missions started to collect a huge amount of photometric observations, with over ∼1,000,000 new light curves generated every month from the Transiting Exoplanet Survey Satellite (TESS) full-frame images alone. In order to analyze such an unprecedented volume of data, automated planet-candidate detection has become an appreciable replacement to human vetting. In this work, we present a Machine Learning approach, based on Deep Neural Networks, to perform a binary classification of TESS light curves in terms of planet candidate and not-planet. Since few TESS labeled data exist to date, we pre-train the network with Kepler DR24 data set, including ≳15,000 labeled light curves. Our pre-trained model is then tested on ExoFOP data, showing an appreciable gain in terms of reliability with respect to a randomly initialized model.
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