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arxiv: 2606.27262 · v1 · pith:NSPHCAAPnew · submitted 2026-06-25 · 🌌 astro-ph.IM · astro-ph.HE

The SPOTLIGHT Multibeam Real-Time Transient Detection System

Ujjwal Panda , Jayanta Roy , Kshitij Bane , Chahat Dudeja , Sridhar Gajendran , Param Joshi , Sanjay Kudale , Arpan Pal
show 12 more authors
Raghav Wani Karel Adamek Wes Armour Kenil Ajudiya Jayaram Chengalur Jyotirmoy Das Nishant Pradeep Deo Deepak Bhong Shelton Gnanaraj Santaji N. Katore Mekhala Muley Harshavardhan Reddy
This is my paper

Pith reviewed 2026-06-26 02:31 UTC · model grok-4.3

classification 🌌 astro-ph.IM astro-ph.HE
keywords fast radio burstsreal-time detectionmultibeam pipelineradio transientsdedispersionGPU accelerationcommensal searchsignal injection
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The pith

A real-time multibeam pipeline processes 2000 beams to detect radio transients at 0.2 Jy ms sensitivity.

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

The paper describes a GPU-based system for searching fast radio bursts and other radio transients in real time from a telescope array. The pipeline handles up to 2000 simultaneous beams by performing dedispersion to correct for interstellar delays, searching for single pulses, optimizing candidates through multiple stages, and triggering capture of detailed data. It incorporates a method to inject synthetic signals into the live stream for ongoing performance checks. During its first use running alongside regular observations, the system recorded 2870 bursts from 42 known sources, achieving the expected detection threshold. This setup allows continuous monitoring for rare transient events without needing separate telescope time.

Core claim

The pipeline is capable of processing up to 2000 post-correlation beams in real time by combining dedispersion and single-pulse search with a multi-stage candidate optimisation framework and triggering system. A real-time signal injection framework validates performance. Initial deployment detected 2870 bursts from 42 known sources and demonstrated sensitivity consistent with the predicted survey threshold of ∼0.2 Jy ms while operating commensally.

What carries the argument

The multibeam real-time transient search pipeline integrating accelerated dedispersion, pulse search, candidate optimisation, and signal injection validation.

Load-bearing premise

The signal injection framework and candidate optimisation accurately reflect the detection of real astrophysical signals without biases or data loss over the full dispersion measure range.

What would settle it

A significant discrepancy between the observed detection rate of known sources and the rate predicted by the 0.2 Jy ms threshold, or failure to recover a substantial fraction of injected test signals.

Figures

Figures reproduced from arXiv: 2606.27262 by Arpan Pal, Chahat Dudeja, Deepak Bhong, Harshavardhan Reddy, Jayanta Roy, Jayaram Chengalur, Jyotirmoy Das, Karel Adamek, Kenil Ajudiya, Kshitij Bane, Mekhala Muley, Nishant Pradeep Deo, Param Joshi, Raghav Wani, Sanjay Kudale, Santaji N. Katore, Shelton Gnanaraj, Sridhar Gajendran, Ujjwal Panda, Wes Armour.

