Related papers: Supernova scores for active anomaly detection
Modern wide-field time-domain surveys produce alert streams whose scientific potential is often concentrated in rare and unusual events. Efficient discovery therefore requires automated pipelines to be combined with rapid expert validation…
We present a new method for probabilistically classifying supernovae (SNe) without using SN spectral or photometric data. Unlike all previous studies to classify SNe without spectra, this technique does not use any SN photometry. Instead,…
We apply deep recurrent neural networks, which are capable of learning complex sequential information, to classify supernovae\footnote{Code available at \href{https://github.com/adammoss/supernovae}{https://github.com/adammoss/supernovae}}.…
In the era of large astronomical surveys, photometric classification of supernovae (SNe) has become an important research field due to limited spectroscopic resources for candidate follow-up and classification. In this work, we present a…
(abridged) Ongoing supernova (SN) surveys find hundreds of candidates, that require confirmation for their use. Traditional classification based on followup spectroscopy of all candidates is virtually impossible for these large samples. We…
Anomaly detection is being regarded as an unsupervised learning task as anomalies stem from adversarial or unlikely events with unknown distributions. However, the predictive performance of purely unsupervised anomaly detection often fails…
Active learning has been utilized as an efficient tool in building anomaly detection models by leveraging expert feedback. In an active learning framework, a model queries samples to be labeled by experts and re-trains the model with the…
Recent high-cadence transient surveys have discovered rapid transients whose light curve timescales are shorter than those of typical supernovae. In this paper, we present a systematic search for rapid transients at medium-high redshifts…
We present a model-independent, photometry-only framework for identifying strongly lensed supernovae when multiple images are unresolved and blended into a single point source. Building on the simulation-based methodology of Bag et al.…
Gravitationally lensed supernovae (SNe) are extremely rare and fade quickly; as a result, they are challenging to detect. To identify lensed SNe in large imaging datasets, current surveys primarily rely on the {\it magnification} effect of…
Identification of anomalous light curves within time-domain surveys is often challenging. In addition, with the growing number of wide-field surveys and the volume of data produced exceeding astronomers ability for manual evaluation,…
Supernovae (SNe) that have been multiply-imaged by gravitational lensing are rare and powerful probes for cosmology. Each detection is an opportunity to develop the critical tools and methodologies needed as the sample of lensed SNe…
The Pan-STARRS1 (PS1) survey has obtained imaging in 5 bands (grizy_P1) over 10 Medium Deep Survey (MDS) fields covering a total of 70 square degrees. This paper describes the search for apparently hostless supernovae (SNe) within the first…
We present a novel method of classifying Type Ia supernovae using convolutional neural networks, a neural network framework typically used for image recognition. Our model is trained on photometric information only, eliminating the need for…
The Zwicky Transient Facility (ZTF), a state-of-the-art optical robotic sky survey, registers on the order of a million transient events - such as supernova explosions, changes in brightness of variable sources, or moving object detections…
We present SNIascore, a deep-learning based method for spectroscopic classification of thermonuclear supernovae (SNe Ia) based on very low-resolution (R $\sim100$) data. The goal of SNIascore is fully automated classification of SNe Ia with…
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…
Over the past decade wide-field optical time-domain surveys have increased the discovery rate of transients to the point that $\lesssim 10\%$ are being spectroscopically classified. Despite this, these surveys have enabled the discovery of…
Current and future surveys rely on machine learning classification to obtain large and complete samples of transients. Many of these algorithms are restricted by training samples that contain a limited number of spectroscopically confirmed…
In the era of multi-messenger astronomy, early classification of photometric alerts from wide-field and high-cadence surveys is a necessity to trigger spectroscopic follow-ups. These classifications are expected to play a key role in…