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We study how type Ia supernovae (SNe Ia) are spatially distributed within their host galaxies, using data taken from the Sloan Digital Sky Survey (SDSS). This paper specifically tests the hypothesis that the SNe Ia rate traces the r-band light of the morphological component to which supernovae belong. A sample of supernovae is taken from the SDSS SN Survey, and host galaxies are identified. Each host galaxy is decomposed into a bulge and disk, and the distribution of supernovae is compared to the distribution of disk and bulge light. Our methodology is relatively unaffected by seeing. We find that, in galaxies dominated by disk light, SNe Ia trace light closely. The situation is less clear for bulges and ellipticals, because of resolution effects, but the available evidence is also consistent with the hypothesis that bulge/elliptical SNe Ia follow light.

Examining the detailed structure of galaxy populations provides valuable insights into their formation and evolution mechanisms. Significant barriers to such analysis are the nontrivial noise properties of real astronomical images and the point-spread function, which blurs structure. Here we present a framework which combines recent advances in score-based likelihood characterization and diffusion model priors to perform a Bayesian analysis of image deconvolution. The method, when applied to minimally processed Hubble Space Telescope data, recovers structures which have otherwise only become visible in next-generation James Webb Space Telescope imaging.

We present HaloFlow, a new machine-learning approach for inferring the mass of host dark matter halos, M h , from the photometry and morphology of galaxies (https://github.com/changhoonhahn/haloflow/). HaloFlow uses simulation-based inference with normalizing flows to conduct rigorous Bayesian inference. It is trained on state-of-the-art synthetic galaxy images from Bottrell et al. that are constructed from the IllustrisTNG hydrodynamic simulation and include realistic effects of the Hyper Suprime-Cam Subaru Strategy Program observations. We design HaloFlow to infer M h and stellar mass, M *, using grizy band magnitudes, morphological properties quantifying characteristic size, concentration and asymmetry, total measured satellite luminosity, and number of satellites. We demonstrate that HaloFlow infers accurate and unbiased posteriors of M h . Furthermore, we quantify the full information content in the photometric observations of galaxies in constraining M h . With magnitudes alone, we infer M h with σlogMh∼0.115 and 0.182 dex for field and group galaxies. Including morphological properties significantly improves the precision of M h constraints, as does total satellite luminosity: σlogMh∼0.095 and 0.132 dex. Compared to the standard approach using the stellar-to-halo mass relation, we improve M h constraints by ∼40%. In subsequent papers, we will validate and calibrate HaloFlow with galaxy–galaxy lensing measurements on real observational data.

We employ the Feedback In Realistic Environments (FIRE-2) physics model to study how the properties of giant molecular clouds (GMCs) evolve during galaxy mergers. We conduct a pixel-by-pixel analysis of molecular gas properties in both the simulated control galaxies and galaxy major mergers. The simulated GMC pixels in the control galaxies follow a similar trend in a diagram of velocity dispersion (σ v ) versus gas surface density (Σmol) to the one observed in local spiral galaxies in the Physics at High Angular resolution in Nearby GalaxieS (PHANGS) survey. For GMC pixels in simulated mergers, we see a significant increase of a factor of 5–10 in both Σmol and σ v , which puts these pixels above the trend of PHANGS galaxies in the σ v versus Σmol diagram. This deviation may indicate that GMCs in the simulated mergers are much less gravitationally bound compared with simulated control galaxies with virial parameters (α vir) reaching 10–100. Furthermore, we find that the increase in α vir happens at the same time as the increase in global star formation rate, which suggests that stellar feedback is responsible for dispersing the gas. We also find that the gas depletion time is significantly lower for high-α vir GMCs during a starburst event. This is in contrast to the simple physical picture that low-α vir GMCs are easier to collapse and form stars on shorter depletion times. This might suggest that some other physical mechanisms besides self-gravity are helping the GMCs in starbursting mergers collapse and form stars.

