Bayesian parameter estimation for targeted anisotropic gravitational-wave background
POSTER
Abstract
Extended sources of the stochastic gravitational backgrounds have been conventionally searched on the spherical harmonics bases. The analysis during the previous observing runs by the ground-based gravitational wave detectors, such as LIGO and Virgo, have yielded the constraints on the angular power spectrum $C_ell$, yet it lacks the capability of estimating other parameters such as a spectral index.
In this work, we introduce an alternative Bayesian formalism to search for such stochastic signals with a particular distribution of anisotropies on the sky. This approach provides a Bayesian posterior of model parameters and also enables selection tests among different signal models. While the conventional analysis fixes the highest angular scale a priori, here we show a more systematic and quantitative way to determine the cut-off scale based on a Bayes factor, which depends on the amplitude and the angular scale of observed signals.
Also, we analyze the third observing runs of LIGO and Virgo for the population of milli-second pulsars and obtain the 95 \% constraints of the signal amplitude, $epsilon < 2.7 imes 10^{-8}$.
In this work, we introduce an alternative Bayesian formalism to search for such stochastic signals with a particular distribution of anisotropies on the sky. This approach provides a Bayesian posterior of model parameters and also enables selection tests among different signal models. While the conventional analysis fixes the highest angular scale a priori, here we show a more systematic and quantitative way to determine the cut-off scale based on a Bayes factor, which depends on the amplitude and the angular scale of observed signals.
Also, we analyze the third observing runs of LIGO and Virgo for the population of milli-second pulsars and obtain the 95 \% constraints of the signal amplitude, $epsilon < 2.7 imes 10^{-8}$.
*L.T is supported by the National Science Foundation through OAC-2103662 and PHY-2011865.S.J. is supported by grants PRE2019-088741 funded by MCIN/AEI/10.13039/501100011033 and FSE, PGC2018-094773-B-C32 [MCIN-AEI-FEDER] and CEX2020-001007-S [MCIN].The authors are also grateful for computational resources provided by the LIGO Laboratory and supported by National Science Foundation Grants PHY-0757058 and PHY-0823459. This material is based upon work supported by NSF's LIGO Laboratory which is a major facility fully funded by the National Science Foundation.
Publication: arXiv:2208.14421
Presenters
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Leo Tsukada
- Pennsylvania State University