12 research outputs found
Deep-HiTS: Rotation Invariant Convolutional Neural Network for Transient Detection
We introduce Deep-HiTS, a rotation invariant convolutional neural network
(CNN) model for classifying images of transients candidates into artifacts or
real sources for the High cadence Transient Survey (HiTS). CNNs have the
advantage of learning the features automatically from the data while achieving
high performance. We compare our CNN model against a feature engineering
approach using random forests (RF). We show that our CNN significantly
outperforms the RF model reducing the error by almost half. Furthermore, for a
fixed number of approximately 2,000 allowed false transient candidates per
night we are able to reduce the miss-classified real transients by
approximately 1/5. To the best of our knowledge, this is the first time CNNs
have been used to detect astronomical transient events. Our approach will be
very useful when processing images from next generation instruments such as the
Large Synoptic Survey Telescope (LSST). We have made all our code and data
available to the community for the sake of allowing further developments and
comparisons at https://github.com/guille-c/Deep-HiTS
Towards an Automated Classification of Transient Events in Synoptic Sky Surveys
We describe the development of a system for an automated, iterative,
real-time classification of transient events discovered in synoptic sky
surveys. The system under development incorporates a number of Machine Learning
techniques, mostly using Bayesian approaches, due to the sparse nature,
heterogeneity, and variable incompleteness of the available data. The
classifications are improved iteratively as the new measurements are obtained.
One novel feature is the development of an automated follow-up recommendation
engine, that suggest those measurements that would be the most advantageous in
terms of resolving classification ambiguities and/or characterization of the
astrophysically most interesting objects, given a set of available follow-up
assets and their cost functions. This illustrates the symbiotic relationship of
astronomy and applied computer science through the emerging discipline of
AstroInformatics.Comment: Invited paper, 15 pages, to appear in Statistical Analysis and Data
Mining (ASA journal), ref. proc. CIDU 2011 conf., eds. A. Srivasatva & N.
Chawla, in press (2011
Flashes in a Star Stream: Automated Classification of Astronomical Transient Events
An automated, rapid classification of transient events detected in the modern
synoptic sky surveys is essential for their scientific utility and effective
follow-up using scarce resources. This presents some unusual challenges: the
data are sparse, heterogeneous and incomplete; evolving in time; and most of
the relevant information comes not from the data stream itself, but from a
variety of archival data and contextual information (spatial, temporal, and
multi-wavelength). We are exploring a variety of novel techniques, mostly
Bayesian, to respond to these challenges, using the ongoing CRTS sky survey as
a testbed. The current surveys are already overwhelming our ability to
effectively follow all of the potentially interesting events, and these
challenges will grow by orders of magnitude over the next decade as the more
ambitious sky surveys get under way. While we focus on an application in a
specific domain (astrophysics), these challenges are more broadly relevant for
event or anomaly detection and knowledge discovery in massive data streams.Comment: 8 pages, to appear in refereed proceedings of the IEEE eScience 2012
conference, October 2012, IEEE Pres
Supernova Recognition using Support Vector
Abstract We introduce a novel application of Support Vector Machines (SVMs) to the problem of identifying potential supernovae using photometric and geometric features computed from astronomical imagery. The challenges of this supervised learning application are significant: 1) noisy and corrupt imagery resulting in high levels of feature uncertainty, 2) features with heavy-tailed, peaked distributions, 3) extremely imbalanced and overlapping positive and negative data sets, and 4) the need to reach high positive classification rates, i.e. to find all potential supernovae, while reducing the burdensome workload of manually examining false positives. High accuracy is achieved via a sign-preserving, shifted log transform applied to features with peaked, heavy-tailed distributions. The imbalanced data problem is handled by oversampling positive examples, selectively sampling misclassified negative examples, and iteratively training multiple SVMs for improved supernova recognition on unseen test data. We present crossvalidation results and demonstrate the impact on a largescale supernova survey that currently uses the SVM decision value to rank-order 600,000 potential supernovae each night
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Sunfall: a collaborative visual analytics system for astrophysics
Computational and experimental sciences produce and collect ever-larger and complex datasets, often in large-scale, multi-institution projects. The inability to gain insight into complex scientific phenomena using current software tools is a bottleneck facing virtually all endeavors of science. In this paper, we introduce Sunfall, a collaborative visual analytics system developed for the Nearby Supernova Factory, an international astrophysics experiment and the largest data volume supernova search currently in operation. Sunfall utilizes novel interactive visualization and analysis techniques to facilitate deeper scientific insight into complex, noisy, high-dimensional, high-volume, time-critical data. The system combines novel image processing algorithms, statistical analysis, and machine learning with highly interactive visual interfaces to enable collaborative, user-driven scientific exploration of supernova image and spectral data. Sunfall is currently in operation at the Nearby Supernova Factory; it is the first visual analytics system in production use at a major astrophysics project