Related papers: VarStar Detect, a Python library dedicated to the …
There is a strong recent emphasis on trustworthy AI. In particular, international regulations, such as the AI Act, demand that AI practitioners measure data quality on the input and estimate bias on the output of high-risk AI systems.…
Detecting changes in high-dimensional time series is difficult because it involves the comparison of probability densities that need to be estimated from finite samples. In this paper, we present the first feature extraction method tailored…
Variational system identification is a new formulation of maximum likelihood for estimation of parameters of dynamical systems subject to process and measurement noise, such as aircraft flying in turbulence. This formulation is an…
Astronomy is entering the era of large surveys of the variable sky such as Zwicky Transient Facility (ZTF) and the forthcoming Legacy Survey of Space and Time (LSST) which are intended to produce up to a million alerts per night. Such an…
Stellar spectropolarimetry is a relatively new remote sensing tool for exploring stellar atmospheres and circumstellar environments. We present the results of our HiVIS survey and a multi-wavelength ESPaDOnS follow-up campaign showing…
We present the first results from the SuperWASP Variable Stars (SVS) citizen science project. The photometry archive of the Wide Angle Search for Planets has previously been searched for periodic variations and the results of this search…
The number of known periodic variable stars has increased rapidly in recent years. As an all-sky transit survey, the Transiting Exoplanet Survey Satellite (TESS) plays an important role in detecting low-amplitude variable stars. Using…
The rate of image acquisition in modern synoptic imaging surveys has already begun to outpace the feasibility of keeping astronomers in the real-time discovery and classification loop. Here we present the inner workings of a framework,…
With the advent of surveys generating multi-epoch photometry and their discoveries of large numbers of variable stars, the classification of the obtained times series has to be automated. We have developed a classification algorithm for the…
Combination frequencies are not solutions of the perturbed stellar structure equations. In dense power spectra from a light curve of a given multi-periodic pulsating star, they can compromise the mode identification in an asteroseismic…
PySensors is a Python package for selecting and placing a sparse set of sensors for classification and reconstruction tasks. Specifically, PySensors implements algorithms for data-driven sparse sensor placement optimization for…
VARCLUST algorithm is proposed for clustering variables under the assumption that variables in a given cluster are linear combinations of a small number of hidden latent variables, corrupted by the random noise. The entire clustering task…
Anomaly detection using dimensionality reduction has been an essential technique for monitoring multidimensional data. Although deep learning-based methods have been well studied for their remarkable detection performance, their…
LiDAR has become one of the primary 3D object detection sensors in autonomous driving. However, LiDAR's diverging point pattern with increasing distance results in a non-uniform sampled point cloud ill-suited to discretized volumetric…
We present ViSta, a Visibility Stacking method to combine interferometric observations in the Fourier domain at radio to sub-millimeter wavelengths for galaxies. The goal of our method is to maximize the exploitation of available archival…
Objective detection of specific patterns in statistical distributions, like groupings or gaps or abrupt transitions between different subsets, is a task with a rich range of applications in astronomy: Milky Way stellar population analysis,…
The light curves from a variety of celestial objects display aperiodic variations, often giving rise to red-noise components in their power spectra. Searching for a narrow power spectrum peak resulting from a periodic modulation over the…
Investigating variability at the earliest stages of low-mass star formation is fundamental in understanding how a protostar assembles mass. While many simulations of protostellar disks predict non-steady accretion onto protostars, deeper…
Discovery rates of supernovae are expected to surpass one million events annually with the Vera C. Rubin Observatory. With unprecedented sample sizes of both common and rare transient types, photometric classification alone will be…
Predictive constitutive equations that connect easy-to-measure transport properties (e.g., viscosity and conductivity) with system performance variables (e.g., power consumption and efficiency) are needed to design advanced thermal and…