Figure 1
Figure 1. Figure 1: A flowchart illustrating how SPOTLIGHT’s real-time transient search pipeline is structured. The pipeline is divided into two modules: aamulti and spltpipe, in order to maximise GPU utilisation and throughput. All results are written to the LUSTRE filesystem, mounted across all hosts. A detailed description of individual modules can be found in the text. the dispersion measure. The dispersion measure quan￾t… view at source ↗
Figure 2
Figure 2. Figure 2 [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Scatter plots of the dispersion measure (DM) versus the arrival time for events detected in a single beam from a single time block of 134.217728 seconds, as seen at each stage of SPOTLIGHT’s transient search; from left to right, these stages are A) single pulse search, B) clustering, C) filtering, and D) classification. These plots illustrate how spurious candidates are eliminated at each stage, starting f… view at source ↗
Figure 4
Figure 4. Figure 4: A census of detections obtained from SPOTLIGHT’s real-time transient search pipeline via commensal observations in GMRT’s Cycle 49 and Cycle 50. A total of 2870 detections were made from 42 known sources; each colour in the plot represents a particular source. The fluence of each detected bursts is plotted, versus its dispersion measure (DM). Each point’s size is scaled by its detected width. The red dashe… view at source ↗
Figure 5
Figure 5. Figure 5: A galactic map of the sources detected obtained from SPOTLIGHT’s real-time transient search pipeline via com￾mensal observations in GMRT’s Cycle 49 and Cycle 50. A total of 39 unique sources were detected. The colour represents the dispersion measure (DM) of each source. A100 GPUs, can be seen in [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Variation of the DM transform (DMT) over different frequency bands at the GMRT, for actual bursts detected from FRB 20180916B and FRB 20201124A. From left to right, we have Band 3 (300 to 500 MHz), Band 4 (550 to 750 MHz), and Band 5 (1060 to 1460 MHz). For real signals, we expect a bow-tie pattern in the DMT, which becomes more and more difficult to observe at lower frequencies. Plots in the first row wer… view at source ↗
Figure 7
Figure 7. Figure 7: Histograms of S/N for 74 bursts detected from FRB 20180916, and 142 bursts detected from FRB 20201124, using the GMRT. These bursts were passed through both your + FETCH (left) and candies + FETCH (right). The bursts wrongly labelled as RFI are in red, while the bursts correctly labelled as FRBs are in blue. From the plots, it is clear that candies helps improve the classification done by FETCH, particular… view at source ↗
Figure 9
Figure 9. Figure 9: Real-time factors (that is, the time taken by the pipeline versus the time processed by the pipeline) for aamulti, spltpipe, and both combined for GMRT’s Band 3 (300 − 500 MHz), Band 4 550 − 750 MHz), and Band 5 (1060 − 1460 MHz). The dashed black line represents a real-time factor of 1; ideally, the pipeline’s real-time factor should lie firmly below this limit. point’s colour represents the DM of each so… view at source ↗
Figure 8
Figure 8. Figure 8: The real-time speed-up factor of candies v/s your, plotted v/s the number of CPU cores used. Note that both your and candies use multiple CPU cores to process several candidates in parallel on the GPU; each candidate is still processed on the GPU. This benchmark was obtained on one of SPOTLIGHT’s compute nodes, using one of its NVIDIA A100 GPUs, for 50 candidates in each run. For each point, we take the me… view at source ↗
Figure 10
Figure 10. Figure 10: A diagram illustrating how a bit is shifted based on the amount of signal injected. Assuming the bit occupies the nth level, the probability of it switching to the (n + m)th level is calculated. This probability depends on the amount of overlap between the two levels (marked in red), which, in turn, depends on the amount of signal injected. The bit is then shifted to the level for which this probability i… view at source ↗
read the original abstract

Fast Radio Bursts (FRBs) are among the most enigmatic transient phenomena in the Universe. In order to unravel the mystery behind these events, one requires instruments that possess the ability to search, detect, localise, and capture these events in high resolution over large fields-of-view in real-time. The SPOTLIGHT project is one such backend, leveraging the upgraded Giant Metrewave Radio Telescope (uGMRT) to conduct a commensal search for FRBs and other radio transients, using a dedicated high-performance computing facility, comprised of 90 NVIDIA A100 GPUs and 60 compute servers. Here we present the design, implementation, and performance of SPOTLIGHT's real-time transient search pipeline, a GPU-accelerated system capable of processing up to 2000 post-correlation beams in real time. The pipeline combines AstroAccelerate-powered brute-force dedispersion and single pulse search, with a multi-stage and robust candidate optimisation framework, as well as a triggering system for automatic capture of high-resolution visibility and baseband data. To ensure continuous validation of pipeline performance, we have also developed a real-time signal injection framework capable of injecting synthetic bursts directly into SPOTLIGHT's beamformed data stream. The system operates commensally with routine uGMRT observations, processing data streams in real-time while maintaining high sensitivity to ms-duration transients across dispersion measures extending up to 2000 pc cm$^{-3}$. During its initial deployment in uGMRT Cycle 49 and Cycle 50, the pipeline detected 2870 bursts from 42 known sources, and demonstrated sensitivity consistent with the predicted survey threshold of $\sim$ 0.2 Jy ms. The SPOTLIGHT system establishes a scalable framework for wide-field, low-frequency transient discovery and localisation, and provides a key technological foundation for next-generation radio transient surveys.

