2022(year)

GalaxyFlow: Upsampling Hydrodynamical Simulations for Realistic Gaia Mock Catalogs

First author: Sung Hak Lim Cosmological N-body simulations of galaxies operate at the level of “star particles” with a mass resolution on the scale of thousands of solar masses. Turning these simulations into stellar mock catalogs requires “upsampling” the star particles into individual stars following the same phase-space density. In this paper, we demonstrate that normalizing flows provide a viable upsampling method that greatly improves on conventionally-used kernel smoothing algorithms such as EnBiD.

MaNGIA: 10,000 mock galaxies for stellar population analysis

First author: Regina Sarmiento Modern astronomical observations give unprecedented access to the physical properties of nearby galaxies, including spatially resolved stellar populations. However, observations can only give a present-day view of the Universe, whereas cosmological simulations give access to the past record of the processes that galaxies have experienced in their evolution. To connect the events that happened in the past with galactic properties as seen today, simulations must be taken to a common ground before being compared to observations.

Modelling the accretion and feedback of supermassive black hole binaries in gas-rich galaxy mergers

First author: Shihong Liao We introduce a new model for the accretion and feedback of supermassive black hole (SMBH) binaries to the KETJU code, which enables us to resolve the evolution of SMBH binaries down to separations of tens of Schwarzschild radii in gas-rich galaxy mergers. Our subgrid binary accretion model extends the widely used Bondi–Hoyle–Lyttleton accretion into the binary phase and incorporates preferential mass accretion onto the secondary SMBH, which is motivated by results from small-scale hydrodynamical circumbinary disc simulations.

Peering into the Milky Way by FAST: IV. Identification of two new Galactic supernova remnants G203.1+6.6 and G206.7+5.9

First author: Xuyang Gao A $5^{\circ} \times 7^{\circ}$ sky area containing two large radio structures of G203.1+6.6 and G206.7+5.9 with a size of about 2.5$^{\circ}$ and 3.5$^{\circ}$ respectively is scanned by using the L-band 19-beam receiver of the Five-hundred-meter Aperture Spherical radio Telescope (FAST). The FAST L-band receiver covers a frequency range of 1.0 GHz $-$ 1.5 GHz. Commissioning of the receiving system, including the measurements of the half-power beam width, gain, and main-beam efficiency is made by observing the calibrators.

Predicting sub-millimeter flux densities from global galaxy properties

First author: R. K. Cochrane Recent years have seen growing interest in post-processing cosmological simulations with radiative transfer codes to predict observable fluxes for simulated galaxies. However, this can be slow, and requires a number of assumptions in cases where simulations do not resolve the ISM. Zoom-in simulations better resolve the detailed structure of the ISM and the geometry of stars and gas, however statistics are limited due to the computational cost of simulating even a single halo.

Reconstructing and Classifying SDSS DR16 Galaxy Spectra with Machine-Learning and Dimensionality Reduction Algorithms

First author: Felix Pat Optical spectra of galaxies and quasars from large cosmological surveys are used to measure redshifts and infer distances. They are also rich with information on the intrinsic properties of these astronomical objects. However, their physical interpretation can be challenging due to the substantial number of degrees of freedom, various sources of noise, and degeneracies between physical parameters that cause similar spectral characteristics. To gain deeper insights into these degeneracies, we apply two unsupervised machine learning frameworks to a sample from the Sloan Digital Sky Survey data release 16 (SDSS DR16).

The dark side of galaxy stellar populations II: The dependence of star formation histories on halo mass and on the scatter of the main sequence

First author: Laura Scholz-Diaz Nearby galaxies are the end result of their cosmological evolution, which is predicted to be influenced by the growth of their host dark matter halos. This co-evolution potentially leaves signatures in present-day observed galaxy properties, which might be essential to further understand how the growth and properties of galaxies are connected to those of their host halos. In this work, we study the evolutionary histories of nearby galaxies both in terms of their host halos and the scatter of the star-forming main sequence by investigating their time-resolved stellar populations using absorption optical spectra drawn from the Sloan Digital Sky Survey.

Magneto hydrodynamic simulations of the supernova remnant G1.9+0.3

First author: Shaobo Zhang The youngest Galactic supernova remnant G1.9+0.3 shows a discrete feature between its radio and X-ray morphologies. The observed radio morphology features a single maximum in the north, while the X-ray observation shows two opposite ’ears’ on the east and west sides. Using 3D magneto hydrodynamical simulations, we investigate the formation of the discrete feature of the remnant. We have tested different parameters for better simulation and reproduced similar discrete features under an environment with density gradient and an environment with clump, which provides a possible explanation of the observation.

A unified model for the co-evolution of galaxies and their circumgalactic medium: the relative roles of turbulence and atomic cooling physics

First author: Viraj Pandya The circumgalactic medium (CGM) plays a pivotal role in regulating gas flows around galaxies and thus shapes their evolution. However, the details of how galaxies and their CGM co-evolve remain poorly understood. We present a new time-dependent two-zone model that self-consistently tracks not just mass and metal flows between galaxies and their CGM but also the evolution of the global thermal and turbulent kinetic energy of the CGM.

Deep Learning-based galaxy image deconvolution

First author: Utsav Akhaury With the onset of large-scale astronomical surveys capturing millions of images, there is an increasing need to develop fast and accurate deconvolution algorithms that generalize well to different images. A powerful and accessible deconvolution method would allow for the reconstruction of a cleaner estimation of the sky. The deconvolved images would be helpful to perform photometric measurements to help make progress in the fields of galaxy formation and evolution.