Galaxy formation and evolution

Abstract:

The advent of integral field data has revolutionised the study of galaxy evolution. A key component of this is dynamical modelling methods which have allowed for crucial insights to be made from kinematic data. Despite this importance, most dynamical models make a number of key assumptions which do not hold for real galaxies. These include assumptions about the geometry (axisymmetry or triaxiality), the shape of the velocity ellipsoid, and the shape of the underlying stellar distribution. At the same time, machine learning methods are becoming increasingly powerful, with many applications appearing in astronomy. As a first step towards building new dynamical modelling methods with machine learning, it is important to understand the types of machine learning architectures that are best fit for dynamical modelling. To investigate this, we construct a training set of dynamical models of early-type galaxies using Jeans Anisotropic Modelling (JAM). We then train a neural network on this data using the parameters of JAM and mock photometry as the input. We are able to accurately model JAM galaxies with relatively simple machine learning architectures, leading to a significant speed increase over traditional JAM modelling.

Abstract:

Abstract We present morphological classifications of over 41 000 galaxies out to zphot ∼ 2.5 across six square degrees of the Euclid Deep Field North (EDFN) from the Hawaii Twenty Square Degree (H20) survey, a part of the wider Cosmic Dawn survey. Galaxy Zoo citizen scientists play a crucial role in the examination of large astronomical data sets through crowdsourced data mining of extragalactic imaging. This iteration, Galaxy Zoo: Cosmic Dawn (GZCD), saw tens of thousands of volunteers and the deep learning foundation model Zoobot collectively classify objects in ultra-deep multiband Hyper Suprime-Cam (HSC) imaging down to a depth of mHSC − i = 21.5. Here, we present the details and general analysis of this iteration, including the use of Zoobot in an active learning cycle to improve both model performance and volunteer experience, as well as the discovery of 51 new gravitational lenses in the EDFN. We also announce the public data release of the classifications for over 45 000 subjects, including more than 41 000 galaxies (median zphot of 0.42 ± 0.23), along with their associated image cutouts. This data set provides a valuable opportunity for follow-up imaging of objects in the EDFN as well as acting as a truth set for training deep learning models for application to ground-based surveys like that of the Ultraviolet Near-Infrared Optical Northern Survey (UNIONS) collaboration and the newly operational Vera C. Rubin Observatory.

Abstract:

Type-2 quasars (QSO2s) are active galactic nuclei (AGN) seen through a significant amount of dust and gas that obscures the central supermassive black hole and the broad line region. Despite this, recent mid-infrared spectra of the central 0.5-1.1 kpc of five QSO2s at z∼0.1, obtained with the MRS module of JWST/MIRI, revealed 9.7, 18, and 23 . These are the CLUMPY and the CAT3D-WIND models. The CAT3D-WIND model is preferred by the observations based on the marginal likelihood and fit residuals, although the two torus models successfully reproduce the spectrum by means of intermediate covering factors (̊m C_T=0.45±^ silicate features in emission in two of them. This indicates that the high angular resolution of JWST/MIRI now allows us to peer into their nuclear region, exposing some of the directly illuminated dusty clouds that produce silicate emission. To test this, we fit the nuclear mid-infrared spectrum of the QSO2 with the strongest silicate features, J1010, with two different sets of torus models implemented in an updated version of the Bayesian tool BayesClumpy 0.26 _ 0.18 and ̊m C_T=0.66±^ 0.16 _ 0.17 for the CLUMPY and CAT3D-WIND models) and low inclinations (̊m i=50^̧irc±^ 8^̧irc _ 9^̧irc and ̊m i=13^̧irc±^ 7^̧irc _ 6^̧irc ). Indeed, four of the five QSO2s with JWST/MIRI observations, including J1010, are in the blowout or ``forbidden'' region of the Eddington ratio-column density diagram, indicating that they are actively clearing gas and dust from their nuclear regions, leading to reduced covering factors. This is in contrast with Seyfert 2 galaxies observed with JWST, which are in the ``permitted'' regions of the diagram and show 9.7 a scenario where the more luminous the AGN and the higher their Eddington ratio, the lower the torus covering factor, driven by radiation pressure on dusty gas. silicate features in absorption. This ̧olor black supports

Abstract:

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.