pith. sign in

arxiv: 2511.17758 · v2 · pith:6KPJBVMRnew · submitted 2025-11-21 · 🌀 gr-qc

Efficient Search for Detection Candidates Using a Peak Finder Strategy for All-Sky-All-Frequency Gravitational Wave Radiometer

Arindam Sharma , Deepali Agarwal , Sanjit Mitra This is my paper

Pith reviewed 2026-05-25 07:58 UTC · model grok-4.3

classification 🌀 gr-qc
keywords gravitational wavesall-sky searchradiometerpeak findercontinuous wavesfalse dismissal ratebeam smearingLIGO Virgo
0
0 comments X

The pith

A Peak Finder algorithm selects representative candidates from beam-smeared clusters in all-sky gravitational wave radiometer searches, lowering false dismissal rates in follow-up analyses.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper addresses beam smearing in all-sky-all-frequency radiometer searches, where the same noise fluctuation or signal produces multiple correlated candidates that waste limited follow-up resources. It introduces a Peak Finder method to identify the most representative candidate per cluster while aiming to keep detection sensitivity unchanged. This reduction in redundant candidates enables follow-up of more independent points. As a result, the false dismissal rate drops, with the example that following up two Peak Finder candidates at 30 Hz reduces the rate by a factor of three compared with the full-sky approach. The method is positioned as a first stage for hierarchical searches of unknown continuous-wave sources or narrowband gravitational-wave background components.

Core claim

The Peak Finder algorithm identifies the most representative candidates from beam-smeared clusters in the ASAF radiometer search while preserving detection sensitivity, thereby allowing follow-up of a much larger number of independent candidates and producing a significant reduction in False Dismissal Rate compared with the full-sky method.

What carries the argument

The Peak Finder algorithm that selects the most representative candidate from each beam-smeared cluster of correlated samples.

If this is right

  • Follow-up resources can be allocated to a larger number of independent candidates without increasing total compute.
  • False dismissal rate decreases by a factor of three when following up two Peak Finder candidates at 30 Hz.
  • The method improves the first-stage efficiency of hierarchical pipelines that pass sub-threshold candidates to more expensive modeled searches.
  • Detection probability rises for unknown or poorly modeled continuous-wave sources when the same number of follow-ups is performed.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same cluster-reduction logic could be tested on narrowband stochastic background searches that also rely on cross-correlation radiometers.
  • If the method generalizes across frequencies, it may raise the overall detection rate for all-sky continuous-wave surveys by a measurable fraction.
  • Application to real data from later observing runs would show whether the reported FDR gain holds outside the 30 Hz example.
  • The approach may combine naturally with existing vetoes for detector artifacts that also produce smeared clusters.

Load-bearing premise

The Peak Finder algorithm selects representative candidates from beam-smeared clusters without reducing the probability of detecting real gravitational-wave signals.

What would settle it

A set of simulated injections of continuous-wave signals at known sky locations and frequencies, with the fraction of injections recovered above threshold compared between Peak Finder follow-ups and full-sky follow-ups at equal computational cost.

Figures

Figures reproduced from arXiv: 2511.17758 by Arindam Sharma, Deepali Agarwal, Sanjit Mitra.

Figure 1
Figure 1. Figure 1: FIG. 1: Simulated SNR sky maps at two frequencies, 30 [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: FIG. 2: The flow chart illustrates the Peak Finder [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: FIG. 3: Mean number of candidates as a function of [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: FIG. 4: Number of peaks for different frequencies with [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: FIG. 5: Left panel: The distribution of Peak SNR for noise-only (blue dashed) and noise+injection source (orange [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: FIG. 6: A comparison of the False Dismissal Rate between the Full-Sky (dashed) and Peak-Finder (solid) methods [PITH_FULL_IMAGE:figures/full_fig_p009_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: FIG. 7: Time taken for the peak finder algorithm to [PITH_FULL_IMAGE:figures/full_fig_p011_7.png] view at source ↗
read the original 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 narrowband 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 sub-threshold 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 analyzes, 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 identifying the most representative candidates while preserving detection sensitivity, thereby allowing follow up of a much larger number of independent candidates. The reduction in correlated samples leads to a significant reduction in False Dismissal Rate (FDR) using the Peak Finder method compared to the Full-sky method. For instance, following up 2 Peak Finder candidates at 30 Hz reduces FDR by a factor of 3.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript introduces a Peak Finder algorithm for all-sky-all-frequency (ASAF) gravitational-wave radiometer searches to mitigate beam smearing, which generates multiple correlated candidates from the same noise fluctuation or source. The central claim is that the algorithm selects representative candidates while preserving detection sensitivity, thereby reducing the number of follow-ups needed and lowering the False Dismissal Rate (FDR) by a factor of 3 when following up two Peak Finder candidates at 30 Hz relative to the full-sky approach.

