Zobrazeno 1 - 10
of 3 616
pro vyhledávání: '"McGibbon, A."'
Autor:
Clark, Spencer K., Watt-Meyer, Oliver, Kwa, Anna, McGibbon, Jeremy, Henn, Brian, Perkins, W. Andre, Wu, Elynn, Bretherton, Christopher S., Harris, Lucas M.
While autoregressive machine-learning-based emulators have been trained to produce stable and accurate rollouts in the climate of the present-day and recent past, none so far have been trained to emulate the sensitivity of climate to substantial chan
Externí odkaz:
http://arxiv.org/abs/2412.04418
Autor:
Broxterman, Jeger C., Schaller, Matthieu, Hoekstra, Henk, Schaye, Joop, McGibbon, Robert J., Moreno, Victor J. Forouhar, Kugel, Roi, Elbers, Willem
Weak gravitational lensing (WL) convergence peaks contain valuable cosmological information in the regime of non-linear collapse. Using the FLAMINGO suite of cosmological hydrodynamical simulations, we study the physical origin and redshift distribut
Externí odkaz:
http://arxiv.org/abs/2412.02736
Autor:
Watt-Meyer, Oliver, Henn, Brian, McGibbon, Jeremy, Clark, Spencer K., Kwa, Anna, Perkins, W. Andre, Wu, Elynn, Harris, Lucas, Bretherton, Christopher S.
Existing machine learning models of weather variability are not formulated to enable assessment of their response to varying external boundary conditions such as sea surface temperature and greenhouse gases. Here we present ACE2 (Ai2 Climate Emulator
Externí odkaz:
http://arxiv.org/abs/2411.11268
Autor:
McCarthy, Ian G., Amon, Alexandra, Schaye, Joop, Schaan, Emmanuel, Angulo, Raul E., Salcido, Jaime, Schaller, Matthieu, Bigwood, Leah, Elbers, Willem, Kugel, Roi, Helly, John C., Moreno, Victor J. Forouhar, Frenk, Carlos S., McGibbon, Robert J., Ondaro-Mallea, Lurdes, van Daalen, Marcel P.
Energetic feedback processes associated with accreting supermassive black holes can expel gas from massive haloes and significantly alter various measures of clustering on ~Mpc scales, potentially biasing the values of cosmological parameters inferre
Externí odkaz:
http://arxiv.org/abs/2410.19905
Autor:
Kugel, Roi, Schaye, Joop, Schaller, Matthieu, Moreno, Victor J. Forouhar, McGibbon, Robert J.
Galaxy cluster counts have historically been important for the measurement of cosmological parameters and upcoming surveys will greatly reduce the statistical errors. To exploit the potential of current and future cluster surveys, theoretical uncerta
Externí odkaz:
http://arxiv.org/abs/2408.17217
Autor:
Kugel, Roi, Schaye, Joop, Schaller, Matthieu, McCarthy, Ian G., Braspenning, Joey, Helly, John C., Moreno, Victor J. Forouhar, McGibbon, Robert J.
Galaxy clusters provide an avenue to expand our knowledge of cosmology and galaxy evolution. Because it is difficult to accurately measure the total mass of a large number of individual clusters, cluster samples are typically selected using an observ
Externí odkaz:
http://arxiv.org/abs/2406.03180
Autor:
McGibbon, Bridget
Magnetohydrodynamics (MHD), combining fluid dynamics and Maxwell's equations, provides a useful means of analysing the dynamic evolution of plasmas and plasma instabilities. JOREK is a non-linear MHD code which solves these equations in the context o
Externí odkaz:
http://arxiv.org/abs/2402.00458
Autor:
Srivastava, Prakhar, Yang, Ruihan, Kerrigan, Gavin, Dresdner, Gideon, McGibbon, Jeremy, Bretherton, Christopher, Mandt, Stephan
In climate science and meteorology, high-resolution local precipitation (rain and snowfall) predictions are limited by the computational costs of simulation-based methods. Statistical downscaling, or super-resolution, is a common workaround where a l
Externí odkaz:
http://arxiv.org/abs/2312.06071
Autor:
Watt-Meyer, Oliver, Dresdner, Gideon, McGibbon, Jeremy, Clark, Spencer K., Henn, Brian, Duncan, James, Brenowitz, Noah D., Kashinath, Karthik, Pritchard, Michael S., Bonev, Boris, Peters, Matthew E., Bretherton, Christopher S.
Existing ML-based atmospheric models are not suitable for climate prediction, which requires long-term stability and physical consistency. We present ACE (AI2 Climate Emulator), a 200M-parameter, autoregressive machine learning emulator of an existin
Externí odkaz:
http://arxiv.org/abs/2310.02074
Autor:
McGibbon, Robert, Khochfar, Sadegh
Using a novel machine learning method, we investigate the buildup of galaxy properties in different simulations, and in various environments within a single simulation. The aim of this work is to show the power of this approach at identifying the phy
Externí odkaz:
http://arxiv.org/abs/2306.07728