Spectroscopic redshift determination with Bayesian convolutional networks

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985815%3A_____%2F22%3A00562014" target="_blank" >RIV/67985815:_____/22:00562014 - isvavai.cz</a>

Alternative codes found

RIV/68407700:21240/22:00358947

Result on the web

<a href="https://doi.org/10.1016/j.ascom.2022.100615" target="_blank" >https://doi.org/10.1016/j.ascom.2022.100615</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.ascom.2022.100615" target="_blank" >10.1016/j.ascom.2022.100615</a>

Result language

angličtina

Original language name

Spectroscopic redshift determination with Bayesian convolutional networks

Original language description

Astronomy is facing large amounts of data, so astronomers have to rely on automated methods to analyse them. However, automated methods might produce incorrect values. Therefore, we need to develop different automated methods and perform a consistency check to identify them. If there is a lot of labelled data, convolutional neural networks are a powerful method for any task. We illustrate the consistency check on spectroscopic redshift determination with a method based on a Bayesian convolutional neural network inspired by VGG networks. The method provides predictive uncertainties that enable us to (1.) determine unusual or problematic spectra for visual inspection (2.) do thresholding that allows us to balance between the error of redshift predictions and coverage. We used the 12th Sloan Digital Sky Survey quasar superset as the training set for the method. We evaluated its generalisation capability on about three-quarters of a million spectra from the 16th quasar superset of the same survey. On the 16th quasar superset, the method performs better in terms of the root-mean-squared error than the most used template fitting method. Using redshift predictions of the proposed method, we identified spectra with incorrectly determined redshifts that are unrecognised quasars or were misclassified as them.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10308 - Astronomy (including astrophysics,space science)

Project

<a href="/en/project/EF16_019%2F0000765" target="_blank" >EF16_019/0000765: Research Center for Informatics</a><br>

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Astronomy and Computing

ISSN

2213-1337

e-ISSN

2213-1345

Volume of the periodical

40

Issue of the periodical within the volume

July

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

14

Pages from-to

100615

UT code for WoS article

000876694100002

EID of the result in the Scopus database

2-s2.0-85134881627