Related papers: Deep learning detection of transients
The Cherenkov Telescope Array (CTA) is planned to be the next generation ground based observatory for very high energy (VHE) gamma-ray astronomy. Gamma-rays provide a powerful insight into the non-thermal universe and hopefully a unique…
Convolutional Neural Networks (CNNs) have demonstrated potential for the real-time analysis of data from gravitational-wave detector networks for the specific case of signals from coalescing compact-object binaries such as black-hole…
The Cherenkov Telescopic Array (CTA), the next-generation ground-based gamma-ray observatory, will have unprecedented sensitivity, providing answers to open questions in gamma-ray cosmology and fundamental physics. Using simulations of…
The H.E.S.S. Imaging Air Cherenkov Telescope system is very well suited to perform follow-up observations of detections at other wavelengths or messengers due to its fast reaction and its comparably low energy threshold. These advantages…
In the era of time-domain, multi-messenger astronomy, the detection of transient events on the high-energy electromagnetic sky has become more important than ever. Previous attempts to systematically search for onboard-untriggered events in…
In this article, we propose using deep learning and transformer architectures combined with classical machine learning algorithms to detect and identify text anomalies in texts. Deep learning model provides a very crucial context…
The ANTARES telescope has the opportunity to detect transient neutrino sources, such as gamma-ray bursts, core-collapse supernovae, flares of active nuclei... To enhance the sensitivity to these sources, we have developed a new detection…
The introduction of deep wide-field surveys in recent years and the adoption of machine learning techniques have led to the discoveries of $\mathcal{O}(10^4)$ strong gravitational lensing systems and candidates. However, the discovery of…
Noise of non-astrophysical origin will contaminate science data taken by the Advanced Laser Interferometer Gravitational-wave Observatory (aLIGO) and Advanced Virgo gravitational-wave detectors. Prompt characterization of instrumental and…
Known for their efficiency in analyzing large data sets, machine learning classifiers are widely used in wide-field sky surveys. The upcoming Vera C. Rubin Observatory Legacy of Time and Space Survey (LSST) will generate millions of alerts…
The coincidental detection of the gravitational wave event GW 170817 and the associated gamma-ray burst GRB 170817A marked the advent of multi-messenger astronomy and represented a milestone in the study of GRBs. In this context, the launch…
Strong gravitational lensing is a promising probe of the substructure of dark matter halos. Deep learning methods have the potential to accurately identify images containing substructure, and differentiate WIMP dark matter from other well…
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…
The scientific interest in studying high-energy transient phenomena in the Universe has largely grown for the last decade. Now, multiple ground-based survey projects have emerged to continuously monitor the optical (and multi-messenger)…
We have searched for ultra-long (> 100 s) gamma-ray transients in the data from the anticoincidence shield (ACS) of the SPI gamma-ray spectrometer onboard the INTEGRAL orbital observatory and classified them by machine learning methods. We…
In this work, we present a new, high performance algorithm for background rejection in imaging atmospheric Cherenkov telescopes. We build on the already popular machine-learning techniques used in gamma-ray astronomy by the application of…
The sensitivity of astronomical X-ray detectors is limited by the instrumental background. The background is especially important when observing low surface brightness sources that are critical for many of the science cases targeted by…
Existing computer vision and object detection methods strongly rely on neural networks and deep learning. This active research area is used for applications such as autonomous driving, aerial photography, protection, and monitoring.…
Gamma-ray astronomy from hundreds of GeV to PeV is confined to ground-based experiments that detect air showers induced by $\gamma$-rays entering Earth's atmosphere. While particle detector arrays feature huge detection areas, accurately…
The influence of atmospheric turbulence on acquired surveillance imagery poses significant challenges in image interpretation and scene analysis. Conventional approaches for target classification and tracking are less effective under such…