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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
physics and astronomy
Supermassive binary black hole evolution can be traced by a small SKA pulsar timing array
Physical Review D, Volume 102, No. 2, Article 023014, Year 2020
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Description
Supermassive black holes are commonly found in the centers of galaxies and evolve with their hosts. Supermassive binary black holes (SMBBH) are thus expected to exist in close galaxy pairs; however, none has been unequivocally detected. The square kilometer array (SKA) is a multipurpose radio telescope with a collecting area approaching 106 square meters, with great potential for detecting nanohertz gravitational waves (GWs). In this paper, we quantify the GW detectability by SKA for a realistic SMBBH population using a pulsar timing array (PTA) technique, and we quantify its impact on revealing SMBBH evolution with redshift for the first time. With only ∼20 pulsars, a much smaller requirement than in previous work, the SKAPTA is expected to obtain detection within about 5 years of operation and to achieve a detection rate of more than 100 SMBBHs/yr after about 10 years. Although beyond the scope of this paper, we must acknowledge that the presence of persistent red noise will reduce the number of expected detections here. It is thus imperative to understand and mitigate red noise in the PTA data. The GW signatures from a few well-known SMBBH candidates, such as OJ 287, 3C 66B, NGC 5548, and Ark 120, will be detected given the currently best-known parameters of each system. Within 30 years of operation, about 60 individual SMBBH detections with z<0.05 and more than 104 with z<1 are expected. The detection rate drops precipitately beyond z=1. The substantial number of expected detections and their discernible evolution with redshift by the SKAPTA will make SKA a significant tool for studying SMBBHs. © 2020 American Physical Society.
Authors & Co-Authors
Feng, Yi
China, Beijing
Chinese Academy of Sciences
Li, D. H.
China, Beijing
Chinese Academy of Sciences
Zheng, Zheng
China, Beijing
Chinese Academy of Sciences
Tsai, Chaowei
China, Beijing
Chinese Academy of Sciences
Statistics
Citations: 8
Authors: 4
Affiliations: 1
Identifiers
Doi:
10.1103/PhysRevD.102.023014
ISSN:
24700010
Study Design
Cross Sectional Study