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

1 major / 0 minor

Summary. The manuscript describes the design, implementation, and initial performance of the SPOTLIGHT real-time transient detection pipeline deployed on the uGMRT. The system uses a dedicated HPC facility with 90 NVIDIA A100 GPUs to process up to 2000 post-correlation beams in real time, combining AstroAccelerate-powered dedispersion and single-pulse search with a multi-stage candidate optimisation framework and an automatic triggering system for high-resolution data capture. A real-time signal injection framework is included for continuous validation, and the pipeline operates commensally with routine observations up to DM = 2000 pc cm^{-3}. Initial deployment results in Cycles 49 and 50 report 2870 burst detections from 42 known sources with sensitivity matching the predicted survey threshold of ∼0.2 Jy ms.

Significance. If the quantitative end-to-end validation holds, the work supplies a practical, scalable example of a commensal, wide-field, low-frequency transient search system that integrates GPU acceleration, candidate optimisation, and live injection testing. The reported operational detections from actual deployment data provide empirical grounding that could inform next-generation survey designs.

major comments (1)
  1. [Abstract] Abstract: The statement that the pipeline 'demonstrated sensitivity consistent with the predicted survey threshold of ∼0.2 Jy ms' by detecting 2870 bursts from 42 known sources is not supported by any quantitative recovery statistics (e.g., efficiency curves versus DM, fluence, or S/N) from the real-time signal injection framework when run on live commensal data streams. Without these metrics, it is not possible to confirm that the multi-stage optimisation and injection tests recover genuine signals without unaccounted biases or data loss across the full DM range.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive review and for recognizing the practical value of the SPOTLIGHT system. We address the single major comment below.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The statement that the pipeline 'demonstrated sensitivity consistent with the predicted survey threshold of ∼0.2 Jy ms' by detecting 2870 bursts from 42 known sources is not supported by any quantitative recovery statistics (e.g., efficiency curves versus DM, fluence, or S/N) from the real-time signal injection framework when run on live commensal data streams. Without these metrics, it is not possible to confirm that the multi-stage optimisation and injection tests recover genuine signals without unaccounted biases or data loss across the full DM range.

    Authors: We agree with the referee that the abstract claim requires stronger quantitative backing. The 2870 detections from known sources were obtained during live commensal runs in which the injection framework was operating, but the manuscript does not present explicit recovery-efficiency curves versus DM, fluence or S/N derived from those live injections. We will therefore revise the abstract to remove the phrase 'demonstrated sensitivity consistent with the predicted survey threshold of ∼0.2 Jy ms' and replace it with a factual statement limited to the number of detections achieved. In the main text we will add a short paragraph (or table) summarising the injection tests that were performed on the live data streams, including any available recovery fractions, and will explicitly note the current limitations in the quantitative validation. This change will be made in the revised manuscript. revision: yes

Circularity Check

0 steps flagged

No circularity: performance claims rest on reported deployment data and injection tests, not on self-referential derivations or fitted inputs.

full rationale

The paper describes the design and empirical performance of a real-time GPU pipeline for transient detection, reporting metrics such as processing 2000 beams, detection of 2870 bursts from 42 sources, and sensitivity of ~0.2 Jy ms directly from initial uGMRT deployment and signal injection tests. No equations, parameter fits, or predictions are presented that reduce by construction to the inputs; there are no self-citations invoked as load-bearing uniqueness theorems or ansatzes. The central claims are externally falsifiable via the described hardware deployment and do not rely on renaming known results or smuggling assumptions through citations. This is a standard engineering report with independent empirical content.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The work is an engineering description of an implemented system. No free parameters are fitted to data, no new physical entities are postulated, and the few background assumptions are standard radio-astronomy processing steps.

