4.2 Article

Cross-correlating galaxy catalogs and gravitational waves: A tomographic approach

Journal

PHYSICAL REVIEW RESEARCH
Volume 2, Issue 2, Pages -

Publisher

AMER PHYSICAL SOC
DOI: 10.1103/PhysRevResearch.2.023314

Keywords

-

Funding

  1. IDEX Univ. Grenoble Alpes, under the program Initiatives de Recherche Strategiques, project Multimessenger avenues in gravitational waves
  2. Departments of Excellence 2018-2022 grant - Italian Ministry of Education, University and Research (MIUR)
  3. Research grant The Dark Universe: A Synergic Multimessenger Approach - MIUR [2017X7X85K]
  4. Research grant TAsP (Theoretical Astroparticle Physics) - INFN

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Unveiling the origin of the coalescing binaries detected via gravitational waves (GWs) is challenging, notably if no multiwavelength counterpart is detected. One important diagnostic tool is the coalescing binary distribution with respect to the large-scale structures (LSSs) of the Universe, which we quantify via the cross-correlation of galaxy catalogs with GW ones. By using both existing and forthcoming galaxy catalogs and using realistic Monte Carlo simulations of GW events, we find that the cross-correlation signal should be marginally detectable in 10-year data taking of advanced LIGO-Virgo detectors at design sensitivity, at least for binary neutron star mergers. The expected addition of KAGRA and LIGO-India to the GW detector network would allow for a firmer detection of this signal, and, in combination with future cosmological surveys, would also permit the detection of cross-correlation for coalescing black holes. Such a measurement may unveil, for instance, a primordial origin of coalescing black holes. To attain this goal, we find that it is crucial to adopt a tomographic approach and to reach a sufficiently accurate localization of GW events. The depth of forthcoming surveys will be fully exploited by third-generation GW detectors such as the Einstein Telescope or the Cosmic Explorer, which will allow one to perform precision studies of the coalescing black hole LSS distribution and attain rather advanced model discrimination capabilities.

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