pith. sign in

arxiv: 2511.00164 · v2 · submitted 2025-10-31 · 🌌 astro-ph.IM

BOOM and Babamul: a real-time, multi-survey, optical alert broker system operating at scale

Theophile Jegou Du Laz , Michael W. Coughlin , Peter Bachant , Jacob E. Simones , Thomas Culino , Antoine Le Calloch , Sushant Sharma Chaudhary , Xander J. Hall
show 12 more authors
Tyler Barna Daniel Warshofsky Matthew Graham Mansi M. Kasliwal Ashish Mahabal Joshua S. Bloom Antonella Palmese Frank J. Masci Steven L. Groom Richard Dekany Reed L. Riddle George Helou
This is my paper

Pith reviewed 2026-05-18 02:13 UTC · model grok-4.3

classification 🌌 astro-ph.IM
keywords alert brokerreal-time processingZTFLSSTastronomical transientssoftware architecturemulti-survey dataKafka pipeline
0
0 comments X

The pith

BOOM processes ZTF alerts at seven times the prior throughput while preserving full feature parity.

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

The paper presents BOOM as a new real-time analysis framework for jointly brokering alert streams from wide-field optical surveys. It relies on a Rust software stack that pairs a non-relational MongoDB database with Valkey in-memory queues and a Kafka messaging cluster. The authors report that this design matches every capability of the existing ZTF production system yet sustains roughly seven times higher alert rates. A sympathetic reader would care because the upcoming LSST survey is expected to deliver twenty million alerts each night, an order-of-magnitude jump that current brokers cannot handle without scaling. The work also introduces Babamul as the public-facing LSST broker built directly on the BOOM foundation.

Core claim

BOOM is an analysis framework for real-time, joint brokering of alert streams from multiple surveys. Built on a Rust-based stack that uses MongoDB for storage, Valkey for in-memory processing, and Kafka for message sharing, the system achieves feature parity with the prior ZTF alert broker while delivering approximately seven times higher throughput. The workflow supports custom filters for targeted transient detection, and the architecture is positioned to scale to the alert volumes expected from LSST, with Babamul serving as its public-facing interface.

What carries the argument

The Rust-MongoDB-Valkey-Kafka architecture, which supplies real-time storage, queuing, and message distribution for high-volume astronomical alert streams.

If this is right

  • The broker can ingest and filter the full nightly LSST alert volume in real time.
  • Custom filters enable immediate identification of specific transient classes across surveys.
  • Babamul will expose the processed LSST alerts to the broader astronomical community.
  • The same pipeline supports simultaneous handling of alerts from multiple optical surveys plus multi-messenger triggers.

Where Pith is reading between the lines

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

  • The same stack could be reused for other high-rate data streams such as gravitational-wave or neutrino alerts once interfaces are added.
  • Embedding lightweight machine-learning classifiers inside the Valkey layer might reduce the time from alert arrival to classification without extra hardware.
  • Public release of Babamul would let external teams test their own filters against real LSST data at scale.

Load-bearing premise

The architecture will retain feature parity and the stated throughput when alert input rates rise from current ZTF levels to the LSST expectation of roughly twenty million alerts per night.

What would settle it

A throughput and completeness test that feeds the system with simulated alert streams at two times ten to the seventh alerts per night and measures both processing latency and retention of all ZTF-equivalent filters.

Figures

Figures reproduced from arXiv: 2511.00164 by Antoine Le Calloch, Antonella Palmese, Ashish Mahabal, Daniel Warshofsky, Frank J. Masci, George Helou, Jacob E. Simones, Joshua S. Bloom, Mansi M. Kasliwal, Matthew Graham, Michael W. Coughlin, Peter Bachant, Reed L. Riddle, Richard Dekany, Steven L. Groom, Sushant Sharma Chaudhary, Theophile Jegou Du Laz, Thomas Culino, Tyler Barna, Xander J. Hall.

