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The NANOGrav 15 yr Data Set: Detector Characterization and Noise Budget

Agazie, Gabriella; Anumarlapudi, Akash; Archibald, Anne M.; Arzoumanian, Zaven; Baker, Paul T.; Bécsy, Bence; Blecha, Laura; Brazier, Adam; Brook, Paul R.; Burke-Spolaor, Sarah; Charisi, Maria; Chatterjee, Shami; Cohen, Tyler; Cordes, James M.; Cornish, Neil J.; Crawford, Fronefield; Cromartie, H. Thankful; Crowter, Kathryn; DeCesar, Megan E.; Demorest, Paul B.; Dolch, Timothy; Drachler, Brendan; Ferrara, Elizabeth C.; Fiore, William; Fonseca, Emmanuel; Freedman, Gabriel E.; Garver-Daniels, Nate; Gentile, Peter A.; Glaser, Joseph; Good, Deborah C.; Guertin, Lydia; Gültekin, Kayhan; Hazboun, Jeffrey S.; Jennings, Ross J.; Johnson, Aaron D.; Jones, Megan L.; Kaiser, Andrew R.; Kaplan, David L.; Kelley, Luke Zoltan; Kerr, Matthew; Key, Joey S.; Laal, Nima; Lam, Michael T.; Lamb, William G.; W. Lazio, T. Joseph; Lewandowska, Natalia; Liu, Tingting; Lorimer, Duncan R.; Luo, Jing; Lynch, Ryan S.; Ma, Chung Pei; Madison, Dustin R.; McEwen, Alexander; McKee, James W.; McLaughlin, Maura A.; McM...

Authors

Gabriella Agazie

Akash Anumarlapudi

Anne M. Archibald

Zaven Arzoumanian

Paul T. Baker

Bence Bécsy

Laura Blecha

Adam Brazier

Paul R. Brook

Sarah Burke-Spolaor

Maria Charisi

Shami Chatterjee

Tyler Cohen

James M. Cordes

Neil J. Cornish

Fronefield Crawford

H. Thankful Cromartie

Kathryn Crowter

Megan E. DeCesar

Paul B. Demorest

Timothy Dolch

Brendan Drachler

Elizabeth C. Ferrara

William Fiore

Emmanuel Fonseca

Gabriel E. Freedman

Nate Garver-Daniels

Peter A. Gentile

Joseph Glaser

Deborah C. Good

Lydia Guertin

Kayhan Gültekin

Jeffrey S. Hazboun

Ross J. Jennings

Aaron D. Johnson

Megan L. Jones

Andrew R. Kaiser

David L. Kaplan

Luke Zoltan Kelley

Matthew Kerr

Joey S. Key

Nima Laal

Michael T. Lam

William G. Lamb

T. Joseph W. Lazio

Natalia Lewandowska

Tingting Liu

Duncan R. Lorimer

Jing Luo

Ryan S. Lynch

Chung Pei Ma

Dustin R. Madison

Alexander McEwen

Maura A. McLaughlin

Natasha McMann

Bradley W. Meyers

Chiara M.F. Mingarelli

Andrea Mitridate

Cherry Ng

David J. Nice

Stella Koch Ocker

Ken D. Olum

Timothy T. Pennucci

Benetge B.P. Perera

Nihan S. Pol

Henri A. Radovan

Scott M. Ransom

Paul S. Ray

Joseph D. Romano

Shashwat C. Sardesai

Ann Schmiedekamp

Carl Schmiedekamp

Kai Schmitz

Brent J. Shapiro-Albert

Xavier Siemens

Joseph Simon

Magdalena S. Siwek

Ingrid H. Stairs

Daniel R. Stinebring

Kevin Stovall

Abhimanyu Susobhanan

Joseph K. Swiggum

Stephen R. Taylor

Jacob E. Turner

Caner Unal

Michele Vallisneri

Sarah J. Vigeland

Haley M. Wahl

Caitlin A. Witt

Olivia Young



Abstract

Pulsar timing arrays (PTAs) are galactic-scale gravitational wave (GW) detectors. Each individual arm, composed of a millisecond pulsar, a radio telescope, and a kiloparsecs-long path, differs in its properties but, in aggregate, can be used to extract low-frequency GW signals. We present a noise and sensitivity analysis to accompany the NANOGrav 15 yr data release and associated papers, along with an in-depth introduction to PTA noise models. As a first step in our analysis, we characterize each individual pulsar data set with three types of white-noise parameters and two red-noise parameters. These parameters, along with the timing model and, particularly, a piecewise-constant model for the time-variable dispersion measure, determine the sensitivity curve over the low-frequency GW band we are searching. We tabulate information for all of the pulsars in this data release and present some representative sensitivity curves. We then combine the individual pulsar sensitivities using a signal-to-noise ratio statistic to calculate the global sensitivity of the PTA to a stochastic background of GWs, obtaining a minimum noise characteristic strain of 7 × 10−15 at 5 nHz. A power-law-integrated analysis shows rough agreement with the amplitudes recovered in NANOGrav’s 15 yr GW background analysis. While our phenomenological noise model does not model all known physical effects explicitly, it provides an accurate characterization of the noise in the data while preserving sensitivity to multiple classes of GW signals.

Citation

Agazie, G., Anumarlapudi, A., Archibald, A. M., Arzoumanian, Z., Baker, P. T., Bécsy, B., …Young, O. (2023). The NANOGrav 15 yr Data Set: Detector Characterization and Noise Budget. Astrophysical journal. Letters, 951(1), Article L10. https://doi.org/10.3847/2041-8213/acda88

Journal Article Type Article
Acceptance Date May 31, 2023
Online Publication Date Jun 29, 2023
Publication Date Jul 1, 2023
Deposit Date Jul 14, 2023
Publicly Available Date Jul 24, 2023
Journal Astrophysical Journal Letters
Print ISSN 2041-8205
Electronic ISSN 2041-8213
Publisher American Astronomical Society
Peer Reviewed Peer Reviewed
Volume 951
Issue 1
Article Number L10
DOI https://doi.org/10.3847/2041-8213/acda88
Public URL https://hull-repository.worktribe.com/output/4321463

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Copyright Statement
© 2023. The Author(s). Published by the American Astronomical Society.
Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.





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