arXiv subjects

C. Daoutis

Publications and source records attributed to C. Daoutis.

5 recordsLinked to original sources

arXiv2026

HECATEv2: An all-sky galaxy catalogue for multimessenger astrophysics

We present HECATEv2, the second release of the Heraklion Extragalactic Catalogue (HECATE), an all-sky, value-added galaxy catalogue comprising 204733 galaxies from the HyperLEDA database with recession velocity <14000 km/s (D~200 Mpc). This release focuses on qualitative upgrades of the provided information while maintaining the same parent galaxy sample as HECATEv1. Improvements include a new cosmology-based distance framework, expanded and homogenised optical and mid-infrared photometry from SDSS-DR17/NSA, PS1-DR2, and AllWISE, and new quality-control flags for stellar contamination, incorrect photometry, and coordinate inconsistencies. We also extend the galaxy-size coverage and derive stellar population parameters for a substantially larger fraction of the sample. Star-formation rates (SFR) and stellar masses (Mstar) are now available for >70% of galaxies using updated mid-IR/optical calibrations that account for stellar population age and dust attenuation, while gas-phase metallicities are derived for ~90%. Activity classifications are provided for >50% of galaxies based on spectroscopic and/or photometric diagnostics, and supermassive black hole masses for ~86%. In terms of L$_{B}$,L$_{Ks}$,SFR, and Mstar, HECATEv2 is among the most complete local-Universe catalogues with spectroscopic redshifts. We also provide spatial completeness maps as a function of distance and luminosity, highlighting variations across the sky. Compared to other catalogues (e.g. GLADE+, NED-LVS), HECATEv2 offers broader (optical, near- and far-IR photometry, metallicity, activity classifications) or comparable (mid-IR photometry, SFR, Mstar) coverage, making it a robust reference for studies of SMBH-host galaxy connections, gravitational-wave and high-energy transient hosts, population analyses, and rare galaxy subpopulations.

astro-ph.GA↗

arXiv2026

An automated activity classification tool for optical galaxy spectra

Reliable, versatile galaxy activity diagnostics are essential for understanding galaxy evolution. Traditional methods frequently necessitate extensive preprocessing, such as starlight subtraction and emission line deblending (e.g., H{\alpha} and [N II]), which can introduce substantial biases and uncertainties due to their model-dependent nature. In this work we developed an automated, diagnostic tool capable of distinguishing between star-forming (SF), active galactic nuclei (AGN), low-ionization nuclear emission-line regions (LINERs), composite, and passive galaxies. We developed a diagnostic tool based on a support vector machine trained on data from optical emission-line ratios and color selection criteria. From literature studies and exploring combinations of discriminatory feature schemes, we found that the equivalent widths of H{\beta}, [O III]{\lambda}5007, and H{\alpha}+[N II]{\lambda}6548,84 as key diagnostic features. Additionally, galaxies classified as AGN can be distinguished into broad- and narrow-line AGN by measuring the full quarter at the half-maximum of H{\alpha} and [N II] complex. We have developed a diagnostic tool that encompasses all activities of galaxies while achieving high performance scores across all of them. Our diagnostic achieves overall accuracy of 83% and recall of 79% for SF, 94% for AGN, 85% for LINER, 77% for composite, and 96% for passive galaxies. Our diagnostic tool significantly improves upon existing diagnostics as it eliminates the need for preprocessing (i.e., starlight subtraction or flux calibration) and spectral line fitting, includes all activity classes under one scheme, and distinguishes the two main AGN types. In addition, omitting starlight subtraction does not significantly reduce performance. Furthermore, Its narrow wavelength requirement enables use across a wide redshift range, making it ideal for high-z studies.

astro-ph.GA↗

arXiv2025

Mid-Infrared diagnostics for identifying main sequence galaxies in the local Universe