Figure 1
Figure 1. Figure 1: Flowchart for BOOM. the client to develop pipelines around it. For these rea￾sons, adopting Kafka as our downstream data sharing system ensures compatibility with existing downstream services, which are also designed to consume alerts from Kafka . For each survey supported by our software, an associated Kafka consumer has been developed to feed from the Avro-formatted alerts. The consumers take advantage o… view at source ↗
Figure 2
Figure 2. Figure 2: Decision tree workflow of each boom worker ever changing, but cross-matches with static cata￾logs are not relevant for these to begin with. Here, the radius used is the maximum between the posi￾tional uncertainty of the alert survey and the posi￾tional uncertainty of the instrument that was used to build the static catalog. This value can be con￾figured. • Similarly, we cross-match every alert with the ob￾… view at source ↗
Figure 3
Figure 3. Figure 3: shows throughput testing results for both BOOM and Kowalski in terms of alerts processed per second versus the number of worker processes. In addi￾tion to its increased throughput, BOOM performs better as more computing resources are added, though since there are three different worker counts to vary there is 5 10 15 20 25 Number of worker processes 0 200 400 600 800 Throughput (alerts/s) Kowalski BOOM [P… view at source ↗
Figure 4
Figure 4. Figure 4: Filter Builder light curves for thousands of extragalactic transients at a z > 0.2 or a peak brightness of r ≈ 20.5. As part of the 2DTS experiment, ZTF has also begun observations with a daily cadence of the same fields in the sky, offering intra-night observations at an even greater cadence. DTS uses the Saccadic Fast Fourier Transform (SFFT) algorithm developed in Hu et al. (2022) to en￾able fast and ac… view at source ↗
Figure 5
Figure 5. Figure 5: Alert photometry of SN 2025kwy, a young supernova candidate first detected by DECam (C202505201402422m202612) and later observed by ZTF (ZTF25aaqsuda). to simulate what may be expected from the LSST alert stream, we sub-sampled from the DECAM lightcurve and only kept the first—and fainter—detection, 3 days before ZTF’s first observation. This leaves us with a ZTF + LSST joint-stream example as illustrated … view at source ↗
read the original abstract

With the arrival of ever higher throughput wide-field surveys and a multitude of multi-messenger and multi-wavelength instruments to complement them, software capable of harnessing these associated data streams is urgently required. To meet these needs, a number of community supported alert brokers have been built, currently focused on processing of Zwicky Transient Facility (ZTF; $\sim 10^5$-$10^6$ alerts per night) with an eye towards Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST; $\sim 2 \times 10^7$ alerts per night). Building upon the system that successfully ran in production for ZTF's first seven years of operation, we introduce BOOM (Burst & Outburst Observations Monitor), an analysis framework focused on real-time, joint brokering of these alert streams. BOOM harnesses the performance of a Rust-based software stack relying on a non-relational MongoDB database combined with a Valkey in-memory processing queue and a Kafka cluster for message sharing. With this system, we demonstrate feature parity with the existing ZTF system with a throughput $\sim 7 \times$ higher. We describe the workflow that enables the real-time processing as well as the results with custom filters we have built to demonstrate the system's capabilities. In conclusion, we present the development roadmap for both BOOM and Babamul - the public-facing LSST alert broker built atop BOOM - as we begin the Rubin era.

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 / 2 minor

Summary. The manuscript introduces BOOM, a Rust-based real-time analysis framework for joint brokering of optical alert streams that employs a MongoDB non-relational database, Valkey in-memory queue, and Kafka cluster. Building on the prior ZTF production system, it claims feature parity at ∼7× higher throughput and describes custom filters, the workflow for real-time processing, and the development roadmap for Babamul, the public LSST-facing broker built atop BOOM.

Significance. If the performance claims hold under quantified conditions, the work would offer a concrete, modern-technology path toward handling LSST-scale alert volumes (∼2×10^7 per night) while preserving the functionality already demonstrated on ZTF. The architecture choices and emphasis on multi-survey capability address a recognized community need, though the absence of detailed benchmarks currently limits the strength of the readiness argument.

