Conveners
Classification
- Sung Hak Lim (Rutgers University)
- Cheng-Wei Chiang (National Taiwan University)
Classification
- Johnny Raine (Universite de Geneve (CH))
- Michael David Sokoloff (University of Cincinnati (US))
CMS has a wide search program making use of ML for jet tagging and event reconstruction. This talk will report recent usage of ML in searches for heavy resonances involving boosted W, Z, H and top quark jets.
This talk will present the performance of constituent-based jet taggers on large radius boosted top quark jets reconstructed from optimized jet input objects in simulated collisions at s√=13 TeV. Several taggers which consider all the information contained in the kinematic information of the jet constituents are tested, and compared to a tagger which relies on high-level summary quantities...
Deep learning is a standard tool in high-energy physics, facilitating identification of physics objects. In particular, complex neural network architectures play a major role for jet flavor tagging. However, these methods are reliant on accurate simulations and a calibration is required to treat non-negligible performance differences with respect to data. In order to reduce residual...
In high-energy physics experiments, estimating the efficiency of a process using selection cuts is a widely used technique. However, this method is limited by the number of events that could be simulated in the required analysis phase space. A way to improve this sensitivity is to use efficiency weights instead of selecting events by selection cuts. This method of efficiency measurements is...
We present a new algorithm that identifies reconstructed jets originating from hadronic decays of tau leptons against those from quarks or gluons. No tau lepton reconstruction algorithm is used. Instead, the algorithm represents jets as heterogeneous graphs using the associated low-level objects such as tracks and energy clusters and trains a Graph Neural Network (GNN) to identify hadronically...
Tau leptons are a key ingredient to perform many Standard Model measurements and searches for new physics at LHC. The CMS experiment has released a new algorithm to discriminate hadronic tau lepton decays against jets, electrons, and muons. The algorithm is based on a deep neural network and combines fully connected and convolutional layers. It combines information from all individual...
New physics searches are usually done by training a supervised classifier to separate a signal model from a background model. However, even when the signal model is correct, systematic errors in the background model can influence supervised classifiers and might adversely affect the signal detection procedure. To tackle this problem, one approach is to find a classifier constrained to be...
We study the benefits of jet- and event-level deep learning methods in distinguishing vector boson fusion (VBF) from gluon-gluon fusion (GGF) Higgs production at the LHC. We show that a variety of classifiers (CNNs, attention-based networks) trained on the complete low-level inputs of the full event achieve significant performance gains over shallow machine learning methods (BDTs) trained on...
In this talk, we explore machine learning-based event and jet identification at the future Electron-Ion Collider (EIC). We study the effectiveness of machine learning-based classifiers at the relatively low EIC energies, focusing on (i) identifying the flavor of the jet, in terms of both quark flavor tagging and quark vs. gluon tagging, and (ii) identifying the hard-scattering process, using...
The dominant neutrino-nucleon interaction above 100 GeV is Deep Inelastic Scattering (DIS) in which an incoming neutrino scatters off a quark in the nucleon by exchanging a weak boson, producing an outgoing lepton accompanied by a hadron shower. Two sub-dominant processes are expected to produce two high energy charged leptons in the final state. The first one is a subset of DIS where a...