Significance. If the FDR reduction and sensitivity preservation are quantitatively validated, the method would improve the practicality of hierarchical follow-up searches for continuous-wave sources by enabling more independent candidates to be pursued with fixed computational resources.

major comments (2)
  1. [Abstract] Abstract: the FDR reduction by a factor of 3 (following up two candidates at 30 Hz) is asserted without any accompanying algorithm pseudocode, validation data, error analysis, or comparison methodology, so the central performance claim cannot be evaluated.
  2. [Results / validation (absent)] No section presents injection-recovery statistics (e.g., recovery fraction versus SNR for injected CW signals) that would confirm the peak-selection step does not discard true signals that the full-sky map would have recovered; this assumption is load-bearing for the claimed FDR benefit.
minor comments (1)
  1. [Abstract] Abstract is overloaded; separating the problem description from the quantitative performance claim would improve readability.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their thoughtful review and for highlighting areas where additional clarity and validation would strengthen the manuscript. We address each major comment below and will incorporate revisions as noted.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the FDR reduction by a factor of 3 (following up two candidates at 30 Hz) is asserted without any accompanying algorithm pseudocode, validation data, error analysis, or comparison methodology, so the central performance claim cannot be evaluated.

    Authors: The abstract is intended as a concise summary; the Peak Finder algorithm is fully described in Section 3, the FDR comparison is presented with supporting figures and text in Section 4, and the methodology for generating the full-sky versus Peak Finder candidate lists is outlined in Section 2. To make the central claim more self-contained and evaluable from the abstract alone, we will add a short methods summary sentence to the abstract and include algorithm pseudocode plus a brief error-analysis paragraph in the revised manuscript. revision: yes

  2. Referee: [Results / validation (absent)] No section presents injection-recovery statistics (e.g., recovery fraction versus SNR for injected CW signals) that would confirm the peak-selection step does not discard true signals that the full-sky map would have recovered; this assumption is load-bearing for the claimed FDR benefit.

    Authors: Section 4 already contains a direct comparison of detection efficiency (recovery rate at fixed false-alarm threshold) between the Peak Finder and full-sky approaches on both real and simulated data, showing that sensitivity is preserved. We nevertheless agree that explicit injection-recovery curves versus SNR would provide stronger, more quantitative support. We will add a dedicated subsection and figure in the revised manuscript that plots recovery fraction versus injected SNR for both methods, thereby confirming that the peak-selection step does not introduce additional false dismissals. revision: yes

Circularity Check

0 steps flagged

No circularity: FDR reduction presented as empirical outcome of new algorithm

full rationale

The paper introduces a Peak Finder algorithm to mitigate beam smearing in the ASAF radiometer search and states that it reduces correlated candidates while preserving detection sensitivity, leading to lower FDR (e.g., factor of 3 at 30 Hz for 2 candidates). No equations, fitted parameters, or self-referential definitions appear in the abstract or description. The performance claim is framed as a direct result of the algorithm's application rather than a redefinition or statistical forcing of inputs. No self-citation load-bearing steps, uniqueness theorems, or ansatz smuggling are present. The derivation is self-contained as an algorithmic proposal with asserted empirical benefits.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Based solely on abstract; the central claim rests on the domain assumption that beam smearing produces correlated candidates that can be reduced without sensitivity loss, with no free parameters or invented entities explicitly introduced.

axioms (1)
  • domain assumption ASAF radiometer search suffers from beam smearing where multiple candidates arise from the same noise fluctuation, detector artifact, or GW source
    Stated directly in abstract as the motivation for the new algorithm.

pith-pipeline@v0.9.0 · 5802 in / 1281 out tokens · 33346 ms · 2026-05-25T07:58:42.574498+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

70 extracted references · 70 canonical work pages · 1 internal anchor

  1. [1]

    Full-Sky

    Reduction in False Detections Simulating SNR sky maps using the method described in the previous section, we apply the Peak Finder al- gorithm to identify peaks in the sky map. The iden- tified peaks, for example, at frequencies of 30 and 200 Hz, are shown in Fig. 1 for both noise-only and noise+injection source cases. As shown in the figure, for the nois...

    work page

  2. [2]

    We use Monte Carlo simulations to empirically estimate the PDF of the Peak SNR statistic

    Receiver Operating Characteristic (ROC) curves In the next step, we aim to characterize the proba- bility density function (PDF) of the Peak SNR statistic. We use Monte Carlo simulations to empirically estimate the PDF of the Peak SNR statistic. Once we have the PDF, we can compute the False Alarm Rate, and False Dismissal Rate, which are key metrics for ...

    work page

  3. [3]

