Gravitational-Wave Data Science with a LIGO at Caltech

加州理工学院利用 LIGO 进行引力波数据科学

• 批准号: 1912594
• 负责人: Alan Weinstein
• 金额: $ 90万
• 依托单位: California Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1912594&HistoricalAwards=false
• 关键词: Gravitational; Wave; Data; Science; LIGO

This award supports research in gravitational wave detector commissioning and characterization, as well as data analysis and it addresses the priority areas of NSF's "Windows on the Universe" Big Idea. Gravitational-wave (GW) astronomy has entered a new era, when detected events (so far, from the merger of compact binary systems: binary black holes and binary neutron stars) will become frequent. The Advanced LIGO detectors are continuously improving their sensitivity to GWs; this will result in unprecedented rates of discoveries of compact object mergers, and the potential for many discoveries of GWs from other astrophysical sources. Whole new classes of sources may be discovered. These observations will enable a wealth of studies and new scientific results in astronomy, cosmology, and fundamental physics. Observations of binary black hole mergers are used to understand stellar evolution and the formation, evolution and death of binaries. They enable uniquely powerful tests of general relativity as a theory of gravity in the strong-field, highly-relativistic regime where detectable GWs are produced. Observations of binary neutron star mergers enable constraints on the nuclear equation of state, unique measurements of the local Hubble-Lemaitre parameter, studies of gamma-ray burst physics, and insights into the origins of the heavy elements in the universe (including gold, platinum and uranium). All of these studies are made possible through the construction and operation of the incredibly sensitive LIGO and Virgo detectors. The Caltech group, working with the LIGO Laboratory and LIGO Scientific Collaboration (LSC), continuously improves the detectors' sensitivity, precisely calibrates the strain data, characterizes detector behavior to evaluate and improve data quality, and releases the data to the public. These studies also depend crucially on the ability to identify weak GW signals from astrophysical sources in the noisy data, using highly optimized search pipelines. We also use the scientific computing required to analyze LIGO data as a tool to engage high school juniors and seniors in the local area in gravitational-wave physics and astronomy, introducing programming and more broadly applying the scientific method through hands-on GW data science projects.Our work is devoted to discovering and analyzing gravitational-wave (GW) signals in the LIGO, Virgo and KAGRA detectors during LIGO's observing runs O3 and O4. The proposed work focuses on: (a) Identifying GW signals from compact binary mergers in data from LIGO, Virgo, and KAGRA, using both of the LSC ``flagship'' search pipelines, PyCBC and gstlal; (b) using the Hidden Markov Model Viterbi algorithm to search for long-duration burst GWs from compact binary mergers, continuous GWs (CGWs) from newly-formed, rapidly spinning neutron stars in our galaxy, and other sources that produce narrow-band signals with potentially wandering central frequency; and (c) validation of candidate events (including CGW candidates) found by these pipelines, with special attention to data quality, calibration, and search pipeline specifics. We will build on our existing expertise and experience in all of these efforts, and extend them to the era of frequent detections by automating routine tasks. We will exploit our expertise in low-latency data handling, strain calibration, detector characterization and data quality evaluation, and search pipelines. The results of these efforts will make it possible to make confident detections of GW events as they become frequent, and make the information obtained available to the LSC, to the astronomical community, and to the public, promptly, so that they can be fully exploited by those communities to advance gravitational-wave astronomy.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该奖项支持引力波探测器调试和表征以及数据分析方面的研究，并解决了NSF“宇宙窗口”大创意的优先领域。引力波（GW）天文学已经进入了一个新的时代，当检测到的事件（到目前为止，从紧凑的双星系统合并：双黑洞和双中子星）将变得频繁。先进的LIGO探测器正在不断提高其对GWs的灵敏度;这将导致紧凑物体合并的发现率达到前所未有的水平，并有可能从其他天体物理来源发现许多GWs。可能会发现全新种类的放射源。 这些观测将使天文学、宇宙学和基础物理学的大量研究和新的科学成果成为可能。 双星黑洞合并的观测被用来了解恒星演化和双星的形成、演化和死亡。它们使广义相对论作为引力理论在强场，高度相对论性制度中产生可检测的GWs的独特强大的测试成为可能。对双中子星星合并的观测可以限制核状态方程，对局部哈勃-勒梅特参数进行独特的测量，研究伽马射线暴物理学，并深入了解宇宙中重元素（包括金，铂和铀）的起源。 所有这些研究都是通过令人难以置信的灵敏度LIGO和Virgo探测器的建造和运行而实现的。加州理工学院团队与LIGO实验室和LIGO科学合作组织（LSC）合作，不断提高探测器的灵敏度，精确校准应变数据，表征探测器行为以评估和提高数据质量，并向公众发布数据。这些研究还非常依赖于使用高度优化的搜索管道从噪声数据中的天体物理源识别弱GW信号的能力。我们还使用分析LIGO数据所需的科学计算作为工具，让当地高中生和高中生参与引力波物理和天文学，通过动手GW数据科学项目介绍编程并更广泛地应用科学方法。我们的工作致力于发现和分析LIGO中的引力波（GW）信号，Virgo和KAGRA探测器在LIGO的O3和O 4观测运行期间。拟议的工作重点是：（a）利用LSC“旗舰”搜索管道PyCBC和gstlal，从LIGO、Virgo和KAGRA的数据中识别来自紧凑型二元合并的GW信号;（B）使用隐马尔可夫模型维特比算法来搜索来自致密双星合并的长持续时间爆发GW，来自我们银河系中新形成的快速旋转中子星的连续GW（CGW），以及产生具有潜在漂移中心频率的窄带信号的其他源;以及（c）验证由这些流水线发现的候选事件（包括CGW候选），特别注意数据质量、校准和搜索流水线细节。我们将在所有这些努力中建立我们现有的专业知识和经验，并通过自动化日常任务将其扩展到频繁检测的时代。我们将利用我们在低延迟数据处理、应变校准、探测器表征和数据质量评估以及搜索管道方面的专业知识。这些努力的结果将使人们有可能在GW事件变得频繁时对其进行可靠的检测，并将所获得的信息及时提供给LSC、天文学界和公众，这样它们就可以被这些社区充分利用，该奖项反映了国家科学基金会的法定使命，并被认为是值得通过使用基金会的知识价值和更广泛的影响审查评估的支持的搜索.

