arXiv subjects

Tobias Geron

Publications and source records attributed to Tobias Geron.

5 recordsLinked to original sources

arXiv2026

The effects of bar strength and kinematics on galaxy evolution II: The global and local impacts of slow-strong bars

There is now clear evidence, from a variety of studies, that galactic bars contribute to and/or accelerate processes which quench galaxies. However, bars have a variety of strengths and pattern speeds, and previous work has suggested that slow and strong bars impact their hosts the most. In this paper, we continue to investigate the impact of bar strength and bar speed on host galaxy evolution in a sample of barred galaxies identified via classifications from Galaxy Zoo. We perform a comprehensive assessment of star-formation tracers spanning a variety of timescales, based on spatially resolved spectroscopic information from the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey. Specifically, we examine the radial distributions of EW[Halpha], HdeltaA, Hbeta, and Dn4000; spectral data that trace star-formation on current, intermediate, and much longer timescales. We investigate how these star-formation tracers vary with respect to each other in diagnostic evolutionary planes for eight categories of barred galaxies (combinations of star forming or quenching; strong and weak; fast and slow). We continue to find that slow-strong bars drive the quenching of their hosts the most by triggering active star-formation throughout the barred region; however, we note some additional complexity: we observe that stronger bars boost star-formation at the bar centre while slower bars have increased star-formation along the bar. This work adds to the growing evidence that galactic bars have both global and local impacts on their host galaxies.

astro-ph.GA↗

arXiv2026

AT 2024ahzi: A Type IIP Supernova Discovered by the LSST Commissioning Camera

As part of its commissioning, the Vera C. Rubin Observatory observed several fields repeatedly for a month with ComCam, an instrument that uses the same hardware as the LSST camera but covers a smaller field of view. We photometrically classify AT 2024ahzi, a transient discovered by ComCam, as a Type IIP supernova (SN IIP) using both ComCam and DECam photometry. We find that the duration, luminosity, and color of AT 2024ahzi's photometric plateau are all consistent with those from a large sample of SNe II. By comparing its multi-band light curves to SN II models and analytic relations, we place constraints on the SN progenitor, explosion dynamics, and circumstellar environment. We argue that the progenitor has an extended density profile indistinguishable from a slowly accelerating CSM. We discuss how a similar workflow can identify and characterize future Rubin SNe II.

astro-ph.HE↗

arXiv2025

Strong Bars, Strong Inflow: The Effect of Bar Strength on Gas Inflow

Stellar bars are elongated structures in disk galaxies that can torque and funnel gas inward, influencing galaxy evolution. While strong bars are known to induce rapid inflow, the impact of weaker bars remains less certain. We collected spectroscopic data using the Isaac Newton Telescope to analyze 18 nearby galaxies (strongly barred, weakly barred, and unbarred) drawn from Galaxy Zoo DESI. We obtained spatial profiles of equivalent width (EW) and ionized gas velocity dispersion by fitting Gaussian profiles to the Hα emission line. Strongly barred galaxies exhibit a distinctive three-peaked EW[Hα] structure, consistent with inward funneling of gas. Weakly barred systems lack this pattern, which suggests limited inflow. Velocity dispersion distributions further distinguish the bar types, with strongly barred galaxies showing significantly higher values than weakly barred and unbarred systems. These results suggest that strong bars drive gas inflow, while weak bars exert a limited dynamical influence.

astro-ph.GA↗

arXiv2022

Galaxy And Mass Assembly: Galaxy Morphology in the Green Valley, Prominent rings and looser Spiral Arms

Galaxies broadly fall into two categories: star-forming (blue) galaxies and quiescent (red) galaxies. In between, one finds the less populated ``green valley". Some of these galaxies are suspected to be in the process of ceasing their star-formation through a gradual exhaustion of gas supply or already dead and are experiencing a rejuvenation of star-formation through fuel injection. We use the Galaxy And Mass Assembly database and the Galaxy Zoo citizen science morphological estimates to compare the morphology of galaxies in the green valley against those in the red sequence and blue cloud. Our goal is to examine the structural differences within galaxies that fall in the green valley, and what brings them there. Previous results found disc features such as rings and lenses are more prominently represented in the green valley population. We revisit this with a similar sized data set of galaxies with morphology labels provided by the Galaxy Zoo for the GAMA fields based on new KiDS images. Our aim is to compare qualitatively the results from expert classification to that of citizen science. We observe that ring structures are indeed found more commonly in green valley galaxies compared to their red and blue counterparts. We suggest that ring structures are a consequence of disc galaxies in the green valley actively exhibiting characteristics of fading discs and evolving disc morphology of galaxies. We note that the progression from blue to red correlates with loosening spiral arm structure.

astro-ph.GA↗

arXiv2022

Galaxy Zoo DECaLS: Detailed Visual Morphology Measurements from Volunteers and Deep Learning for 314,000 Galaxies

We present Galaxy Zoo DECaLS: detailed visual morphological classifications for Dark Energy Camera Legacy Survey images of galaxies within the SDSS DR8 footprint. Deeper DECaLS images (r=23.6 vs. r=22.2 from SDSS) reveal spiral arms, weak bars, and tidal features not previously visible in SDSS imaging. To best exploit the greater depth of DECaLS images, volunteers select from a new set of answers designed to improve our sensitivity to mergers and bars. Galaxy Zoo volunteers provide 7.5 million individual classifications over 314,000 galaxies. 140,000 galaxies receive at least 30 classifications, sufficient to accurately measure detailed morphology like bars, and the remainder receive approximately 5. All classifications are used to train an ensemble of Bayesian convolutional neural networks (a state-of-the-art deep learning method) to predict posteriors for the detailed morphology of all 314,000 galaxies. When measured against confident volunteer classifications, the networks are approximately 99% accurate on every question. Morphology is a fundamental feature of every galaxy; our human and machine classifications are an accurate and detailed resource for understanding how galaxies evolve.

astro-ph.GA↗