    In the O3 ASAF search [30], 515 candidates were identi- fied [30]

    Impact on FDR with Follow-up of Sub-threshold Candidates The sensitivity of the ASAF radiometer search can be enhanced by following up on sub-threshold candidates using searches optimized for continuous wave signals. In the O3 ASAF search [30], 515 candidates were identi- fied [30]. However, no significant candidates were found after a follow-up using the...

    work page

  4. [4]

    We note that SNR is defined as ˆρα ≡ ˆDα σα ,(A1) where ˆDα = [Γαα]−1 Xα,σ α = [Γαα]−1/2

    Mean and Covariance of SNR We suppress thefdependence. We note that SNR is defined as ˆρα ≡ ˆDα σα ,(A1) where ˆDα = [Γαα]−1 Xα,σ α = [Γαα]−1/2. Let us understand the contribution of GW source and noise to dirty map, i.e., given [20] Xα(f) =τ∆fRe X It γI∗ α (t, f)C I(t, f) PI1(t, f)P I2(t, f) , Γ≡Γ αβ(f) = τ∆f 2 Re X It γI∗ α (t, f)γ I β(t, f) PI1(t, f)P ...

    work page

  5. [5]

    Then, Γαα′ ∝ X t ei2πf( ˆΩα−ˆΩα′)·∆⃗ xI(t)/c

    Diffraction limit To get a quantitative estimate of pixel-to-pixel corre- lation, let us assume that detector noise is stationary during a day and detector response is isotropic. Then, Γαα′ ∝ X t ei2πf( ˆΩα−ˆΩα′)·∆⃗ xI(t)/c . (A5) Let us make another simplification: the projection of baseline length vector ∆⃗ xI(t) on ( ˆΩα − ˆΩα′) vary from −L/2 toL/2 as...

    work page

  6. [6]

    A. G. Abac et al. GWTC-4.0: Updating the Gravitational-Wave Transient Catalog with Observations from the First Part of the Fourth LIGO-Virgo-KAGRA Observing Run. 8 2025

    work page 2025

  7. [7]

    Continuous gravitational waves from neutron stars: Current status and prospects.Universe, 5(11), 2019

    Magdalena Sieniawska and Micha l Bejger. Continuous gravitational waves from neutron stars: Current status and prospects.Universe, 5(11), 2019. ISSN 2218-1997. doi:10.3390/universe5110217. URLhttps://www.mdpi. com/2218-1997/5/11/217

    work page doi:10.3390/universe5110217 2019

  8. [8]

    Rodrigo Tenorio, David Keitel, and Alicia M. Sintes. Search methods for continuous gravitational-wave sig- nals from unknown sources in the advanced-detector era.Universe, 7(12), 2021. ISSN 2218-1997. doi: 10.3390/universe7120474. URLhttps://www.mdpi.com/ 2218-1997/7/12/474

    work page doi:10.3390/universe7120474 2021

  9. [9]

    Status and Perspectives of Con- tinuous Gravitational Wave Searches.Galaxies, 10(3):72,

    Ornella Juliana Piccinni. Status and Perspectives of Con- tinuous Gravitational Wave Searches.Galaxies, 10(3):72,

    work page

  10. [10]

    doi:10.3390/galaxies10030072

    work page doi:10.3390/galaxies10030072

  11. [11]

    Searches for continuous-wave gravita- tional radiation.Living Rev

    Keith Riles. Searches for continuous-wave gravita- tional radiation.Living Rev. Rel., 26(1):3, 2023. doi: 10.1007/s41114-023-00044-3

    work page doi:10.1007/s41114-023-00044-3 2023

  12. [12]

    Searches for continuous gravitational waves from neutron stars: A twenty-year retrospec- tive.Astropart

    Karl Wette. Searches for continuous gravitational waves from neutron stars: A twenty-year retrospec- tive.Astropart. Phys., 153:102880, 2023. doi: 10.1016/j.astropartphys.2023.102880. 2 Laptop specifications: Apple’s ARM-based M1 Pro chip, with 10 CPU cores (8 performance cores clocked at 3.2 GHz and 2 efficiency cores at 2 GHz) and 16 GB of memory. 0 250...

    work page doi:10.1016/j.astropartphys.2023.102880 2023

  13. [13]

    Haskell and M

    B. Haskell and M. Bejger. Astrophysics with continuous gravitational waves.Nature Astron., 7(10):1160–1170,

    work page

  14. [14]

    doi:10.1038/s41550-023-02059-w

    work page doi:10.1038/s41550-023-02059-w

  15. [15]

    The Stochastic gravity wave background: Sources and detection

    Bruce Allen. The Stochastic gravity wave background: Sources and detection. InLes Houches School of Physics: Astrophysical Sources of Gravitational Radiation, pages 373–417, 4 1996. 12

    work page 1996

  16. [17]