axioms (1)
  • domain assumption AstroAccelerate correctly implements brute-force dedispersion and single-pulse search for the relevant DM range.
    The pipeline description states it combines AstroAccelerate-powered dedispersion and single pulse search.

pith-pipeline@v0.9.1-grok · 5961 in / 1347 out tokens · 80479 ms · 2026-06-26T02:31:17.716631+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

38 extracted references · 37 canonical work pages

  1. [1]

    2020, The Astrophysical Journal Supplement Series, 247, 56, doi:10.3847/1538-4365/ab7994

    Ad´ amek, K., & Armour, W. 2020, The Astrophysical Journal Supplement Series, 247, 56, doi:10.3847/1538-4365/ab7994

    work page doi:10.3847/1538-4365/ab7994 2020

  2. [2]

    R., & Garver-Daniels, N

    Agarwal, D., Aggarwal, K., Burke-Spolaor, S., Lorimer, D. R., & Garver-Daniels, N. 2020, Monthly Notices of the Royal Astronomical Society, 497, 1661, doi:10.1093/mnras/staa1856

    work page doi:10.1093/mnras/staa1856 2020

  3. [3]

    C., Bandura, K., et al

    Amiri, M., Andersen, B. C., Bandura, K., et al. 2021, The Astrophysical Journal Supplement Series, 257, 59, doi:10.3847/1538-4365/ac33ab

    work page doi:10.3847/1538-4365/ac33ab 2021

  4. [4]

    W., Deller, A

    Bannister, K. W., Deller, A. T., Phillips, C., et al. 2019, Science, 365, 565, doi:10.1126/science.aaw5903

    work page doi:10.1126/science.aaw5903 2019

  5. [5]

    J., Wharton, R

    Chatterjee, S., Law, C. J., Wharton, R. S., et al. 2017, Nature, 541, 58, doi:10.1038/nature20797 CHIME/FRB Collaboration, Amiri, M., Bandura, K., et al. 2018, The Astrophysical Journal, 863, 48, doi:10.3847/1538-4357/aad188

    work page doi:10.1038/nature20797 2017

  6. [6]

    C., Amiri, M., Andersen, B

    Collaboration, T. C., Amiri, M., Andersen, B. C., et al. 2023, The Astrophysical Journal Supplement Series, 264, 53, doi:10.3847/1538-4365/acb54c

    work page doi:10.3847/1538-4365/acb54c 2023

  7. [7]

    M., & McLaughlin, M

    Cordes, J. M., & McLaughlin, M. A. 2003, The Astrophysical Journal, 596, 1142, doi:10.1086/378231

    work page doi:10.1086/378231 2003

  8. [8]

    2017, The Astrophysical Journal, 849, 162, doi:10.3847/1538-4357/aa90b9 The SPOTLIGHT Real-time Transient Search Pipeline17

    Eftekhari, T., & Berger, E. 2017, The Astrophysical Journal, 849, 162, doi:10.3847/1538-4357/aa90b9 The SPOTLIGHT Real-time Transient Search Pipeline17

    work page doi:10.3847/1538-4357/aa90b9 2017

  9. [9]

    1996, in Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, KDD’96 (Portland, Oregon: AAAI Press), 226–231

    Ester, M., Kriegel, H.-P., Sander, J., & Xu, X. 1996, in Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, KDD’96 (Portland, Oregon: AAAI Press), 226–231

    1996

  10. [10]

    Gajjar, V., Siemion, A. P. V., Price, D. C., et al. 2018, The Astrophysical Journal, 863, 2, doi:10.3847/1538-4357/aad005

    work page doi:10.3847/1538-4357/aad005 2018

  11. [11]

    2024, in 2024 United States National Committee of URSI National Radio Science Meeting (USNC-URSI NRSM), 396–396, doi:10.23919/USNC-URSINRSM60317.2024.10465039

    Khairy, K. 2024, in 2024 United States National Committee of URSI National Radio Science Meeting (USNC-URSI NRSM), 396–396, doi:10.23919/USNC-URSINRSM60317.2024.10465039

    work page doi:10.23919/usnc-ursinrsm60317.2024.10465039 2024

  12. [12]