major comments (2)
  1. [Abstract] Abstract: the claim that the system demonstrates “feature parity with the existing ZTF system with a throughput ∼7× higher” provides no information on the alert rate or latency distribution at which the factor was measured, the hardware baseline used for the comparison, or the quantitative criteria employed to establish feature parity. These omissions are load-bearing for the central assertion of suitability for LSST rates.
  2. [Results / workflow description] Performance and scaling discussion: the architecture description does not report direct throughput or latency measurements at input rates approaching or exceeding current ZTF levels, nor does it contain scaling tests, queue-depth analysis, or credible extrapolation to the ∼2×10^7 alerts/night LSST expectation. Without such data the claim that the MongoDB–Valkey–Kafka stack will remain stable under a further 20–200× volume increase cannot be evaluated.
minor comments (2)
  1. [Abstract] The abstract would benefit from a single sentence stating the peak sustained alert rate and median latency achieved in the reported tests.
  2. Figure captions and table headings should explicitly define all throughput and latency units and the exact hardware configuration used for each measurement.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their careful and constructive review of our manuscript on the BOOM alert broker. We address each major comment below and will revise the manuscript to incorporate additional performance details and clarifications.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claim that the system demonstrates “feature parity with the existing ZTF system with a throughput ∼7× higher” provides no information on the alert rate or latency distribution at which the factor was measured, the hardware baseline used for the comparison, or the quantitative criteria employed to establish feature parity. These omissions are load-bearing for the central assertion of suitability for LSST rates.

    Authors: We agree that the abstract would be strengthened by specifying the conditions of the measurement. In the revised manuscript we will state that the ∼7× throughput improvement was measured at ZTF-comparable input rates (∼10^5–10^6 alerts per night) on a standard multi-core server with the MongoDB–Valkey–Kafka stack, using end-to-end processing latency and filter execution time as the primary metrics. Feature parity is defined by replication of the prior ZTF system’s core alert ingestion, filtering, and classification functions. These details will be added to the abstract and elaborated in a new performance subsection. revision: yes

  2. Referee: [Results / workflow description] Performance and scaling discussion: the architecture description does not report direct throughput or latency measurements at input rates approaching or exceeding current ZTF levels, nor does it contain scaling tests, queue-depth analysis, or credible extrapolation to the ∼2×10^7 alerts/night LSST expectation. Without such data the claim that the MongoDB–Valkey–Kafka stack will remain stable under a further 20–200× volume increase cannot be evaluated.

    Authors: The current text emphasizes architectural design and initial ZTF-scale results. We acknowledge that explicit scaling data and extrapolation are not yet presented. In revision we will add a dedicated performance section reporting measured throughput and latency at ZTF rates, preliminary load tests at elevated rates, queue-depth statistics under sustained operation, and a resource-based extrapolation to LSST volumes that accounts for the horizontal scaling properties of the Rust/Kafka components. This will allow readers to evaluate stability under the projected volume increase. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical demonstration of system performance

full rationale

The paper reports construction and operation of a software broker (BOOM/Babamul) and states an empirical result: feature parity with the prior ZTF broker plus ~7x throughput. This is presented as a measured outcome from running the Rust-MongoDB-Valkey-Kafka stack on real alert streams, not as a mathematical derivation, fitted parameter renamed as prediction, or self-referential definition. No equations, ansatzes, or uniqueness theorems appear. The architecture description stands independently of the performance numbers, and the throughput claim is benchmarked against an external prior system rather than reducing to the paper's own inputs by construction. Self-citations, if any, are not load-bearing for the central claim.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 2 invented entities

The central performance claim rests on the untested assumption that the chosen technology stack will scale linearly to LSST alert rates while retaining all prior filtering functionality; no free parameters are introduced, but the scalability premise is domain-specific and not independently verified in the abstract.

axioms (1)
  • domain assumption Alert streams from ZTF and future surveys can be processed in real time using the described database and queue technologies without loss of essential features.
    Invoked when the authors state feature parity and the 7× throughput result.
invented entities (2)
  • BOOM framework no independent evidence
    purpose: Real-time multi-survey optical alert brokering
    New software system introduced to meet higher throughput needs.
  • Babamul broker no independent evidence
    purpose: Public-facing LSST alert broker built on BOOM
    New component presented as the LSST extension.

pith-pipeline@v0.9.0 · 5895 in / 1443 out tokens · 42418 ms · 2026-05-18T02:13:34.351223+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.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. NOMAI : A real-time photometric classifier for superluminous supernovae identification. A science module for the Fink broker

    astro-ph.IM 2026-04 unverdicted novelty 4.0

    NOMAI applies XGBoost to SALT2 and Rainbow-derived features on ZTF alerts to reach 66% completeness and 58% purity for SLSNe, recovering 22 of 24 known active SLSNe in a two-month real-time test.

  2. The ZTF-ULTRASAT experiment: Characterizing the non-transients in ULTRASAT's high cadence survey

    astro-ph.SR 2026-04 unverdicted novelty 4.0

    ZTF high-cadence data shows RR Lyrae stars and flaring sources can mimic UV transients, with pre-existing ML catalogs offering a concrete mitigation approach.