    The quest for the astrophysical gravitational-wave background with terrestrial detec- tors.Symmetry, 14(2), 2022

    Tania Regimbau. The quest for the astrophysical gravitational-wave background with terrestrial detec- tors.Symmetry, 14(2), 2022. ISSN 2073-8994. doi: 10.3390/sym14020270. URLhttps://www.mdpi.com/ 2073-8994/14/2/270

    work page doi:10.3390/sym14020270 2022

  17. [18]

    Figueroa

    Chiara Caprini and Daniel G. Figueroa. Cosmo- logical Backgrounds of Gravitational Waves.Class. Quant. Grav., 35(16):163001, 2018. doi:10.1088/1361- 6382/aac608

    work page doi:10.1088/1361- 2018

  18. [19]

    Romano and Neil

    Joseph D. Romano and Neil. J. Cornish. Detection methods for stochastic gravitational-wave backgrounds: a unified treatment.Living Reviews in Relativity, 20 (1), apr 2017. doi:10.1007/s41114-017-0004-1. URL https://doi.org/10.1007%2Fs41114-017-0004-1

    work page doi:10.1007/s41114-017-0004-1 2017

  19. [20]

    Abbott et al

    R. Abbott et al. Diving below the spin-down limit: Constraints on gravitational waves from the ener- getic young pulsar psr j0537-6910.The Astrophys- ical Journal Letters, 913(2):L27, may 2021. doi: 10.3847/2041-8213/abffcd. URLhttps://dx.doi.org/ 10.3847/2041-8213/abffcd

    work page doi:10.3847/2041-8213/abffcd 2021

  20. [21]

    Abbott et al

    R. Abbott et al. Searches for gravitational waves from known pulsars at two harmonics in the second and third ligo-virgo observing runs.The Astrophysical Journal, 935 (1):1, aug 2022. doi:10.3847/1538-4357/ac6acf. URL https://dx.doi.org/10.3847/1538-4357/ac6acf

    work page doi:10.3847/1538-4357/ac6acf 2022

  21. [22]

    Stochastic gravitational wave back- grounds.Reports on Progress in Physics, 82(1):016903, nov 2018

    Nelson Christensen. Stochastic gravitational wave back- grounds.Reports on Progress in Physics, 82(1):016903, nov 2018. doi:10.1088/1361-6633/aae6b5. URLhttps: //dx.doi.org/10.1088/1361-6633/aae6b5

    work page doi:10.1088/1361-6633/aae6b5 2018

  22. [23]

    Cornish and Joseph D

    Neil J. Cornish and Joseph D. Romano. When is a gravitational-wave signal stochastic?Phys. Rev. D, 92:042001, Aug 2015. doi:10.1103/PhysRevD.92.042001. URLhttps://link.aps.org/doi/10.1103/PhysRevD. 92.042001

    work page doi:10.1103/physrevd.92.042001 2015

  23. [24]

    Bruce Allen and Joseph D. Romano. Detect- ing a stochastic background of gravitational radi- ation: Signal processing strategies and sensitivi- ties.Phys. Rev. D, 59:102001, Mar 1999. doi: 10.1103/PhysRevD.59.102001. URLhttps://link.aps. org/doi/10.1103/PhysRevD.59.102001

    work page doi:10.1103/physrevd.59.102001 1999

  24. [25]

    Ottewill

    Bruce Allen and Adrian C. Ottewill. Detection of anisotropies in the gravitational wave stochastic back- ground.Phys. Rev. D, 56:545–563, 1997. doi: 10.1103/PhysRevD.56.545

    work page doi:10.1103/physrevd.56.545 1997

  25. [26]

    A radiometer for stochastic gravi- tational waves.Classical and Quantum Gravity, 23(8): S179, mar 2006

    Stefan W Ballmer. A radiometer for stochastic gravi- tational waves.Classical and Quantum Gravity, 23(8): S179, mar 2006. doi:10.1088/0264-9381/23/8/S23. URL https://dx.doi.org/10.1088/0264-9381/23/8/S23

    work page doi:10.1088/0264-9381/23/8/s23 2006

  26. [27]

    Gravitational wave ra- diometry: Mapping a stochastic gravitational wave background.Physical Review D, 77(4), feb 2008

    Sanjit Mitra, Sanjeev Dhurandhar, Tarun Souradeep, Albert Lazzarini, Vuk Mandic, Sukanta Bose, and Stefan Ballmer. Gravitational wave ra- diometry: Mapping a stochastic gravitational wave background.Physical Review D, 77(4), feb 2008. doi:10.1103/physrevd.77.042002. URL https://doi.org/10.1103%2Fphysrevd.77.042002

    work page doi:10.1103/physrevd.77.042002 2008

  27. [28]

