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PRODID:UW-Madison-Physics-Events
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UID:UW-Physics-Event-9105
DTSTART:20250205T210000Z
DURATION:PT1H0M0S
DTSTAMP:20260413T164637Z
LAST-MODIFIED:20250204T184246Z
LOCATION:https://cern.zoom.us/j/2709027422
SUMMARY:Search for New Physics Phenomenon via Higgs Boson Pair Product
 ion and Novel Machine Learning Methods\, Preliminary Exam\, Chi Lung C
 heng\, Physics PhD student
DESCRIPTION:Abstract 1: Search for New Physics via Higgs Boson Pair Pr
 oduction    Higgs boson pair production provides a direct probe of the
  Higgs self-coupling and potential physics beyond the Standard Model (
 BSM). This study explores non-resonant HH production\, focusing on con
 straints from collider data and theoretical models. Key results highli
 ght the sensitivity of current and future experiments to BSM phenomena
  through precise measurements of the HH production cross-section and k
 inematic distributions.<br>\n<br>\nAbstract 2: Novel Machine Learning 
 Methods in High-Energy Physics    This work introduces innovative semi
 -weakly supervised machine learning techniques for enhancing sensitivi
 ty to rare physics processes in collider experiments. By leveraging ne
 ural networks tailored to domain-specific features\, the approach impr
 oves signal extraction and classification efficiency. Applications to 
 simulated and real data demonstrate the effectiveness of these methods
  in uncovering subtle signatures of new physics phenomena.
URL:https://www.physics.wisc.edu/events/?id=9105
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