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Autonomous Real-Time Science-Driven Follow-up of Survey Transients

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Big-Data-Analytics in Astronomy, Science, and Engineering (BDA 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 13167))

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Abstract

Astronomical surveys continue to provide unprecedented insights into the time-variable Universe and will remain the source of groundbreaking discoveries for years to come. However, their data throughput has overwhelmed the ability to manually synthesize alerts for devising and coordinating necessary follow-up with limited resources. The advent of Rubin Observatory, with alert volumes an order of magnitude higher at otherwise sparse cadence, presents an urgent need to overhaul existing human-centered protocols in favor of machine-directed infrastructure for conducting science inference and optimally planning expensive follow-up observations.

We present the first implementation of autonomous real-time science-driven follow-up using value iteration to perform sequential experiment design. We demonstrate it for strategizing photometric augmentation of Zwicky Transient Facility Type Ia supernova light-curves given the goal of minimizing SALT2 parameter uncertainties. We find a median improvement of 2–6% for SALT2 parameters and 3–11% for photometric redshift with 2–7 additional data points in g, r and/or i compared to random augmentation. The augmentations are automatically strategized to complete gaps and for resolving phases with high constraining power (e.g. around peaks). We suggest that such a technique can deliver higher impact during the era of Rubin Observatory for precision cosmology at high redshift and can serve as the foundation for the development of general-purpose resource allocation systems.

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Notes

  1. 1.

    https://github.com/fritz-marshal/fritz.

  2. 2.

    https://mars.lco.global/.

  3. 3.

    https://pitt-broker.readthedocs.io/en/latest/.

  4. 4.

    Object Recommender for Augmentation and Coordinating Liaison Engine.

  5. 5.

    Bin edges at 12, 15.5, 17.5, 19.5, and 21.5 mag.

  6. 6.

    Gaussian kernel with bandwidth of 0.005.

References

  1. Abbott, B.P., et al.: A gravitational-wave standard siren measurement of the Hubble constant. Nature 551(7678), 85–88 (2017). https://doi.org/10.1038/nature24471

    Article  Google Scholar 

  2. Ackley, K., et al.: Observational constraints on the optical and near-infrared emission from the neutron star-black hole binary merger candidate S190814bv. A&A 643, A113 (2020). https://doi.org/10.1051/0004-6361/202037669

    Article  Google Scholar 

  3. Anand, S., et al.: Optical follow-up of the neutron star-black hole mergers S200105ae and S200115j. Nat. Astron. 5, 46–53 (2021). https://doi.org/10.1038/s41550-020-1183-3

    Article  Google Scholar 

  4. Arcavi, I.: Rapidly rising transients in the supernova—superluminous supernova gap. ApJ 819(1), 35 (2016). https://doi.org/10.3847/0004-637X/819/1/35

    Article  Google Scholar 

  5. Astudillo, J., Protopapas, P., Pichara, K., Huijse, P.: An information theory approach on deciding spectroscopic follow-ups. AJ 159(1), 16 (2020). https://doi.org/10.3847/1538-3881/ab557d

    Article  Google Scholar 

  6. Baldeschi, A., Miller, A., Stroh, M., Margutti, R., Coppejans, D.L.: Star formation and morphological properties of galaxies in the pan-STARRS 3\(\pi \) survey. I. A machine-learning approach to galaxy and supernova classification. ApJ 902(1), 60 (2020). https://doi.org/10.3847/1538-4357/abb1c0

    Article  Google Scholar 

  7. Bellm, E.C., et al.: The zwicky transient facility: system overview, performance, and first results. PASP 131(995), 018002 (2019). https://doi.org/10.1088/1538-3873/aaecbe

    Article  Google Scholar 

  8. Betoule, M., et al.: Improved cosmological constraints from a joint analysis of the SDSS-II and SNLS supernova samples. A&A 568, A22 (2014). https://doi.org/10.1051/0004-6361/201423413

    Article  Google Scholar 

  9. Boone, K.: Avocado: photometric classification of astronomical transients with Gaussian process augmentation. AJ 158(6), 257 (2019). https://doi.org/10.3847/1538-3881/ab5182

    Article  Google Scholar 

  10. Brout, D., et al.: First cosmology results using Type Ia supernovae from the Dark Energy Survey: photometric pipeline and light-curve data release. ApJ 874, 106 (2019). https://doi.org/10.3847/1538-4357/ab06c1

