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SUMMARY:Neural Boltzmann Equations
DTSTART:20261030T143000Z
DTEND:20261030T150000Z
DTSTAMP:20261102T114900Z
UID:indico-event-1596@conference.ippp.dur.ac.uk
DESCRIPTION:Speakers: Jonas Spinner (Heidelberg University)\n\nThe dynamic
 s of particles in the early universe are described by Boltzmann equations\
 , which involve high-dimensional phase-space integrals. Classical ap- proa
 ches use quadrature integration and evolve the system on a fixed momentum 
 grid\, which scales poorly to complicated systems and parameter scans\, se
 verely limiting the complexity of processes that can be studied. We introd
 uce Neural Boltzmann Equations (NBEs)\, which combine three coupled concep
 ts to overcome these limitations. First\, particle properties are encoded 
 in physics-inspired neural distribution functions\, with parameters that c
 an be predicted using neural networks\, enabling efficient parameter scans
 . Second\, phase-space integrals are evaluated with Monte Carlo\, using im
 portance sampling tools from collider physics. Third\, we use the natural 
 gradient method to evolve the system. After demonstrating the individual b
 enefits of NBEs\, we use the framework to perform the most numerically pre
 cise calculation to date of the effective number of relativistic neutrino 
 degrees of freedom\, Neff\, in the Standard Model.\n\nhttps://conference.i
 ppp.dur.ac.uk/event/1596/
URL:https://conference.ippp.dur.ac.uk/event/1596/
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