Consistency check of automatic pipeline measurements of quasar redshifts with Bayesian convolutional networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F23%3A00362346" target="_blank" >RIV/68407700:21240/23:00362346 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Consistency check of automatic pipeline measurements of quasar redshifts with Bayesian convolutional networks

Popis výsledku v původním jazyce

Spectroscopic redshifts of quasars are important inputs for constructing many cosmological models. Redshift measurement is generally considered to be a straightforward task performed by automatic pipelines based on template matching. Due to the millions of spectra delivered by surveys of SDSS or LAMOST telescopes, it is impossible to verify all redshift measurements of automatic pipelines by a human visual inspection. However, the pipeline results are still taken as the "ground truth" for further statistical inferences. Nevertheless, because of the similarity of patterns of quasar emission lines in different spectral ranges, an optimal match may be found for a completely different template position, causing severe errors in the measured redshift. For example, it may easily happen that a faint emission star with a noisy spectrum is identified as a high redshift quasar and vice versa. We show such examples discovered by the consistency check of redshift measurements of the SDSS pipeline and redshift predictions of a regression Bayesian convolutional network. The network is trained on a large amount of human-inspected redshifts and predicts redshifts together with their predictive uncertainties. Therefore, it can also identify cases where predictions are uncertain and thus require human visual inspection.

Název v anglickém jazyce

Consistency check of automatic pipeline measurements of quasar redshifts with Bayesian convolutional networks

Popis výsledku anglicky

Spectroscopic redshifts of quasars are important inputs for constructing many cosmological models. Redshift measurement is generally considered to be a straightforward task performed by automatic pipelines based on template matching. Due to the millions of spectra delivered by surveys of SDSS or LAMOST telescopes, it is impossible to verify all redshift measurements of automatic pipelines by a human visual inspection. However, the pipeline results are still taken as the "ground truth" for further statistical inferences. Nevertheless, because of the similarity of patterns of quasar emission lines in different spectral ranges, an optimal match may be found for a completely different template position, causing severe errors in the measured redshift. For example, it may easily happen that a faint emission star with a noisy spectrum is identified as a high redshift quasar and vice versa. We show such examples discovered by the consistency check of redshift measurements of the SDSS pipeline and redshift predictions of a regression Bayesian convolutional network. The network is trained on a large amount of human-inspected redshifts and predicts redshifts together with their predictive uncertainties. Therefore, it can also identify cases where predictions are uncertain and thus require human visual inspection.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/EF16_019%2F0000765" target="_blank" >EF16_019/0000765: Výzkumné centrum informatiky</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název statě ve sborníku

Astronomical Data Analysis Software and Systems XXXII

ISBN

978-1-58381-964-7

ISSN

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e-ISSN

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Počet stran výsledku

4

Strana od-do

134-137

Název nakladatele

Astronomical Society of the Pacific

Místo vydání

San Francisco

Místo konání akce

Victoria

Datum konání akce

31. 10. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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