A galaxy's mid-IR spectrum encodes key information on its radiation field, star formation, and dust properties. Characterizing this spectrum therefore offers strong constraints on a galaxy's activity. This project describes a diagnostic tool for identifying main-sequence (MS) star-forming galaxies (SFGs) in the local Universe using IR dust emission features that are characteristic of galaxy activity. A physically-motivated sample of mock galaxy spectra has been generated to simulate the IR emission of SFGs. Using this sample, we developed a diagnostic tool for identifying MS SFGs based on machine learning methods. Custom photometric bands were defined to target dust emission features, including polycyclic aromatic hydrocarbons (PAHs) and the dust continuum. Three bands were chosen to trace PAH features at 6.2 {\mu}m, 7.7 {\mu}m, 8.6 {\mu}m, and 11.3 {\mu}m, along with an additional band to probe the radiation field strength responsible for heating the dust. This diagnostic was subsequently applied to observed galaxies to evaluate its effectiveness in real-world applications. Our diagnostic achieves high performance, with an accuracy of 90.9% on MS SFGs (observed sample of SFGs). Additionally, it shows low contamination, with only 16.2% of AGN galaxies being misidentified as SF. Combining observational data with stellar population synthesis models enables the creation of physically-motivated samples of SFGs that match the spectral properties of real galaxies. By positioning custom photometric bands targeting key dust features, our diagnostic can extract valuable information without the need to measure emission lines. Although PAHs are sensitive indicators of star formation and interstellar medium radiation hardness, PAH emission alone is insufficient for identifying MS SFGs. Finally, we developed a physically-motivated spectral library of MS SFGs spanning from UV to FIR wavelengths.

astro-ph.GA↗

arXiv2024

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

Context. A major challenge in astrophysics is classifying galaxies by their activity. Current methods often require multiple diagnostics to capture the full range of galactic activity. Furthermore, overlapping excitation sources with similar observational signatures complicate the analysis of a galaxy's activity. Aims. This study aims to create an activity diagnostic tool that overcomes the limitations of current emission line diagnostics by identifying the underlying excitation mechanisms in mixed-activity galaxies (e.g., star formation, active nucleus, or old stellar populations) and determining the dominant ones. Methods. We use the random forest machine-learning algorithm, trained on three main activity classes -- star-forming, AGN, and passive -- that represent key gas excitation mechanisms. This diagnostic employs four distinguishing features: the equivalent widths of [O iii] ${\lambda}$5007, [N ii] ${\lambda}$6584, H${\alpha}$, and the D4000 continuum break index. Results. The classifier achieves near-perfect performance, with an overall accuracy of ~ 99% and recall scores of ~ 100% for star-forming, ~ 98% for AGN, and ~ 99% for passive galaxies. These exceptional scores allow for confident decomposition of mixed activity classes into the primary gas excitation mechanisms, overcoming the limitations of current classification methods. Additionally, the classifier can be simplified to a two-dimensional diagnostic using the D4000 index and log$_{10}$(EW([O iii])$^{2}$) without significant loss of diagnostic power. Conclusions. We present a diagnostic for classifying galaxies by their primary gas excitation mechanisms and deconstructing the activity of mixed-activity galaxies into these components. This method covers the full range of galaxy activity. Aditionally, D4000 index serves as an indicator for resolving the degeneracy among various activity components.

astro-ph.GA↗

arXiv2024

Blueberry galaxies up to 200 Mpc and their optical and infrared properties

Dwarf highly star-forming galaxies (SFGs) dominated the early Universe and are considered the main driver of its reionization. However, direct observations of these distant galaxies are mainly confined to rest-frame ultraviolet and visible light, limiting our understanding of their complete properties. Therefore, it is still paramount to study their local analogs, the green pea (GP) and blueberry (BB) galaxies. This work aims to expand our knowledge of BBs by identifying a new sample that is closer and in the southern sky. In addition to the already known BBs, this new sample will allow for a statistically significant study of their properties probed by visible and infrared (IR) light. By utilizing the HECATE catalog, which provides photometry and characterization of galaxies, along with data from Pan-STARSS and SDSS, this study selects and analyzes a new sample of BBs. We employed spectral energy distribution fitting to derive homogeneous measurements of star-formation rates and stellar masses. Additionally, we measured emission-line fluxes, including $\rm HeII~\lambda 4686$, through spectral fitting. Through this work, we identified 48 BBs, of which 40 were first recognized as such, with the nearest at 19~Mpc. 14 of the BBs are in the south sky. The BBs tend to be extremely IR red in both WISE $W1-W2$ and $W2-W3$ colors, distinguishing them from typical SFGs. Dwarf SFGs with higher specific star-formation rates tend to have redder IR colors. Blueberry galaxies stand out as the most intensely star-forming sources in the local Universe among dwarf galaxies. They exhibit unique characteristics, such as being intrinsically bluer in visible light, redder in the infrared, and less massive. They also have higher specific star-formation rates, equivalent widths, lower metallicities, and the most strongly ionized interstellar medium compared to typical SFGs and GPs.

astro-ph.GA↗