Zobrazeno 1 - 10
of 14
pro vyhledávání: '"Sheridan B. Green"'
Publikováno v:
Monthly Notices of the Royal Astronomical Society. 509:2624-2636
The demographics of dark matter substructure depend sensitively on the nature of dark matter. Optimally leveraging this probe requires accurate theoretical predictions regarding the abundance of subhaloes. These predictions are hampered by artificial
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a964f57c07ef06b07996c418bd21d5bb
https://doi.org/10.1093/mnras/stab3130
https://doi.org/10.1093/mnras/stab3130
Publikováno v:
Monthly Notices of the Royal Astronomical Society. 503:4075-4091
Several recent studies have indicated that artificial subhalo disruption (the spontaneous, non-physical disintegration of a subhalo) remains prevalent in state-of-the-art dark matter-only cosmological simulations. In order to quantify the impact of d
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::87d7651015737515099f5a8ff6f3fd94
https://doi.org/10.1093/mnras/stab696
https://doi.org/10.1093/mnras/stab696
Publikováno v:
CHANCE. 32:6-13
Originally a speculative branch of natural philosophy, cosmology is an ancient field concerned with answering some of the big-picture questions about the origin and evolution of our universe. Our m...
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d6cfb41e9964b12fd9e718547d11640b
https://doi.org/10.1080/09332480.2019.1662695
https://doi.org/10.1080/09332480.2019.1662695
Autor:
Frank C. van den Bosch, Fangzhou Jiang, Philip F. Hopkins, Jonathan Freundlich, Avishai Dekel, Sheridan B. Green, Xiaolong Du, Andrew J. Benson
Publikováno v:
Monthly Notices of the Royal Astronomical Society
Monthly Notices of the Royal Astronomical Society, Oxford University Press (OUP): Policy P-Oxford Open Option A, 2021, 502 (1), pp.621-641. ⟨10.1093/mnras/staa4034⟩
Monthly Notices of the Royal Astronomical Society, Oxford University Press (OUP): Policy P-Oxford Open Option A, 2021, 502 (1), pp.621-641. ⟨10.1093/mnras/staa4034⟩
We present a semi-analytic model of satellite galaxies, SatGen, which can generate large samples of satellite populations for a host halo of desired mass, redshift, and assembly history. The model combines dark-matter halo merger trees, empirical rel
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2af675166659c7c71c7ac3815d77132b
https://hal.archives-ouvertes.fr/hal-03148336
https://hal.archives-ouvertes.fr/hal-03148336
Publikováno v:
Monthly Notices of the Royal Astronomical Society. 508:2944-2945
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::86d265c2e2b68949722834d35f475567
https://doi.org/10.1093/mnras/stab2786
https://doi.org/10.1093/mnras/stab2786
We develop a machine learning algorithm that generates high-resolution thermal Sunyaev-Zeldovich (SZ) maps of novel galaxy clusters given only halo mass and mass accretion rate. The algorithm uses a conditional variational autoencoder (CVAE) in the f
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3799cef3501c547ba822fb265bad5950
X-ray and microwave cluster scaling relations are immensely valuable for cosmological analysis. However, their power is limited by astrophysical systematics that bias mass estimates and introduce additional scatter. Turbulence injected into the intra
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ba8ffee32fe1c53ad3be9896502cabd0
Accurately predicting the abundance and structural evolution of dark matter subhaloes is crucial for understanding galaxy formation, modeling galaxy clustering, and constraining the nature of dark matter. Due to the nonlinear nature of subhalo evolut
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a10509a7441099efae27e576ba3f6df9
http://arxiv.org/abs/1908.08537
http://arxiv.org/abs/1908.08537
Autor:
Daisuke Nagai, Lorenzo Lovisari, Michelle Ntampaka, John ZuHone, Klaus Dolag, Dominique Eckert, Sheridan B. Green
We present a machine learning approach for estimating galaxy cluster masses, trained using both Chandra and eROSITA mock X-ray observations of 2,041 clusters from the Magneticum simulations. We train a random forest regressor, an ensemble learning me
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d9fbaa0797de21b0b2734cd81353e3f0
Publikováno v:
Biophysical Journal. 110(3)
Molecular packing is studied over a dataset of crystal structures representing 500 diverse globular proteins in their native state ranging in size from 34 to 839 residues. Different size cavities are ubiquitously present due to the presence of void b
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5f4a34015e6f40894db0e9b28d8e891c