Erratum: Identification of single spectral lines in large spectroscopic surveys using UMLAUT: An unsupervised machine-learning algorithm based on unbiased topology (Astrophysical Journal, Supplement Series (2021) 257 (67) DOI: 10.3847/1538-4365/ac250c)

I. Baronchelli, C. M. Scarlata, L. Rodríguez-Muñoz, M. Bonato, L. Morselli, M. Vaccari, R. Carraro, L. Barrufet, A. Henry, V. Mehta, G. Rodighiero, A. Baruffolo, M. Bagley, A. Battisti, J. Colbert, Y. S. Dai, M. De Pascale, H. Dickinson, M. Malkan, C. ManciniM. Rafelski, H. I. Teplitz

Research output: Contribution to journalComment/debatepeer-review

Abstract

Due to an error at the publisher two symbol errors were introduced. 1. In Section 3.1, in list item 1 “Data samples that are similar in a sufficiently large amount of independent dimensions (N=) tend to be similar also when considering an additional independent dimension (N + 1).” the = symbol should be replaced by ?. 2. In Section 3.2.1, paragraph 3, in “Conversely, the magnitude of a source would not play any role when trying to identify the “most similar” data points in the N-dimensional space, as (Equation presented). IOP Publishing sincerely regrets these errors.

Original languageEnglish (US)
Article number19
JournalAstrophysical Journal, Supplement Series
Volume263
Issue number1
DOIs
StatePublished - Nov 1 2022

Bibliographical note

Publisher Copyright:
© 2022. The Author(s). Published by the American Astronomical Society.

Fingerprint

Dive into the research topics of 'Erratum: Identification of single spectral lines in large spectroscopic surveys using UMLAUT: An unsupervised machine-learning algorithm based on unbiased topology (Astrophysical Journal, Supplement Series (2021) 257 (67) DOI: 10.3847/1538-4365/ac250c)'. Together they form a unique fingerprint.

Cite this