Parametrization and Classification of 20 Billion LSST Objects: Lessons from SDSS

TL;DR

This paper discusses the challenges and methods for parametrization and classification of 20 billion objects observed by LSST, drawing lessons from SDSS data to prepare for future large-scale astronomical surveys.

Contribution

It introduces classification techniques and lessons learned from SDSS data to handle LSST's massive data volume for source classification and property determination.

Findings

Demonstrated color classification of stars using SDSS data

Developed methods for binary star and asteroid classification

Outlined challenges for real-time source classification in LSST

Abstract

The Large Synoptic Survey Telescope (LSST) will be a large, wide-field ground-based system designed to obtain, starting in 2015, multiple images of the sky that is visible from Cerro Pachon in Northern Chile. About 90% of the observing time will be devoted to a deep-wide-fast survey mode which will observe a 20,000 deg$^2$ region about 1000 times during the anticipated 10 years of operations (distributed over six bands, $ugrizy$). Each 30-second long visit will deliver 5$\sigma$ depth for point sources of $r\sim24.5$ on average. The co-added map will be about 3 magnitudes deeper, and will include 10 billion galaxies and a similar number of stars. We discuss various measurements that will be automatically performed for these 20 billion sources, and how they can be used for classification and determination of source physical and other properties. We provide a few classification examples based on SDSS data, such as color classification of stars, color-spatial proximity search for wide-angle binary stars, orbital-color classification of asteroid families, and the recognition of main Galaxy components based on the distribution of stars in the position-metallicity-kinematics space. Guided by these examples, we anticipate that two grand classification challenges for LSST will be 1) rapid and robust classification of sources detected in difference images, and 2) {\it simultaneous} treatment of diverse astrometric and photometric time series measurements for an unprecedentedly large number of objects.