LSST : Image Subtraction & Transient Detection Techniques

Details LSST : Image Subtraction & Transient Detection Techniques LSST : Image Subtraction & Transient Detection Techniques

Dec 2004

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2004aas...20510812b&link_type=abstract

American Astronomical Society Meeting 205, #108.12; Bulletin of the American Astronomical Society, Vol. 36, p.1529

Astronomy and Astrophysics

Astronomy

1

Scientific paper

The LSST will open a window to new classes of optical variability with its enterprising combination of depth, cadence, and sky coverage. The recent detection of faint, short timescale events suggests a wealth of phenomena occurring below the sky background and within the intervals when one returns to look at a given part of the sky. The LSST should detect thousands of these short timescale (< 1000 second) events per night; the transient detection pipeline must recognize events and disseminate alerts on a timescale short enough to enable followup. In addition, even a small misclassification allowance will result in a flood of false positives. This requires making use of current datasets to examine and classify the spectrum of astronomical variability. We describe here successes, failures, and lessons learned in difference imaging survey data from the Deep Lens, Sloan, SuperMACHO, ESSENCE, MCELS, and MACHO surveys. This includes the need to propagate noise and bad/saturated pixel masks through image convolution stages, which allows for the setting of correct detection thresholds. As well, variability pipelines must enable efficiency analysis for the calculation of event rates. Particular attention must be paid to the quality and stability of the system PSF, a requirement both for weak lensing science and for image subtraction techniques.

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