Gravitational-wave parameter inference with the Newman-Penrose scalar

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Detection and parameter inference of gravitational-wave signals from compact mergers rely on the comparison of the incoming detector strain data 𝑑⁡(𝑡) to waveform templates for the gravitational-wave strain ℎ⁡(𝑡) that ultimately rely on the resolution of Einstein’s equations via numerical relativity simulations. These, however, commonly output a quantity known as the Newman-Penrose scalar 𝜓4⁡(𝑡) which, under the Bondi gauge, is related to the gravitational-wave strain by 𝜓4⁡(𝑡) =𝑑2⁢ℎ⁡(𝑡)/𝑑⁢𝑡2. Therefore, obtaining strain templates involves an integration process that introduces artifacts that need to be treated in a rather manual way. By taking second-order finite differences on the detector data and inferring the corresponding background noise distribution, we develop a framework to perform gravitational-wave data analysis directly using 𝜓4⁡(𝑡) templates. We first demonstrate this formalism, and the impact of integration artifacts in strain templates, through the recovery of numerically simulated signals from head-on collisions of Proca stars injected in Advanced LIGO noise. Next, we reanalyze the event GW190521 under the hypothesis of a Proca-star merger, obtaining results equivalent to those previously published [Phys. Rev. Lett. 126, 081101 (2021)], where we used the classical strain framework. We find, however, that integration errors would strongly impact our analysis if GW190521 was 4 times louder. Finally, we show that our framework fixes significant biases in the interpretation of the high-mass gravitational-wave trigger S200114f arising from the usage of strain templates. We remove the need to obtain strain waveforms from numerical relativity simulations, avoiding the associated systematic errors

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Calderón Bustillo, J., Wong, Isaac C. F., Sanchís-Gual, Nicolás et al. Gravitational-Wave Parameter Inference with the Newman-Penrose Scalar. Physical Review X, 13, 4, december 2023. https://doi.org/10.1103/PhysRevX.13.041048

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K. A. G. R. A. is supported by Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan Society for the Promotion of Science (JSPS) in Japan; National Research Foundation (NRF) and Ministry of Science and ICT (MSIT) in Korea; Academia Sinica (AS) and National Science and Technology Council (NSTC) in Taiwan. J. C. B. is supported by a fellowship from “la Caixa” Foundation (No. 100010434) and from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No. 847648. The fellowship code is LCF/BQ/PI20/11760016. J. C. B. is also supported by the Research Grant No. PID2020–118635 GB-I00 from the Spain-Ministerio de Ciencia e Innovación. K. C. acknowledges the MHRD, Government of India, for the fellowship support. J. A. F. is supported by the Spanish Agencia Estatal de Investigación (Grants No. PGC2018-095984-B-I00 and No. PID2021-125485NB-C21) and by the Generalitat Valenciana (PROMETEO/2019/071). This work is supported by the Center for Research and Development in Mathematics and Applications (CIDMA) through the Portuguese Foundation for Science and Technology (FCT—Fundação para a Ciência e a Tecnologia), Reference No. UIDB/04106/2020, and by national funds (OE), through FCT, I. P., in the scope of the framework contract foreseen in nos. 4, 5, and 6 of the article 23, of the Decree-Law 57/2016, changed by Law 57/2017. We also acknowledge support from the projects PTDC/FIS-OUT/28407/2017, CERN/FIS-PAR/0027/2019, PTDC/FIS-AST/3041/2020, CERN/FIS-PAR/0024/2021, and 2022.04560.PTDC. N. S.-G. is supported by the Spanish Ministerio de Universidades, through a María Zambrano grant (ZA21-031) with reference UP2021-044, funded within the European Union-Next Generation EU. This work has further been supported by the European Union’s Horizon 2020 research and innovation (RISE) programme H2020-MSCA-RISE-2017 Grant No. FuNFiCO-777740 and by the European Horizon Europe staff exchange (SE) programme HORIZON-MSCA-2021-SE-01 Grant No. NewFunFiCO-101086251. We acknowledge the use of IUCAA LDG cluster Sarathi for the computational and numerical work. The authors acknowledge computational resources provided by the CIT cluster of the LIGO Laboratory and supported by National Science Foundation Grants No. PHY-0757058 and No. PHY0823459, and the support of the NSF CIT cluster for the provision of computational resources for our parameter inference runs. This material is based upon work supported by NSF’s LIGO Laboratory which is a major facility fully funded by the National Science Foundation

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