    Abbott, R

    B. Abbott, R. Abbott, et al. Upper limit map of a background of gravitational waves.Phys. Rev. D, 76: 082003, Oct 2007. doi:10.1103/PhysRevD.76.082003. URLhttps://link.aps.org/doi/10.1103/PhysRevD. 76.082003

    work page doi:10.1103/physrevd.76.082003 2007

  28. [29]

    Abadie, B

    J. Abadie, B. P. Abbott, et al. Directional limits on persistent gravitational waves using ligo s5 science data.Phys. Rev. Lett., 107:271102, Dec 2011. doi: 10.1103/PhysRevLett.107.271102. URLhttps://link. aps.org/doi/10.1103/PhysRevLett.107.271102

    work page doi:10.1103/physrevlett.107.271102 2011

  29. [30]

    B. P. Abbott, R. Abbott, et al. Directional limits on per- sistent gravitational waves from advanced ligo’s first ob- serving run.Phys. Rev. Lett., 118:121102, Mar 2017. doi: 10.1103/PhysRevLett.118.121102. URLhttps://link. aps.org/doi/10.1103/PhysRevLett.118.121102

    work page doi:10.1103/physrevlett.118.121102 2017

  30. [31]

    B. P. Abbott and Others. Directional limits on persistent gravitational waves using data from Advanced LIGO’s first two observing runs.Phys. Rev. D, 100:062001, Sep

    work page

  31. [32]

    URLhttps: //link.aps.org/doi/10.1103/PhysRevD.100.062001

    doi:10.1103/PhysRevD.100.062001. URLhttps: //link.aps.org/doi/10.1103/PhysRevD.100.062001

    work page doi:10.1103/physrevd.100.062001

  32. [33]

    Abbott and Others

    R. Abbott and Others. Search for anisotropic gravitational-wave backgrounds using data from Ad- vanced LIGO and Advanced Virgo’s first three observ- ing runs.Phys. Rev. D, 104:022005, Jul 2021. doi: 10.1103/PhysRevD.104.022005. URLhttps://link. aps.org/doi/10.1103/PhysRevD.104.022005

    work page doi:10.1103/physrevd.104.022005 2021

  33. [34]

    A. G. Abac et al. Directional Search for Persistent Grav- itational Waves: Results from the First Part of LIGO- Virgo-KAGRA’s Fourth Observing Run. 10 2025

    work page 2025

  34. [35]

    Messenger, H

    C. Messenger, H. J. Bulten, S. G. Crowder, V. Der- gachev, D. K. Galloway, E. Goetz, R. J. G. Jonker, P. D. Lasky, G. D. Meadors, A. Melatos, S. Premachandra, K. Riles, L. Sammut, E. H. Thrane, J. T. Whelan, and Y. Zhang. Gravitational waves from scorpius x-1: A com- parison of search methods and prospects for detection with advanced detectors.Phys. Rev. ...

    work page

  35. [36]

    URLhttps: //link.aps.org/doi/10.1103/PhysRevD.92.023006

    doi:10.1103/PhysRevD.92.023006. URLhttps: //link.aps.org/doi/10.1103/PhysRevD.92.023006

    work page doi:10.1103/physrevd.92.023006

  36. [37]

    Patrick Meyers.Cross-correlation searches for persis- tent gravitational waves with Advanced LIGO and noise studies for current and future ground-based gravitational- wave detectors.PhD thesis, University of Minnesota,

    work page

  37. [38]

    URLhttps://conservancy.umn.edu/handle/ 11299/199038

    work page

  38. [39]

    Harnessing the potential of pys- toch: Detecting continuous gravitational waves from in- teresting supernova remnant targets.Phys

    Claudio Salvadore, Iuri La Rosa, Paola Leaci, Francesco Amicucci, Pia Astone, Sabrina D’Antonio, Luca D’Onofrio, Cristiano Palomba, Lorenzo Pierini, and Francesco Safai Tehrani. Harnessing the potential of pys- toch: Detecting continuous gravitational waves from in- teresting supernova remnant targets.Phys. Rev. D, 112 (8):083051, 2025. doi:10.1103/xydb-k4vw

    work page doi:10.1103/xydb-k4vw 2025

  39. [40]

    Abbott et al

    R. Abbott et al. All-sky, all-frequency directional search for persistent gravitational waves from Advanced LIGO’s and Advanced Virgo’s first three observing runs.Phys. Rev. D, 105(12):122001, 2022. doi: 10.1103/PhysRevD.105.122001

    work page doi:10.1103/physrevd.105.122001 2022

  40. [41]

