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The Architecture of the LISA Science Analysis

Teukolsky, Saul A. and Vallisneri, Michele and Chua, Alvin and Akeson, Rachel and Archibald, Anne M. and Babak, Stanislav and Breivik, Katelyn and Brown, C. Titus and Cornish, Neil J. and Cutler, Curt and Davidoff, Scott and Foucart, Francois and Galley, Chad R. and Kidder, Lawrence E. and Kumar, Prayush and Lamberts, Astrid and Lovelace, Geoffrey and Mahabal, Ashish and Corbett Moran, Christine and Nuttall, Laura and Okounkova, Maria and Robson, Travis and Scheel, Mark A. and Shoemaker, Deirdre and Taylor, Stephen and Tinto, Massimo and Varma, Vijay and Vecchio, Alberto (2019) The Architecture of the LISA Science Analysis. , Pasadena, CA. (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20191002-102632020

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Abstract

The space-based gravitational-wave (henceforth, GW!) observatory LISA (Amaro-Seoane et al., 2017) will o˙er unparalleled science returns, including a view of massive black hole mergers up to high redshifts, precision tests of general relativity and black hole structure, a census of thousands of compact binaries in the Galaxy, and the possibility of detecting stochastic signals from the early Universe. While the Mock LISA Data Challenges (2006–2011) gave us confidence that LISA will be able to fulfill its scientific potential, we still have a rather incomplete idea of what the end-to-end LISA science analysis should look like. The task at hand is substantial: 1) Our data reduction process needs to ensure the phase coherence of GW measurements across data gaps and instrument glitches over multiple years. 2) Our waveform models need to reach part-in-105 accuracy to maximize the science payo˙ of the mission, with suÿcient computational eÿciency to sample parameter space broadly. 3) Our algorithms need to resolve thousands of individual sources of di˙erent types and strengths, all of them superimposed on the same multi-year dataset, while simultaneously characterizing the underlying noise-like stochastic background. 4) Our catalogs need to represent the complex and high-dimensional joint distributions of estimated source parameters for all sources. It is tempting to assume that current algorithms and prototype codes will scale up to this challenge, thanks to the greatly increased computational power that will become available by the time of LISA’s launch in the early 2030s. In reality, harnessing that power will require very di˙erent methods, adapted to future high-performance computational architectures that we can only glimpse now. Thus, we need to begin our exploration at this time, seeking inspiration from other disciplines (e.g., big data processing, computational biology, the most advanced applications in astroinformatics) and learning to pose the same physical questions in di˙erent, future-proof ways—or even daring to imagine questions that will be tractable only with future machines. The broad objective of this study was to imagine how evolved or rethought data analysis algorithms and source-modeling codes will perform the LISA science analysis on the computers of the future, with the hope of guiding LISA science and data analysis research and development in the years to come.


Item Type:Report or Paper (Technical Report)
ORCID:
AuthorORCID
Teukolsky, Saul A.0000-0001-9765-4526
Vallisneri, Michele0000-0002-4162-0033
Akeson, Rachel0000-0001-9674-1564
Brown, C. Titus0000-0001-6001-2677
Foucart, Francois0000-0003-4617-4738
Lamberts, Astrid0000-0001-8740-0127
Lovelace, Geoffrey0000-0002-7084-1070
Mahabal, Ashish0000-0003-2242-0244
Okounkova, Maria0000-0001-7869-5496
Taylor, Stephen0000-0003-0264-1453
Varma, Vijay0000-0002-9994-1761
Vecchio, Alberto0000-0002-6254-1617
Additional Information:© September 2019. This work had its inception at the Architecture of the LISA Science Analysis study funded by the W. M. Keck Institute for Space Studies. The study organizers and all study participants are deeply grateful to KISS Executive Director Michele Judd and to her amazing staff for creating the perfect environment for insight, creativity, and collegiality. Deirdre Shoemaker acknowledges support from NSF award PHY1806580. Michele Vallisneri, Alvin Chua, Curt Cutler, and Chad Galley acknowledge support from the JPL RTD program. Part of this work was performed at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. Government sponsorship acknowledged.
Group:Keck Institute for Space Studies
Funders:
Funding AgencyGrant Number
Keck Institute for Space Studies (KISS)UNSPECIFIED
NSFPHY-1806580
JPL Research and Technology Development FundUNSPECIFIED
NASA/JPL/CaltechUNSPECIFIED
DOI:10.26206/NTBV-YA50
Record Number:CaltechAUTHORS:20191002-102632020
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20191002-102632020
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:99015
Collection:CaltechAUTHORS
Deposited By: Iryna Chatila
Deposited On:03 Oct 2019 17:42
Last Modified:22 Nov 2019 09:58

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