Reference graph

Works this paper leans on

61 extracted references · 61 canonical work pages · cited by 2 Pith papers · 2 internal anchors

  1. [1]

    , " * write output.state after.block = add.period write newline

    ENTRY address archivePrefix author booktitle chapter doi edition editor eprint howpublished institution journal key month number organization pages publisher school series title misctitle type volume year version url label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts ...

    work page

  2. [2]

    write newline

    " write newline "" before.all 'output.state := FUNCTION format.url url empty "" new.block "" url * "" * if FUNCTION format.eprint eprint empty "" archivePrefix empty "" archivePrefix "arXiv" = new.block " " eprint * " " * new.block " " eprint * " " * if if if FUNCTION format.doi doi empty "" " " doi * " " * if FUNCTION format.pid doi empty eprint empty ur...

    work page

  3. [3]

    - [1] #1 = = ^ ^ ^ .\!\!^ d .\!\!^ h .\!\!^ m .\!\!^ s .\!\!^ @mss

    thebibliography [1] 20pt to REFERENCES 6pt =0pt -12pt 10pt plus 3pt =0pt =0pt =1pt plus 1pt =0pt =0pt -12pt =13pt plus 1pt =20pt =13pt plus 1pt \@M =10000 =-1.0em =0pt =0pt 0pt =0pt =1.0em @enumiv\@empty 10000 10000 `\.\@m \@noitemerr \@latex@warning Empty `thebibliography' environment \@ifnextchar \@reference \@latexerr Missing key on reference command E...

    work page doi:10.5281/zenodo.831784 2021

  4. [4]

    G., Ackermann, M., Adams, J., et al

    Aartsen, M., Ackermann, M., Adams, J., et al. 2017, Journal of Instrumentation, 12, P03012, 10.1088/1748-0221/12/03/p03012

    work page doi:10.1088/1748-0221/12/03/p03012 2017

  5. [5]

    2015, Classical and Quantum Gravity, 32, 074001

    Aasi et al . 2015, Classical and Quantum Gravity, 32, 074001

    work page 2015

  6. [6]

    P., Abbott, R., Abbott, T

    Abbott et al. 2017 a , Phys. Rev. Lett., 119, 161101, 10.1103/PhysRevLett.119.161101

    work page doi:10.1103/physrevlett.119.161101 2017

  7. [7]

    2017 b , The Astrophysical Journal Letters, 848, L12

    ---. 2017 b , The Astrophysical Journal Letters, 848, L12. http://stacks.iop.org/2041-8205/848/i=2/a=L12

    work page 2017

  8. [8]

    2015, Classical and Quantum Gravity, 32, 024001

    Acernese et al . 2015, Classical and Quantum Gravity, 32, 024001

    work page 2015

  9. [9]

    W., Perley, D

    Andreoni, I., Coughlin, M. W., Perley, D. A., et al. 2022, Nature, 612, 430–434, 10.1038/s41586-022-05465-8

    work page doi:10.1038/s41586-022-05465-8 2022

  10. [10]

    C., Kulkarni , S

    Bellm , E. C., Kulkarni , S. R., Barlow , T., et al. 2019, , 131, 068003

    work page 2019

  11. [11]

    S., Giannios, D., Metzger, B

    Bloom , J. S., Giannios , D., Metzger , B. D., et al. 2011, Science, 333, 203, 10.1126/science.1207150

    work page doi:10.1126/science.1207150 2011

  12. [12]

    2024, Phys

    Cabrera , T., Palmese , A., Hu , L., et al. 2024, Phys. Rev. D, 110, 123029, 10.1103/PhysRevD.110.123029

    work page doi:10.1103/physrevd.110.123029 2024

  13. [13]

    R., Howell, D

    Cao, Y., Kulkarni, S. R., Howell, D. A., et al. 2015, Nature, 521, 328, 10.1038/nature14440

    work page doi:10.1038/nature14440 2015

  14. [14]

    The Pan-STARRS1 Surveys

    Chambers , K. C., Magnier , E. A., Metcalfe , N., et al. 2016, arXiv e-prints, arXiv:1612.05560. 1612.05560

    work page internal anchor Pith review Pith/arXiv arXiv 2016

  15. [15]

