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SUMMARY:Machine learning the proton structure - Professor Maria Ubiali\, P
 rofessor of Theoretical Particle Physics and Phenomenology\, DAMTP\, Unive
 rsity of Cambridge
DTSTART:20241016T150000Z
DTEND:20241016T160000Z
UID:TALK216502@talks.cam.ac.uk
CONTACT:Alison Warrington
DESCRIPTION:The wealth of precise data gathered from the Large Hadron Coll
 ider (LHC) at CERN presents both opportunities and challenges in the deter
 mination of fundamental parameters of the Standard Model (SM) of particle 
 physics and in the search for physics beyond the SM. Central to this effor
 t is a detailed understanding of the proton's subnuclear structure\, descr
 ibed in terms of quarks and gluons via the parton distribution functions (
 PDFs). \n\nIn this talk\, I will explore how cutting-edge machine learning
  techniques provide a robust solution to the inverse problem of extracting
  PDFs from experimental data.\nI will discuss recent advancements in globa
 l PDF fits and the broader potential of machine learning in this context. 
 Additionally\, I will present new insights into how the parametrization of
  the proton structure interacts with signals of new physics at the LHC\, u
 sing two complementary approaches. First\, I will introduce a novel framew
 ork that simultaneously determines PDFs and the Wilson coefficients of an 
 effective field theory (EFT)\, allowing for a model-independent exploratio
 n of heavy new physics. Second\, I will outline a systematic methodology t
 o investigate whether global PDF fits could inadvertently "wash out" hints
  of new physics in the high-energy tails of observed distributions.\n\nThi
 s talk aims to shed light on the interplay between established proton stru
 cture models and emerging theories\, demonstrating how modern computationa
 l tools can drive discovery in high-energy physics.\n
LOCATION:  Centre for Mathematical Sciences MR2\, CMS
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