Galaxy velocity bias in cosmological simulations: towards per cent-level calibration
Galaxy cluster masses, rich with cosmological information, can be estimated from internal
dark matter (DM) velocity dispersions, which in turn can be observationally inferred from …
dark matter (DM) velocity dispersions, which in turn can be observationally inferred from …
the three hundred project: a machine learning method to infer clusters of galaxy mass radial profiles from mock Sunyaev–Zel'dovich maps
A Ferragamo, D de Andres, A Sbriglio… - Monthly Notices of …, 2023 - academic.oup.com
We develop a machine learning algorithm to infer the three-dimensional cumulative radial
profiles of total and gas masses in galaxy clusters from thermal Sunyaev–Zel'dovich effect …
profiles of total and gas masses in galaxy clusters from thermal Sunyaev–Zel'dovich effect …
Stellar velocity dispersion: Linking quiescent galaxies to their dark matter halos
HJ Zahid, J Sohn, MJ Geller - The Astrophysical Journal, 2018 - iopscience.iop.org
We analyze the Illustris-1 hydrodynamical cosmological simulation to explore the stellar
velocity dispersion of quiescent galaxies as an observational probe of dark matter halo …
velocity dispersion of quiescent galaxies as an observational probe of dark matter halo …
Using convolutional neural networks to predict galaxy metallicity from three-colour images
JF Wu, S Boada - Monthly Notices of the Royal Astronomical …, 2019 - academic.oup.com
We train a deep residual convolutional neural network (CNN) to predict the gas-phase
metallicity (Z) of galaxies derived from spectroscopic information () using only three-band gri …
metallicity (Z) of galaxies derived from spectroscopic information () using only three-band gri …
The importance of being interpretable: Toward an understandable machine learning encoder for galaxy cluster cosmology
M Ntampaka, A Vikhlinin - The Astrophysical Journal, 2022 - iopscience.iop.org
We present a deep machine-learning (ML) approach to constraining cosmological
parameters with multiwavelength observations of galaxy clusters. The ML approach has two …
parameters with multiwavelength observations of galaxy clusters. The ML approach has two …
Multiwavelength cluster mass estimates and machine learning
JD Cohn, N Battaglia - Monthly Notices of the Royal …, 2020 - academic.oup.com
One emerging application of machine learning methods is the inference of galaxy cluster
masses. In this note, machine learning is used to directly combine five simulated …
masses. In this note, machine learning is used to directly combine five simulated …
Cluster Cosmology with the Velocity Distribution Function of the HeCS-SZ Sample
M Ntampaka, K Rines, H Trac - The Astrophysical Journal, 2019 - iopscience.iop.org
Cluster Cosmology with the Velocity Distribution Function of the HeCS-SZ Sample - IOPscience
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Dark Matter
C Balazs - Multimessenger Astronomy in Practice, 2021 - iopscience.iop.org
The existence of dark matter has been firmly established at astrophysical and cosmological
scales via its gravitational effects. In contrast, we do not know what dark matter is made of …
scales via its gravitational effects. In contrast, we do not know what dark matter is made of …
Multimessenger Astronomy in Practice
C Balazs - jpconf.iop.org
The existence of dark matter has been firmly established at astrophysical and cosmological
scales via its gravitational effects. In contrast, we do not know what dark matter is made of …
scales via its gravitational effects. In contrast, we do not know what dark matter is made of …