    O., Mazzarella, J

    Cook, D. O., Mazzarella, J. M., Helou, G., et al. 2023, The Astrophysical Journal Supplement Series, 268, 14, 10.3847/1538-4365/acdd06

    work page doi:10.3847/1538-4365/acdd06 2023

  16. [16]

    W., Bloom, J

    Coughlin, M. W., Bloom, J. S., Nir, G., et al. 2023, The Astrophysical Journal Supplement Series, 267, 31, 10.3847/1538-4365/acdee1

    work page doi:10.3847/1538-4365/acdee1 2023

  17. [17]

    A., Jones , D

    Coulter , D. A., Jones , D. O., McGill , P., et al. 2023, arXiv e-prints, arXiv:2303.02154. 2303.02154

    work page arXiv 2023

  18. [18]

    A., et al., 2017, @doi [Science] 10.1126/science.aap9811 , 358, 1556

    Coulter et al. 2017, Science, 358, 1556, 10.1126/science.aap9811

    work page doi:10.1126/science.aap9811 2017

  19. [19]

    M., Riddle, R., et al

    Dekany, R., Smith, R. M., Riddle, R., et al. 2020, Publications of the Astronomical Society of the Pacific, 132, 038001, 10.1088/1538-3873/ab4ca2

    work page doi:10.1088/1538-3873/ab4ca2 2020

  20. [20]

    J., Lang, D., et al

    Dey, A., Schlegel, D. J., Lang, D., et al. 2019, The Astronomical Journal, 157, 168, 10.3847/1538-3881/ab089d

    work page doi:10.3847/1538-3881/ab089d 2019

  21. [21]

    A., & van der Walt, S

    Duev, D. A., & van der Walt, S. J. 2021, Phenomenological classification of the Zwicky Transient Facility astronomical event alerts. 2111.12142

    work page arXiv 2021

  22. [22]

    A., & van der Walt, S

    Duev , D. A., & van der Walt , S. J. 2021, arXiv e-prints, arXiv:2111.12142, 10.48550/arXiv.2111.12142

    work page doi:10.48550/arxiv.2111.12142 2021

  23. [23]

    A., Mahabal, A., Masci, F

    Duev, D. A., Mahabal, A., Masci, F. J., et al. 2019, Monthly Notices of the Royal Astronomical Society, 489, 3582–3590, 10.1093/mnras/stz2357

    work page doi:10.1093/mnras/stz2357 2019

  24. [24]

    Flesch, E. W. 2023, The Open Journal of Astrophysics, 6, 10.21105/astro.2308.01505

    work page doi:10.21105/astro.2308.01505 2023

  25. [25]

    2021, The Astronomical Journal, 161, 242, 10.3847/1538-3881/abe9bc

    Förster, F., Cabrera-Vives, G., Castillo-Navarrete, E., et al. 2021, The Astronomical Journal, 161, 242, 10.3847/1538-3881/abe9bc

    work page doi:10.3847/1538-3881/abe9bc 2021

  26. [26]

    2012, Science, 337, 932, doi: 10.1126/science.1216793

    Gehrels, N., & M \'e sz \'a ros, P. 2012, Science, 337, 932, 10.1126/science.1216793

    work page doi:10.1126/science.1216793 2012

  27. [27]

    Gehrels, G

    Gehrels et al. 2004, , 611, 1005, 10.1086/422091

    work page doi:10.1086/422091 2004

  28. [28]

    J., Kulkarni, S

    Graham, M. J., Kulkarni, S. R., Bellm, E. C., et al. 2019, Publications of the Astronomical Society of the Pacific, 131, 078001, 10.1088/1538-3873/ab006c

    work page doi:10.1088/1538-3873/ab006c 2019

  29. [29]

    J., Hu, L., Palmese, A., et al

    Hall, X. J., Hu, L., Palmese, A., et al. 2025, Transient Name Server AstroNote, 84, 1. https://ui.adsabs.harvard.edu/abs/2025TNSAN..84....1H

    work page 2025

  30. [30]

    Ho, A. Y. Q., Goldstein, D. A., Schulze, S., et al. 2019, The Astrophysical Journal, 887, 169, 10.3847/1538-4357/ab55ec

    work page doi:10.3847/1538-4357/ab55ec 2019

  31. [31]

