Related papers: Seeking Spectroscopic Binaries with Data-Driven Mo…
We have developed a procedure for the classification of eclipsing binaries from their light-curve parameters and spectral type. The procedure was tested on more than 1000 systems with known classification, and its efficiency was estimated…
Machine Learning is an efficient method for analyzing and interpreting the increasing amount of astronomical data that is available. In this study, we show, a pedagogical approach that should benefit anyone willing to experiment with Deep…
We present differential stellar population models, which allow improved determinations of the ages, iron and alpha-element abundances of old stellar populations from spectral fitting. These new models are calibrated at solar abundances…
In an effort to better understand the details of the stellar structure and evolution of metal poor stars, the Gemini North telescope was used on two occasions to take speckle imaging data of a sample of known spectroscopic binary stars and…
Galaxies are often modelled as composites of separable components with distinct spectral signatures, implying that different wavelength ranges are only weakly correlated. They are not. We present a data-driven model which exploits subtle…
The data driven extrapolation requires the definition of a functional model depending on the available data and has the application scope of providing reliable predictions on the unknown dynamics. Since data might be scattered, we drive our…
With the plentiful information available in the Gaia BP/RP spectra, there is significant scope for applying discriminative models to extract stellar atmospheric parameters and abundances. We describe an approach to leverage an `Uncertain…
Two ubiquitous aspects of large-scale data analysis are that the data often have heavy-tailed properties and that diffusion-based or spectral-based methods are often used to identify and extract structure of interest. Perhaps surprisingly,…
Machine learning models in astrophysics are often limited in scope and cannot adapt to data from new instruments or tasks. We introduce SpectraFM, a Transformer-based foundation model architecture that can be pre-trained on stellar spectra…
The spectrophotometric flux calibration of recent spectroscopic surveys has reached a limiting systematic precision of approximately 1-3 percent, and is often biased near the wavelengths associated with H I Balmer absorption. As we prepare…
Massive stars condition the evolution of the interstellar medium by the amount of energy released during their lives and especially by their deaths as supernova explosions. The vast amounts of spectroscopic data for massive stars provided…
Traditional lost-in-space algorithms, such as those implemented in astrometry.net, solve for spacecraft orientation by matching observed star fields to celestial catalogs using geometric asterisms alone. In this work, we propose a novel…
Classifying variable stars is crucial for advancing our understanding of stellar evolution and dynamics. As large-scale surveys generate increasing volumes of light curve data, the demand for automated and reliable classification techniques…
We present a data-driven technique to analyze multifrequency images from upcoming cosmological surveys mapping large sky area. Using full information from the data at the two-point level, our method can simultaneously constrain the…
Stellar parameters for large samples of stars play a crucial role in constraining the nature of stars and stellar populations in the Galaxy. An increasing number of medium-band photometric surveys are presently used in estimating stellar…
Although primarily aimed at the galactic archeology and evolution, automated all-sky spectroscopic surveys (RAVE, SDSS) are also a valuable source for the binary star research community. Identification of double-lined spectra is easy and it…
Vision foundation models, which have demonstrated significant potential in many multimedia applications, are often underutilized in the natural sciences. This is primarily due to mismatches between the nature of domain-specific scientific…
With the large amounts of spectroscopic data available today and the very large surveys to come (e.g. Gaia), the need for automatic data analysis software is unquestionable. We thus developed an automatic spectra analysis program for the…
In this work, we propose a novel method to classify close binary stars, derived from the dynamical structure inherent in their light curves. We apply the technique to light curves of binaries from the revised Kepler Eclipsing binary…
With nearly two billion stars observed and their corresponding astrometric parameters evaluated in the recent Gaia mission, the number of astrometric binary candidates have risen significantly. Due to the surplus of astrometric data, the…