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We present zephyr, a novel method that integrates cutting-edge normalizing flow techniques into a mixture density estimation framework, enabling the effective use of heterogeneous training data for photometric redshift inference. Compared…
We describe a new neural-net based light curve classifier and provide it with documentation as a ready-to-use tool for the community. While optimized for identification and classification of eclipsing binary stars, the classifier is general…
The advent of space-based photometry missions in the early 21st century enabled the application to asteroseismic data of advanced inference techniques until then restricted to the field of helioseismology. The high quality of the…
We present scope (Simulated CCD Observations for Photometric Experimentation), a Python package to create a forward model of telescope detectors and simulate stellar targets with motion relative to the CCD. The primary application of this…
Variability in the time series brightness of a star on a timescale of 8 hours, known as 'flicker', has been previously demonstrated to serve as a proxy for the surface gravity of a star by Bastien et al. (2013). Although surface gravity is…
Lensed quasars are key to many areas of study in astronomy, offering a unique probe into the intermediate and far universe. However, finding lensed quasars has proved difficult despite significant efforts from large collaborations. These…
We search for transits around all known pulsating {\delta} Sct variables (6500 K < Teff < 10 000 K) in the long-cadence Kepler data after subtracting the pulsation signal through an automated routine. To achieve this, we devise a simple and…
The rapid development of new generation radio interferometers such as the Square Kilometer Array (SKA) has opened up unprecedented opportunities for astronomical research. However, anthropogenic Radio Frequency Interference (RFI) from…
Eclipsing binary systems (EBs), as foundational objects in stellar astrophysics, have garnered significant attention in recent years. These systems exhibit periodic decreases in light intensity when one star obscures the other from the…
Stellar photometric variability and instrumental effects, like cosmic ray hits, data discontinuities, data leaks, instrument aging etc. cause difficulties in the characterization of exoplanets and have an impact on the accuracy and…
We developed, tested and validated a new Fast, Robust and Automated (FRA) tool to detect solar-like oscillations. FRA is based on the detection and measurement of the frequency of maximum oscillation power $\nu_{max}$, without relying on…
This study presents a bidirectional Long Short-Term Memory (LSTM) neural network for classifying transient astronomical object light curves from the Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC) dataset. The…
The transit method is one of the most relevant exoplanet detection techniques, which consists of detecting periodic eclipses in the light curves of stars. This is not always easy due to the presence of noise in the light curves, which is…
Feature engineering, a crucial step of machine learning, aims to extract useful features from raw data to improve data quality. In recent years, great efforts have been devoted to Automated Feature Engineering (AutoFE) to replace expensive…
Extreme helium stars (EHes) are low-mass hydrogen-deficient stars thought to be the products of double white dwarf mergers. Despite prolonged ground-based observations, there is no consensus on the properties of their photometric…
We focus on the automated classification of eclipsing binary stars using deep learning methods to handle the vast data generated by large-scale photometric sky surveys. These surveys produce extensive datasets that are impractical for…
High-quality, short-cadence photometry from TESS enables the detection of rapid oscillators with unprecedented sensitivity. In this work, we conduct a homogeneous search for high-frequency variability using 20-second cadence light curves…
We develop a novel method based on machine learning principles to achieve optimal initiation of CPU-intensive computations for forward asteroseismic modeling in a multi-D parameter space. A deep neural network is trained on a precomputed…
As white-dwarf (WD) stars cool, they go through one or more stages of $g$(gravity)-mode pulsational instability, becoming multiperiodic variable stars. Stars passing through these instability domains allow astronomers to study their…
A novel artificial intelligence (AI) technique that uses machine learning (ML) methodologies combines several algorithms, which were developed by ThetaRay, Inc., is applied to NASA's Transiting Exoplanets Survey Satellite (TESS) dataset to…