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arxiv: 1907.07817 · v1 · pith:EV26GUJEnew · submitted 2019-07-17 · 🌌 astro-ph.IM

Scheduling Discovery in the 2020s

Eric C. Bellm , Eric B. Ford , Aaron Tohuvavohu , Michael W. Coughlin , Brett Morris , Bryan Miller , Jennifer Sobeck , Reed Riddle
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Chuanfei Dong Peter Yoachim
This is my paper

Pith reviewed 2026-05-24 19:43 UTC · model grok-4.3

classification 🌌 astro-ph.IM
keywords astronomical schedulingsurvey optimizationopen-source softwareobservation planningdata analysis integrationlarge-scale surveys2020s astronomy
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The pith

Astronomers must develop high-quality scheduling approaches as open-source software and link observation directly with data analysis for the 2020s.

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

This review argues that the 2020s will produce more astronomical data than any prior decade because survey scale and complexity are rising rapidly. It claims that scheduling will therefore become a central bottleneck, requiring new high-quality methods implemented openly and tied to analysis pipelines. A sympathetic reader would care because without these steps the volume of incoming observations risks being gathered and processed inefficiently.

Core claim

The 2020s will be the most data-rich decade of astronomy in history. As the scale and complexity of surveys increase, the problem of scheduling becomes more critical. High-quality scheduling approaches must be developed, implemented as open-source software, and used to link the typically separate stages of observation and data analysis.

What carries the argument

Scheduling approaches that optimize observation sequences while connecting planning directly to downstream data analysis pipelines.

If this is right

  • Large surveys will collect data more efficiently once scheduling accounts for analysis needs.
  • Open-source scheduling code will allow rapid community testing and refinement across facilities.
  • Linking observation planning to analysis will reduce wasted telescope time on low-value targets.
  • The separation between planning and processing stages will shrink as a standard practice.

Where Pith is reading between the lines

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

  • Similar scheduling integration could become useful in other data-heavy observational sciences facing survey growth.
  • Pilot implementations on existing telescopes in the late 2010s could test the claimed benefits before the decade peak.
  • Optimizing schedules for specific science goals might uncover previously overlooked observing strategies.

Load-bearing premise

The scale and complexity of astronomical surveys will increase enough in the 2020s to make existing scheduling methods inadequate.

What would settle it

A demonstration that current scheduling software handles the largest planned 2020s surveys at full efficiency without new methods or integration with analysis.

read the original abstract

The 2020s will be the most data-rich decade of astronomy in history. As the scale and complexity of our surveys increase, the problem of scheduling becomes more critical. We must develop high-quality scheduling approaches, implement them as open-source software, and begin linking the typically separate stages of observation and data analysis.

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

0 major / 1 minor

Summary. The manuscript is a short position paper asserting that the 2020s will be the most data-rich decade in astronomy due to increasing survey scale and complexity, which will render scheduling a critical problem. It advocates developing high-quality scheduling approaches, implementing them as open-source software, and linking the typically separate stages of observation and data analysis.

Significance. If the premise on survey scale holds, the paper usefully flags a methodological gap in astronomical methods and calls for community action on open tools and integrated pipelines. As a qualitative advocacy piece without quantitative projections, case studies, or derivations, its value lies in prompting discussion rather than providing a technical solution; no machine-checked proofs or reproducible elements are present.

minor comments (1)
  1. [Abstract] The premise that scheduling will become critical is presented as background without references to existing scheduling challenges in current large surveys (e.g., LSST or SKA precursors) or any illustrative metrics; adding 1-2 concrete examples would strengthen the call to action without altering the position-paper format.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their review and recommendation of minor revision. We are pleased that the referee recognizes the paper's role in flagging a methodological gap and calling for community action. We respond to the points in the report below.

read point-by-point responses

  1. Referee: REFEREE SUMMARY: The manuscript is a short position paper asserting that the 2020s will be the most data-rich decade in astronomy due to increasing survey scale and complexity, which will render scheduling a critical problem. It advocates developing high-quality scheduling approaches, implementing them as open-source software, and linking the typically separate stages of observation and data analysis.

    Authors: This is an accurate summary of the manuscript's content and intent. revision: no

  2. Referee: REFEREE SIGNIFICANCE: If the premise on survey scale holds, the paper usefully flags a methodological gap in astronomical methods and calls for community action on open tools and integrated pipelines. As a qualitative advocacy piece without quantitative projections, case studies, or derivations, its value lies in prompting discussion rather than providing a technical solution; no machine-checked proofs or reproducible elements are present.

    Authors: We agree with this characterization. The manuscript is intentionally a concise position paper to highlight an emerging issue and advocate for community efforts on open-source tools and integrated pipelines, rather than to deliver quantitative projections or a specific technical solution. This format aligns with the goal of prompting discussion on scheduling challenges for upcoming surveys. revision: no

Circularity Check

0 steps flagged

No significant circularity

full rationale

The manuscript is a short position paper whose central claim is a normative recommendation to develop open-source schedulers and link observation/analysis stages. No equations, quantitative models, fitted parameters, or technical derivations exist in the text. The premise that survey scale will increase is presented as background context rather than derived from internal logic or self-citation chains. The recommendation stands independently of any internal reduction, making the paper self-contained against external benchmarks with no load-bearing circular steps.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This position paper introduces no free parameters, axioms, or invented entities as it contains no technical derivations or models.