    Kouwenhoven, M. L. A., & Voˆ ute, J. L. L. 2001, Astronomy & Astrophysics, 378, 700, doi:10.1051/0004-6361:20011226

    work page doi:10.1051/0004-6361:20011226 2001

  13. [13]

    Kulkarni, S. R. 2020, Dispersion measure: Confusion, Constants & Clarity, arXiv, doi:10.48550/arXiv.2007.02886

    work page doi:10.48550/arxiv.2007.02886 2020

  14. [14]

    E., Andrew, S., Lazda, M., et al

    Lanman, A. E., Andrew, S., Lazda, M., et al. 2024, CHIME/FRB Outriggers: KKO Station System and Commissioning Results, arXiv, doi:10.48550/arXiv.2402.07898

    work page doi:10.48550/arxiv.2402.07898 2024

  15. [15]

    J., Bower, G

    Law, C. J., Bower, G. C., Burke-Spolaor, S., et al. 2018, The Astrophysical Journal Supplement Series, 236, 8, doi:10.3847/1538-4365/aab77b

    work page doi:10.3847/1538-4365/aab77b 2018

  16. [16]

    J., Sharma, K., Ravi, V., et al

    Law, C. J., Sharma, K., Ravi, V., et al. 2023, Deep Synoptic Array Science: First FRB and Host Galaxy Catalog, doi:10.48550/arXiv.2307.03344

    work page doi:10.48550/arxiv.2307.03344 2023

  17. [17]

    2020, Monthly Notices of the Royal Astronomical Society: Letters, 496, L28, doi:10.1093/mnrasl/slaa070

    Li, Z., Gao, H., Wei, J.-J., et al. 2020, Monthly Notices of the Royal Astronomical Society: Letters, 496, L28, doi:10.1093/mnrasl/slaa070

    work page doi:10.1093/mnrasl/slaa070 2020

  18. [18]

    , keywords =

    Lin, H.-H., Lin, K.-y., Li, C.-T., et al. 2022, Publications of the Astronomical Society of the Pacific, 134, 094106, doi:10.1088/1538-3873/ac8f71

    work page doi:10.1088/1538-3873/ac8f71 2022

  19. [19]

    R., Bailes, M., McLaughlin, M

    Lorimer, D. R., Bailes, M., McLaughlin, M. A., Narkevic, D. J., & Crawford, F. 2007, Science, 318, 777, doi:10.1126/science.1147532

    work page doi:10.1126/science.1147532 2007

  20. [20]

    Y., et al

    Luo, R., Hobbs, G., Yong, S. Y., et al. 2022, Monthly Notices of the Royal Astronomical Society, 513, 5881, doi:10.1093/mnras/stac1168

    work page doi:10.1093/mnras/stac1168 2022

  21. [21]

    Macquart, J.-P., Bailes, M., Bhat, N. D. R., et al. 2010, Publications of the Astronomical Society of Australia, 27, 272, doi:10.1071/AS09082

    work page doi:10.1071/as09082 2010

  22. [22]

    P., Prochaska, J

    Macquart, J.-P., Prochaska, J. X., McQuinn, M., et al. 2020, Nature, 581, 391, doi:10.1038/s41586-020-2300-2

    work page doi:10.1038/s41586-020-2300-2 2020

  23. [23]

    D., & Sironi, L

    Margalit, B., Metzger, B. D., & Sironi, L. 2020, Monthly Notices of the Royal Astronomical Society, 494, 4627, doi:10.1093/mnras/staa1036

    work page doi:10.1093/mnras/staa1036 2020

  24. [24]

    J., Carli, E., & Desvignes, G

    Men, Y., Barr, E., Clark, C. J., Carli, E., & Desvignes, G. 2023, Astronomy & Astrophysics, 679, A20, doi:10.1051/0004-6361/202347356

    work page doi:10.1051/0004-6361/202347356 2023

  25. [25]

    and Berger, Edo and Margalit, Ben , title =

    Metzger, B. D., Berger, E., & Margalit, B. 2017,\apj, 841, 14, doi:10.3847/1538-4357/aa633d

    work page doi:10.3847/1538-4357/aa633d 2017

  26. [26]