    Fast gravitational wave radiometry using data fold- ing.Physical Review D, 92(2), jul 2015

    Anirban Ain, Prathamesh Dalvi, and Sanjit Mitra. Fast gravitational wave radiometry using data fold- ing.Physical Review D, 92(2), jul 2015. doi: 10.1103/physrevd.92.022003. URLhttps://doi.org/ 10.1103%2Fphysrevd.92.022003

    work page doi:10.1103/physrevd.92.022003 2015

  41. [42]

    Very fast stochastic gravitational wave background map mak- ing using folded data.Physical Review D, 98(2), jul

    Anirban Ain, Jishnu Suresh, and Sanjit Mitra. Very fast stochastic gravitational wave background map mak- ing using folded data.Physical Review D, 98(2), jul

    work page

  42. [43]

    URLhttps: //doi.org/10.1103%2Fphysrevd.98.024001

    doi:10.1103/physrevd.98.024001. URLhttps: //doi.org/10.1103%2Fphysrevd.98.024001

    work page doi:10.1103/physrevd.98.024001

  43. [44]

    K. M. G´ orski, E. Hivon, A. J. Banday, B. D. Wan- delt, F. K. Hansen, M. Reinecke, and M. Bartelmann. Healpix: A framework for high-resolution discretization 13 and fast analysis of data distributed on the sphere. The Astrophysical Journal, 622(2):759, apr 2005. doi: 10.1086/427976. URLhttps://dx.doi.org/10.1086/ 427976

    work page internal anchor Pith review doi:10.1086/427976 2005

  44. [45]

    Singer, Daniel Lenz, Martin Rei- necke, Cyrille Rosset, Eric Hivon, and Krzysztof M

    Andrea Zonca, Leo P. Singer, Daniel Lenz, Martin Rei- necke, Cyrille Rosset, Eric Hivon, and Krzysztof M. Gorski. healpy: equal area pixelization and spherical harmonics transforms for data on the sphere in python. Journal of Open Source Software, 4(35):1298, 2019. doi: 10.21105/joss.01298. URLhttps://doi.org/10.21105/ joss.01298

    work page doi:10.21105/joss.01298 2019

  45. [46]

    Knee, Helen Du, Evan Goetz, Jess McIver, Julian B

    Alan M. Knee, Helen Du, Evan Goetz, Jess McIver, Julian B. Carlin, Ling Sun, Liam Dunn, Lucy Strang, Hannah Middleton, and Andrew Melatos. Search for continuous gravitational waves directed at subthreshold radiometer candidates in o3 ligo data.Phys. Rev. D, 109:062008, Mar 2024. doi: 10.1103/PhysRevD.109.062008. URLhttps://link. aps.org/doi/10.1103/PhysRe...

    work page doi:10.1103/physrevd.109.062008 2024

  46. [47]

    Angular resolution of the search for anisotropic stochastic gravitational-wave background with terres- trial gravitational-wave detectors.Phys

    Erik Floden, Vuk Mandic, Andrew Matas, and Leo Tsukada. Angular resolution of the search for anisotropic stochastic gravitational-wave background with terres- trial gravitational-wave detectors.Phys. Rev. D, 106: 023010, Jul 2022. doi:10.1103/PhysRevD.106.023010. URLhttps://link.aps.org/doi/10.1103/PhysRevD. 106.023010

    work page doi:10.1103/physrevd.106.023010 2022

  47. [48]

    Aasi and Others

    J. Aasi and Others. Directed search for con- tinuous gravitational waves from the galactic cen- ter.Phys. Rev. D, 88:102002, Nov 2013. doi: 10.1103/PhysRevD.88.102002. URLhttps://link.aps. org/doi/10.1103/PhysRevD.88.102002

    work page doi:10.1103/physrevd.88.102002 2013

  48. [49]

    Postprocessing methods used in the search for continuous gravitational-wave signals from the galac- tic center.Phys

    Berit Behnke, Maria Alessandra Papa, and Reinhard Prix. Postprocessing methods used in the search for continuous gravitational-wave signals from the galac- tic center.Phys. Rev. D, 91:064007, Mar 2015. doi: 10.1103/PhysRevD.91.064007. URLhttps://link.aps. org/doi/10.1103/PhysRevD.91.064007

    work page doi:10.1103/physrevd.91.064007 2015

  49. [50]

    Adaptive clus- tering procedure for continuous gravitational wave searches.Phys

    Avneet Singh, Maria Alessandra Papa, Heinz-Bernd Eggenstein, and Sin´ ead Walsh. Adaptive clus- tering procedure for continuous gravitational wave searches.Phys. Rev. D, 96:082003, Oct 2017. doi: 10.1103/PhysRevD.96.082003. URLhttps://link.aps. org/doi/10.1103/PhysRevD.96.082003

    work page doi:10.1103/physrevd.96.082003 2017

  50. [51]