A key question in galaxy evolution has been the importance of the apparent “clumpiness” of high-redshift galaxies. Until now, this property has been primarily investigated in rest-frame UV, limiting our understanding of their relevance. Are they short-lived or are they associated with more long-lived massive structures that are part of the underlying stellar disks? We use JWST/NIRCam imaging from the Cosmic Evolution and Epoch of Reionization Survey to explore the connection between the presence of these “clumps” in a galaxy and its overall stellar morphology, in a mass-complete ( logM*/M⊙>10.0 ) sample of galaxies at 1.0 < z < 2.0. Exploiting the uninterrupted access to rest-frame optical and near-IR light, we simultaneously map the clumps in galactic disks across our wavelength coverage, along with measuring the distribution of stars among their bulges and disks. First, we find that the clumps are not limited to the rest-frame UV and optical, but are also apparent in near-IR with ∼60% spatial overlap. This rest-frame near-IR detection indicates that clumps would also feature in the stellar-mass distribution of the galaxy. A secondary consequence is that these will hence be expected to increase the dynamical friction within galactic disks leading to gas inflow. We find a strong negative correlation between how clumpy a galaxy is and strength of the bulge. This firmly suggests an evolutionary connection, either through clumps driving bulge growth or the bulge stabilizing the galaxy against clump formation, or a combination of the two. Finally, we find evidence of this correlation differing from rest-frame optical to near-IR, which could suggest a combination of varying formation modes for the clumps.

Typically, binary classification lens-finding schemes are used to discriminate between lens candidates and non-lenses. However, these models often suffer from substantial false-positive classifications. Such false positives frequently occur due to images containing objects such as crowded sources, galaxies with arms, and also images with a central source and smaller surrounding sources. Therefore, a model might confuse the stated circumstances with an Einstein ring. It has been proposed that by allowing such commonly misclassified image types to constitute their own classes, machine learning models will more easily be able to learn the difference between images that contain real lenses, and images that contain lens imposters. Using Hubble Space Telescope images, in the F814W filter, we compare the usage of binary and multi-class classification models applied to the lens finding task. From our findings, we conclude there is not a significant benefit to using the multi-class model over a binary model. We will also present the results of a simple lens search using a multi-class machine learning model, and potential new lens candidates.

We perform a thorough analysis of the projected shapes of nearby galaxies in both observations and cosmological simulations. We implement a forward-modeling approach to overcome the limitations in previous studies, which hinder accurate comparisons between observations and simulations. We measure axis ratios of z = 0 (snapshot 99) TNG50 galaxies from their synthetic Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) images and compare them with those obtained from real HSC-SSP images of a matched galaxy sample. Remarkably, the comparison shows excellent agreement between the observations and the TNG50 simulation, challenging previous claims that ΛCDM models underproduced the abundance of thin galaxies. Specifically, for galaxies with stellar masses 10≤log(M⋆/M☉)≤11.5 , we find ≲0.1σ tensions between the observations and the simulation, a stark contrast to the previously reported ≳10σ tensions. We reveal that low-mass galaxies (M⋆≲109.5M☉) in TNG50 are thicker than their observed counterparts in HSC-SSP and attribute this to the spurious dynamical heating effects that artificially puff up galaxies. We also find that, despite the overall broad agreement, TNG50 galaxies are more concentrated than the HSC-SSP ones at the low- and high-mass end of the stellar mass range of 9.0≤log(M⋆/M☉)≤11.2 and are less concentrated at intermediate stellar masses. But we argue that the higher concentrations of the low-mass TNG50 galaxies are not likely the cause of their thicker/rounder appearances. Our study underscores the critical importance of conducting mock observations of simulations and applying consistent measurement methodologies to facilitate proper comparison with observations.

