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Published December 10, 2021 | Version Published
Journal Article Open

Identification of Single Spectral Lines in Large Spectroscopic Surveys Using UMLAUT: an Unsupervised Machine-learning Algorithm Based on Unbiased Topology

Creators

  • 1. ROR icon Osservatorio Astronomico di Padova
  • 2. ROR icon University of Padua
  • 3. ROR icon University of Minnesota
  • 4. ROR icon Istituto di Radioastronomia di Bologna
  • 5. ROR icon University of the Western Cape
  • 6. ROR icon University of Valparaíso
  • 7. ROR icon University of Geneva
  • 8. ROR icon Space Telescope Science Institute
  • 9. ROR icon The University of Texas at Austin
  • 10. ROR icon Australian National University
  • 11. ROR icon Infrared Processing and Analysis Center
  • 12. ROR icon Chinese Academy of Sciences
  • 13. ROR icon The Open University
  • 14. ROR icon University of California, Los Angeles
  • 15. ROR icon Johns Hopkins University

Abstract

The identification of an emission line is unambiguous when multiple spectral features are clearly visible in the same spectrum. However, in many cases, only one line is detected, making it difficult to correctly determine the redshift. We developed a freely available unsupervised machine-learning algorithm based on unbiased topology (UMLAUT) that can be used in a very wide variety of contexts, including the identification of single emission lines. To this purpose, the algorithm combines different sources of information, such as the apparent magnitude, size and color of the emitting source, and the equivalent width and wavelength of the detected line. In each specific case, the algorithm automatically identifies the most relevant ones (i.e., those able to minimize the dispersion associated with the output parameter). The outputs can be easily integrated into different algorithms, allowing us to combine supervised and unsupervised techniques and increasing the overall accuracy. We tested our software on WISP (WFC3 IR Spectroscopic Parallel) survey data. WISP represents one of the closest existing analogs to the near-IR spectroscopic surveys that are going to be performed by the future Euclid and Roman missions. These missions will investigate the large-scale structure of the universe by surveying a large portion of the extragalactic sky in near-IR slitless spectroscopy, detecting a relevant fraction of single emission lines. In our tests, UMLAUT correctly identifies real lines in 83.2% of the cases. The accuracy is slightly higher (84.4%) when combining our unsupervised approach with a supervised approach we previously developed.

Additional Information

© 2021. The American Astronomical Society. Received 2020 December 30; revised 2021 August 3; accepted 2021 September 6; published 2021 December 10.

Attached Files

Published - Baronchelli_2021_ApJS_257_67.pdf

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Additional details

Identifiers

Eprint ID
112360
Resolver ID
CaltechAUTHORS:20211210-240656000

Related works

Describes
https://arxiv.org/abs/2111.01807 (URL)
https://github.com/Ibaronch/UMLAUT (URL)
10.3847/1538-4365/ac9963 (DOI)

Dates

Created
2021-12-10
Created from EPrint's datestamp field
Updated
2022-12-22
Created from EPrint's last_modified field

Caltech Custom Metadata

Caltech groups
Infrared Processing and Analysis Center (IPAC)