|
09:00
|
Classification
-
Sung Hak Lim
(Rutgers University) Prof.
Cheng-Wei Chiang
(National Taiwan University)
(until 10:40)
|
|
09:00
|
Recent ML-usage in searches with boosted jets in CMS
-
Oz Amram
(Johns Hopkins University (US))
|
|
09:20
|
Constituent-Based Top-Quark Tagging with the ATLAS Detector
-
Kevin Thomas Greif
(University of California Irvine (US))
|
|
09:40
|
Adversarial training for b-tagging algorithms in CMS
-
CMS Collaboration
(CMS Experiment, CERN)
Annika Stein
(Rheinisch Westfaelische Tech. Hoch. (DE))
|
|
10:00
|
Truth tagging for efficiency parametrization of b-jets using Graph Neural Networks
-
Krunal Bipin Gedia
(ETH Zurich (CH))
|
|
10:20
|
Heterogeneous Graph Representation for Identifying Hadronically Decayed Tau Leptons at the High Luminosity LHC
-
Xiangyang Ju
(Lawrence Berkeley National Lab. (US))
Andris Huang
(University of California-Berkeley)
|
|
09:00
|
Measurement
-
Eva Halkiadakis
(Rutgers State Univ. of New Jersey (US))
Manuel Szewc
(until 10:40)
|
|
09:00
|
Multi-differential Jet Substructure Measurement in High $Q^{2}$ Deep-Inelastic Scattering with the H1 Detector
-
Vinicius Massami Mikuni
(Lawrence Berkeley National Lab. (US))
|
|
09:20
|
Machine learning for top physics in CMS
-
Philip Daniel Keicher
(Hamburg University (DE))
|
|
09:40
|
ML Unfolding based on conditional Invertible Neural Networks using iterative training
-
Mathias Josef Backes
(Universität Heidelberg)
|
|
10:00
|
Moment Unfolding using Deep Learning
-
Krish Desai
|
|
10:20
|
Invertible Networks for the Matrix Element Method
-
Theo Heimel
(Heidelberg University)
|
|
10:40
|
--- Coffee ---
|
|
11:10
|
Classification
-
Michael David Sokoloff
(University of Cincinnati (US))
Johnny Raine
(Universite de Geneve (CH))
(until 12:50)
|
|
11:10
|
Identification of hadronic tau decays using a deep neural network with the CMS experiment at LHC
-
Mykyta Shchedrolosiev
(Deutsches Elektronen-Synchrotron (DE))
|
|
11:30
|
Robust Signal Detection using a Classifier Decorrelated through Optimal Transport (CDOT)
-
Purvasha Chakravarti
(University College London)
|
|
11:50
|
VBF vs. GGF Higgs with Full-Event Deep Learning: Towards a Decay-Agnostic Tagger
-
Cheng-Wei Chiang
(National Taiwan University)
|
|
12:10
|
Machine learning based jet and event classification at the Electron-Ion Collider
-
James Mulligan
(University of California, Berkeley (US))
|
|
12:30
|
Search for dimuon events in IceCube using decision trees
-
Nakul Aggarwal
(University of Alberta)
|
|
11:10
|
Measurement
-
Jesse Thaler
(MIT)
Oz Amram
(Johns Hopkins University (US))
(until 12:50)
|
|
11:10
|
Constraining quark and gluon jet energy loss distributions in quark-gluon plasma using Bayesian inference
-
Alexandre Falcão
(University of Bergen)
|
|
11:30
|
Estimating Uncertainties for Trained Neural Networks
-
Sebastian Guido Bieringer
(Hamburg University)
|
|
11:50
|
How can Bayesian networks be used for uncertainty quantification in particle physics?
-
Christina Peters
(University of Delaware)
|
|
12:10
|
Using Machine Learning to Improve our Understanding of the Jet Background in Nucleus-Nucleus Collisions.
-
Tanner Mengel
(University of Tennessee)
|
|
12:30
|
Loop Amplitudes from Precision Networks
-
Tilman Plehn
|
