Related papers: Probabilistic neural network approach to determini…
Eclipsing binaries serve as a valuable source of stellar masses and radii that inform stellar evolutionary models and provide insight into additional astrophysical processes. The exquisite light curves generated by space-based missions such…
Eclipsing, spectroscopic double-lined (SB2) binaries remain to be the prime source of precise and accurate fundamental properties of stars. Furthermore, high-cadence spectroscopic observations of the eclipse phases allow us to resolve the…
While deep learning offers powerful capabilities for scientific research, its application is often hindered by a lack of quantitative reliability. To address this, we introduce a probabilistic denoising framework that simultaneously…
Spotted stars in eclipsing binary systems allow us to gather significant information about the stellar surface inhomogeneities that is otherwise impossible from only photometric data. Starspots can be scanned using the eclipse (or transit)…
We present a new machine learning model for estimating photometric redshifts with improved accuracy for galaxies in Pan-STARRS1 data release 1. Depending on the estimation range of redshifts, this model based on neural networks can handle…
Binary neural networks have attracted tremendous attention due to the efficiency for deploying them on mobile devices. Since the weak expression ability of binary weights and features, their accuracy is usually much lower than that of…
Robust quantification of predictive uncertainty is critical for understanding factors that drive weather and climate outcomes. Ensembles provide predictive uncertainty estimates and can be decomposed physically, but both physics and machine…
Double-lined spectroscopic binaries which are also eclipsing provide the most accurate stellar data, and are consequently of first importance to improve stellar evolution modelling. While the mass and radius of each component of detached…
Neural networks are powerful function approximators with tremendous potential in learning complex distributions. However, they are prone to overfitting on spurious patterns. Bayesian inference provides a principled way to regularize neural…
A fundamental limitation of traditional Neural Networks (NN) in predictive modelling is their inability to quantify uncertainty in their outputs. In critical applications like positioning systems, understanding the reliability of…
In this work, we seek to characterise a large sample of 14377 main sequence eclipsing binaries in terms of their stellar, asteroseismic, and orbital properties. We conduct manual vetting on a 4000-target subset of our full 14377-target…
We present an algorithm that can detect blends of bright stars with fainter, un-associated eclipsing binaries. Such systems contaminate searches for transiting planets, in particular in crowded fields where blends are common. Spectroscopic…
Likelihood ratios are used for a variety of applications in particle physics data analysis, including parameter estimation, unfolding, and anomaly detection. When the data are high-dimensional, neural networks provide an effective tools for…
Spectrum sensing is of critical importance in any cognitive radio system. When the primary user's signal has uncertain parameters, the likelihood ratio test, which is the theoretically optimal detector, generally has no closed-form…
The mass-luminosity relation is a fundamental law of astrophysics. We have suggested that the currently used mass-luminosity relation is not correct for the M/M_sun > 2.7 range of mass since it was created using double-lined eclipsing…
Accurately predicting eclipse events around irregular small bodies is crucial for spacecraft navigation, orbit determination, and spacecraft systems management. This paper introduces a novel approach leveraging neural implicit…
The majority of binary stars do not eclipse. Current searches for transiting circumbinary planets concentrate on eclipsing binaries, and are therefore restricted to a small fraction of potential hosts. We investigate the concept of finding…
The East Lansing Model is a global, uncertainty-quantified optical potential for neutron and proton projectiles, with a novel form for the neutron-proton asymmetry component, with the goal to improve extrapolations away from stability. Our…
Asteroseismology has become an indispensable method for measuring stellar ages and radii, while binary systems remain the most prevalent tool for determining stellar masses. The synergy of the two, namely pulsating stars in binary systems,…
In recent times, neural networks have become a powerful tool for the analysis of complex and abstract data models. However, their introduction intrinsically increases our uncertainty about which features of the analysis are model-related…