    Article  Google Scholar 

  11. Burns, C.R., et al.: The Carnegie supernova project: absolute calibration and the Hubble constant. ApJ 869(1), 56 (2018). https://doi.org/10.3847/1538-4357/aae51c

    Article  Google Scholar 

  12. Carbone, D., Corsi, A.: An optimized radio follow-up strategy for stripped-envelope core-collapse supernovae. ApJ 889(1), 36 (2020). https://doi.org/10.3847/1538-4357/ab6227

    Article  Google Scholar 

  13. Cardelli, J.A., Clayton, G.C., Mathis, J.S.: The relationship between infrared, optical, and ultraviolet extinction. ApJ 345, 245 (1989). https://doi.org/10.1086/167900

    Article  Google Scholar 

  14. Carrasco-Davis, R., et al.: Alert classification for the ALeRCE broker system: the real-time stamp classifier. arXiv e-prints arXiv:2008.03309, August 2020

  15. Coughlin, M.W., Dietrich, T.: Can a black hole-neutron star merger explain GW170817, AT2017gfo, and GRB170817A? Phys. Rev. D 100(4), 043011 (2019). https://doi.org/10.1103/PhysRevD.100.043011

    Article  Google Scholar 

  16. Coughlin, M.W., Dietrich, T., Margalit, B., Metzger, B.D.: Multimessenger Bayesian parameter inference of a binary neutron star merger. MNRAS 489(1), L91–L96 (2019). https://doi.org/10.1093/mnrasl/slz133

    Article  Google Scholar 

  17. Coughlin, M.W., et al.: GROWTH on S190425z: searching thousands of square degrees to identify an optical or infrared counterpart to a binary neutron star merger with the zwicky transient facility and palomar gattini-IR. ApJ 885(1), L19 (2019). https://doi.org/10.3847/2041-8213/ab4ad8

    Article  Google Scholar 

  18. Cranmer, M., Melchior, P., Nord, B.: Unsupervised resource allocation with graph neural networks. arXiv e-prints arXiv:2106.09761, June 2021

  19. Dietrich, T., et al.: Multimessenger constraints on the neutron-star equation of state and the Hubble constant. Science 370(6523), 1450–1453 (2020). https://doi.org/10.1126/science.abb4317

    Article  MathSciNet  MATH  Google Scholar 

  20. Djorgovski, S.G., et al.: Real-time data mining of massive data streams from synoptic sky surveys. arXiv e-prints arXiv:1601.04385, January 2016

  21. Folatelli, G., et al.: The Carnegie supernova project: analysis of the first sample of low-redshift type-Ia supernovae. AJ 139(1), 120–144 (2010). https://doi.org/10.1088/0004-6256/139/1/120

    Article  Google Scholar 

  22. Förster, F., et al.: The automatic learning for the rapid classification of events (ALeRCE) alert broker. AJ 161(5), 242 (2021). https://doi.org/10.3847/1538-3881/abe9bc

    Article  Google Scholar 

  23. Fremling, C., et al.: The zwicky transient facility bright transient survey. I. Spectroscopic classification and the redshift completeness of local galaxy catalogs. ApJ 895(1), 32 (2020). https://doi.org/10.3847/1538-4357/ab8943

    Article  Google Scholar 

  24. Graham, M.J., et al.: The zwicky transient facility: science objectives. PASP 131(1001), 078001 (2019). https://doi.org/10.1088/1538-3873/ab006c

    Article  Google Scholar 

  25. Graur, O., et al.: LOSS revisited. II. The relative rates of different types of supernovae vary between low- and high-mass galaxies. ApJ 837, 121 (2017). https://doi.org/10.3847/1538-4357/aa5eb7

    Article  Google Scholar 

  26. Guy, J., et al.: SALT2: using distant supernovae to improve the use of type Ia supernovae as distance indicators. A&A 466(1), 11–21 (2007). https://doi.org/10.1051/0004-6361:20066930

    Article  Google Scholar 

  27. Hinderer, T., et al.: Distinguishing the nature of comparable-mass neutron star binary systems with multimessenger observations: GW170817 case study. Phys. Rev. D 100(6), 063021 (2019). https://doi.org/10.1103/PhysRevD.100.063021

    Article  Google Scholar 

  28. Hotokezaka, K., et al.: A Hubble constant measurement from superluminal motion of the jet in GW170817. Nat. Astron. 3, 940–944 (2019). https://doi.org/10.1038/s41550-019-0820-1

