LSST: From Science Drivers to Reference Design and Anticipated Data Products

Abstract

We describe here the most ambitious survey currently planned in the optical, the Large Synoptic Survey Telescope (LSST). The LSST design is driven by four main science themes: probing dark energy and dark matter, taking an inventory of the solar system, exploring the transient optical sky, and mapping the Milky Way. LSST will be a large, wide-field ground-based system designed to obtain repeated images covering the sky visible from Cerro Pachón in northern Chile. The telescope will have an 8.4 m (6.5 m effective) primary mirror, a 9.6 deg 2 field of view, a 3.2-gigapixel camera, and six filters (ugrizy) covering the wavelength range 320-1050 nm. The project is in the construction phase and will begin regular survey operations by 2022. About 90% of the observing time will be devoted to a deep-wide-fast survey mode that will uniformly observe a 18,000 deg 2 region about 800 times (summed over all six bands) during the anticipated 10 yr of operations and will yield a co-added map to r∼27.5. These data will result in databases including about 32 trillion observations of 20 billion galaxies and a similar number of stars, and they will serve the majority of the primary science programs. The remaining 10% of the observing time will be allocated to special projects such as Very Deep and Very Fast time domain surveys, whose details are currently under discussion. We illustrate how the LSST science drivers led to these choices of system parameters, and we describe the expected data products and their characteristics.

Asteroids’ Size Distribution and Colors from HITS

“…the two images per visit should be in different filters to retrieve good colors. Although LSST will deliver large lightcurves for many asteroids, allowing to measure colors even if the two images per visit were in the same filter (Ivezić et al 2018), this will be possible only for asteroids well tracked through many nights for a long time, while if the color is measured per visit we would be able to have colors for nightly tracklets even for those cases when the body is detected only once (generally the case for small bodies at the limiting magnitude). The slopes we got in our size distributions are much steeper than in any other survey, only comparable to the ones found by Parker et al (2008) for bright bodies.…”

Surveying the reach and maturity of machine learning and artificial intelligence in astronomy

“…A key motivation for many applications of ML and AI to astronomical data is the need to prepare for the data streams expected from near-term observatories and space missions. The Large Synoptic Survey Telescope (Ivezi c et al, 2019;LSST Science Collaboration and LSST Project, 2009); the Euclid satellite (Amendola et al, 2013;Laureijs et al, 2011); MeerKAT (Booth, de Blok, Jonas, & Fanaroff, 2009); the Australian Square Kilometer Array Pathfinder (Johnston et al, 2007(Johnston et al, , 2008; and the Square Kilometer Array (Dewdney, Hall, Schilizzi, & Lazio, 2009), among others, will all generate datasets on scales (volumes and velocities) that vastly exceed the discovery capabilities of humans. In the interim, the SDDS (Abazajian et al, 2009;Stoughton et al, 2002;York et al, 2000), the Panoramaic Survey Telescope and Rapid Response System (Kaiser, 2004), the Catalina Real-Time Transient Survey (CRTS; Drake et al, 2009;Mahabal et al, 2011) and the Zwicky Transient Facility (ZTF; Bellm et al, 2019), the Kilo Degree Survey (KiDS; de Jong, Verdoes Kleijn, Kuijken, & Valentijn, 2013), and the Fornax Deep Survey (Iodice et al, 2016), both using the VLT Survey Telescope, 9 LOFAR (van Haarlem et al, 2013), the Solar Dynamic Observatory (SDO; Lemen et al, 2012;Pesnell, Thompson, & Chamberlin, 2012), the Kepler Planet-Detection Mission (Borucki et al, 2010), and the GAIA space mission (Gaia Collaboration, Gaia Collaboration, Prusti, et al, 2016;Gaia Collaboration et al, 2018), are generating data with which ML and AI has enabled classification, regression, forecasting, and discovery, leading to new knowledge and new insights.…”

The HSC-SSP Transient Survey: Implications from Early Photometry and Rise Time of Normal Type Ia Supernovae

“…Standard image reduction procedures including bias, dark, flat, and fringe corrections, as well as astrometric and photometric calibrations against the Pan-STARRS1 (PS1) 3π catalog (Tonry et al 2012;Magnier et al 2013) were done with the HSC pipeline, a version of the Large Synoptic Survey Telescope (LSST) stack (Axelrod et al 2010;Bosch et al 2018;Ivezić et al 2019). The image subtraction was applied using deep, coadded reference images created from data taken in March 2014, April 2016, and May 2019 2 .…”

“…Software: hscPipe (Bosch et al 2018), LSST pipeline (Axelrod et al 2010;Ivezić et al 2019), SALT2 (Guy et al 2007(Guy et al , 2010, Astropy (Astropy Collaboration et al 2013…”