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Proceedings Paper • Open Access

The Palomar transient factory
Author(s): Peter Nugent; Yi Cao; Mansi Kasliwal

Paper Abstract

Astrophysics is transforming from a data-starved to a data-swamped discipline, fundamentally changing the nature of scientific inquiry and discovery. New technologies are enabling the detection, transmission, and storage of data of hitherto unimaginable quantity and quality across the electromagnetic, gravity and particle spectra. The observational data obtained during this decade alone will supersede everything accumulated over the preceding four thousand years of astronomy. Currently there are 4 large-scale photometric and spectroscopic surveys underway, each generating and/or utilizing hundreds of terabytes of data per year. Some will focus on the static universe while others will greatly expand our knowledge of transient phenomena. Maximizing the science from these programs requires integrating the processing pipeline with high-performance computing resources. These are coupled to large astrophysics databases while making use of machine learning algorithms with near real-time turnaround. Here we present an overview of one of these programs, the Palomar Transient Factory (PTF). We will cover the processing and discovery pipeline we developed at LBNL and NERSC for it and several of the great discoveries made during the 4 years of observations with PTF.

Paper Details

Date Published: 8 February 2015
PDF: 7 pages
Proc. SPIE 9397, Visualization and Data Analysis 2015, 939702 (8 February 2015); doi: 10.1117/12.2085383
Show Author Affiliations
Peter Nugent, Lawrence Berkeley National Lab. (United States)
Univ. of California, Berkeley (United States)
Yi Cao, California Institute of Technology (United States)
Mansi Kasliwal, California Institute of Technology (United States)
Carnegie Institution for Science (United States)

Published in SPIE Proceedings Vol. 9397:
Visualization and Data Analysis 2015
David L. Kao; Ming C. Hao; Mark A. Livingston; Thomas Wischgoll, Editor(s)

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