相关论文: ROOSTER: a machine-learning analysis tool for Kepl…
The present study reports the discovery of Sun-like stars, namely main-sequence stars with $T_{\rm eff}$, $\log g$ and rotation periods $P_{rot}$ similar to solar values, presenting evidence of surface differential rotation. An…
Variable stars play a key role in understanding the Milky Way and the universe. The era of astronomical big data presents new challenges for quick identification of interesting and important variable stars. Accurately estimating the periods…
We test the viability of training machine learning algorithms with synthetic H alpha line profiles to determine the inclination angles of Be stars (the angle between the central B star's rotation axis and the observer's line of sight) from…
The recently launched NASA Transiting Exoplanet Survey Satellite (TESS) mission is going to collect lightcurves for a few hundred million of stars and we expect to increase the number of pulsating stars to analyze compared to the few…
Modelling isolated rotating stars at any rotation rate is a challenge for the next generation of stellar models. These models will couple dynamical aspects of rotating stars, like angular momentum and chemicals transport, with classical…
Time-resolved photometry of tens of thousands of red giant stars from space missions like Kepler and K2 has created the need for automated asteroseismic analysis methods. The first and most fundamental step in such analysis, is to identify…
(abridged) We develop a tool for the automated spectral classification of OB stars according to their sub-types. We use the regular Random Forest (RF) algorithm, the Probabilistic RF (PRF), and we introduce the KDE-RF method which is a…
Stellar rotation plays a key role in stellar activity. The rotation period could be detected through light curve variations caused by starspots. Kepler provides two types of light curves, one is the Pre-search Data Conditioning (PDC) light…
We provide classifications for all 143 million non-repeat photometric objects in the Third Data Release of the Sloan Digital Sky Survey (SDSS) using decision trees trained on 477,068 objects with SDSS spectroscopic data. We demonstrate that…
The Transiting Exoplanet Survey Satellite (TESS) has provided stellar rotation periods across much of the sky through high-precision light curves, but the reliability and completeness of these measurements require careful evaluation. We…
Photometric missions such as Kepler and TESS have generated millions of light curves covering almost the entire sky, offering unprecedented opportunities to study stellar variability and advance our understanding of the Universe. In this…
Since the end of 2018, the Transiting Exoplanet Survey Satellite (TESS) has provided stellar photometry to the astronomical community. We have used TESS data to study rotational modulation in the light curves of a sample of chemically…
We present rotation period measurements for 107 M dwarfs in the mass range $0.15-0.70 M_\odot$ observed within the context of the APACHE photometric survey. We measure rotation periods in the range 0.5-190 days, with the distribution…
In recent years it has been claimed that the length of stellar activity cycles is determined by the stellar rotation rate. It is observed that the cycle period increases with rotation period along the so-called active and inactive…
We study the relation between stellar rotation and magnetic activity for a sample of 134 bright, nearby M dwarfs observed in the Kepler Two-Wheel (K2) mission during campaigns C0 to C4. The K2 lightcurves yield photometrically derived…
This work brings together some of the most common machine learning (ML) algorithms, and the objective is to make a comparison at the level of obtained results from a set of unbalanced data. This dataset is composed of almost 17 thousand…
Tens of thousands of rotation periods have been measured in the Kepler fields, including a substantial fraction of rapid rotators. We use Gaia parallaxes to distinguish photometric binaries (PBs) from single stars on the unevolved lower…
The plethora of photometric data collected by the Kepler space telescope has promoted the detection of tens of thousands of stellar rotation periods. However, these periods are not found to an equal extent among different spectral types.…
The immense amount of time series data produced by astronomical surveys has called for the use of machine learning algorithms to discover and classify several million celestial sources. In the case of variable stars, supervised learning…
Nowadays, Machine Learning techniques offer fast and efficient solutions for classification problems that would require intensive computational resources via traditional methods. We examine the use of a supervised Random Forest to classify…