58 citations · 265 across the 18 of their papers we have counts for
21 papers
Comparing Active Anomaly Detection Frameworks: From Light Curves to Images
Sreevarsha Sreejith, Robert C. Nichol
The increasing volume and complexity of data from modern astronomical surveys are making comprehensive visual inspection increasingly impractical, motivating automated methods for…
SNAD: enabling discovery in the era of big data
Maria Pruzhinskaya, Emille E. O. Ishida, Konstantin Malanchev +8
In the era of wide-field surveys and big data in astronomy, the SNAD team is exploiting the potential of modern datasets for discovering new, unforeseen, or rare astrophysical obje…
What ZTF Saw Where Rubin Looked: Anomaly Hunting in DR23
Maria V. Pruzhinskaya, Anastasia D. Lavrukhina, Timofey A. Semenikhi +7
We present results from the SNAD VIII Workshop, during which we conducted the first systematic anomaly search in the ZTF fields also observed by LSSTComCam during Rubin Scientific…
Signatures to help interpretability of anomalies
Emmanuel Gangler, Emille E. O. Ishida, Matwey V. Kornilov +8
Machine learning is often viewed as a black box when it comes to understanding its output, be it a decision or a score. Automatic anomaly detection is no exception to this rule, an…
Dataset of artefacts for machine learning applications in astronomy
Sreevarsha Sreejith, Maria V. Pruzhinskaya, Alina A. Volnova +8
Accurate photometry in astronomical surveys is challenged by image artefacts, which affect measurements and degrade data quality. Due to the large amount of available data, this ta…
Coniferest: a complete active anomaly detection framework
M. V. Kornilov, V. S. Korolev, K. L. Malanchev +8
We present coniferest, an open source generic purpose active anomaly detection framework written in Python. The package design and implemented algorithms are described. Currently,…
Exploring the Universe with SNAD: Anomaly Detection in Astronomy
Alina A. Volnova, Patrick D. Aleo, Anastasia Lavrukhina +9
SNAD is an international project with a primary focus on detecting astronomical anomalies within large-scale surveys, using active learning and other machine learning algorithms. T…
SNAD catalogue of M-dwarf flares from the Zwicky Transient Facility
A. S. Voloshina, A. D. Lavrukhina, M. V. Pruzhinskaya +11
Most of the stars in the Universe are M spectral class dwarfs, which are known to be the source of bright and frequent stellar flares. In this paper, we propose new approaches to d…
Rainbow: a colorful approach on multi-passband light curve estimation
E. Russeil, K. L. Malanchev, P. D. Aleo +10
We present Rainbow, a physically motivated framework which enables simultaneous multi-band light curve fitting. It allows the user to construct a 2-dimensional continuous surface a…
Point Spread Function Deconvolution Using a Convolutional Autoencoder for Astronomical Applications
Sreevarsha Sreejith, Anže Slosar, Hong Wang
A major issue in optical astronomical image analysis is the combined effect of the instrument's point spread function (PSF) and the atmospheric seeing that blurs images and changes…
Are classification metrics good proxies for SN Ia cosmological constraining power?
Alex I. Malz, Mi Dai, Kara A. Ponder +10
Context: When selecting a classifier to use for a supernova Ia (SN Ia) cosmological analysis, it is common to make decisions based on metrics of classification performance, i.e. co…
The SNAD Viewer: Everything You Want to Know about Your Favorite ZTF Object
Konstantin Malanchev, Matwey V. Kornilov, Maria V. Pruzhinskaya +9
We describe the SNAD Viewer, a web portal for astronomers which presents a centralized view of individual objects from the Zwicky Transient Facility's (ZTF) data releases, includin…
Neural Network Based Point Spread Function Deconvolution For Astronomical Applications
Hong Wang, Sreevarsha Sreejith, Yuewei Lin +3
Optical astronomical images are strongly affected by the point spread function (PSF) of the optical system and the atmosphere (seeing) which blurs the observed image. The amount of…
Supernova search with active learning in ZTF DR3
Maria V. Pruzhinskaya, Emille E. O. Ishida, Alexandra K. Novinskaya +9
We provide the first results from the complete SNAD adaptive learning pipeline in the context of a broad scope of data from large-scale astronomical surveys. The main goal of this…
SNAD Transient Miner: Finding Missed Transient Events in ZTF DR4 using k-D trees
P. D. Aleo, K. L. Malanchev, M. V. Pruzhinskaya +7
We report the automatic detection of 11 transients (7 possible supernovae and 4 active galactic nuclei candidates) within the Zwicky Transient Facility fourth data release (ZTF DR4…
Galaxy Deblending using Residual Dense Neural networks
Hong Wang, Sreevarsha Sreejith, Anže Slosar +2
We present a new neural network approach for deblending galaxy images in astronomical data using Residual Dense Neural network (RDN) architecture. We train the network on synthetic…
Anomaly detection in the Zwicky Transient Facility DR3
K. L. Malanchev, M. V. Pruzhinskaya, V. S. Korolev +11
We present results from applying the SNAD anomaly detection pipeline to the third public data release of the Zwicky Transient Facility (ZTF DR3). The pipeline is composed of 3 stag…
Active learning with RESSPECT: Resource allocation for extragalactic astronomical transients
Noble Kennamer, Emille E. O. Ishida, Santiago Gonzalez-Gaitan +12
The recent increase in volume and complexity of available astronomical data has led to a wide use of supervised machine learning techniques. Active learning strategies have been pr…
Newly discovered dwarf galaxies in the MATLAS low density fields
Rebecca Habas, Francine R. Marleau, Pierre-Alain Duc +16
We present the photometric properties of 2210 newly identified dwarf galaxy candidates in the MATLAS fields. The Mass Assembly of early Type gaLAxies with their fine Structures (MA…
Active Anomaly Detection for time-domain discoveries
Emille E. O. Ishida, Matwey V. Kornilov, Konstantin L. Malanchev +7
We present the first evidence that adaptive learning techniques can boost the discovery of unusual objects within astronomical light curve data sets. Our method follows an active l…
Galaxy And Mass Assembly: Automatic Morphological Classification of Galaxies Using Statistical Learning
Sreevarsha Sreejith, Sergiy Pereverzyev, Lee S. Kelvin +15
We apply four statistical learning methods to a sample of galaxies () from the Galaxy and Mass Assembly (GAMA) survey to test the feasibility of using automated algo…