Related papers: Using machine learning for transient classificatio…
Supervised classification of temporal sequences of astronomical images into meaningful transient astrophysical phenomena has been considered a hard problem because it requires the intervention of human experts. The classifier uses the…
Current synoptic sky surveys monitor large areas of the sky to find variable and transient astronomical sources. As the number of detections per night at a single telescope easily exceeds several thousand, current detection pipelines make…
In the era of large all-sky surveys, there will be a need for rapid, automatic classifications of newly discovered transient objects. Our focus here is the classification of supernovae (SNe). We consider random forest machine learning…
Photometric data-driven classification of supernovae becomes a challenge due to the appearance of real-time processing of big data in astronomy. Recent studies have demonstrated the superior quality of solutions based on various machine…
The robust association of electromagnetic candidates discovered during follow-up of gravitational-wave alerts is challenging, not only due to the large sky areas and broad distance uncertainties, but also due to the tens to hundreds of…
Thanks to the advances in robotic telescopes, the time domain astronomy leads to a large number of transient events detected in images every night. Data mining and machine learning tools used for object classification are presented. The…
Real-time ranking of optical transient candidates during gravitational-wave (GW) and multimessenger follow-up is challenging when only sparse early-time, multi-band photometry is available.We present \texttt{KilonovaSCORER}, an open-source…
With the upcoming Vera C.~Rubin Observatory Legacy Survey of Space and Time (LSST), it is expected that only $\sim 0.1\%$ of all transients will be classified spectroscopically. To conduct studies of rare transients, such as Type I…
Vast amounts of astronomical photometric data are generated from various projects, requiring significant effort to identify variable stars and other object classes. In light of this, a general, widely applicable classification framework…
The ability to discover new transients via image differencing without direct human intervention is an important task in observational astronomy. For these kind of image classification problems, machine Learning techniques such as…
We present RAPID (Real-time Automated Photometric IDentification), a novel time-series classification tool capable of automatically identifying transients from within a day of the initial alert, to the full lifetime of a light curve. Using…
Substantial effort has been devoted to the characterization of transient phenomena from photometric information. Automated approaches to this problem have taken advantage of complete phase-coverage of an event, limiting their use for…
Exploration of time domain is now a vibrant area of research in astronomy, driven by the advent of digital synoptic sky surveys. While panoramic surveys can detect variable or transient events, typically some follow-up observations are…
Estimates of the source sky location for gravitational wave signals are likely span areas ranging up to hundreds of square degrees or more, making it very challenging for most telescopes to search for counterpart signals in the…
During the LIGO and Virgo joint science runs in 2009-2010, gravitational wave (GW) data from three interferometer detectors were analyzed within minutes to select GW candidate events and infer their apparent sky positions. Target…
We present the results of the detailed analysis of an optical imaging survey conducted using the Subaru / Hyper Suprime-Cam (HSC), which aims to identify an optical counterpart to the gravitational wave event GW151226. In half a night, the…
The rise of multi-messenger astronomy has brought with it the need to exploit all available data streams and learn more about the astrophysical objects that fall within its breadth. One possible avenue is the search for serendipitous…
Automating real-time anomaly detection is essential for identifying rare transients, with modern survey telescopes generating tens of thousands of alerts per night, and future telescopes, such as the Vera C. Rubin Observatory, projected to…
Kilonovae are the electromagnetic transients created by the radioactive decay of freshly synthesized elements in the environment surrounding a neutron star merger. To study the fundamental physics in these complex environments, kilonova…
Gravitational wave (GW) transient searches rely on signal-noise discriminators to distinguish astrophysical signals from noise artefacts. These discriminators are typically tuned towards expected signal morphologies, which may limit their…