We consider stellar-origin black hole binaries, which are among the main astrophysical sources for next generation gravitational wave (GW) detectors such as the Einstein Telescope (ET) and Cosmic Explorer (CE). Using population models calibrated with the most recent LIGO/Virgo results from O3b run, we show that ET and CE will be capable of detecting tens of thousands of such sources (and virtually all of those present in our past light cone up to [Formula: see text] for ET and [Formula: see text] for CE) with a signal-to-noise ratio up to several hundreds, irrespective of the detector design. When it comes to parameter estimation, we use a Fisher-matrix analysis to assess the impact of the design on the estimation of the intrinsic and extrinsic parameters. We find that the CE detector, consisting of two distinct [Formula: see text]shape interferometers, has better sky localization performance compared to ET in its triangular configuration. We also find that the network is typically capable of measuring the chirp mass, symmetric mass ratio and spins of the binary at order of [Formula: see text], [Formula: see text] and [Formula: see text] fractional error respectively. While the fractional errors for the extrinsic parameters are of order [Formula: see text] for the sky localization, luminosity distance and inclination.
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http://dx.doi.org/10.1038/s41598-022-19540-7 | DOI Listing |
Sci Rep
December 2024
Radiating Systems Group, Department of Applied Physics, University of Santiago de Compostela, 15782, Santiago de Compostela, Spain.
The following paper presents the findings of a study conducted on the distances at which the field generated by a ϕ-symmetric circular Taylor aperture distribution can be classified as far-field, and also the efficiency across various study parameters. The [Formula: see text] transition integers that produce a monotonic distribution, which have been traditionally used and analyzed, are compared with those that yield a peaked distribution, and offer greater efficiency. Additionally, modified circular Taylor [Formula: see text] distributions featuring synthesized patterns with one or two depressed inner sidelobes, which have not been previously explored, are also examined.
View Article and Find Full Text PDFAnn Surg Oncol
December 2024
Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA.
Background: We sought to define whether and how hepatic ischemia/reperfusion (I/R) as manifested by perioperative aspartate aminotransferase (AST) and alanine aminotransaminase (ALT) levels impact long-term outcomes after curative-intent resection of hepatocellular carcinoma (HCC).
Patients And Methods: Intrasplenic injection of HCC cells was used to establish a murine model of HCC recurrence with versus without I/R injury. Patients who underwent curative resection for HCC were identified from a multi-institutional derivative cohort (DC) and separate external validation (VC) cohort.
Sci Rep
December 2024
Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Vellore, Tamil Nadu, 632014, India.
This study explores the problem of train scheduling (or) train timetabling and its impact on the administration of railway management. This is a highly dependable and effective public transportation system. The problem considers both single and multiple tracks along with multiple platforms with varying train capacities (like speed, passengers, and so on).
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December 2024
Department of Mathematics, Faculty of Science, The Hashemite University, P.O.Box 330127, Zarqa, 13133, Jordan.
In this study, we developed a Caputo-Fractional Chlamydia pandemic model to describe the disease's spread. We demonstrated the model's positivity and boundedness, ensuring biological relevance. The existence and uniqueness of the model's solution were established, and we investigated the stability of the α-fractional order model.
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December 2024
ETH Zurich, Zurich, Switzerland.
Artificial intelligence (AI) provides considerable opportunities to assist human work. However, one crucial challenge of human-AI collaboration is that many AI algorithms operate in a black-box manner where the way how the AI makes predictions remains opaque. This makes it difficult for humans to validate a prediction made by AI against their own domain knowledge.
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