Related papers: Is the `Known' Enough? An Integrated Machine Learn…
Predicting the melting temperature (Tm) of multi-component and high-entropy alloys (HEAs) is critical for high-temperature applications but computationally expensive using traditional CALPHAD or DFT methods. In this work, we develop a…
Identification of specific stellar populations using photometry for spectroscopic follow-up is a first step to confirm and better understand their nature. In this context, we present an unsupervised machine learning approach to identify…
EXONEST is an algorithm dedicated to detecting and characterizing the photometric signatures of exoplanets, which include reflection and thermal emission, Doppler boosting, and ellipsoidal variations. Using Bayesian Inference, we can test…
Ocular biometric systems working in unconstrained environments usually face the problem of small within-class compactness caused by the multiple factors that jointly degrade the quality of the obtained data. In this work, we propose an…
XGBoost, a scalable tree boosting algorithm, has proven effective for many prediction tasks of practical interest, especially using tabular datasets. Hyperparameter tuning can further improve the predictive performance, but unlike neural…
Breast cancer is the most common cancer among women worldwide. Early-stage diagnosis of breast cancer can significantly improve the efficiency of treatment. Computer-aided diagnosis (CAD) systems are widely adopted in this issue due to…
In high energy density physics (HEDP) and inertial confinement fusion (ICF), predictive modeling is complicated by uncertainty in parameters that characterize various aspects of the modeled system, such as those characterizing material…
Light curves of the contact systems visible in the direction of Baade's Window have been analyzed using the first coefficients of the Fourier representation. The results confirm that the geometric contact between components is usually weak.…
The Southern Photometric Local Universe Survey (S-PLUS) is a novel project that aims to map the Southern Hemisphere using a twelve filter system, comprising five broad-band SDSS-like filters and seven narrow-band filters optimized for…
Random Forests have been one of the most popular bagging methods in the past few decades, especially due to their success at handling tabular datasets. They have been extensively studied and compared to boosting models, like XGBoost, which…
We present a novel approach for classifying stars as binary or exoplanet using deep learning techniques. Our method utilizes feature extraction, wavelet transformation, and a neural network on the light curves of stars to achieve…
We use a random forest model to predict the critical cooling rate (RC) for glass formation of various alloys from features of their constituent elements. The random forest model was trained on a database that integrates multiple sources of…
We present an updated collection of eclipsing and ellipsoidal binary systems in the Large and Small Magellanic Clouds (LMC and SMC), as observed by the Optical Gravitational Lensing Experiment (OGLE) survey. The catalog comprises a total of…
Young eclipsing binaries (EBs) are powerful probes of early stellar evolution. Current models are unable to simultaneously reproduce the measured and derived properties that are accessible for EB systems (e.g., mass, radius, temperature,…
Context. Intermediate- to high-mass stars are the least numerous types of stars and they are less well understood than their more numerous low-mass counterparts in terms of their internal physical processes. Modelling the photometric…
A variety of fundamental astrophysical science topics require the determination of very accurate photometric redshifts (photo-z's). A wide plethora of methods have been developed, based either on template models fitting or on empirical…
The use of machine learning is becoming ubiquitous in astronomy, but remains rare in the study of the atmospheres of exoplanets. Given the spectrum of an exoplanetary atmosphere, a multi-parameter space is swept through in real time to find…
The taxonomy of galaxy morphology is critical in astrophysics as the morphological properties are powerful tracers of galaxy evolution. With the upcoming Large-scale Imaging Surveys, billions of galaxy images challenge astronomers to…
Aims: Our goal was to increase number of known doubly eclipsing systems such that the resulting dataset would allow to study them via statistical means, as well as prove that they constitute gravitationally bound 2+2 quadruple system.…
We present photodynamical models of four eclipsing binary systems that are members of higher-order multiple systems. We provide some radial velocities measurements and use recent TESS data for three of the systems. KIC 7668648 consists of…