Towards real-time classification of astronomical transients
| Autor: | A. Mahabal, S. G. Djorgovski, R. Williams, A. Drake, C. Donalek, M. Graham, B. Moghaddam, M. Turmon, J. Jewell, A. Khosla, B. Hensley, Coryn A.L. Bailer-Jones |
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| Přispěvatelé: | Bailer-Jones, Coryn A. L. |
| Jazyk: | angličtina |
| Rok vydání: | 2008 |
| Předmět: |
Physics
Artificial neural network Data stream mining Astrophysics (astro-ph) Bayesian probability FOS: Physical sciences Astrophysics computer.software_genre 01 natural sciences 010305 fluids & plasmas Support vector machine Variable (computer science) Naive Bayes classifier Kriging 0103 physical sciences Transient (computer programming) Data mining 010306 general physics computer |
| Popis: | Exploration of time domain is now a vibrant area of research in astronomy, driven by the advent of digital synoptic sky surveys. While panoramic surveys can detect variable or transient events, typically some follow-up observations are needed; for short-lived phenomena, a rapid response is essential. Ability to automatically classify and prioritize transient events for follow-up studies becomes critical as the data rates increase. We have been developing such methods using the data streams from the Palomar-Quest survey, the Catalina Sky Survey and others, using the VOEventNet framework. The goal is to automatically classify transient events, using the new measurements, combined with archival data (previous and multi-wavelength measurements), and contextual information (e.g., Galactic or ecliptic latitude, presence of a possible host galaxy nearby, etc.); and to iterate them dynamically as the follow-up data come in (e.g., light curves or colors). We have been investigating Bayesian methodologies for classification, as well as discriminated follow-up to optimize the use of available resources, including Naive Bayesian approach, and the non-parametric Gaussian process regression. We will also be deploying variants of the traditional machine learning techniques such as Neural Nets and Support Vector Machines on datasets of reliably classified transients as they build up. Comment: 7 pages, 3 figures, to appear in proceedings of the Class2008 conference (Classification and Discovery in Large Astronomical Surveys, Ringberg Castle, 14-17 October 2008) |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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