scholarly journals Measuring the Hubble Constant with Neutron Star Black Hole Mergers

2018 ◽  
Vol 121 (2) ◽  
Author(s):  
Salvatore Vitale ◽  
Hsin-Yu Chen
2019 ◽  
Vol 485 (3) ◽  
pp. 4260-4273 ◽  
Author(s):  
Christian N Setzer ◽  
Rahul Biswas ◽  
Hiranya V Peiris ◽  
Stephan Rosswog ◽  
Oleg Korobkin ◽  
...  

Abstract We investigate the ability of the Large Synoptic Survey Telescope (LSST) to discover kilonovae (kNe) from binary neutron star (BNS) and neutron star–black hole (NSBH) mergers, focusing on serendipitous detections in the Wide-Fast-Deep (WFD) survey. We simulate observations of kNe with proposed LSST survey strategies, focusing on cadence choices that are compatible with the broader LSST cosmology programme. If all kNe are identical to GW170817, we find the baseline survey strategy will yield 58 kNe over the survey lifetime. If we instead assume a representative population model of BNS kNe, we expect to detect only 27 kNe. However, we find the choice of survey strategy significantly impacts these numbers and can increase them to 254 and 82 kNe over the survey lifetime, respectively. This improvement arises from an increased cadence of observations between different filters with respect to the baseline. We then consider the detectability of these BNS mergers by the Advanced LIGO/Virgo (ALV) detector network. If the optimal survey strategy is adopted, 202 of the GW170817-like kNe and 56 of the BNS population model kNe are detected with LSST but are below the threshold for detection by the ALV network. This represents, for both models, an increase by a factor greater than 4.5 in the number of detected sub-threshold events over the baseline strategy. These sub-threshold events would provide an opportunity to conduct electromagnetic-triggered searches for signals in gravitational-wave data and assess selection effects in measurements of the Hubble constant from standard sirens, e.g. viewing angle effects.


2021 ◽  
Vol 126 (17) ◽  
Author(s):  
Stephen M. Feeney ◽  
Hiranya V. Peiris ◽  
Samaya M. Nissanke ◽  
Daniel J. Mortlock

2021 ◽  
Vol 502 (1) ◽  
pp. L72-L78
Author(s):  
K Mohamed ◽  
E Sonbas ◽  
K S Dhuga ◽  
E Göğüş ◽  
A Tuncer ◽  
...  

ABSTRACT Similar to black hole X-ray binary transients, hysteresis-like state transitions are also seen in some neutron-star X-ray binaries. Using a method based on wavelets and light curves constructed from archival Rossi X-ray Timing Explorer observations, we extract a minimal timescale over the complete range of transitions for 4U 1608-52 during the 2002 and 2007 outbursts and the 1999 and 2000 outbursts for Aql X-1. We present evidence for a strong positive correlation between this minimal timescale and a similar timescale extracted from the corresponding power spectra of these sources.


2021 ◽  
Vol 103 (6) ◽  
Author(s):  
Francois Foucart ◽  
Alexander Chernoglazov ◽  
Michael Boyle ◽  
Tanja Hinderer ◽  
Max Miller ◽  
...  

Author(s):  
R Pattnaik ◽  
K Sharma ◽  
K Alabarta ◽  
D Altamirano ◽  
M Chakraborty ◽  
...  

Abstract Low Mass X-ray binaries (LMXBs) are binary systems where one of the components is either a black hole or a neutron star and the other is a less massive star. It is challenging to unambiguously determine whether a LMXB hosts a black hole or a neutron star. In the last few decades, multiple observational works have tried, with different levels of success, to address this problem. In this paper, we explore the use of machine learning to tackle this observational challenge. We train a random forest classifier to identify the type of compact object using the energy spectrum in the energy range 5-25 keV obtained from the Rossi X-ray Timing Explorer archive. We report an average accuracy of 87±13% in classifying the spectra of LMXB sources. We further use the trained model for predicting the classes for LMXB systems with unknown or ambiguous classification. With the ever-increasing volume of astronomical data in the X-ray domain from present and upcoming missions (e.g., SWIFT, XMM-Newton, XARM, ATHENA, NICER), such methods can be extremely useful for faster and robust classification of X-ray sources and can also be deployed as part of the data reduction pipeline.


2015 ◽  
Vol 115 (23) ◽  
Author(s):  
Chris L. Fryer ◽  
F. G. Oliveira ◽  
J. A. Rueda ◽  
R. Ruffini

2021 ◽  
Vol 103 (12) ◽  
Author(s):  
Andreas Bauswein ◽  
Sebastian Blacker ◽  
Georgios Lioutas ◽  
Theodoros Soultanis ◽  
Vimal Vijayan ◽  
...  

2008 ◽  
Author(s):  
Masaru Shibata ◽  
Keisuke Taniguchi ◽  
Koji Uryū ◽  
Ye-Fei Yuan ◽  
Xiang-Dong Li ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document