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

Connecting the time domain community with the Virtual Astronomical Observatory

The time domain has been identified as one of the most important areas of astronomical research for the next decade. The Virtual Observatory is in the vanguard with dedicated tools and services that enable and facilitate the discovery, dissemination and analysis of time domain data. These range in scope from rapid notifications of time-critical astronomical transients to annotating long-term variables with the latest modeling results. In this paper, we will review the prior art in these areas and focus on the capabilities that the VAO is bringing to bear in support of time domain science. In particular, we will focus on the issues involved with the heterogeneous collections of (ancillary) data associated with astronomical transients, and the time series characterization and classification tools required by the next generation of sky surveys, such as LSST and SKA.Comment: Submitted to Proceedings of SPIE Observatory Operations: Strategies, Processes and Systems IV, Amsterdam, 2012 July 2-

Online classification for time-domain astronomy

The advent of synoptic sky surveys has spurred the development of techniques for real-time classification of astronomical sources in order to ensure timely follow-up with appropriate instruments. Previous work has focused on algorithm selection or improved light curve representations, and naively convert light curves into structured feature sets without regard for the time span or phase of the light curves. In this paper, we highlight the violation of a fundamental machine learning assumption that occurs when archival light curves with long observational time spans are used to train classifiers that are applied to light curves with fewer observations. We propose two solutions to deal with the mismatch in the time spans of training and test light curves. The first is the use of classifier committees where each classifier is trained on light curves of different observational time spans. Only the committee member whose training set matches the test light curve time span is invoked for classification. The second solution uses hierarchical classifiers that are able to predict source types both individually and by sub-group, so that the user can trade-off an earlier, more robust classification with classification granularity. We test both methods using light curves from the MACHO survey, and demonstrate their usefulness in improving performance over similar methods that naively train on all available archival data.Comment: Astroinformatics workshop, IEEE International Conference on Data Mining 201

Automated Real-Time Classification and Decision Making in Massive Data Streams from Synoptic Sky Surveys

The nature of scientific and technological data collection is evolving rapidly: data volumes and rates grow exponentially, with increasing complexity and information content, and there has been a transition from static data sets to data streams that must be analyzed in real time. Interesting or anomalous phenomena must be quickly characterized and followed up with additional measurements via optimal deployment of limited assets. Modern astronomy presents a variety of such phenomena in the form of transient events in digital synoptic sky surveys, including cosmic explosions (supernovae, gamma ray bursts), relativistic phenomena (black hole formation, jets), potentially hazardous asteroids, etc. We have been developing a set of machine learning tools to detect, classify and plan a response to transient events for astronomy applications, using the Catalina Real-time Transient Survey (CRTS) as a scientific and methodological testbed. The ability to respond rapidly to the potentially most interesting events is a key bottleneck that limits the scientific returns from the current and anticipated synoptic sky surveys. Similar challenge arise in other contexts, from environmental monitoring using sensor networks to autonomous spacecraft systems. Given the exponential growth of data rates, and the time-critical response, we need a fully automated and robust approach. We describe the results obtained to date, and the possible future developments.Comment: 8 pages, IEEE conference format, to appear in the refereed proceedings of the IEEE e-Science 2014 conf., eds. C. Medeiros et al., IEEE, in press (2014). arXiv admin note: substantial text overlap with arXiv:1209.1681, arXiv:1110.465

Data challenges of time domain astronomy

Astronomy has been at the forefront of the development of the techniques and methodologies of data intensive science for over a decade with large sky surveys and distributed efforts such as the Virtual Observatory. However, it faces a new data deluge with the next generation of synoptic sky surveys which are opening up the time domain for discovery and exploration. This brings both new scientific opportunities and fresh challenges, in terms of data rates from robotic telescopes and exponential complexity in linked data, but also for data mining algorithms used in classification and decision making. In this paper, we describe how an informatics-based approach-part of the so-called "fourth paradigm" of scientific discovery-is emerging to deal with these. We review our experiences with the Palomar-Quest and Catalina Real-Time Transient Sky Surveys; in particular, addressing the issue of the heterogeneity of data associated with transient astronomical events (and other sensor networks) and how to manage and analyze it.Comment: 15 pages, 3 figures, to appear in special issue of Distributed and Parallel Databases on Data Intensive eScienc

Sources of Gravitational Waves: Theory and Observations

Gravitational-wave astronomy will soon become a new tool for observing the Universe. Detecting and interpreting gravitational waves will require deep theoretical insights into astronomical sources. The past three decades have seen remarkable progress in analytical and numerical computations of the source dynamics, development of search algorithms and analysis of data from detectors with unprecedented sensitivity. This Chapter is devoted to examine the advances and future challenges in understanding the dynamics of binary and isolated compact-object systems, expected cosmological sources, their amplitudes and rates, and highlights of results from gravitational-wave observations. All of this is a testament to the readiness of the community to open a new window for observing the cosmos, a century after gravitational waves were first predicted by Albert Einstein