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discussion (0)

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Reference graph

Works this paper leans on

39 extracted references · 39 canonical work pages · 2 internal anchors

  1. [1]

    Bellm, E. C. 2016, PASP, 128, 084501

    work page 2016

  2. [2]

    C., Kulkarni, S

    Bellm, E. C., Kulkarni, S. R., Barlow, T., et al. 2019, PASP, 1 31, 068003, doi: 10.1088/1538-3873/ab0c2a

    work page doi:10.1088/1538-3873/ab0c2a 2019

  3. [3]

    2019, in BAAS, V ol

    Bryson, S., Bennett, D., Gaudi, S., et al. 2019, in BAAS, V ol. 51, 443

    work page 2019

  4. [4]

    2019, in BAAS, V ol

    Capak, P ., Scolnic, D., & Davidzon, I. 2019, in BAAS, V ol. 51, 470

    work page 2019

  5. [5]

    H., Greathouse, T., et al

    Chanover, N., Wong, M. H., Greathouse, T., et al. 2019, in BAA S, V ol. 51, 340

    work page 2019

  6. [6]

    E., & Katsavounidis, E

    Chen, H.-Y ., Essick, R., Vitale, S., Holz, D. E., & Katsavounidis, E. 2017, The Astrophysical Journal, 835, 31, doi: 10.3847/1538-4357/835/1/31 Colom´ e, J., Casteels, K., Ribas, I., & Francisco, X. 2010, in Proc. SPIE, V ol. 7740, Software and Cyberinfrastructure for Astronomy, 77403K

    work page doi:10.3847/1538-4357/835/1/31 2017

  7. [7]

    2012, in Proc

    Colome, J., Colomer, P ., Gu` ardia, J., et al. 2012, in Proc. SPIE, V ol. 8448, Observatory Operations: Strategies, Processes, and Systems IV , 84481L

    work page 2012

  8. [8]

    W., Tao, D., Chan, M

    Coughlin, M. W., Tao, D., Chan, M. L., et al. 2018, Monthly Not ices of the Royal Astronomical 8 Society, 478, 692, doi: 10.1093/mnras/sty1066

    work page doi:10.1093/mnras/sty1066 2018

  9. [9]

    Denny, R. B. 2006, Dispatch Scheduling of Automated Telesco pes, http://scheduler.dc3.com/Scheduler2006.pdf Eifler, T., Simet, M., Hirata, C., et al. 2019, in BAAS, V ol. 51 , 418

    work page 2006

  10. [10]

    R., et al

    Ford, E., Bedell, M., Ciardi, D. R., et al. 2019, Astro2020: D ecadal Survey on Astronomy and Astrophysics, 2020, 466

    work page 2019

  11. [11]

    Ford, E. B. 2008, AJ, 135, 1008, doi: 10.1088/0004-6256/135/3/1008

    work page doi:10.1088/0004-6256/135/3/1008 2008

  12. [12]

    2014, in Proc

    Garcia-Piquer, A., Gu` ardia, J., Colom´ e, J., et al. 2014, in Proc. SPIE, V ol. 9152, Software and Cyberinfrastructure for Astronomy III, 915221

    work page 2014

  13. [13]

    2019, in BAAS , V ol

    Graham, M., Milisavljevic, D., Rest, A., et al. 2019, in BAAS , V ol. 51, 339

    work page 2019

  14. [14]

    Hessman, F. V . 2014, in The Third Hot-wiring the Transient Universe Workshop, ed. P . R

    work page 2014

  15. [15]

    2009, The Fourth Paradigm: Data-Intensive Scientific Discovery (Microsoft Research)

    Tolle, K. 2009, The Fourth Paradigm: Data-Intensive Scientific Discovery (Microsoft Research). https://www.microsoft.com/en-us/research/publication/fourth-paradigm-data-intensive-scientific-discove ry/

    work page 2009

  16. [16]

    D., & Miller, G

    Johnston, M. D., & Miller, G. E. 1994, in Intelligent Schedul ing, ed. M. Zweben & M. S. Fox (Morgan Kaufmann Publishers), 391

    work page 1994

  17. [17]

    2019, in BAAS, V ol

    Kim, A., Aldering, G., Antilogus, P ., et al. 2019, in BAAS, V ol. 51, 140

    work page 2019

  18. [18]

    2019, in BAA S, V ol

    Kollmeier, J., Fuller, J., Gaensicke, B., et al. 2019, in BAA S, V ol. 51, 503

    work page 2019

  19. [19]

    2019, in BAAS, V ol

    Kupfer, T., Kilic, M., Maccarone, T., et al. 2019, in BAAS, V ol. 51, 188

    work page 2019

  20. [20]

    An Integer Linear Programming Solution to the Telescope Network Scheduling Problem