Internal gas inflows driven by galaxy mergers are considered to enhance star formation rates (SFRs), fuel supermassive black hole growth, and stimulate active galactic nuclei (AGNs). However, quantifying these phenomena remains a challenge, due to difficulties both in classifying mergers and in quantifying galaxy and AGN properties. We quantitatively examine the merger–SFR–AGN connection using Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) galaxies using novel methods for both galaxy classification and property measurements. Mergers in HSC-SSP observational images are identified through fine-tuning Zoobot, a pretrained deep representation learning model, using images and labels based on the Galaxy Cruise project. We use galaxy and AGN properties that were produced by fitting Galaxy and Mass Assembly spectra using the spectral energy distribution fitting code ProSpect, which fits panchromatically across the far-ultraviolet through far-infrared wavelengths and obtains galaxy and AGN properties simultaneously. Small differences are seen in SFR and AGN activity between mergers and controls, with ΔSFR = −0.009 ± 0.003 dex, ΔfAGN = −0.010 ± 0.033 dex, and ΔLAGN = 0.002 ± 0.025 dex. After further visual purification of the merger sample, we find ΔSFR = −0.033 ± 0.014 dex, ΔfAGN = −0.024 ± 0.170 dex, and ΔLAGN = 0.019 ± 0.129 dex for pairs, and ΔSFR = −0.057 ± 0.024 dex, ΔfAGN = 0.286 ± 0.270 dex, and ΔLAGN = 0.329 ± 0.195 dex for postmergers. These numbers suggest secular processes being an important driver for star formation and AGN activity, and present a cautionary tale when using longer-timescale tracers.

This study probes the inside-out and outside-in quenching status of galaxies to understand the internal and external quenching sources responsible and their roles in galaxy evolution. We utilize data from the MaNGA survey and MaNGIA, a mock MaNGA sample derived from the high-resolution TNG50 simulation, comparing their spatially resolved galaxy properties to address this knowledge gap. Our analysis begins with an assessment of the integrated and spatially resolved star-forming main sequence, finding good agreement between the two datasets. We also observe excellent consistency in radial profiles of stellar mass surface density. Using the surface density of the specific star formation rate (ΣsSFR) to identify quenched regions, we investigate inside-out and outside-in quenching modes across different stellar masses and environments via three classification methods. We find broad consistency between MaNGA and MaNGIA for high-mass galaxies, where inside-out quenching dominates regardless of environment. However, for lower-mass galaxies, we find discrepancies in the dominant quenching mode in middle halo mass environments. The environmental dependence of inside-out quenching in both MaNGA and MaNGIA aligns with an internal quenching scenario, such as feedback from active galactic nuclei or morphology quenching. In contrast, MaNGA reveals a weaker environmental dependence for outside-in quenching, suggesting a roughly even contribution of multiple physical processes, whereas MaNGIA indicates a stronger environmental role, with group environments likely playing a significant part.

The conditions under which galactic nuclear regions become active are largely unknown, although it has been hypothesized that secular processes related to galaxy morphology could play a significant role. We investigate this question using optical i-band images of 3096 SDSS quasars and galaxies at 0.3 < z < 0.6 from the Hyper Suprime-Cam Subaru Strategic Program, which possesses a unique combination of area, depth, and resolution, allowing the use of residual images, after removal of the quasar and smooth galaxy model, to investigate internal structural features. We employ a variational auto-encoder, which is a generative model that acts as a form of dimensionality reduction. We analyze the lower-dimensional latent space in search of features that correlate with nuclear activity. We find that the latent space does separate images based on the presence of nuclear activity, which appears to be associated with more pronounced components (i.e., arcs, rings, and bars) as compared to a matched control sample of inactive galaxies. These results suggest the importance of secular processes and possibly mergers (by their remnant features) in activating or sustaining black hole growth. Our study highlights the breadth of information available in ground-based imaging taken under optimal seeing conditions and having an accurate characterization of the point-spread function (PSF), thus demonstrating future science to come from the Rubin Observatory.