Towards a real‐time transient classification engine

Bloom, J.; Starr, D.; Butler, Nathaniel R.; Nugent, P.; Rischard, Maxime; Eads, Damian; Poznanski, D.

doi:10.1002/asna.200710957

Cited by 23 publications

(24 citation statements)

References 9 publications

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“…Software to perform the classification as well as the list of candidate quasars can be downloaded from the project Web site. 4 This work has been conducted under the auspices of a broader project (The Time-domain Classification Project; Bloom et al 2008), a goal of which is to characterize the allowed range of variability versus timescale for the full diversity of astrophysical objects. We have shown above how the temporal profile of quasars stands out against the signature of variable stars, predominantly on long timescales but with strong power to separate strong variables from quasars on short timescale.…”

Section: Discussionmentioning

confidence: 99%

Optimal Time-Series Selection of Quasars

Butler¹,

Bloom²

2011

The Astronomical Journal

128

163

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We present a novel method for the optimal selection of quasars using time-series observations in a single photometric bandpass. Utilizing the damped random walk model of Kelly et al., we parameterize the ensemble quasar structure function in Sloan Stripe 82 as a function of observed brightness. The ensemble model fit can then be evaluated rigorously for and calibrated with individual light curves with no parameter fitting. This yields a classification in two statistics-one describing the fit confidence and the other describing the probability of a false alarm-which can be tuned, a priori, to achieve high quasar detection fractions (99% completeness with default cuts), given an acceptable rate of false alarms. We establish the typical rate of false alarms due to known variable stars as 3% (high purity). Applying the classification, we increase the sample of potential quasars relative to those known in Stripe 82 by as much as 29%, and by nearly a factor of two in the redshift range 2.5 < z < 3, where selection by color is extremely inefficient. This represents 1875 new quasars in a 290 deg 2 field. The observed rates of both quasars and stars agree well with the model predictions, with >99% of quasars exhibiting the expected variability profile. We discuss the utility of the method at high redshift and in the regime of noisy and sparse data. Our time-series selection complements well-independent selection based on quasar colors and has strong potential for identifying high-redshift quasars for Baryon Acoustic Oscillations and other cosmology studies in the LSST era.

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Section: Discussionmentioning

confidence: 99%

Optimal Time-Series Selection of Quasars

Butler¹,

Bloom²

2011

The Astronomical Journal

128

163

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“…Considerable insight into the classification problem can be gained from the current efforts on classification of transients in optical synoptic sky surveys (Bloom et al 2008;Donalek et al 2008;Mahabal et al 2008aMahabal et al , 2008bMahabal et al , 2010Mahabal et al , 2011Djorgovski et al 2011). We experimented with several approaches, using the data from the Palomar-Quest and Catalina Real-Time Transient Surveys, as well as selected data sets from the literature.…”

Section: Other Machine-learning Approachesmentioning

confidence: 99%

VAST: An ASKAP Survey for Variables and Slow Transients

Murphy

Chatterjee

Kaplan

et al. 2013

Publ. Astron. Soc. Aust.

112

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The Australian Square Kilometre Array Pathfinder (ASKAP) will give us an unprecedented opportunity to investigate the transient sky at radio wavelengths. In this paper we present VAST, an ASKAP survey for Variables and Slow Transients. VAST will exploit the wide-field survey capabilities of ASKAP to enable the discovery and investigation of variable and transient phenomena from the local to the cosmological, including flare stars, intermittent pulsars, X-ray binaries, magnetars, extreme scattering events, interstellar scintillation, radio supernovae, and orphan afterglows of gamma-ray bursts. In addition, it will allow us to probe unexplored regions of parameter space where new classes of transient sources may be detected. In this paper we review the known radio transient and variable populations and the current results from blind radio surveys. We outline a comprehensive program based on a multi-tiered survey strategy to characterise the radio transient sky through detection and monitoring of transient and variable sources on the ASKAP imaging timescales of 5 s and greater. We also present an analysis of the expected source populations that we will be able to detect with VAST.

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“…Classification of stars and other phenomena into one of the known types of variability (described in Section 1) is a very common and traditional astronomy application [Bloo08]. Usually the first step is to determine the type of variability (e.g., periodic, transient, stochastic) and then to determine the specific astrophysical class of object within that variability type.…”

Section: Astronomical Applications: Current and Futurementioning

confidence: 99%

Pattern Recognition in Time Series

Lin¹,

Williamson²,

Borne³

et al. 2012

Advances in Machine Learning and Data Mining for Astronomy

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Massive amount of time series data are generated daily, in areas as diverse as astronomy, industry, sciences, and aerospace, to name just a few. One obvious problem of handling time series databases concerns with its typically massive size-gigabytes or even terabytes are common, with more and more databases reaching the petabyte scale. Most classic data mining algorithms do not perform or scale well on time series data. The intrinsic structural characteristics of time series data such as the high dimensionality and feature correlation, combined with the measurement-induced noises that beset real-world time series data, pose challenges that render classic data mining algorithms ineffective and inefficient for time series. As a result, time series data mining has attracted enormous amount of attention in the past two decades.In this chapter, we discuss the state-of-the-art techniques for time series pattern recognition, the process of mapping an input representation for an entity or relationship to an output category. Approaches to pattern recognition tasks vary by representation for the input data, similarity/distance measurement function, and pattern recognition technique. We will discuss the various pattern recognition techniques, representations, and similarity measures commonly used for time series. While the majority of work concentrated on univariate time series, we will also discuss the applications of some of the techniques on multivariate time series.

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Towards a real‐time transient classification engine

Cited by 23 publications

References 9 publications

Optimal Time-Series Selection of Quasars

Optimal Time-Series Selection of Quasars

VAST: An ASKAP Survey for Variables and Slow Transients

Pattern Recognition in Time Series

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