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Periodic Astrometric Signal Recovery Through Convolutional Autoencoders

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Intelligent Astrophysics

Part of the book series: Emergence, Complexity and Computation ((ECC,volume 39))

Abstract

Astrometric detection involves precise measurements of stellar positions, and it is widely regarded as the leading concept presently ready to find Earth-mass planets in temperate orbits around nearby sun-like stars. The TOLIMAN space telescope [39] is a low-cost, agile mission concept dedicated to narrow-angle astrometric monitoring of bright binary stars. In particular the mission will be optimised to search for habitable-zone planets around \(\alpha \) Centauri AB. If the separation between these two stars can be monitored with sufficient precision, tiny perturbations due to the gravitational tug from an unseen planet can be witnessed and, given the configuration of the optical system, the scale of the shifts in the image plane are about one-millionth of a pixel. Image registration at this level of precision has never been demonstrated (to our knowledge) in any setting within science. In this paper, we demonstrate that a Deep Convolutional Auto-Encoder is able to retrieve such a signal from simplified simulations of the TOLIMAN data and we present the full experimental pipeline to recreate out experiments from the simulations to the signal analysis. In future works, all the more realistic sources of noise and systematic effects present in the real-world system will be injected into the simulations.

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Notes

  1. 1.

    http://exoplanet.eu/catalog/.

  2. 2.

    https://cosmostatistics-initiative.org/focus/toliman1/.

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Acknowledgements

This work was partially produced during the \({2}^{\text {nd}}\) COIN-Focus: Toliman Event (COIN-Focus # 2) held in Rome, Italy, in November 2019. The COIN-Focus: Toliman participants acknowledge the fundamental support of the Breakthrough Initiatives. The Breakthrough Watch initiative and committee (notably Olivier Guyon, Pete Klupar & Pete Worden) have supported and framed the problem. We also acknowledge input and ideas from people in the wider TOLIMAN collaboration including Ben Pope, Barnaby Norris, Bryn Jeffries, Anthony Horton and others. MB acknowledges financial contributions from the agreement ASI/INAF 2018-23-HH.0, Euclid ESA mission - Phase D and the INAF PRIN-SKA 2017 program 1.05.01.88.04. EEOI acknowledges financial support from CNRS 2017 MOMENTUM grant under project Active Learning for Large Scale Sky Surveys. AKM acknowledges the support from the Portuguese Fundação para a Ciência e a Tecnologia (FCT) through grants SFRH/BPD/74697/2010, PTDC/FIS-AST/31546/2017 and from the Portuguese Strategic Programme UID/FIS/00099/2013 for CENTRA.

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Authors and Affiliations

  1. University of Naples Federico II, DIETI, Naples, Italy

    Michele Delli Veneri & Antonio Picariello

  2. School of Physics, The University of Sydney, Sydney, NSW, 2006, Australia

    Louis Desdoigts & Peter Tuthill

  3. Department of Astrophysical Sciences, Princeton University, 4 Ivy Ln., Princeton, NJ08544, USA

    Morgan A. Schmitz

  4. Donald Bren School of Information and Computer Sciences, University of California, Irvine, CA, 92697, USA

    Alberto Krone-Martins

  5. CENTRA/SIM, Faculdade de Ciências, Universidade de Lisboa, Ed. C8, Campo Grande, 1749-016, Lisboa, Portugal

    Alberto Krone-Martins

  6. Université Clermont Auvergne, CNRS/IN2P3, LPC, 63000, Clermont-Ferrand, France

    Emille E. O. Ishida

  7. Key Laboratory for Research in Galaxies and Cosmology, Shanghai Astronomical Observatory, Chinese Academy of Sciences, 80 Nandan Road, Shanghai, 200030, China

    Rafael S. de Souza

  8. Centre for Translational Data Science, University of Sydney, Darlington, NSW, 2008, Australia

    Richard Scalzo

  9. University of Naples Federico II, Department of Physics E. Pancini, Naples, Italy

    Giuseppe Longo

  10. INAF - Astronomical Observatory of Capodimonte, Naples, Italy

    Massimo Brescia

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  1. Michele Delli Veneri
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Correspondence to Michele Delli Veneri .

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Editors and Affiliations

  1. Faculty of Electrical Engineering and Computer Science, VŠB-TU Ostrava, Ostrava, Czech Republic

    Ivan Zelinka

  2. INAF, Astronomical Observatory of Capodimonte, NAPOLI, Italy

    Massimo Brescia

  3. School of Physics and Astronomy, Tel Aviv University, Tel Aviv, Israel

    Dalya Baron

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Veneri, M.D. et al. (2021). Periodic Astrometric Signal Recovery Through Convolutional Autoencoders. In: Zelinka, I., Brescia, M., Baron, D. (eds) Intelligent Astrophysics. Emergence, Complexity and Computation, vol 39. Springer, Cham. https://doi.org/10.1007/978-3-030-65867-0_8

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