Engineering topics
Publications and source records attributed to Patterson, Maria T..
We describe the proposed design and implementation of the LSST Alert Distribution System, which provides rapid dissemination of alerts to community alert brokers. At time of writing, this service is still under development; this “living document” describes current thinking, but is expected to evolve over the course of LSST construction.
This document describes the data products and processing services to be delivered by the NSF-DOE Vera C. Rubin Observatory whilst performing the Legacy Survey of Space and Time (LSST). LSST will deliver three levels of data products and services. Prompt data products are computed and released within 24 hours of observation, and include images, difference images, catalogs of sources and objects detected in difference images, and catalogs of Solar System objects. Their primary purpose is to enable rapid follow-up of time-domain events. Data Release data products are computed during annual processing campaigns, and include well-calibrated single-epoch images, deep coadds, and catalogs of objects, sources, and forced sources, enabling static sky and precision time-domain science. The Science Platform will allow for the creation of User Generated data products and will enable science cases that greatly benefit from co-location of user processing and/or data within the Rubin Observatory Data Access Center. LSST will also devote 10% of observing time to programs with special cadence. Their data products will be created using the same software and hardware as Prompt and Data Release products. All data products will be made available using user-friendly databases and web services.
There is significant interest in the models for production of short gamma-ray bursts (GRBs). Until now, the number of known short GRBs with multi-wavelength afterglows has been small. While the Fermi GRB Monitor detects many GRBs relative to the Neil Gehrels Swift Observatory, the large localization regions makes the search for counterparts difficult. With the Zwicky Transient Facility (ZTF - part of Palomar Observatory) recently achieving first light, it is now fruitful to use its combination of depth (m (sub AB) approximating 20.6), field of view (approximately 47 square degrees), and survey cadence (every approximately 3 days) to perform Target of Opportunity observations. We demonstrate this capability on GRB 180523B, which was recently announced by the Fermi GRB Monitor as a short GRB. ZTF imaged 2900 square degrees of the localization region, resulting in the coverage of 61.6 percent of the enclosed probability over two nights to a depth of m (sub AB) approximating 20.5. We characterized 14 previously unidentified transients, and none were found to be consistent with a short GRB counterpart. This search with the ZTF shows it is an efficient camera for searching for coarsely localized short GRB and gravitational-wave counterparts, allowing for a sensitive search with minimal interruption to its nominal cadence.
Project Matsu is a collaboration between the Open Commons Consortium and NASA focused on developing open source technology for cloud-based processing of Earth satellite imagery with practical applications to aid in natural disaster detection and relief. Project Matsu has developed an open source cloud-based infrastructure to process, analyze, and reanalyze large collections of hyperspectral satellite image data using OpenStack, Hadoop, MapReduce and related technologies. We describe a framework for efficient analysis of large amounts of data called the Matsu "Wheel." The Matsu Wheel is currently used to process incoming hyperspectral satellite data produced daily by NASA's Earth Observing-1 (EO-1) satellite. The framework allows batches of analytics, scanning for new data, to be applied to data as it flows in. In the Matsu Wheel, the data only need to be accessed and preprocessed once, regardless of the number or types of analytics, which can easily be slotted into the existing framework. The Matsu Wheel system provides a significantly more efficient use of computational resources over alternative methods when the data are large, have high-volume throughput, may require heavy preprocessing, and are typically used for many types of analysis. We also describe our preliminary Wheel analytics, including an anomaly detector for rare spectral signatures or thermal anomalies in hyperspectral data and a land cover classifier that can be used for water and flood detection. Each of these analytics can generate visual reports accessible via the web for the public and interested decision makers. The result products of the analytics are also made accessible through an Open Geospatial Compliant (OGC)-compliant Web Map Service (WMS) for further distribution. The Matsu Wheel allows many shared data services to be performed together to efficiently use resources for processing hyperspectral satellite image data and other, e.g., large environmental datasets that may be analyzed for many purposes.