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CATEGORIES:DAMTP Departmental Colloquia
SUMMARY:Machine learning the proton structure - Professor 
 Maria Ubiali\, Professor of Theoretical Particle P
 hysics and Phenomenology\, DAMTP\, University of C
 ambridge
DTSTART;TZID=Europe/London:20241016T160000
DTEND;TZID=Europe/London:20241016T170000
UID:TALK216502AThttp://talks.cam.ac.uk
URL:http://talks.cam.ac.uk/talk/index/216502
DESCRIPTION:The wealth of precise data gathered from the Large
  Hadron Collider (LHC) at CERN presents both oppor
 tunities and challenges in the determination of fu
 ndamental parameters of the Standard Model (SM) of
  particle physics and in the search for physics be
 yond the SM. Central to this effort is a detailed 
 understanding of the proton's subnuclear structure
 \, described in terms of quarks and gluons via the
  parton distribution functions (PDFs). \n\nIn this
  talk\, I will explore how cutting-edge machine le
 arning techniques provide a robust solution to the
  inverse problem of extracting PDFs from experimen
 tal data.\nI will discuss recent advancements in g
 lobal PDF fits and the broader potential of machin
 e 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\, using two complementary a
 pproaches. First\, I will introduce a novel framew
 ork that simultaneously determines PDFs and the Wi
 lson coefficients of an effective field theory (EF
 T)\, allowing for a model-independent exploration 
 of heavy new physics. Second\, I will outline a sy
 stematic methodology to investigate whether global
  PDF fits could inadvertently "wash out" hints of 
 new physics in the high-energy tails of observed d
 istributions.\n\nThis talk aims to shed light on t
 he interplay between established proton structure 
 models and emerging theories\, demonstrating how m
 odern computational tools can drive discovery in h
 igh-energy physics.\n
LOCATION:  Centre for Mathematical Sciences MR2\, CMS
CONTACT:Alison Warrington
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