    Ho, A. Y. Q., Perley, D. A., Kulkarni, S. R., et al. 2020, The Astrophysical Journal, 895, 49, 10.3847/1538-4357/ab8bcf

    work page doi:10.3847/1538-4357/ab8bcf 2020

  32. [32]

    J., Valenti, S., et al

    Hosseinzadeh, G., Sand, D. J., Valenti, S., et al. 2017, The Astrophysical Journal Letters, 845, L11, 10.3847/2041-8213/aa8402

    work page doi:10.3847/2041-8213/aa8402 2017

  33. [33]

    2022, ApJ, 936, 157, doi: 10.3847/1538-4357/ac7394

    Hu, L., Wang, L., Chen, X., & Yang, J. 2022, The Astrophysical Journal, 936, 157, 10.3847/1538-4357/ac7394

    work page doi:10.3847/1538-4357/ac7394 2022

  34. [34]

    2025, arXiv e-prints, arXiv:2506.22626, 10.48550/arXiv.2506.22626

    Hu , L., Cabrera , T., Palmese , A., et al. 2025, arXiv e-prints, arXiv:2506.22626, 10.48550/arXiv.2506.22626

    work page doi:10.48550/arxiv.2506.22626 2025

  35. [35]

    LSST: From Science Drivers to Reference Design and Anticipated Data Products , journal =

    Ivezi \'c , Z ., Kahn , S. M., Tyson , J. A., et al. 2019, , 873, 111, 10.3847/1538-4357/ab042c

    work page doi:10.3847/1538-4357/ab042c 2019

  36. [36]

    W., Bachant, P., et al

    Jegou du Laz, T., Coughlin, M. W., Bachant, P., et al. 2025, 10.22002/640bx-nbn45

    work page doi:10.22002/640bx-nbn45 2025

  37. [37]

    M., Nakar, E., Singer, L

    Kasliwal , M. M., Nakar , E., Singer , L. P., et al. 2017, Science, 358, 1559, 10.1126/science.aap9455

    work page doi:10.1126/science.aap9455 2017

  38. [38]

    M., Cannella, C., Bagdasaryan, A., et al

    Kasliwal , M. M., Cannella , C., Bagdasaryan , A., et al. 2019, , 131, 038003, 10.1088/1538-3873/aafbc2

    work page doi:10.1088/1538-3873/aafbc2 2019

  39. [39]

    A., Bloom, J

    Liu, C., Miller, A. A., Bloom, J. S., Knop, R. A., & Nugent, P. E. 2025, A Morphological Model to Separate Resolved --unresolved Sources in the DESI Legacy Surveys : Application in the LS4 Alert Stream , arXiv, 10.48550/arXiv.2505.17174

    work page doi:10.48550/arxiv.2505.17174 2025

  40. [40]

    The Zwicky Transient Facility: Data Processing, Products, and Archive

    Masci , F. J., Laher , R. R., Rusholme , B., et al. 2019, , 131, 018003, 10.1088/1538-3873/aae8ac

    work page internal anchor Pith review doi:10.1088/1538-3873/aae8ac 2019

  41. [41]

    2021, The Astronomical Journal, 161, 107, 10.3847/1538-3881/abd703

    Matheson, T., Stubens, C., Wolf, N., et al. 2021, The Astronomical Journal, 161, 107, 10.3847/1538-3881/abd703

    work page doi:10.3847/1538-3881/abd703 2021

  42. [42]

    2009, The Astrophysical Journal, 702, 791

    Meegan et al. 2009, The Astrophysical Journal, 702, 791. http://stacks.iop.org/0004-637X/702/i=1/a=791

    work page 2009

  43. [43]

    A., Magee, M

    Miller, A. A., Magee, M. R., Polin, A., et al. 2020, The Astrophysical Journal, 898, 56, 10.3847/1538-4357/ab9e05

    work page doi:10.3847/1538-4357/ab9e05 2020

  44. [44]

    A., Abrams, N

    Miller, A. A., Abrams, N. S., Aldering, G., et al. 2025, The La Silla Schmidt Southern Survey. 2503.14579

    work page arXiv 2025

  45. [45]

    Möller, A., Peloton, J., Ishida, E. E. O., et al. 2020, Monthly Notices of the Royal Astronomical Society, 501, 3272, 10.1093/mnras/staa3602

    work page doi:10.1093/mnras/staa3602 2020

  46. [46]

