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.

[1]  Oxford,et al.  Exploring the Optical Transient Sky with the Palomar Transient Factory , 2009, 0906.5355.

[2]  David Heckerman,et al.  A Tutorial on Learning with Bayesian Networks , 1998, Learning in Graphical Models.

[3]  Robert J. Brunner,et al.  Exploration of parameter spaces in a virtual observatory , 2001, SPIE Optics + Photonics.

[4]  Ciro Donalek,et al.  Mixing Bayesian Techniques for Effective Real-time Classification of Astronomical Transients , 2010 .

[5]  Gábor Lugosi,et al.  Prediction, learning, and games , 2006 .

[6]  Chris H. Q. Ding,et al.  Supernova Recognition Using Support Vector Machines , 2006, 2006 5th International Conference on Machine Learning and Applications (ICMLA'06).

[7]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[8]  D. Titterington,et al.  Comparison of Discrimination Techniques Applied to a Complex Data Set of Head Injured Patients , 1981 .

[9]  A. A. Mahabal,et al.  The Catalina Real-Time Transient Survey (CRTS) , 2011, 1102.5004.

[10]  P. J. Green,et al.  Density Estimation for Statistics and Data Analysis , 1987 .

[11]  S. G. Djorgovski,et al.  The Palomar-Quest digital synoptic sky survey , 2007, 0801.3005.

[12]  Robert J. Brunner,et al.  The Digitized Second Palomar Observatory Sky Survey (DPOSS). III. Star-Galaxy Separation , 2004 .

[13]  D. Hand,et al.  Idiot's Bayes—Not So Stupid After All? , 2001 .

[14]  S. G. Djorgovski,et al.  Automated probabilistic classification of transients and variables , 2008, 0802.3199.

[15]  Ashok N. Srivastava,et al.  Advances in Machine Learning and Data Mining for Astronomy , 2012 .

[16]  Ciro Donalek,et al.  Classification of Optical Transients: Experiences from PQ and CRTS Surveys , 2010 .

[17]  Usama Fayyad,et al.  THE SKICAT SYSTEM FOR PROCESSING AND ANALYZING DIGITAL IMAGING SKY SURVEYS , 1995 .

[18]  David J. Spiegelhalter,et al.  Probabilistic Networks and Expert Systems - Exact Computational Methods for Bayesian Networks , 1999, Information Science and Statistics.

[19]  H. Robbins.  Some aspects of the sequential design of experiments , 1952 .

[20]  E. Glikman,et al.  Some Pattern Recognition Challenges in Data-Intensive Astronomy , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[21]  Michael Turmon,et al.  Statistical Pattern Recognition for Labeling Solar Active Regions: Application to SOHO/MDI Imagery , 2002 .

[22]  Pat Langley,et al.  Estimating Continuous Distributions in Bayesian Classifiers , 1995, UAI.

[23]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[24]  Yoshua Bengio,et al.  Pattern Recognition and Neural Networks , 1995 .

[25]  Canada.,et al.  Data Mining and Machine Learning in Astronomy , 2009, 0906.2173.

[26]  C. Donalek,et al.  New Approaches to Object Classification in Synoptic Sky Surveys , 2008 .

[27]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

[28]  S. Bailey,et al.  How to Find More Supernovae with Less Work: Object Classification Techniques for Difference Imaging , 2006, 0705.0493.

[29]  Bohdan Paczynski.  Monitoring All Sky for Variability , 2000 .

[30]  Asu,et al.  Exploration of Large Digital Sky Surveys , 2000, astro-ph/0012489.