    D., Margalit, B., & Sironi, L

    Metzger, B. D., Margalit, B., & Sironi, L. 2019, Monthly Notices of the Royal Astronomical Society, 485, 4091, doi:10.1093/mnras/stz700

    work page doi:10.1093/mnras/stz700 2019

  27. [27]

    W., Mckinven, R., et al

    Michilli, D., Masui, K. W., Mckinven, R., et al. 2021, The Astrophysical Journal, 910, 147, doi:10.3847/1538-4357/abe626 Novotn´ y, J., Ad´ amek, K., Clark, M. A., Giles, M., &

    work page doi:10.3847/1538-4357/abe626 2021

  28. [28]

    2023, The Astrophysical Journal Supplement Series, 269, 29, doi:10.3847/1538-4365/acfef6

    Armour, W. 2023, The Astrophysical Journal Supplement Series, 269, 29, doi:10.3847/1538-4365/acfef6

    work page doi:10.3847/1538-4365/acfef6 2023

  29. [29]

    G., et al

    Pleunis, Z., Michilli, D., Bassa, C. G., et al. 2021, The Astrophysical Journal Letters, 911, L3, doi:10.3847/2041-8213/abec72

    work page doi:10.3847/2041-8213/abec72 2021

  30. [30]

    N., & Pen, U.-L

    Roy, J., Chengalur, J. N., & Pen, U.-L. 2018, The Astrophysical Journal, 864, 160, doi:10.3847/1538-4357/aad815

    work page doi:10.3847/1538-4357/aad815 2018

  31. [31]

    W., Deller, A

    Sammons, M. W., Deller, A. T., Glowacki, M., et al. 2023, Two-Screen Scattering in CRAFT FRBs, arXiv, doi:10.48550/arXiv.2305.11477

    work page doi:10.48550/arxiv.2305.11477 2023

  32. [32]

    2017, Proceedings of the International Astronomical Union, 13, 406, doi:10.1017/S1743921317009310

    Sanidas, S., Caleb, M., Driessen, L., et al. 2017, Proceedings of the International Astronomical Union, 13, 406, doi:10.1017/S1743921317009310

    work page doi:10.1017/s1743921317009310 2017

  33. [33]

    N., Prochaska, J

    Simha, S., Burchett, J. N., Prochaska, J. X., et al. 2020, The Astrophysical Journal, 901, 134, doi:10.3847/1538-4357/abafc3

    work page doi:10.3847/1538-4357/abafc3 2020

  34. [34]

    P., Bassa, C

    Tendulkar, S. P., Bassa, C. G., Cordes, J. M., et al. 2017, The Astrophysical Journal Letters, 834, L7, doi:10.3847/2041-8213/834/2/L7

    work page doi:10.3847/2041-8213/834/2/l7 2017

  35. [35]

    P., Gil de Paz, A., Kirichenko, A

    Tendulkar, S. P., Gil de Paz, A., Kirichenko, A. Y., et al. 2021, The Astrophysical Journal, 908, L12, doi:10.3847/2041-8213/abdb38

    work page doi:10.3847/2041-8213/abdb38 2021

  36. [36]

    Wang et al

    Wang, Z., Bannister, K. W., Gupta, V., et al. 2025, Publications of the Astronomical Society of Australia, 42, e005, doi:10.1017/pasa.2024.107

    work page doi:10.1017/pasa.2024.107 2025

  37. [37]

    R., Chen, P., et al

    Xu, H., Niu, J. R., Chen, P., et al. 2022, Nature, 609, 685, doi:10.1038/s41586-022-05071-8

    work page doi:10.1038/s41586-022-05071-8 2022

  38. [38]

    Zackay, B., & Ofek, E. O. 2017, The Astrophysical Journal, 835, 11, doi:10.3847/1538-4357/835/1/11

    work page doi:10.3847/1538-4357/835/1/11 2017