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    Efficient search for detection candidates using a peak finder strategy for all-sky-all-frequency gravitational wave radiometer

    Arindam Sharma1,*, Deepali Agarwal2,3,4,†, and Sanjit Mitra4,‡

    • *Contact author: asharm17@go.olemiss.edu
    • †Contact author: deepali.agarwal@utrgv.edu
    • ‡Contact author: sanjit@iucaa.in

    Phys. Rev. D 113, 063057 – Published 27 March, 2026

    DOI: https://doi.org/10.1103/8922-4h7n

    Abstract

    The first all-sky-all-frequency (ASAF) radiometer search was conducted using data from the first three observing runs of the Advanced LIGO and Advanced Virgo detectors. The significance of this search lies in its fast and unmodeled approach, leveraging a cross-correlation technique to identify common signals across the detector network. As a result, this method serves as an excellent alternative to search for unknown or poorly modeled continuous wave sources and narrow band components of the gravitational wave (GW) background. For continuous wave sources whose waveform can be modeled, this method can serve as the first stage of a hierarchical scheme by identifying subthreshold candidates to be followed up with more optimal but computationally expensive searches. The ASAF search, however, presently suffers from beam smearing, where multiple candidates may arise due to the same noise fluctuations, detector artifact, or a GW source. This can reduce the detection probability in follow-up analyses, especially with limited computing resources. To mitigate this issue and reduce the number of correlated and unnecessary candidates, we introduce a novel peak finder algorithm. This algorithm helps identify the most representative candidates while preserving detection sensitivity, thereby allowing follow-up of a much larger number of independent candidates. This reduction in correlated samples leads to a significant reduction in the number of trial factors and also the false dismissal rate (FDR), depending upon the frequency, strength of the injected signal, and the number of candidates, N, that can be followed up, with the reduction in FDR being most significant at low frequencies, and small N. For instance, at 30 Hz, following up N=2 peak finder candidates reduces FDR by a factor of ∼3. At high N, the peak finder FDR is higher than the full sky FDR. However, this is of no practical consequence as we can only follow up a small number of candidates per frequency bin.

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