The LSST Data Management System

The Large Synoptic Survey Telescope (LSST; Ivezić et al. 2008) is a large-aperture, wide-field, ground-based survey system that will image the sky in six optical bands from 320 to 1050 nm, uniformly covering approximately 18000 deg2 of the sky over 800 times. The LSST is currently under construction on Cerro Pachón in Chile, and expected to enter operations in 2022. Once operational, the LSST will explore a wide range of astrophysical questions, from discovering “killer” asteroids to examining the nature of Dark Energy. The LSST will generate on average 15 TB of data per night, and will require a comprehensive Data Management system to reduce the raw data to scientifically useful catalogs and images with minimum human intervention. These reductions will result in a real-time alert stream, and eleven data releases over the 10-year duration of LSST operations. To enable this processing, the LSST project is developing a new, general-purpose, high-performance, scalable, well documented, open source data processing software stack for O/IR surveys. Prototypes of this stack are already capable of processing data from existing cameras (e.g., SDSS, DECam, MegaCam), and form the basis of the Hyper-Suprime Cam (HSC) Survey data reduction pipeline

LSST Active Optics System Software Architecture

The Large Synoptic Survey Telescope (LSST) is an 8-meter class wide-field telescope now under construction on Cerro Pachon, near La Serena, Chile. This ground-based telescope is designed to conduct a decade-long time domain survey of the optical sky. In order to achieve the LSST scientific goals, the telescope requires delivering seeing limited image quality over the 3.5 degree field-of-view. Like many telescopes, LSST will use an Active Optics System (AOS) to correct in near real-time the system aberrations primarily introduced by gravity and temperature gradients. The LSST AOS uses a combination of 4 curvature wavefront sensors (CWS) located on the outside of the LSST field-of-view. The information coming from the 4 CWS is combined to calculate the appropriate corrections to be sent to the 3 different mirrors composing LSST. The AOS software incorporates a wavefront sensor estimation pipeline (WEP) and an active optics control system (AOCS). The WEP estimates the wavefront residual error from the CWS images. The AOCS determines the correction to be sent to the different degrees of freedom every 30 seconds. In this paper, we describe the design and implementation of the AOS. More particularly, we will focus on the software architecture as well as the AOS interactions with the various subsystems within LSST

LSST Active Optics System Software Architecture

The Large Synoptic Survey Telescope (LSST) is an 8-meter class wide-field telescope now under construction on Cerro Pachon, near La Serena, Chile. This ground-based telescope is designed to conduct a decade-long time domain survey of the optical sky. In order to achieve the LSST scientific goals, the telescope requires delivering seeing limited image quality over the 3.5 degree field-of-view. Like many telescopes, LSST will use an Active Optics System (AOS) to correct in near real-time the system aberrations primarily introduced by gravity and temperature gradients. The LSST AOS uses a combination of 4 curvature wavefront sensors (CWS) located on the outside of the LSST field-of-view. The information coming from the 4 CWS is combined to calculate the appropriate corrections to be sent to the 3 different mirrors composing LSST. The AOS software incorporates a wavefront sensor estimation pipeline (WEP) and an active optics control system (AOCS). The WEP estimates the wavefront residual error from the CWS images. The AOCS determines the correction to be sent to the different degrees of freedom every 30 seconds. In this paper, we describe the design and implementation of the AOS. More particularly, we will focus on the software architecture as well as the AOS interactions with the various subsystems within LSST

Software Architecture and System Design of Rubin Observatory

Starting from a description of the Rubin Observatory Data Management System Architecture, and drawing on our experience with and involvement in a range of other projects including Gaia, SDSS, UKIRT, and JCMT, we derive a series of generic design patterns and lessons learned.Comment: 10 pages ADASS XXXII submissio

Investigating interoperability of the LSST Data Management software stack with Astropy

The Large Synoptic Survey Telescope (LSST) will be an 8.4m optical survey telescope sited in Chile and capable of imaging the entire sky twice a week. The data rate of approximately 15TB per night and the requirements to both issue alerts on transient sources within 60 seconds of observing and create annual data releases means that automated data management systems and data processing pipelines are a key deliverable of the LSST construction project. The LSST data management software has been in development since 2004 and is based on a C++ core with a Python control layer. The software consists of nearly a quarter of a million lines of code covering the system from fundamental WCS and table libraries to pipeline environments and distributed process execution. The Astropy project began in 2011 as an attempt to bring together disparate open source Python projects and build a core standard infrastructure that can be used and built upon by the astronomy community. This project has been phenomenally successful in the years since it has begun and has grown to be the de facto standard for Python software in astronomy. Astropy brings with it considerable expectations from the community on how astronomy Python software should be developed and it is clear that by the time LSST is fully operational in the 2020s many of the prospective users of the LSST software stack will expect it to be fully interoperable with Astropy. In this paper we describe the overlap between the LSST science pipeline software and Astropy software and investigate areas where the LSST software provides new functionality. We also discuss the possibilities of re-engineering the LSST science pipeline software to build upon Astropy, including the option of contributing affliated packages

A framework for modeling the detailed optical response of thick, multiple segment, large format sensors for precision astronomy applications

Near-future astronomical survey experiments, such as LSST, possess system requirements of unprecedented fidelity that span photometry, astrometry and shape transfer. Some of these requirements flow directly to the array of science imaging sensors at the focal plane. Availability of high quality characterization data acquired in the course of our sensor development program has given us an opportunity to develop and test a framework for simulation and modeling that is based on a limited set of physical and geometric effects. In this paper we describe those models, provide quantitative comparisons between data and modeled response, and extrapolate the response model to predict imaging array response to astronomical exposure. The emergent picture departs from the notion of a fixed, rectilinear grid that maps photo-conversions to the potential well of the channel. In place of that, we have a situation where structures from device fabrication, local silicon bulk resistivity variations and photo-converted carrier patterns still accumulating at the channel, together influence and distort positions within the photosensitive volume that map to pixel boundaries. Strategies for efficient extraction of modeling parameters from routinely acquired characterization data are described. Methods for high fidelity illumination/image distribution parameter retrieval, in the presence of such distortions, are also discussed. © (2014) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.Astronom