First author: Liam Parker
We present a new method which leverages conditional Generative Adversarial Networks (cGAN) to reconstruct galaxy cluster convergence from lensed CMB temperature maps. Our model is constructed to emphasize structure and high-frequency correctness relative to the Residual U-Net approach presented by Caldeira, et. al. (2019). Ultimately, we demonstrate that while both models perform similarly in the no-noise regime (as well as after random off-centering of the cluster center), cGAN outperforms ResUNet when processing CMB maps noised with 5uK/arcmin white noise or astrophysical foregrounds (tSZ and kSZ); this out-performance is especially pronounced at high l, which is exactly the regime in which the ResUNet under-performs traditional methods.
First author: Fred Jennings
In galaxy clusters, the hot intracluster medium (ICM) can develop a striking multi-phase structure around the brightest cluster galaxy. Much work has been done on understanding the origin of this central nebula, but less work has studied its eventual fate after the originally filamentary structure is broken into individual cold clumps. In this paper we perform a suite of 30 (magneto-)hydrodynamical simulations of kpc-scale cold clouds with typical parameters as found by galaxy cluster simulations, to understand whether clouds are mixed back into the hot ICM or can persist.
First author: Sergio Martin-Alvarez
Enshrouded in several well-known controversies, dwarf galaxies have been extensively studied to learn about the underlying cosmology, notwithstanding that physical processes regulating their properties are poorly understood. To shed light on these processes, we introduce the Pandora suite of 17 high-resolution (3.5 parsec half-cell side) dwarf galaxy formation cosmological simulations. Commencing with thermo-turbulent star formation and mechanical supernova feedback, we gradually increase the complexity of physics incorporated leading to full-physics models combining magnetism, on-the-fly radiative transfer and the corresponding stellar photoheating, and SN-accelerated cosmic rays.
First author: Daizhong Liu
We present a high-resolution kinematic study of the massive main-sequence star-forming galaxy (SFG) SDSS J090122.37+181432.3 (J0901) at z=2.259, using 0.36 arcsec ALMA CO(3-2) and 0.1-0.5 arcsec SINFONI/VLT H-alpha observations. J0901 is a rare, strongly-lensed but otherwise normal massive (log(M_star/M_sun)~11) main sequence SFG, offering a unique opportunity to study a typical massive SFG under the microscope of lensing. Through forward dynamical modeling incorporating lensing deflection, we fit the CO and H-alpha kinematics in the image plane out to about one disk effective radius (R_e ~ 4 kpc) at a ~600pc delensed physical resolution along the kinematic major axis.
First author: Peter Melchior
We introduce the neural network architecture SPENDER as a core differentiable building block for analyzing, representing, and creating galaxy spectra. It combines a convolutional encoder, which pays attention to up to 256 spectral features and compresses them into a low-dimensional latent space, with a decoder that generates a restframe representation, whose spectral range and resolution exceeds that of the observing instrument. The decoder is followed by explicit redshift, resampling, and convolution transformations to match the observations.
First author: Daichi Kashino
We present a first sample of 117 [OIII]$\lambda\lambda$4960,5008-selected star-forming galaxies at $5.33 < z < 6.93$ detected in JWST/NIRCam 3.5$\mu$m slitless spectroscopy of a $6.5 \times 3.4$ arcmin$^2$ field centered on the hyperluminous quasar SDSS J0100+2802, obtained as part of the EIGER (Emission-line galaxies and Intergalactic Gas in the Epoch of Reionization) survey. Three prominent galaxy overdensities are observed, one of them at the redshift of the quasar.
First author: Jorryt Matthee
We present emission-line measurements and physical interpretations for a sample of 117 [OIII] emitting galaxies at $z=5.33-6.93$, using the first deep JWST/NIRCam wide field slitless spectroscopic observations. Our 9.7-hour integration is centered upon the $z=6.3$ quasar J0100+2802 – the first of six fields targeted by the EIGER survey – and covers $\lambda=3-4$ microns. We detect 133 [OIII] doublets, but merge pairs within $\approx$10 kpc and 600 km s$^{-1}$, motivated by their small scale clustering excess.
First author: Dominic Smith
Galaxies broadly fall into two categories: star-forming (blue) galaxies and quiescent (red) galaxies. In between, one finds the less populated ``green valley". Some of these galaxies are suspected to be in the process of ceasing their star-formation through a gradual exhaustion of gas supply or already dead and are experiencing a rejuvenation of star-formation through fuel injection. We use the Galaxy And Mass Assembly database and the Galaxy Zoo citizen science morphological estimates to compare the morphology of galaxies in the green valley against those in the red sequence and blue cloud.
First author: Siddharth Chaini
We present MargNet, a deep learning-based classifier for identifying stars, quasars and compact galaxies using photometric parameters and images from the Sloan Digital Sky Survey (SDSS) Data Release 16 (DR16) catalogue. MargNet consists of a combination of Convolutional Neural Network (CNN) and Artificial Neural Network (ANN) architectures. Using a carefully curated dataset consisting of 240,000 compact objects and an additional 150,000 faint objects, the machine learns classification directly from the data, minimising the need for human intervention.
First author: Josh Borrow
All modern galaxy formation models employ stochastic elements in their sub-grid prescriptions to discretise continuous equations across the time domain. In this paper, we investigate how the stochastic nature of these models, notably star formation, black hole accretion, and their associated feedback, that act on small ($<$ kpc) scales, can back-react on macroscopic galaxy properties (e.g. stellar mass and size) across long ($>$ Gyr) timescales. We find that the scatter in scaling relations predicted by the EAGLE model implemented in the SWIFT code can be significantly impacted by random variability between re-simulations of the same object, even when galaxies are resolved by tens of thousands of particles.
