Related papers: A Hybrid Framework for Kilonova Anomaly Detection …
This study explores the application of autoencoder-based machine learning techniques for anomaly detection to identify exoplanet atmospheres with unconventional chemical signatures using a low-dimensional data representation. We use the…
This work proposes a real-time anomaly detection scheme that leverages the multi-step ahead prediction capabilities of encoder-decoder (ED) deep learning models with recurrent units. Specifically, an encoder-decoder is used to model…
After GW170817, kilonovae have become of great interest for the astronomical, astrophysics and nuclear physics communities, due to their potential in revealing key information on the compact binary merger from which they emerge, such as the…
This paper explores semi-supervised anomaly detection, a more practical setting for anomaly detection where a small additional set of labeled samples are provided. We propose a new KL-divergence based objective function for semi-supervised…
Strong gravitationally lensed supernovae (LSNe) are rare but extremely valuable probes of cosmology and astrophysics. Prompt identification within the alert streams of time-domain surveys such as the Rubin Legacy Survey of Space and Time…
In this work, we present a novel approach to transform supervised classifiers into effective unsupervised anomaly detectors. The method we have developed, termed Discriminatory Detection of Distortions (DDD), enhances anomaly detection by…
Context. Transient astronomical events that exhibit no discernible association with a host galaxy are commonly referred to as hostless. These rare phenomena are associated with extremely energetic events, and they can offer unique insights…
The kilonova (KN) associated with the binary neutron star (BNS) merger GW170817 is the only known electromagnetic counterpart to a gravitational wave source. Here we produce a sequence of radiative transfer models (using $\textsc{tardis}$)…
Precision cosmology with Type Ia supernovae (SNe Ia) requires robust quality control of large, heterogeneous datasets. Current data processing often relies on manual, subjective rejection of photometric data, a practice that is not scalable…
Most of the existing methods for anomaly detection use only positive data to learn the data distribution, thus they usually need a pre-defined threshold at the detection stage to determine whether a test instance is an outlier.…
Modern time-domain surveys, such as the Zwicky Transient Facility (ZTF), detect far more extragalactic transients than can be spectroscopically classified. Photometric classification offers a scalable alternative, enabling the…
Unsupervised anomaly detection is a promising technique for identifying unusual patterns in data without the need for labeled training examples. This approach is particularly valuable for early case detection in epidemic management,…
Automating anomaly detection is an open problem in many scientific fields, particularly in time-domain astronomy, where modern telescopes generate millions of alerts per night. Currently, most anomaly detection algorithms for astronomical…
Hybrid physical systems combine continuous and discrete dynamics, which can be simultaneously affected by faults. Conventional fault detection methods often treat these dynamics separately, limiting their ability to capture interacting…
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…
Anomaly detection aims at identifying images that deviate significantly from the norm. We focus on algorithms that embed the normal training examples in space and when given a test image, detect anomalies based on the features distance to…
We present a comprehensive, configurable open-source framework for estimating the rate of electromagnetic detection of kilonovae (KNe) associated with gravitational wave detections of binary neutron star (BNS) mergers. We simulate the…
Kilonovae, the ultraviolet/optical/infrared counterparts to binary neutron star mergers, are an exceptionally rare class of transients. Optical follow-up campaigns are plagued by contaminating transients, which may mimic kilonovae, but do…
We present two supernovae (SNe) discovered with the Hubble Space Telescope (HST) in the Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey (CANDELS), an HST multi-cycle treasury program. We classify both objects as Type Ia SNe…
Transient sources such as supernovae (SNe) and tidal disruption events are candidates of high energy neutrino sources. However, SNe commonly occur in the universe and a chance coincidence of their detection with a neutrino signal cannot be…