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    Inferring additional physics through unmodeled signal reconstructions

    Rimo Das1,2,*, V. Gayathri3,†, Divyajyoti4,1,2,‡, Sijil Jose1,§, Imre Bartos5,∥, Sergey Klimenko5,¶, and Chandra Kant Mishra1,2,**

    • *Contact author: rimo.physics@gmail.com
    • †Contact author: gayathri.v@ligo.org
    • ‡Contact author: divyajyoti.physics@gmail.com
    • §Contact author: sijiljose.999@gmail.com
    • ∥Contact author: imrebartos@ufl.edu
    • Contact author: klimenko@phys.ufl.edu
    • **Contact author: ckm@physics.iitm.ac.in

    Phys. Rev. D 112, 023011 – Published 9 July, 2025

    DOI: https://doi.org/10.1103/3rmv-b2cq

    Abstract

    Parameter estimation of gravitational wave data is often computationally expensive, requiring simplifying assumptions such as circularization of binary orbits. Although, if included, the subdominant effects like orbital eccentricity may provide crucial insights into the formation channels of compact binary mergers. To address these challenges, we present a pipeline strategy leveraging minimally modeled waveform reconstruction to identify the presence of eccentricity in real time. Using injections (40M⊙ binary black hole signals), we demonstrate that ignoring eccentricity (with values larger than ∼0.15 estimated at 20 Hz (e20)) leads to significant biases in parameter recovery, including chirp mass estimates falling outside the 90% credible interval. Waveform reconstruction shows that inconsistencies increase with eccentricity, and this behavior is consistent for different mass ratios. Our method enables low-latency inferences of binary properties supporting targeted follow-up analyses and can be applied to identify any physical effect of measurable strength.

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