We present 2241 exoplanet candidates identified with data from the Transiting Exoplanet Survey Satellite (TESS) during its 2 yr Prime Mission. We list these candidates in the TESS Objects of Interest (TOI) Catalog, which includes both new planet candidates found by TESS and previously known planets recovered by TESS observations. We describe the process used to identify TOIs, investigate the characteristics of the new planet candidates, and discuss some notable TESS planet discoveries. The TOI catalog includes an unprecedented number of small planet candidates around nearby bright stars, which are well suited for detailed follow-up observations. The TESS data products for the Prime Mission (sectors 1–26), including the TOI catalog, light curves, full-frame images, and target pixel files, are publicly available at the Mikulski Archive for Space Telescopes.

We perform a study of stellar flares for the 24,809 stars observed with 2 minute cadence during the first two months of the TESS mission. Flares may erode exoplanets’ atmospheres and impact their habitability, but might also trigger the genesis of life around small stars. TESS provides a new sample of bright dwarf stars in our galactic neighborhood, collecting data for thousands of M dwarfs that might host habitable exoplanets. Here, we use an automated search for flares accompanied by visual inspection. Then, our public allesfitter code robustly selects the appropriate model for potentially complex flares via Bayesian evidence. We identify 1228 flaring stars, 673 of which are M dwarfs. Among 8695 flares in total, the largest superflare increased the stellar brightness by a factor of 16.1. Bolometric flare energies range from 10^31.0 to 10^36.9 erg, with a median of 10^33.1 erg. Furthermore, we study the flare rate and energy as a function of stellar type and rotation period. We solidify past findings that fast rotating M dwarfs are the most likely to flare and that their flare amplitude is independent of the rotation period. Finally, we link our results to criteria for prebiotic chemistry, atmospheric loss through coronal mass ejections, and ozone sterilization. Four of our flaring M dwarfs host exoplanet candidates alerted on by TESS, for which we discuss how these effects can impact life. With upcoming TESS data releases, our flare analysis can be expanded to almost all bright small stars, aiding in defining criteria for exoplanet habitability.

We describe the Next Generation Transit Survey (NGTS), which is a ground-based project searching for transiting exoplanets orbiting bright stars. NGTS builds on the legacy of previous surveys, most notably WASP, and is designed to achieve higher photometric precision and hence find smaller planets than have previously been detected from the ground. It also operates in red light, maximizing sensitivity to late K and early M dwarf stars. The survey specifications call for photometric precision of 0.1 per cent in red light over an instantaneous field of view of 100 deg², enabling the detection of Neptune-sized exoplanets around Sun-like stars and super-Earths around M dwarfs. The survey is carried out with a purpose-built facility at Cerro Paranal, Chile, which is the premier site of the European Southern Observatory (ESO). An array of twelve 20 cm f/2.8 telescopes fitted with back-illuminated deep-depletion CCD cameras is used to survey fields intensively at intermediate Galactic latitudes. The instrument is also ideally suited to ground-based photometric follow-up of exoplanet candidates from space telescopes such as TESS, Gaia and PLATO. We present observations that combine precise autoguiding and the superb observing conditions at Paranal to provide routine photometric precision of 0.1 per cent in 1 h for stars with I-band magnitudes brighter than 13. We describe the instrument and data analysis methods as well as the status of the survey, which achieved first light in 2015 and began full-survey operations in 2016. NGTS data will be made publicly available through the ESO archive.

We report the detection of a transiting planet around π Men (HD 39091), using data from the Transiting Exoplanet Survey Satellite (TESS). The solar-type host star is unusually bright (V = 5.7) and was already known to host a Jovian planet on a highly eccentric, 5.7 yr orbit. The newly discovered planet has a size of 2.04 ± 0.05 R _⊕ and an orbital period of 6.27 days. Radial-velocity data from the High-Accuracy Radial-velocity Planet Searcher and Anglo-Australian Telescope/University College London Echelle Spectrograph archives also displays a 6.27 day periodicity, confirming the existence of the planet and leading to a mass determination of 4.82 ± 0.85 M _⊕. The star’s proximity and brightness will facilitate further investigations, such as atmospheric spectroscopy, asteroseismology, the Rossiter–McLaughlin effect, astrometry, and direct imaging.

All-sky photometric time-series missions have allowed for the monitoring of thousands of young (t _age < 800 Myr) stars in order to understand the evolution of stellar activity. Here, we developed a convolutional neural network (CNN), stella, specifically trained to find flares in Transiting Exoplanet Survey Satellite (TESS) short-cadence data. We applied the network to 3200 young stars in order to evaluate flare rates as a function of age and spectral type. The CNN takes a few seconds to identify flares on a single light curve. We also measured rotation periods for 1500 of our targets and find that flares of all amplitudes are present across all spot phases, suggesting high spot coverage across the entire surface. Additionally, flare rates and amplitudes decrease for stars t _age > 50 Myr across all temperatures T _eff ≥ 4000 K, while stars from 2300 ≤ T _eff < 4000 K show no evolution across 800 Myr. Stars of T _eff ≤ 4000 K also show higher flare rates and amplitudes across all ages. We investigate the effects of high flare rates on photoevaporative atmospheric mass loss for young planets. In the presence of flares, planets lose 4%–7% more atmosphere over the first 1 Gyr. stella is an open-source Python toolkit hosted on GitHub and PyPI.

We present allesfitter, a public and open-source Python software for flexible and robust inference of stars and exoplanets given photometric and radial velocity data. Allesfitter offers a rich selection of orbital and transit/eclipse models, accommodating multiple exoplanets, multistar systems, transit-timing variations, phase curves, stellar variability, starspots, stellar flares, and various systematic noise models, including Gaussian processes. It features both parameter estimation and Bayesian model selection, allowing either a Markov Chain Monte Carlo or Nested Sampling fit to be easily run. For novice users, a graphical user interface allows all input and perform analyses to be specified; for Python users, all modules can be readily imported into any existing script. Allesfitter also produces publication-ready tables, LaTeX commands, and figures. The software is publicly available (https://github.com/MNGuenther/allesfitter), pip-installable (pip install allesfitter), and well documented (www.allesfitter.com). Finally, we demonstrate the software’s capabilities in several examples and provide updates to the literature where possible for Pi Mensae, TOI-216, WASP-18, KOI-1003, and GJ 1243.