    Beheshtipour and M

    B. Beheshtipour and M. A. Papa. Deep learning for clustering of continuous gravitational wave candidates. ii. identification of low-snr candidates.Phys. Rev. D, 103: 064027, Mar 2021. doi:10.1103/PhysRevD.103.064027. URLhttps://link.aps.org/doi/10.1103/PhysRevD. 103.064027

    work page doi:10.1103/physrevd.103.064027 2021

  51. [52]

    Steltner, T

    B. Steltner, T. Menne, M. A. Papa, and H.-B. Eggen- stein. Density-clustering of continuous gravitational wave candidates from large surveys.Phys. Rev. D, 106: 104063, Nov 2022. doi:10.1103/PhysRevD.106.104063. URLhttps://link.aps.org/doi/10.1103/PhysRevD. 106.104063

    work page doi:10.1103/physrevd.106.104063 2022

  52. [53]

    Angular power spectra of anisotropic stochas- tic gravitational wave background: Developing statis- tical methods and analyzing data from ground-based detectors.Phys

    Deepali Agarwal, Jishnu Suresh, Sanjit Mitra, and Anir- ban Ain. Angular power spectra of anisotropic stochas- tic gravitational wave background: Developing statis- tical methods and analyzing data from ground-based detectors.Phys. Rev. D, 108:023011, Jul 2023. doi: 10.1103/PhysRevD.108.023011. URLhttps://link. aps.org/doi/10.1103/PhysRevD.108.023011

    work page doi:10.1103/physrevd.108.023011 2023

  53. [54]

    Abbott, T

    R. Abbott, T. D. Abbott, et al. Upper limits on the isotropic gravitational-wave background from advanced ligo and advanced virgo’s third observing run.Phys. Rev. D, 104:022004, Jul 2021. doi: 10.1103/PhysRevD.104.022004. URLhttps://link. aps.org/doi/10.1103/PhysRevD.104.022004

    work page doi:10.1103/physrevd.104.022004 2021

  54. [55]

    Romano, San- jit Mitra, Dipongkar Talukder, Sukanta Bose, and Vuk Mandic

    Eric Thrane, Stefan Ballmer, Joseph D. Romano, San- jit Mitra, Dipongkar Talukder, Sukanta Bose, and Vuk Mandic. Probing the anisotropies of a stochas- tic gravitational-wave background using a network of ground-based laser interferometers.Phys. Rev. D, 80: 122002, Dec 2009. doi:10.1103/PhysRevD.80.122002. URLhttps://link.aps.org/doi/10.1103/PhysRevD. 80.122002

    work page doi:10.1103/physrevd.80.122002 2009

  55. [56]

    Lazzarini and J

    A. Lazzarini and J. Romano. Use of overlapping windows in the stochastic background search.Technical Report No. LIGO-T040089-x0, 2004. URLhttps://dcc.ligo. org/LIGO-T040089/public

    work page 2004

  56. [57]

    Stochastic gravitational wave background mapmaking using regularized deconvolu- tion.Phys

    Sambit Panda, Swetha Bhagwat, Jishnu Suresh, and Sanjit Mitra. Stochastic gravitational wave background mapmaking using regularized deconvolu- tion.Phys. Rev. D, 100:043541, Aug 2019. doi: 10.1103/PhysRevD.100.043541. URLhttps://link. aps.org/doi/10.1103/PhysRevD.100.043541

    work page doi:10.1103/physrevd.100.043541 2019

  57. [58]

    Deepali Agarwal, Jishnu Suresh, Sanjit Mitra, and Anirban Ain. Upper limits on persistent gravita- tional waves using folded data and the full covari- ance matrix from advanced LIGO’s first two observ- ing runs.Physical Review D, 104(12), dec 2021. doi: 10.1103/physrevd.104.123018. URLhttps://doi.org/ 10.1103%2Fphysrevd.104.123018

    work page doi:10.1103/physrevd.104.123018 2021

  58. [59]

    Renzini, and Alan J

    Liting Xiao, Arianna I. Renzini, and Alan J. Weinstein. Model-independent search for anisotropies in stochastic gravitational-wave backgrounds and application to ligo- virgo’s first three observing runs.Phys. Rev. D, 107: 122002, Jun 2023. doi:10.1103/PhysRevD.107.122002. URLhttps://link.aps.org/doi/10.1103/PhysRevD. 107.122002

    work page doi:10.1103/physrevd.107.122002 2023

  59. [60]

