From seagull to hummingbird: New diagnostic methods for resolving galaxy activity

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985815%3A_____%2F25%3A00616749" target="_blank" >RIV/67985815:_____/25:00616749 - isvavai.cz</a>

Result on the web

<a href="https://hdl.handle.net/11104/0365442" target="_blank" >https://hdl.handle.net/11104/0365442</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1051/0004-6361/202451323" target="_blank" >10.1051/0004-6361/202451323</a>

Result language

angličtina

Original language name

From seagull to hummingbird: New diagnostic methods for resolving galaxy activity

Original language description

Context. One of the principal challenges in astrophysics involves the classification of galaxies based on their activity. Currently, the characterization of galactic activity usually requires multiple diagnostics to fully cover the diverse spectrum of galaxy activity types. Additionally, the presence of multiple sources of excitation with similar observational signatures hinders the exploration of the activity of a galaxy. Aims. In this study our objective is to develop an activity diagnostic tool that addresses the degeneracy inherent in the existing emission line diagnostics by identifying the underlying excitation mechanisms of the principal components of a mixed-activity galaxy (star formation, active nucleus, or old stellar populations) and identifying the dominant ones. Methods. We utilized the random forest machine-learning algorithm, trained on three primary activity classes: star-forming, active galactic nucleus (AGN), and passive, these classes represent the three key gas excitation mechanisms. This diagnostic relies on four discriminating features: the equivalent widths of three spectral lines, [O III] lambda 5007, [N II] lambda 6584, and H alpha, along with the D4000 continuum break index. Results. We find that this classifier achieves almost perfect performance scores in the principal activity classes. In particular, the achieved overall accuracy is similar to 99%, while the recall scores are similar to 100% for star-forming, similar to 98% for AGN, and similar to 99% for passive. The nearly perfect scores achieved enable the decomposition of mixed-activity classes into the three primary gas excitation mechanisms with high confidence, thereby resolving the degeneracy inherent in current activity classification methods. Furthermore, we find that our classifier scheme can be simplified to a two-dimensional diagnostic diagram of D4000 index versus the log10(EW([O III])2) line without significant loss of its diagnostic power. Conclusions. We introduce a diagnostic capable of classifying galaxies based on their primary gas excitation mechanisms. Simultaneously, it can deconstruct the activity of mixed-activity galaxies into these principal components. This diagnostic encompasses the entire range of galaxy activity. Additionally, the D4000 index serves as a valuable indicator for resolving the degeneracy among various activity components by estimating the age of the stellar populations within a galaxy.

Czech name

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Czech description

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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/EF18_053%2F0016972" target="_blank" >EF18_053/0016972: Support of the international collaboration in astronomy</a><br>

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2025

Confidentiality

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

Name of the periodical

Astronomy & Astrophysics

ISSN

0004-6361

e-ISSN

1432-0746

Volume of the periodical

693

Issue of the periodical within the volume

Jan.

Country of publishing house

FR - FRANCE

Number of pages

19

Pages from-to

A95

UT code for WoS article

001406577300008

EID of the result in the Scopus database

2-s2.0-85214679024