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Found 12 papers, 8 papers with code
A Classifier-Based Approach to Multi-Class Anomaly Detection for Astronomical Transients
In this work, we introduce an alternative approach to detecting anomalies: using the penultimate layer of a neural network classifier as the latent space for anomaly detection.
Real-time Detection of Anomalies in Multivariate Time Series of Astronomical Data
Astronomical transients are stellar objects that become temporarily brighter on various timescales and have led to some of the most significant discoveries in cosmology and astronomy.
Real-Time Detection of Anomalies in Large-Scale Transient Surveys
We demonstrate our methods' ability to provide anomaly scores as a function of time on light curves from the Zwicky Transient Facility.
Practical Galaxy Morphology Tools from Deep Supervised Representation Learning
Models fine-tuned from our representation are better able to identify ring galaxies than models fine-tuned from terrestrial images (ImageNet) or trained from scratch.
Astronomaly: Personalised Active Anomaly Detection in Astronomical Data
Here we introduce Astronomaly: a general anomaly detection framework with a novel active learning approach designed to provide personalised recommendations.
Instrumentation and Methods for Astrophysics Astrophysics of Galaxies
Unsupervised machine learning for transient discovery in Deeper, Wider, Faster light curves
We present an unsupervised method for transient discovery using a clustering technique and the Astronomaly package.
Instrumentation and Methods for Astrophysics
The Role of Machine Learning in the Next Decade of Cosmology
In recent years, machine learning (ML) methods have remarkably improved how cosmologists can interpret data.
Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics
Bayesian Anomaly Detection and Classification
Using simulated data with Gaussian noise, BADAC is shown to be superior to standard algorithms in both classification and anomaly detection performance in the presence of uncertainties, though with significantly increased computational cost.
The Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC): Data set
The Photometric LSST Astronomical Time Series Classification Challenge (PLAsTiCC) is an open data challenge to classify simulated astronomical time-series data in preparation for observations from the Large Synoptic Survey Telescope (LSST), which will achieve first light in 2019 and commence its 10-year main survey in 2022.
Instrumentation and Methods for Astrophysics Solar and Stellar Astrophysics
DeepSource: Point Source Detection using Deep Learning
For uniformly-weighted images it achieves a Purity x Completeness (PC) score at SNR = 3 of 0. 73, compared to 0. 31 for the best PyBDSF model.
Machine learning cosmological structure formation
We train a machine learning algorithm to learn cosmological structure formation from N-body simulations.
Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics
Science-Driven Optimization of the LSST Observing Strategy
The Large Synoptic Survey Telescope is designed to provide an unprecedented optical imaging dataset that will support investigations of our Solar System, Galaxy and Universe, across half the sky and over ten years of repeated observation.
Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics Earth and Planetary Astrophysics Astrophysics of Galaxies Solar and Stellar Astrophysics