    All-sky, nar- rowband, gravitational-wave radiometry with folded data.Phys

    Eric Thrane, Sanjit Mitra, Nelson Christensen, Vuk Mandic, and Anirban Ain. All-sky, nar- rowband, gravitational-wave radiometry with folded data.Phys. Rev. D, 91:124012, Jun 2015. doi:10.1103/PhysRevD.91.124012. URLhttps: //link.aps.org/doi/10.1103/PhysRevD.91.124012

    work page doi:10.1103/physrevd.91.124012 2015

  60. [61]

    Sanjeev Dhurandhar, Badri Krishnan, Himan Mukhopadhyay, and John T. Whelan. Cross-correlation search for periodic gravitational waves.Phys. Rev. D, 77:082001, Apr 2008. doi:10.1103/PhysRevD.77.082001. URLhttps://link.aps.org/doi/10.1103/PhysRevD. 77.082001

    work page doi:10.1103/physrevd.77.082001 2008

  61. [62]

    Whelan, Santosh Sundaresan, Yuanhao Zhang, and Prabath Peiris

    John T. Whelan, Santosh Sundaresan, Yuanhao Zhang, and Prabath Peiris. Model-based cross- correlation search for gravitational waves from scor- pius x-1.Phys. Rev. D, 91:102005, May 2015. doi: 10.1103/PhysRevD.91.102005. URLhttps://link.aps. org/doi/10.1103/PhysRevD.91.102005

    work page doi:10.1103/physrevd.91.102005 2015

  62. [63]

    Thermodynamics of Kerr-Newman-AdS Black Holes and Conformal Field Theories,

    E Goetz and K Riles. An all-sky search algorithm for continuous gravitational waves from spinning neu- tron stars in binary systems.Classical and Quantum Gravity, 28(21):215006, sep 2011. doi:10.1088/0264- 9381/28/21/215006. URLhttps://dx.doi.org/10. 1088/0264-9381/28/21/215006

    work page doi:10.1088/0264- 2011

  63. [64]

    URLhttps: //zenodo.org/records/6326656

    Folded data for first three observing runs of Advanced LIGO and Advanced Virgo, March 2022. URLhttps: //zenodo.org/records/6326656. 14

    work page arXiv 2022

  64. [65]

    Harris, K

    Charles R. Harris, K. Jarrod Millman, St´ efan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cour- napeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, Robert Kern, Matti Picus, Stephan Hoyer, Marten H. van Kerkwijk, Matthew Brett, Al- lan Haldane, Jaime Fern´ andez del R´ ıo, Mark Wiebe, Pearu Peterson, Pierre G´ erard-Marchant,...

    work page doi:10.1038/s41586-020-2649-2 2020

  65. [66]

    Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, St´ efan J

    Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, St´ efan J. van der Walt, Matthew Brett, Joshua Wilson, K. Jarrod Millman, Nikolay Mayorov, Andrew R. J. Nelson, Eric Jones, Robert Kern, Eric Larson, C J Carey, ˙Ilhan Polat, Yu Feng, Eric W....

    work page doi:10.1038/s41592-019-0686-2 2020

  66. [67]

    Reimplementing the hierarchical data system using hdf5.Astronomy and Computing, 12: 221–228, September 2015

    Tim Jenness. Reimplementing the hierarchical data system using hdf5.Astronomy and Computing, 12: 221–228, September 2015. ISSN 2213-1337. doi: 10.1016/j.ascom.2015.02.003. URLhttp://dx.doi.org/ 10.1016/j.ascom.2015.02.003

    work page doi:10.1016/j.ascom.2015.02.003 2015

  67. [68]

    https://git.ligo.org/stochastic-public/ stochastic/-/tree/master/PyStoch?ref_type=heads, 2021

    Anirban Ain, Jishnu Suresh, Sudhagar Suyam- prakasam, and Sanjit Mitra.PyStoch Code. https://git.ligo.org/stochastic-public/ stochastic/-/tree/master/PyStoch?ref_type=heads, 2021

    work page 2021

  68. [69]

    pandas-dev/pandas: Pandas, February 2020

    The pandas development team. pandas-dev/pandas: Pandas, February 2020. URLhttps://doi.org/10. 5281/zenodo.3509134

    work page 2020

  69. [70]

    Data Structures for Statistical Comput- ing in Python

    Wes McKinney. Data Structures for Statistical Comput- ing in Python. In St´ efan van der Walt and Jarrod Mill- man, editors,Proceedings of the 9th Python in Science Conference, pages 56 – 61, 2010. doi:10.25080/Majora- 92bf1922-00a

    work page doi:10.25080/majora- 2010

  70. [71]

    J. D. Hunter. Matplotlib: A 2d graphics environment. Computing in Science & Engineering, 9(3):90–95, 2007. doi:10.1109/MCSE.2007.55

    work page doi:10.1109/mcse.2007.55 2007