    Article  Google Scholar 

  29. Huerta, E.A., et al.: Enabling real-time multi-messenger astrophysics discoveries with deep learning. Nat. Rev. Phys. 1(10), 600–608 (2019). https://doi.org/10.1038/s42254-019-0097-4

    Article  Google Scholar 

  30. Huth, S., et al.: Constraining neutron-star matter with microscopic and macroscopic collisions. arXiv e-prints arXiv:2107.06229, July 2021

  31. Ivezić, Ž, et al.: LSST: from science drivers to reference design and anticipated data products. ApJ 873(2), 111 (2019). https://doi.org/10.3847/1538-4357/ab042c

    Article  Google Scholar 

  32. Kasliwal, M.M., et al.: The GROWTH marshal: a dynamic science portal for time-domain astronomy. PASP 131(997), 038003 (2019). https://doi.org/10.1088/1538-3873/aafbc2

    Article  Google Scholar 

  33. Kennamer, N., et al.: Active learning with RESSPECT: resource allocation for extragalactic astronomical transients. arXiv e-prints arXiv:2010.05941, October 2020

  34. Kim, A.G., et al.: Type Ia supernova Hubble residuals and host-galaxy properties. ApJ 784(1), 51 (2014). https://doi.org/10.1088/0004-637X/784/1/51

    Article  Google Scholar 

  35. Lochner, M., Bassett, B.A.: ASTRONOMALY: personalised active anomaly detection in astronomical data. Astron. Comput. 36, 100481 (2021). https://doi.org/10.1016/j.ascom.2021.100481

    Article  Google Scholar 

  36. Lunnan, R., et al.: Two new calcium-rich gap transients in group and cluster environments. ApJ 836(1), 60 (2017). https://doi.org/10.3847/1538-4357/836/1/60

    Article  Google Scholar 

  37. Malanchev, K.L., et al.: Anomaly detection in the Zwicky Transient Facility DR3. MNRAS 502(4), 5147–5175 (2021). https://doi.org/10.1093/mnras/stab316

    Article  Google Scholar 

  38. Margutti, R., et al.: An embedded X-ray source shines through the aspherical AT 2018cow: revealing the inner workings of the most luminous fast-evolving optical transients. ApJ 872(1), 18 (2019). https://doi.org/10.3847/1538-4357/aafa01

    Article  Google Scholar 

  39. Mnih, V., et al.: Human-level control through deep reinforcement learning. Nature 518(7540), 529–533 (2015). https://doi.org/10.1038/nature14236

    Article  Google Scholar 

  40. Möller, A., de Boissière, T.: SuperNNova: an open-source framework for Bayesian, neural network-based supernova classification. MNRAS 491(3), 4277–4293 (2020). https://doi.org/10.1093/mnras/stz3312

    Article  Google Scholar 

  41. Möller, A., et al.: FINK, a new generation of broker for the LSST community. MNRAS 501(3), 3272–3288 (2021). https://doi.org/10.1093/mnras/staa3602

    Article  Google Scholar 

  42. Muthukrishna, D., Mandel, K.S., Lochner, M., Webb, S., Narayan, G.: Real-time detection of anomalies in large-scale transient surveys. arXiv e-prints arXiv:2111.00036, October 2021

  43. Muthukrishna, D., Narayan, G., Mandel, K.S., Biswas, R., Hložek, R.: RAPID: early classification of explosive transients using deep learning. PASP 131(1005), 118002 (2019). https://doi.org/10.1088/1538-3873/ab1609

    Article  Google Scholar 

  44. Narayan, G., et al.: Machine-learning-based brokers for real-time classification of the LSST alert stream. ApJS 236(1), 9 (2018). https://doi.org/10.3847/1538-4365/aab781

    Article  Google Scholar 

  45. Nordin, J., et al.: Transient processing and analysis using AMPEL: alert management, photometry, and evaluation of light curves. A&A 631, A147 (2019). https://doi.org/10.1051/0004-6361/201935634

    Article  MathSciNet  Google Scholar 

  46. Perley, D.A., et al.: The zwicky transient facility bright transient survey. II. A public statistical sample for exploring supernova demographics. ApJ 904(1), 35 (2020). https://doi.org/10.3847/1538-4357/abbd98

