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VERSION:2.0
CALSCALE:GREGORIAN
PRODID:UW-Madison-Physics-Events
BEGIN:VEVENT
SEQUENCE:3
UID:UW-Physics-Event-9599
DTSTART:20260304T190000Z
DTEND:20260304T210000Z
DTSTAMP:20260403T204109Z
LAST-MODIFIED:20260227T194709Z
LOCATION:Chamberlin 5280
SUMMARY:Hunting heavy di-Higgs resonances in bbtautau final states and
  commissioning of GPUs for the CMS High-Level Trigger.\, Thesis Defens
 e\, Ganesh Parida
DESCRIPTION:This dissertation presents a search for massive\, narrow-w
 idth resonances decaying to pairs of Higgs bosons in the bb̄ττ fina
 l state\, where one Higgs boson decays into a pair of bottom quarks an
 d the other into a pair of tau leptons (X → HH → bb̄ττ). Such r
 esonances are predicted by beyond-the-standard-model theories\, which 
 aim to address the shortcomings of our current understanding of fundam
 ental particles and their interactions. The search uses proton–proto
 n collision data at a center-of-mass energy of 13 TeV recorded by the 
 Compact Muon Solenoid (CMS) experiment during 2016–2018\, correspond
 ing to an integrated luminosity of 138 fb⁻¹\, and targets resonance
 s in the mass range of 1–4.5 TeV. The analysis uses a single large j
 et to reconstruct the H → bb̄ decay\, while the H → ττ decay pr
 oducts can either be contained within a single large jet or appear as 
 two isolated tau leptons. The reconstruction and identification of phy
 sics objects are enhanced using advanced machine learning techniques\,
  including a graph convolutional neural network for merged bb̄ jets a
 nd a convolutional neural network specifically designed for this searc
 h to identify merged ττ decays. Upper limits at the 95% confidence l
 evel are set on the production cross section for resonant HH productio
 n in the mass range considered\, with this analysis providing the most
  sensitive limits to date on X → HH → bb̄ττ decays for masses a
 bove 1.4 TeV.
\n
\nThe second component of the thesis describes the co
 mmissioning and validation of graphics processing unit (GPU)-based rec
 onstruction at the CMS high-level trigger for Run 3 data-taking. To ad
 dress increasing computational demands arising from higher instantaneo
 us luminosity and greater event complexity\, reconstruction algorithms
  for the hadron calorimeter\, electromagnetic calorimeter\, and pixel 
 tracker were offloaded to GPUs to take advantage of parallel processin
 g wherever feasible. Dedicated physics validation was required to ensu
 re that the GPU-offloaded algorithms produce physics results consisten
 t with the central processing unit (CPU)-based reconstruction. The fin
 al trigger configuration seamlessly utilizes GPU hardware when availab
 le while maintaining backward compatibility with CPU-only configuratio
 n\, establishing a foundation for meeting the computational challenges
  of the high-luminosity LHC era.
\n
URL:https://www.physics.wisc.edu/events/?id=9599
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