CaltechAUTHORS
Published November 2023 | Version Published
Journal Article Open

Machine-learning approach for optimal self-calibration and fringe tracking in photonic nulling interferometry

Creators

  • 1. ROR icon University of Sydney
  • 2. Astralis, Astronomical Instrumentation Consortium, Sydney, New South Wales, Australia
  • 3. ROR icon KU Leuven
  • 4. ROR icon Macquarie University
  • 5. ROR icon Observatoire de la Côte d'Azur
  • 6. ROR icon Lagrange Laboratory
  • 7. ROR icon California Institute of Technology
  • 8. ROR icon University of Hawaii at Hilo

Abstract

Photonic technologies have enabled a generation of nulling interferometers, such as the guided light interferometric nulling technology instrument, potentially capable of imaging exoplanets and circumstellar structure at extreme contrast ratios by suppressing contaminating starlight, and paving the way to the characterization of habitable planet atmospheres. But even with cutting-edge photonic nulling instruments, the achievable starlight suppression (null-depth) is only as good as the instrument's wavefront control and its accuracy is only as good as the instrument's calibration. Here, we present an approach wherein outputs from non-science channels of a photonic nulling chip are used as a precise null-depth calibration method and can also be used in real time for fringe tracking. This is achieved using a deep neural network to learn the true in-situ complex transfer function of the instrument and then predict the instrumental leakage contribution (at millisecond timescales) for the science (nulled) outputs, enabling accurate calibration. In this method, this pseudo-real-time approach is used instead of the statistical methods used in other techniques (such as null self calibration, or NSC) and also resolves the severe effect of read-noise seen when NSC is used with some detector types.

Copyright and License

© The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

Acknowledgement

Barnaby R. M. Norris is the recipient of an Australian Research Council Discovery Early Career 230 Award (Grant No. DE210100953) funded by the Australian Government. The development of SCExAO was supported by the Japan Society for the Promotion of Science (Grant-in-Aid for Research Nos. 23340051, 26220704, 23103002, 19H00703 and 19H00695); the Astrobiology Center of the National Institutes of Natural Sciences, Japan; the Mt. Cuba Foundation; and the director’s contingency fund at Subaru Telescope. The authors would like to thank Dr. Eckhart Spalding for his work on the GLINT instrument upgrade. The authors wish to recognize and acknowledge the very significant cultural role and reverence that the summit of Mauna Kea has always had within indigenous Hawaiian communities and are most fortunate to have the opportunity to conduct observations from this mountain.

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Additional details

Funding

Australian Research Council
DE210100953
Japan Society for the Promotion of Science
23340051
Japan Society for the Promotion of Science
26220704
Japan Society for the Promotion of Science
23103002
Japan Society for the Promotion of Science
19H00703
Japan Society for the Promotion of Science
19H00695
National Institutes of Natural Sciences
Mt. Cuba Astronomical Foundation

Dates

Available
2023-11-30
Published

Caltech Custom Metadata

Caltech groups
Palomar Observatory , Division of Physics, Mathematics and Astronomy (PMA)
Publication Status
Published