项目成果

Tests of General Relativity with Binary Black Holes from the second LIGO-Virgo Gravitational-Wave Transient Catalog

• DOI: 10.1103/physrevd.103.122002
• 发表时间: 2020-10
• 作者: The Ligo Scientific Collaboration;T. Abbott;T. Abbott;S. Abraham;F. Acernese;K. Ackley;A. Adams

A follow-up on intermediate-mass black hole candidates in the second LIGO–Virgo observing run with the Bayes Coherence Ratio

使用贝叶斯相干比对第二次 LIGO-Virgo 观测运行中的中等质量黑洞候选者进行后续跟踪

• DOI: 10.1093/mnras/stac2332
• 发表时间: 2022
• 期刊: Monthly Notices of the Royal Astronomical Society
• 影响因子: 4.8
• 作者: Vajpeyi, Avi;Smith, Rory;Thrane, Eric;Ashton, Gregory;Alford, Thomas;Garza, Sierra;Isi, Maximiliano;Kanner, Jonah;Massinger, T. J.;Xiao, Liting
• 通讯作者: Xiao, Liting

Stochastic Gravitational-Wave Backgrounds: Current Detection Efforts and Future Prospects

• DOI: 10.3390/galaxies10010034
• 发表时间: 2022-02
• 期刊: Galaxies
• 影响因子: 2.5
• 作者: A. Renzini;B. Goncharov;A. Jenkins;P. Meyers

Inference with finite time series: Observing the gravitational Universe through windows

• DOI: 10.1103/physrevresearch.3.043049
• 发表时间: 2021-06
• 期刊: Physical Review Research
• 影响因子: 4.2
• 作者: C. Talbot;E. Thrane;S. Biscoveanu;Rory J. E. Smith