    Article  Google Scholar 

  47. Phillips, M.M.: The absolute magnitudes of Type IA supernovae. ApJ 413, L105 (1993). https://doi.org/10.1086/186970

    Article  Google Scholar 

  48. Raaijmakers, G., et al.: The challenges ahead for multimessenger analyses of gravitational waves and kilonova: a case study on GW190425. arXiv e-prints arXiv:2102.11569, February 2021

  49. Richards, J.W., et al.: Active learning to overcome sample selection bias: application to photometric variable star classification. ApJ 744(2), 192 (2012). https://doi.org/10.1088/0004-637X/744/2/192

    Article  Google Scholar 

  50. Rigault, M., et al.: Evidence of environmental dependencies of type Ia supernovae from the nearby supernova factory indicated by local H\(\alpha \). A&A 560, A66 (2013). https://doi.org/10.1051/0004-6361/201322104

    Article  Google Scholar 

  51. Rigault, M., et al.: Fully automated integral field spectrograph pipeline for the SEDMachine: pysedm. A&A 627, A115 (2019). https://doi.org/10.1051/0004-6361/201935344

    Article  Google Scholar 

  52. Saha, A., et al.: ANTARES: a prototype transient broker system. In: Observatory Operations: Strategies, Processes, and Systems V. Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, vol. 9149, p. 914908, July 2014. https://doi.org/10.1117/12.2056988

  53. Sánchez-Sáez, P., et al.: Alert classification for the ALeRCE broker system: the light curve classifier. AJ 161(3), 141 (2021). https://doi.org/10.3847/1538-3881/abd5c1

    Article  Google Scholar 

  54. Schlegel, D.J., Finkbeiner, D.P., Davis, M.: Maps of dust infrared emission for use in estimation of reddening and cosmic microwave background radiation foregrounds. ApJ 500(2), 525–553 (1998). https://doi.org/10.1086/305772

    Article  Google Scholar 

  55. Smith, K.W., et al.: Lasair: the transient alert broker for LSST: UK. Res. Notes Am. Astron. Soc. 3(1), 26 (2019). https://doi.org/10.3847/2515-5172/ab020f

    Article  Google Scholar 

  56. Sravan, N., Milisavljevic, D., Reynolds, J.M., Lentner, G., Linvill, M.: Real-time, value-driven data augmentation in the era of LSST. ApJ 893(2), 127 (2020). https://doi.org/10.3847/1538-4357/ab8128

    Article  Google Scholar 

  57. Street, R.A., Bowman, M., Saunders, E.S., Boroson, T.: General-purpose software for managing astronomical observing programs in the LSST era. In: Software and Cyberinfrastructure for Astronomy V. Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, vol. 10707, p. 1070711, July 2018. https://doi.org/10.1117/12.2312293

  58. Villar, V.A., et al.: A deep-learning approach for live anomaly detection of extragalactic transients. ApJS 255(2), 24 (2021). https://doi.org/10.3847/1538-4365/ac0893

    Article  MathSciNet  Google Scholar 

  59. Williamson, M., Modjaz, M., Bianco, F.B.: Optimal classification and outlier detection for stripped-envelope core-collapse supernovae. ApJ 880(2), L22 (2019). https://doi.org/10.3847/2041-8213/ab2edb

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Division of Physics, Mathematics, and Astronomy, California Institute of Technology, Pasadena, CA, 91125, USA

    Niharika Sravan, Matthew J. Graham & Christoffer Fremling

  2. School of Physics and Astronomy, University of Minnesota, Minneapolis, MN, 55455, USA

    Michael W. Coughlin

Authors
  1. Niharika Sravan
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  2. Matthew J. Graham
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  3. Christoffer Fremling
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  4. Michael W. Coughlin
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Editor information

Editors and Affiliations

  1. National Institute of Technology, Delhi, India

    Shelly Sachdeva

  2. University of Aizu, Aizu-wakamatsu city, Fukushima, Japan

    Yutaka Watanobe

  3. University of Aizu, Aizu-wakamatsu city, Fukushima, Japan

    Subhash Bhalla

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Sravan, N., Graham, M.J., Fremling, C., Coughlin, M.W. (2022). Autonomous Real-Time Science-Driven Follow-up of Survey Transients. In: Sachdeva, S., Watanobe, Y., Bhalla, S. (eds) Big-Data-Analytics in Astronomy, Science, and Engineering. BDA 2021. Lecture Notes in Computer Science(), vol 13167. Springer, Cham. https://doi.org/10.1007/978-3-030-96600-3_5

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  • DOI: https://doi.org/10.1007/978-3-030-96600-3_5

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