    Lampoudi, S., Saunders, E., & Eastman, J. 2015, ArXiv e-prin ts. https://arxiv.org/abs/1503.07170

    work page internal anchor Pith review Pith/arXiv arXiv 2015

  21. [21]

    Loredo, T. J. 2004, in American Institute of Physics Confere nce Series, V ol. 707, Bayesian Inference and Maximum Entropy Methods in Science and Engine ering, ed. G. J. Erickson & Y . Zhai, 330–346

    work page 2004

  22. [22]

    H., fung Leung, H., & Jennings, N

    Luo, X., man Lee, J. H., fung Leung, H., & Jennings, N. R. 2003, Fuzzy Sets and Systems, 136, 151 , doi: https://doi.org/10.1016/S0165-0114(02)00385-8

    work page doi:10.1016/s0165-0114(02)00385-8 2003

  23. [23]

    M., et al

    Milam, S., Hammel, H., Bauer, J. M., et al. 2019, in BAAS, V ol. 51, 327

    work page 2019

  24. [24]

    M., Tollerud, E., Sip˝ ocz, B., et al

    Morris, B. M., Tollerud, E., Sip˝ ocz, B., et al. 2018, AJ, 155, 128, doi: 10.3847/1538-3881/aaa47e

    work page doi:10.3847/1538-3881/aaa47e 2018

  25. [25]

    J., Connolly, A

    Naghib, E., Y oachim, P ., V anderbei, R. J., Connolly, A. J., &Jones, R. L. 2019, AJ, 157, 151, doi: 10.3847/1538-3881/aafece

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

  26. [26]

    2019, in BAAS, V ol

    Price-Whelan, A., Breivik, K., D’Orazio, D., et al. 2019, in BAAS, V ol. 51, 206

    work page 2019

  27. [27]

    2019, in BAAS, V ol

    Rix, H.-W., Ting, Y .-S., Sippel, A., et al. 2019, in BAAS, V ol. 51, 104

    work page 2019

  28. [28]

    M., et al

    Sathyaprakash, B., Bailes, M., Kasliwal, M. M., et al. 2019, in BAAS, V ol. 51, 276

    work page 2019

  29. [29]

    S., Bowman, M., Boroson, T., & Street, R

    Saunders, E. S., Bowman, M., Boroson, T., & Street, R. A. 2018 , in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, V ol. 10704, Proc. SPIE, 107040Z

    work page 2018

  30. [30]

    2019, in BAAS, V ol.51, 274

    Shen, Y ., Anderson, S., Berger, E., et al. 2019, in BAAS, V ol.51, 274

    work page 2019

  31. [31]

    J., et al

    Silver, D., Huang, A., Maddison, C. J., et al. 2016, Nature, 5 29, 484, doi: 10.1038/nature16961

    work page doi:10.1038/nature16961 2016

  32. [32]

    2017, Na ture, 550, 354, doi: 10.1038/nature24270 9

    Silver, D., Schrittwieser, J., Simonyan, K., et al. 2017, Na ture, 550, 354, doi: 10.1038/nature24270 9

    work page doi:10.1038/nature24270 2017

  33. [33]

    A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play

    Silver, D., Hubert, T., Schrittwieser, J., et al. 2018, Scie nce, 362, 1140, doi: 10.1126/science.aar6404

    work page doi:10.1126/science.aar6404 2018

  34. [34]

    2016, Astronomy and Computing, 15, 90

    Solar, M., Michelon, P ., Avarias, J., & Garces, M. 2016, Astronomy and Computing, 15, 90

    work page 2016

  35. [35]

    2019, in American Astronomical Society Meeting A bstracts, V ol

    Szalay, A. 2019, in American Astronomical Society Meeting A bstracts, V ol. 233, American Astronomical Society Meeting Abstracts #233, 400.01

    work page 2019

  36. [36]

    Improving science yield for NASA Swift with automated planning technologies

    Tohuvavohu, A. 2017, arXiv e-prints, arXiv:1712.08111. https://arxiv.org/abs/1712.08111

    work page internal anchor Pith review Pith/arXiv arXiv 2017

  37. [37]

    Tohuvavohu, A., Evans, P ., Kennea, J., & Cenko, S. B. 2019, in American Astronomical Society Meeting Abstracts, V ol. 233, American Astronomical Society Meeting Abstracts #233, 210.08

    work page 2019

  38. [38]

    L., Denneau, L., Heinze, A

    Tonry, J. L., Denneau, L., Heinze, A. N., et al. 2018, PASP, 13 0, 064505 V erfaillie, G., & Jussien, N. 2005, Constraints, 10, 253, doi: 10.1007/s10601-005-2239-9

    work page doi:10.1007/s10601-005-2239-9 2018

  39. [39]

    2015, in 2015 IEEE 11th International C onference on e-Science, IEEE, 362–370 10

    Wetter, J., Akgun, ¨O., Barker, A., et al. 2015, in 2015 IEEE 11th International C onference on e-Science, IEEE, 362–370 10

    work page 2015