LIGO detector characterization in the second and third observing runs

• DOI: 10.1088/1361-6382/abfd85
• 发表时间: 2021-01
• 期刊: Classical and Quantum Gravity
• 影响因子: 3.5
• 作者: D. Davis;J. Areeda;B. Berger;R. Bruntz;A. Effler;R. Essick;R. Fisher;P. Godwin;E. Goetz;A. Helmling-Cornell;B. Hughey;E. Katsavounidis;A. Lundgren;D. Macleod;Z. Márka;T. Massinger;A. Matas;J. McIver;G. Mo;K. Mogushi;P. Nguyen;L. Nuttall;R. Schofield;D. Shoemaker;S. Soni;A. Stuver;A. Urban;G. Valdes;M. Walker;R. Abbott;C. Adams;R. Adhikari;A. Ananyeva;S. Appert;K. Arai;Y. Asali;S. Aston;C. Austin;A. Baer;M. Ball;S. Ballmer;S. Banagiri;D. Barker;C. Barschaw;L. Barsotti;J. Bartlett;J. Betzwieser;R. Beda;D. Bhattacharjee;J. Bidler;G. Billingsley;S. Biscans;C. Blair;R. Blair;N. Bode;P. Booker;R. Bork;A. Bramley;A. Brooks;D. Brown;A. Buikema;C. Cahillane;T. Callister;G. Caneva Santoro;K. Cannon;J. Carlin;K. Chandra;X. Chen;N. Christensen;A. Ciobanu;F. Clara;C. Compton;S. Cooper;K. Corley;M. Coughlin;S. Countryman;P. Covas;D. Coyne;S. Crowder;T. Dal Canton;B. Danila;L. Datrier;G. Davies;T. Dent;N. Didio;C. Di Fronzo;K. Dooley;J. Driggers;P. Dupej;S. Dwyer;T. Etzel;M. Evans;T. Evans;S. Fairhurst;J. Feicht;Á. Fernández-Galiana;R. Frey;P. Fritschel;V. Frolov;P. Fulda;M. Fyffe;B. Gadre;J. Giaime;K. Giardina;G. González;S. Gras;C. Gray;R. Gray;A. Green;A. Gupta;E. Gustafson;R. Gustafson;J. Hanks;J. Hanson;T. Hardwick;I. Harry;R. Hasskew;M. Heintze;J. Heinzel;N. Holland;I. J. Hollows;C. Hoy;S. Hughey;S. Jadhav;K. Janssens;G. Johns;J. Jones;S. Kandhasamy;S. Karki;M. Kasprzack;K. Kawabe;D. Keitel;N. Kijbunchoo;Y. M. Kim;P. King;J. Kissel;S. Kulkarni;Rahul Kumar;M. Landry;B. Lane;B. Lantz;M. Laxen;Y. Lecoeuche;J. Leviton;J. Liu;M. Lormand;R. Macas;A. Macedo;M. Macinnis;V. Mandic;G. Mansell;S. Márka;B. Martinez;K. Martinovic;D. Martynov;K. Mason;F. Matichard;N. Mavalvala;R McCarthy-;D. McClelland;S. Mccormick;L. McCuller;C. McIsaac;T. McRae;G. Mendell;K. Merfeld;E. Merilh;P. Meyers;F. Meylahn;I. Michaloliakos;H. Middleton;J. Mills;T. Mistry;R. Mittleman;G. Moreno;C. Mow-Lowry;S. Mozzon;L. Mueller;N. Mukund;A. Mullavey;J. Muth;T. Nelson;A. Neunzert;S. Nichols;E. Nitoglia;J. Oberling;J. Oh;S. Oh;R. Oram;R. Ormiston;N. Ormsby;C. Osthelder;D. Ottaway;H. Overmier;A. Pai;J. R. Palamos;F. Pannarale;W. Parker;O. Patane;M. Patel;E. Payne;A. Pele;R. Penhorwood;C. Perez;K. S. Phukon;M. Pillas;M. Pirello;H. Radkins;K. Ramirez;J. Richardson;K. Riles;K. Rink;N. Robertson;J. Rollins;C. Romel;J. Romie;M. Ross;K. Ryan;T. Sadecki;M. Sakellariadou;E. Sanchez;L. Sanchez;L. Sandles;T. R. Saravanan;R. Savage;D. Schaetzl;R. Schnabel;E. Schwartz;D. Sellers;T. Shaffer;D. Sigg;A. Sintes;B. Slagmolen;J. R. Smith;K. Soni;B. Sorazu;A. Spencer;K. Strain;D. Strom;L. Sun;M. Szczepańczyk;J. Tasson;R. Tenorio;M. Thomas;P. Thomas;K. Thorne;K. Toland;C. Torrie;A. Tran;G. Traylor;M. Trevor;M. Tse;G. Vajente;N. van Remortel;D. Vander-Hyde;A. Vargas;J. Veitch;P. Veitch;K. Venkateswara;Gautam Venugopalan;A. Viets;V. Villa-Ortega;T. Vo;C. Vorvick;M. Wade;G. Wallace;R. Ward;J. Warner;B. Weaver;A. Weinstein;R. Weiss;K. Wette;D. White;L. White;C. Whittle;A. Williamson;B. Willke;C. Wipf;L. Xiao;R. Xu;H. Yamamoto;Hang Yu;Haocun Yu;L. Zhang;Y. Zheng;M. Zucker;J. Zweizig