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UID:UW-Physics-Event-9204
DTSTART:20250507T150000Z
DTEND:20250507T170000Z
DTSTAMP:20260413T135816Z
LAST-MODIFIED:20250424T182527Z
LOCATION:5280 CH
SUMMARY:Search for dark matter recoiling from pencil-thin jets using C
 MS data with machine learning techniques\, Thesis Defense\, Abhishikth
  Mallampalli\, Physics PhD student
DESCRIPTION:The Standard Model (SM) of particle physics serves as the 
 foundational framework describing the fundamental particles and forces
  that govern the behavior of matter and radiation in the universe\, ex
 cluding gravity. It provides a comprehensive theory of the electromagn
 etic\, weak\, and strong nuclear interactions—three of the four fund
 amental forces of nature. Despite its incredible success in explaining
  a vast range of experimental phenomena\, it is still incomplete and t
 here are several open questions. This thesis attempts to answer some o
 f these open questions in physics today.<br>\n<br>\nSeveral new physic
 s models predict particles that are expected to leave signatures of mi
 ssing transverse momentum in collider experiments. One of the primary 
 motivations for such searches is the astrophysical evidence for dark m
 atter\, including galactic rotation curves\, gravitational lensing\, a
 nd observations of the cosmic microwave background. Weakly Interacting
  Massive Particles (WIMPs) are a leading candidate for dark matter\, a
 nd this thesis explores the parameter space of two WIMP-inspired model
 s\, setting stringent limits on their viability. In addition\, searche
 s for extra spacetime dimensions\, leptoquarks\, and quantum blackhole
 s are also performed. Machine learning techniques are used for these s
 earches. <br>\n<br>\nThis thesis also presents an algorithm to mitigat
 e beam-induced background in a future muon collider using fast machine
  learning<br>\n
URL:https://www.physics.wisc.edu/events/?id=9204
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