相关论文: ROOSTER: a machine-learning analysis tool for Kepl…

We analyse light curves covering four years of 39 fast-rotating ($P_\mathrm{rot}< 1d$) late-type active stars from the Kepler database. Using time-frequency analysis (Short-Term Fourier-Transform), we find hints for activity cycles of…

The advent of space-based observatories such as CoRoT and Kepler has enabled the testing of our understanding of stellar evolution on thousands of stars. Evolutionary models typically require five input parameters, the mass, initial Helium…

The fast classification of new variable stars is an important step in making them available for further research. Selection of science targets from large databases is much more efficient if they have been classified first. Defining the…

We study rotation-robust learning for image inputs using Convolutional Model Trees (CMTs) [1], whose split and leaf coefficients can be structured on the image grid and transformed geometrically at deployment time. In a controlled MNIST…

Clustering is an essential data mining tool for analyzing and grouping similar objects. In big data applications, however, many clustering algorithms are infeasible due to their high memory requirements and/or unfavorable runtime…

Classification of young stellar objects (YSOs) into different evolutionary stages helps us to understand the formation process of new stars and planetary systems. Such classification has traditionally been based on spectral energy…

Accurate surface rotation measurements are crucial to estimate stellar ages and improve our understanding of stellar rotational evolution. Comparisons of datasets obtained from different space missions on common targets represent in this…

Obtaining accurate photometric redshift estimations is an important aspect of cosmology, remaining a prerequisite of many analyses. In creating novel methods to produce redshift estimations, there has been a shift towards using machine…

Understanding the dynamical evolution of asteroids through the secular Yarkovsky effect requires the determination of many physical properties, including the rotation period. We propose a method aimed at obtaining a robust determination of…

One of the most difficult properties to derive for stars is their age. For cool main-sequence stars, gyrochronology relations can be used to infer stellar ages from measured rotation pe- riods and HR Diagram positions. These relations have…

Asteroseismology is the study of resonant oscillations of stars to infer their internal structure and dynamics. It is also a powerful tool for precisely determining stellar parameters such as mass, radius, surface gravity, and age. The…

We apply multi-algorithm machine learning models to TESS 2-minute survey data from Sectors 1-72 to identify stellar flares. Models trained with Deep Neural Network, Random Forest, and XGBoost algorithms, respectively, utilized four flare…

Large-scale photometric surveys are revolutionizing astronomy by delivering unprecedented amounts of data. The rich data sets from missions such as the NASA Kepler and TESS satellites, and the upcoming ESA PLATO mission, are a treasure…

Of the more than 150000 targets followed by the Kepler Mission, about 10% were selected as red giants. Due to their high scientific value, in particular for Galaxy population studies and stellar structure and evolution, their Kepler light…

Context: A number of pulsating stars with rotational splittings have been observed thanks to the CoRoT and Kepler missions. This is particularly true of evolved (sub-giant and giant) stars, and has led various groups to investigate their…

A fundamental challenge for wide-field imaging surveys is obtaining follow-up spectroscopic observations: there are > $10^9$ photometrically cataloged sources, yet modern spectroscopic surveys are limited to ~few x $10^6$ targets. As we…

We analyse Kepler light-curves of the exoplanet KOI-13b transiting its moderately rapidly rotating (gravity-darkened) parent star. A physical model, with minimal ad hoc free parameters, reproduces the time-averaged light-curve at the ca. 10…

We present a machine learning approach for estimating galaxy cluster masses, trained using both Chandra and eROSITA mock X-ray observations of 2,041 clusters from the Magneticum simulations. We train a random forest regressor, an ensemble…

Variable star analysis and classification is an important task in the understanding of stellar features and processes. While historically classifications have been done manually by highly skilled experts, the recent and rapid expansion in…

We present the results of an automated variability analysis of the Kepler public data measured in the first quarter (Q1) of the mission. In total, about 150 000 light curves have been analysed to detect stellar variability, and to identify…