Exploring the Time Domain With Synoptic Sky Surveys

Synoptic sky surveys are becoming the largest data generators in astronomy, and they are opening a new research frontier, that touches essentially every field of astronomy. Opening of the time domain to a systematic exploration will strengthen our understanding of a number of interesting known phenomena, and may lead to the discoveries of as yet unknown ones. We describe some lessons learned over the past decade, and offer some ideas that may guide strategic considerations in planning and execution of the future synoptic sky surveys.Comment: Invited talk, to appear in proc. IAU SYmp. 285, "New Horizons in Time Domain Astronomy", eds. E. Griffin et al., Cambridge Univ. Press (2012). Latex file, 6 pages, style files include

Using R-based VOStat as a low resolution spectrum analysis tool

We describe here an online software suite VOStat written mainly for the Virtual Observatory, a novel structure in which astronomers share terabyte scale data. Written mostly in the public-domain statistical computing language and environment R, it can do a variety of statistical analysis on multidimensional, multi-epoch data with errors. Included are techniques which allow astronomers to start with multi-color data in the form of low-resolution spectra and select special kinds of sources in a variety of ways including color outliers. Here we describe the tool and demonstrate it with an example from Palomar-QUEST, a synoptic sky survey

Using R-based VOStat as a Low-Resolution Spectrum Analysis Tool

We describe here an online software suite VOStat written mainly for the Virtual Observatory, a novel structure in which astronomers share terabyte scale data. Written mostly in the public-domain statistical computing language and environment R, it can do a variety of statistical analysis on multidimensional, multi-epoch data with errors. Included are techniques which allow astronomers to start with multi-color data in the form of low-resolution spectra and select special kinds of sources in a variety of ways including color outliers. Here we describe the tool and demonstrate it with an example from Palomar-QUEST, a synoptic sky survey.

Topic Maps as a Virtual Observatory tool

One major component of the VO will be catalogs measuring gigabytes and terrabytes if not more. Some mechanism like XML will be used for structuring the information. However, such mechanisms are not good for information retrieval on their own. For retrieval we use queries. Topic Maps that have started becoming popular recently are excellent for segregating information that results from a query. A Topic Map is a structured network of hyperlinks above an information pool. Different Topic Maps can form different layers above the same information pool and provide us with different views of it. This facilitates in being able to ask exact questions, aiding us in looking for gold needles in the proverbial haystack. Here we discuss the specifics of what Topic Maps are and how they can be implemented within the VO framework. URL: http://www.astro.caltech.edu/~aam/science/topicmaps/Comment: 11 pages, 5 eps figures, to appear in SPIE Annual Meeting 2001 proceedings (Astronomical Data Analysis), uses spie.st

Effective image differencing with convolutional neural networks for real-time transient hunting

Large sky surveys are increasingly relying on image subtraction pipelines for real-time (and archival) transient detection. In this process one has to contend with varying point-spread function (PSF) and small brightness variations in many sources, as well as artefacts resulting from saturated stars and, in general, matching errors. Very often the differencing is done with a reference image that is deeper than individual images and the attendant difference in noise characteristics can also lead to artefacts. We present here a deep-learning approach to transient detection that encapsulates all the steps of a traditional image-subtraction pipeline – image registration, background subtraction, noise removal, PSF matching and subtraction – in a single real-time convolutional network. Once trained, the method works lightening-fast and, given that it performs multiple steps in one go, the time saved and false positives eliminated for multi-CCD surveys like Zwicky Transient Facility and Large Synoptic Survey Telescope will be immense, as millions of subtractions will be needed per night

Novel Measures for Rare Transients

Data volumes in astronomy have been growing rapidly. Various projects and methodologies are starting to deal with this. As we cross-match and correlate datasets, the number of parameters per object—in other words dimensions we need to deal with— is also growing. This leads to more interesting issues as many values are missing, and many parameters are non-homogeneously redundant. One needs to tease apart clusters in this space which represent different physical properties, and hence phenomena. We describe measures that help to do that for transients from the Catalina Realtime Transient Survey, and project it to near future surveys. The measures are based partly on domain knowledge and are incorporated into statistical and machine learning techniques. We also describe the discriminating role of appropriate followup observations in near-real-time classification of transients. In particular such novel measures will help us find relatively rare transients