相关论文: Data-driven detection of multi-messenger transient…

The detection of anomalies or transitions in complex dynamical systems is of critical importance to various applications. In this study, we propose the use of machine learning to detect changepoints for high-dimensional dynamical systems.…

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…

Automated decision-making is becoming key for automated characterization including electron and scanning probe microscopies and nano indentation. Most machine learning driven workflows optimize a single predefined objective and tend to…

I provide an incomplete inventory of the astronomical variability that will be found by next-generation time-domain astronomical surveys. These phenomena span the distance range from near-Earth satellites to the farthest Gamma Ray Bursts.…

The study of plasma physics under conditions of extreme temperatures, densities and electromagnetic field strengths is significant for our understanding of astrophysics, nuclear fusion and fundamental physics. These extreme physical systems…

Astronomy has been at the forefront of the development of the techniques and methodologies of data intensive science for over a decade with large sky surveys and distributed efforts such as the Virtual Observatory. However, it faces a new…

Electromagnetic observations of the sky have been the basis for our study of the Universe for millennia, cosmic ray studies are now entering their second century, the first neutrinos from an astrophysical source were identified three…

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…

The detection of transient phenomena such as Gamma-Ray Bursts (GRBs), Fast Radio Bursts (FRBs), stellar flares, novae, and supernovae, alongside novel cosmic messengers like high-energy neutrinos and gravitational waves, has transformed…

Machine Learning algorithms are good tools for both classification and prediction purposes. These algorithms can further be used for scientific discoveries from the enormous data being collected in our era. We present ways of discovering…

Deep learning (DL) has introduced a new paradigm in multiple-input multiple-output (MIMO) detection, balancing performance and complexity. However, the practical deployment of DL-based detectors is hindered by poor generalization,…

In the last decade, over a million stars were monitored to detect transiting planets. Manual interpretation of potential exoplanet candidates is labor intensive and subject to human error, the results of which are difficult to quantify.…

Searches for counterparts to multimessenger events with optical imagers use difference imaging to detect new transient sources. However, even with existing artifact detection algorithms, this process simultaneously returns several classes…

This paper presents a novel method for detecting pedestrians under adverse illumination conditions. Our approach relies on a novel cross-modality learning framework and it is based on two main phases. First, given a multimodal dataset, a…

Observations of transient phenomena like Gamma-Ray Bursts (GRBs), Fast Radio Bursts (FRBs), stellar flares and explosions (novae and supernovae), combined with the detection of novel cosmic messengers like high-energy neutrinos and…

The data taken by the advanced LIGO and Virgo gravitational-wave detectors contains short duration noise transients that limit the significance of astrophysical detections and reduce the duty cycle of the instruments. As the advanced…

Over the next decade, we can expect time domain astronomy to flourish at optical and radio wavelengths. In parallel with these efforts, a dedicated transient "machine" operating at higher energies (X-ray band through soft gamma-rays) is…

Fast variability of optical objects is an interesting though poorly explored subject in modern astronomy. Real-time data processing and identification of transient celestial events in the images is very important for such study as it allows…

We describe the development of a system for an automated, iterative, real-time classification of transient events discovered in synoptic sky surveys. The system under development incorporates a number of Machine Learning techniques, mostly…

Experiments searching for rare processes like neutrinoless double beta decay heavily rely on the identification of background events to reduce their background level and increase their sensitivity. We present a novel machine learning based…