相关论文: Machine Learning applications for Cataclysmic Vari…

Recent advances in machine learning have become increasingly popular in the applications of phase transitions and critical phenomena. By machine learning approaches, we try to identify the physical characteristics in the two-dimensional…

Common cross-validation (CV) methods like k-fold cross-validation or Monte-Carlo cross-validation estimate the predictive performance of a learner by repeatedly training it on a large portion of the given data and testing on the remaining…

Upcoming synoptic surveys are set to generate an unprecedented amount of data. This requires an automatic framework that can quickly and efficiently provide classification labels for several new object classification challenges. Using data…

We explore the observational appearance of the merger of a low-mass star with a white dwarf (WD) binary companion. We are motivated by Schreiber et al. (2016), who found that multiple tensions between the observed properties of cataclysmic…

Many rare event transitions involve multiple collective variables (CVs) and the most appropriate combination of CVs is generally unknown a priori. We thus introduce a new method, contour forward flux sampling (cFFS), to study rare events…

In molecular dynamics (MD) simulations, transitions between states are often rare events due to energy barriers that exceed the thermal temperature. Because of their infrequent occurrence and the huge number of degrees of freedom in…

Magnetic cataclysmic variables (CVs) contain a white dwarf with magnetic field strong enough to control the accretion flow from a late type secondary. In this paper, we discover a magnetic CV (CXOGSG J215544.4+380116) from the $Chandra$…

A sample of Cataclysmic Variables (CVs) is presented including spectroscopically identified 380 spectra of 245 objects, of which 58 CV candidates are new discoveries. The BaggingTopPush and the Random Forest algorithms are applied to the…

Understanding the long-time dynamics of complex physical processes depends on our ability to recognize patterns. To simplify the description of these processes, we often introduce a set of reaction coordinates, customarily referred to as…

The Zwicky Transient Facility is a large optical survey in multiple filters producing hundreds of thousands of transient alerts per night. We describe here various machine learning (ML) implementations and plans to make the maximal use of…

We present 855 cataclysmic variable candidates detected by the Catalina Real-time Transient Survey (CRTS) of which at least 137 have been spectroscopically confirmed and 705 are new discoveries. The sources were identified from the analysis…

The Zwicky Transient Facility (ZTF), a state-of-the-art optical robotic sky survey, registers on the order of a million transient events - such as supernova explosions, changes in brightness of variable sources, or moving object detections…

Reactive flows in porous media play an important role in our life and are crucial for many industrial, environmental and biomedical applications. Very often the concentration of the species at the inlet is known, and the so-called…

In this paper, we explore the potential of generative machine learning models as an alternative to the computationally expensive Monte Carlo (MC) simulations commonly used by the Large Hadron Collider (LHC) experiments. Our objective is to…

This study explores the usefulness of machine learning classifiers for modeling freight mode choice. We investigate eight commonly used machine learning classifiers, namely Naive Bayes, Support Vector Machine, Artificial Neural Network,…

We have optically identified a recently discovered INTEGRAL source, IGR J08390--4833, with a cataclysmic variable, i.e. an accreting white dwarf in a binary system. The spectrum exhibits a rising blue continuum together with Balmer and HeII…

The white dwarf (WD) mass distribution of cataclysmic variables (CVs) has recently been found to dramatically disagree with the predictions of the standard CV formation model. The high mean WD mass among CVs is not imprinted in the…

Active learning for classification seeks to reduce the cost of labeling samples by finding unlabeled examples about which the current model is least certain and sending them to an annotator/expert to label. Bayesian theory can provide a…

Studying the early stages of transient events provides crucial information about the fundamental physical processes in cataclysmic variables (CVs). However, determining an appropriate observation mode immediately after the discovery of a…

The evolution of Cataclysmic Variables (CVs) is driven by period-changes ($\dot{P}$), for which the long-venerable consensus is the Magnetic Braking Model (MBM). The MBM has its only distinctive assumption being a power-law `recipe'…