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All-sky photometric time-series missions have allowed for the monitoring of thousands of young ($t_{rm age} < 800$Myr) to understand the evolution of stellar activity. Here we developed a convolutional neural network (CNN), $texttt{stella}$, specifically trained to find flares in $textit{Transiting Exoplanet Survey Satellite}$ ($textit{TESS}$) short-cadence data. We applied the network to 3200 young stars 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 $t_{rm age} > 50$Myr across all temperatures $T_{rm eff} geq 4000$K, while stars from $2300 leq T_{rm eff} < 4000$K show no evolution across 800 Myr. Stars of $T_{rm eff} leq 4000$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. $texttt{stella}$ is an open-source Python tool-kit hosted on GitHub and PyPI.
Phased flaring, or the periodic occurrence of stellar flares, may probe electromagnetic star-planet interaction (SPI), binary interaction, or magnetic conditions in spots. For the first time, we explore flare periodograms for a large sample of flare
The study is devoted to search for flare stars among confirmed members of Galactic open clusters using high-cadence photometry from {it TESS} mission. We analyzed 957 high-cadence light curves of members from 136 open clusters. As a result, 56 flare
We have identified a quadruple system with two close eclipsing binaries in TESS data. The object is unresolved in Gaia and appears as a single source at parallax 1.08~$pm$0.01 mas. Both binaries have observable primary and secondary eclipses and were
Due to the ever-expanding volume of observed spectroscopic data from surveys such as SDSS and LAMOST, it has become important to apply artificial intelligence (AI) techniques for analysing stellar spectra to solve spectral classification and regressi
In this paper, we propose a new data augmentation strategy named Thumbnail, which aims to strengthen the networks capture of global features. We get a generated image by reducing an image to a certain size, which is called as the thumbnail, and pasti