| 1. |
- Abolfathi, Bela, et al.
(författare)
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The LSST DESC DC2 Simulated Sky Survey
- 2021
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Ingår i: Astrophysical Journal Supplement Series. - : American Astronomical Society. - 0067-0049 .- 1538-4365. ; 253:31
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Tidskriftsartikel (refereegranskat)abstract
- We describe the simulated sky survey underlying the second data challenge (DC2) carried out in preparation for analysis of the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) by the LSST Dark Energy Science Collaboration (LSST DESC). Significant connections across multiple science domains will be a hallmark of LSST; the DC2 program represents a unique modeling effort that stresses this interconnectivity in a way that has not been attempted before. This effort encompasses a full end-to-end approach: starting from a large N-body simulation, through setting up LSST-like observations including realistic cadences, through image simulations, and finally processing with Rubin's LSST Science Pipelines. This last step ensures that we generate data products resembling those to be delivered by the Rubin Observatory as closely as is currently possible. The simulated DC2 sky survey covers six optical bands in a wide-fast-deep area of approximately 300 deg2, as well as a deep drilling field of approximately 1 deg2. We simulate 5 yr of the planned 10 yr survey. The DC2 sky survey has multiple purposes. First, the LSST DESC working groups can use the data set to develop a range of DESC analysis pipelines to prepare for the advent of actual data. Second, it serves as a realistic test bed for the image processing software under development for LSST by the Rubin Observatory. In particular, simulated data provide a controlled way to investigate certain image-level systematic effects. Finally, the DC2 sky survey enables the exploration of new scientific ideas in both static and time domain cosmology.
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| 2. |
- Huerta, E. A., et al.
(författare)
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Enabling real-time multi-messenger astrophysics discoveries with deep learning
- 2019
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Ingår i: Nature reviews physics. - : Springer Science and Business Media LLC. - 2522-5820. ; 1:10, s. 600-608
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Forskningsöversikt (refereegranskat)abstract
- Multi-messenger astrophysics is a fast-growing, interdisciplinary field that combines data, which vary in volume and speed of data processing, from many different instruments that probe the Universe using different cosmic messengers: electromagnetic waves, cosmic rays, gravitational waves and neutrinos. In this Expert Recommendation, we review the key challenges of real-time observations of gravitational wave sources and their electromagnetic and astroparticle counterparts, and make a number of recommendations to maximize their potential for scientific discovery. These recommendations refer to the design of scalable and computationally efficient machine learning algorithms; the cyber-infrastructure to numerically simulate astrophysical sources, and to process and interpret multi-messenger astrophysics data; the management of gravitational wave detections to trigger real-time alerts for electromagnetic and astroparticle follow-ups; a vision to harness future developments of machine learning and cyber-infrastructure resources to cope with the big-data requirements; and the need to build a community of experts to realize the goals of multi-messenger astrophysics. A group of experts suggests ways in which deep learning can be used to enhance the potential for discovery in multi-messenger astrophysics.
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| 3. |
- Varghese, Blesson, et al.
(författare)
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Cloud Futurology
- 2019
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Ingår i: Computer. - 0018-9162 .- 1558-0814. ; 52:9, s. 68-77
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- The Cloud has become integral to most Internet-based applications and user gadgets. This article provides a brief history of the Cloud and presents a researcher’s view of the prospects for innovating at the infrastructure, middleware, and application and delivery levels of the already crowded Cloud computing stack.
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