相关论文: Machine Learning applications for Cataclysmic Vari…

We use an independent new sample of cataclysmic variables (CVs), constructed by selecting objects for Halpha emission, to constrain the properties of the intrinsic CV population. This sample is restricted to systems that are likely to be…

With the advent of powerful telescopes such as the Square Kilometer Array and the Vera C. Rubin Observatory, we are entering an era of multiwavelength transient astronomy that will lead to a dramatic increase in data volume. Machine…

We have discovered a new high proper motion cataclysmic variable (CV) in the SUPERBLINK proper motion survey, which is sensitive to stars with proper motions greater than 40 mas/yr. This CV was selected for follow-up observations as part of…

Critical heat flux (CHF) marks the transition from nucleate to film boiling, where heat transfer to the working fluid can rapidly deteriorate. Accurate CHF prediction is essential for efficiency, safety, and preventing equipment damage,…

The evolutionary classification of molecular clumps, crucial for understanding star formation, is commonly based on human-assigned categories derived from infrared (IR) emission and well-established morphological criteria. However, due to…

Short-period cataclysmic variables (spCVs), with orbital periods below the period gap ($P_{orb}$ < 2 hr), offer insight into the evolutionary models of CVs and can serve as strong emitters of gravitational waves (GWs). To identify new spCV…

While highly evolved cataclysmic variables (CVs) with brown dwarf donors, often called "period bouncers", are predicted to make up $\simeq40-70$ % of the Galactic CV population, only a handful of such systems are currently known. The…

We present the results of a systematic study of cataclysmic variables (CVs) and related systems, combining detailed binary-population synthesis (BPS) models with a grid of 120 binary evolution sequences calculated with a Henyey-type stellar…

Cataclysmic Variables (CV) are close binary systems, in which the primary, the more massive star, is a white dwarf. CVs usually exhibit a number of periodicities, most of which are now understood. However, recently, a new phenomenon was…

Machine learning (ML) tools such as encoder-decoder convolutional neural networks (CNN) can represent incredibly complex nonlinear functions which map between combinations of images and scalars. For example, CNNs can be used to map…

I review our current understanding of the evolution of cataclysmic variables (CVs). I first provide a brief introductory "CV primer", in which I describe the physical structure of CVs, as well as their astrophysical significance. The main…

Security researchers grapple with the surge of malicious files, necessitating swift identification and classification of malware strains for effective protection. Visual classifiers and in particular Convolutional Neural Networks (CNNs)…

The exploitation of present and future synoptic (multi-band and multi-epoch) surveys requires an extensive use of automatic methods for data processing and data interpretation. In this work, using data extracted from the Catalina Real Time…

Developing autonomous vehicles (AVs) helps improve the road safety and traffic efficiency of intelligent transportation systems (ITS). Accurately predicting the trajectories of traffic participants is essential to the decision-making and…

Radio emission from non-magnetic cataclysmic variables (CVs, accreting white dwarfs) could allow detailed studies of outflows and possibly accretion flows in these nearby, numerous and non-relativistic compact accretors. Up to now, however,…

Machine learning (ML) can process large sets of data generated from complex systems, which is ideal for classification tasks as often appeared in critical phenomena. Meanwhile ML techniques have been found effective in detecting critical…

We report the discovery of a new cataclysmic variable (CV) among unidentified objects from the ASCA Galactic Plane Survey made using the Virtual Observatory data mining. First, we identified AX J194939+2631 with IPHAS J194938.39+263149.2,…

Identifying suitable machine learning paradigms for intrusion detection remains critical for building effective and generalizable security solutions. In this study, we present a controlled comparison of four representative models -…

Machine learning (ML) is becoming a critical tool for interrogation of large complex data. Labeling, defined as the process of adding meaningful annotations, is a crucial step of supervised ML. However, labeling datasets is time consuming.…

We present a systematic survey for mass-transferring and recently-detached cataclysmic variables (CVs) with evolved secondaries, which are progenitors of extremely low-mass white dwarfs (ELM WDs), AM CVn systems, and detached ultracompact…