    2019, , 631, A147, 10.1051/0004-6361/201935634

    Nordin , J., Brinnel , V., van Santen , J., et al. 2019, , 631, A147, 10.1051/0004-6361/201935634

    work page doi:10.1051/0004-6361/201935634 2019

  47. [47]

    2009, The Astrophysical Journal, 701, 824

    Nysewander, M., Fruchter, A., & Pe'Er, A. 2009, The Astrophysical Journal, 701, 824

    work page 2009

  48. [48]

    2022, Transient Name Server AstroNote, 107, 1

    Palmese, A., Wang, L., Chen, X., et al. 2022, Transient Name Server AstroNote, 107, 1. https://ui.adsabs.harvard.edu/abs/2022TNSAN.107....1P

    work page 2022

  49. [49]

    A., Mazzali , P

    Perley , D. A., Mazzali , P. A., Yan , L., et al. 2019, , 484, 1031, 10.1093/mnras/sty3420

    work page doi:10.1093/mnras/sty3420 2019

  50. [50]

    A., Ho, A

    Perley, D. A., Ho, A. Y. Q., Yao, Y., et al. 2021, Monthly Notices of the Royal Astronomical Society, 508, 5138–5147, 10.1093/mnras/stab2785

    work page doi:10.1093/mnras/stab2785 2021

  51. [51]

    J., Maguire, K., Smartt, S

    Prentice, S. J., Maguire, K., Smartt, S. J., et al. 2018, The Astrophysical Journal, 865, L3, 10.3847/2041-8213/aadd90

    work page doi:10.3847/2041-8213/aadd90 2018

  52. [52]

    A., Jegou Du Laz , T., et al

    Rehemtulla , N., Miller , A. A., Jegou Du Laz , T., et al. 2024, , 972, 7, 10.3847/1538-4357/ad5666

    work page doi:10.3847/1538-4357/ad5666 2024

  53. [53]

    Rizhko, M., & Bloom, J. S. 2024, arXiv preprint arXiv:2411.08842

    work page arXiv 2024

  54. [54]

    2023, Bulletin of the AAS, 55

    Singer, L., & Racusin, J. 2023, Bulletin of the AAS, 55

    work page 2023

  55. [55]

    J., et al., 2017, @doi [Nature] 10.1038/nature24303 , 551, 75

    Smartt et al. 2017, Nature, 551, 75 EP . http://dx.doi.org/10.1038/nature24303

    work page doi:10.1038/nature24303 2017

  56. [56]

    W., Williams, R

    Smith, K. W., Williams, R. D., Young, D. R., et al. 2019, Research Notes of the AAS, 3, 26, 10.3847/2515-5172/ab020f

    work page doi:10.3847/2515-5172/ab020f 2019

  57. [57]

    A., Bowman, M., Saunders, E

    Street, R. A., Bowman, M., Saunders, E. S., & Boroson, T. 2018, in Software and Cyberinfrastructure for Astronomy V, ed. J. C. Guzman & J. Ibsen, Vol. 10707, International Society for Optics and Photonics (SPIE), 274 -- 284, 10.1117/12.2312293

    work page doi:10.1117/12.2312293 2018

  58. [59]

    Summary of the content and survey properties

    ---. 2023 b , Astronomy & Astrophysics, 674, A1, 10.1051/0004-6361/202243940

    work page doi:10.1051/0004-6361/202243940 2023

  59. [60]

    van der Walt and Arien Crellin-Quick and Joshua S

    van der Walt, S. J., Crellin-Quick, A., & Bloom, J. S. 2019, Journal of Open Source Software, 4, 10.21105/joss.01247

    work page doi:10.21105/joss.01247 2019

  60. [61]

    Handbook of X-ray and Gamma-ray Astrophysics , year = 2022, editor =

    Yuan, W., Zhang, C., Chen, Y., & Ling, Z. 2022, The Einstein Probe Mission (Springer Nature Singapore), 1–30, 10.1007/978-981-16-4544-0_151-1

    work page doi:10.1007/978-981-16-4544-0_151-1 2022

  61. [62]

    T., Mishra-Sharma, S., & Villar, V

    Zhang, G., Helfer, T., Gagliano, A. T., Mishra-Sharma, S., & Villar, V. A. 2024, Maven: A Multimodal Foundation Model for Supernova Science. 2408.16829

    work page arXiv 2024