Galaxy formation and evolution

Abstract:

Black holes (BHs) in microquasars can launch powerful relativistic jets that have the capacity to travel up to several parsecs from the compact object and interact with the interstellar medium. Recently, the detection of large-scale very-high-energy (VHE) gamma-ray emission around the black hole transient V4641 Sgr and other BH-jet systems suggested that jets from microquasars may play an important role in the production of galactic cosmic rays. V4641 Sgr is known for its superluminal radio jet discovered in 1999, but no radio counterpart of a large-scale jet has been observed. The goal of this work is to search for a radio counterpart of the extended VHE source. We observed V4641 Sgr with the MeerKAT radio telescope at the and bands and produced deep maps of the field using high dynamic range techniques. L UHF We report the discovery of a large-scale (∼ 35 ), bow-tie-shaped, diffuse, radio structure around V4641 Sgr, with similar angular size to the extended X-ray emission discovered by XRISM. However, it is not spatially coincident with the extended VHE emission. After discussing the association of the structure with V4641 Sgr, we investigate the nature of the emission mechanism. We suggest that the bow-tie structure arose from the long-term action of large-scale jets or disk winds from V4641 Sgr. If the emission mechanism is of synchrotron origin, the radio/X-ray extended structure implies acceleration of electrons up to more than 100 as far as tens of parsecs from the black hole. pc TeV

Abstract:

Dynamical tracers provide key measurements of galaxies' total masses, complementing other methods of determining stellar and gas masses and also providing some of the only possible constraints on their dark matter content. Until recently, the deep spectrally-resolved observations necessary for these measurements were only accessible out to relatively low redshifts. Thanks to new observatories and instruments, particularly near infrared multi-object spectrographs as well as sub-mm interferometers, galaxy dynamical masses can now be measured out to the peak epoch of cosmic star formation and even earlier epochs. Here I give an overview of dynamical mass constraints of galaxy-scale dark matter fractions from the present day out to z ∼ 3. I also discuss comparisons between measurements from different dynamical tracers, and the modeling challenges and degeneracies that complicate our interpretations of observations.

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.