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Assessing the data-analysis impact of LISA orbit approximations using a GPU-accelerated response model

Katz, Michael L. and Bayle, Jean-Baptiste and Chua, Alvin J. K. and Vallisneri, Michele (2022) Assessing the data-analysis impact of LISA orbit approximations using a GPU-accelerated response model. Physical Review D, 106 (10). Art. No. 103001. ISSN 2470-0010. doi:10.1103/physrevd.106.103001.

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The analysis of gravitational-wave (GW) datasets is based on the comparison of measured time series with theoretical templates of the detector’s response to a variety of source parameters. For the Laser Interferometer Space Antenna (LISA), the main scientific observables will be the so-called time-delay interferometry (TDI) combinations, which suppress the otherwise overwhelming laser noise. Computing the TDI response to GWs involves projecting the GW polarizations onto the LISA constellation arms, and then combining projections delayed by a multiple of the light propagation time along the arms. Both computations are difficult to perform efficiently for generic LISA orbits and GW signals. Various approximations are currently used in practice, e.g., assuming constant and equal arm lengths, which yields analytical TDI expressions that are essential to the desirable speed of current analysis codes. In this article, we present fastlisaresponse, a new efficient graphics-processing-unit-accelerated code designed to perform systematics studies on the LISA response in a fully Bayesian context. We examine loud Galactic binary signals first with the typical equal-arm-length approximation and then with a hybrid template that uses accurate orbits for the projections and equal-arm-length orbits for the TDI combinations. The hybrid template is an attempt to preserve the efficient analytical TDI expressions. We conclude that all equal-arm-length parameter-estimation codes, including when only used for TDI, need to be upgraded to the generic response if they are to achieve optimal accuracy for high (but reasonable) SNR sources within the actual LISA data.

Item Type:Article
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URLURL TypeDescription
Katz, Michael L.0000-0002-7605-5767
Bayle, Jean-Baptiste0000-0001-7629-6555
Chua, Alvin J. K.0000-0001-5242-8269
Vallisneri, Michele0000-0002-4162-0033
Additional Information:J.-B. B. was supported by a NASA postdoctoral fellowship administered by USRA. A. J. K. C. acknowledges support from the NASA LISA Preparatory Science Grant No. 20-LPS20-0005. M. V. was supported by the NASA LISA study office. This research was supported in part through the computational resources and staff contributions provided for the Quest/Grail high performance computing facility at Northwestern University. This paper also employed use of scipy [9] and matplotlib [54]. Part of this research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (Grant No. 80NM0018D0004). Copyright 2022. All rights reserved.
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Issue or Number:10
Record Number:CaltechAUTHORS:20221122-564647900.10
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:117986
Deposited By: Research Services Depository
Deposited On:02 Dec 2022 23:49
Last Modified:02 Dec 2022 23:49

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