Conveners
Tools II
- Daneng Yang (Department of Physics, Tsinghua University (CN))
Description
https://pitt.zoom.us/j/93687648384
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Stephen Martin (Northern Illinois University)24/05/2021, 16:30Tools
The projected discovery and exclusion capabilities of searches are often quantified using the median expected $p$-value or its corresponding significance. However, this criterion can lead to flawed results, for example counterintuitively predicting lessened sensitivities if the experiment takes more data or reduces its background. We discuss the merits of several alternatives to the median...
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Dr Aleksas Mazeliauskas (CERN)24/05/2021, 16:45
With current high precision collider data and high-order calculations, the reliable estimation of theoretical uncertainty due to missing higher orders (MHO) terms has become a pressing issue for perturbative QFT predictions. The traditionally used simple but ad hoc scale variation has no probabilistic interpretation. Bayesian approach to MHO introduced by Cacciari and Houdeau and recently...
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Ms Swagata Mukherjee (Rheinisch Westfaelische Tech. Hoch. (DE))24/05/2021, 17:00Tools
The trigger systems of the LHC detectors play a crucial role in determining the physics capabilities of the experiments. The CMS experiment implements a sophisticated two-level triggering system composed of the Level-1(L1), instrumented by custom-design hardware boards, and the High Level Trigger(HLT), a streamlined version of the offline reconstruction software running on a computer farm. For...
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Ms Rhitaja Sengupta (Indian Institute of Science, Bengaluru)24/05/2021, 17:15BSM
Sophisticated machine learning techniques have promising potential in search for physics beyond Standard Model (BSM) in Large Hadron Collider (LHC). Convolutional neural networks (CNN) can provide powerful tools for differentiating between patterns of calorimeter energy deposits by prompt particles of Standard Model and long-lived particles predicted in various models beyond the Standard...
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Taegyun Kim (University of Notre Dame)24/05/2021, 17:30
In this paper we train a Convolutional Neural Network to classify longitudinally and transversely polarized hadronic $W^\pm$ using the images of boosted $W^{\pm}$ jets as input. The images capture angular and energy information from the jet constituents that is faithful to properties of the original quark/anti-quark $W^{\pm}$ decay products without the need for invasive substructure cuts. We...
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Sezen Sekmen (Kyungpook National University (KR))24/05/2021, 17:45Tools
Collider data analysis, which usually requires complicated software frameworks, has a very steep learning curve. Therefore, a sizable barrier exists between data and the physicist wishing to work on different analysis ideas. Recently, a new approach has been under development to address this issue and provide a practical way to collider data analysis. This approach features the so-called...
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Dr Jan Heisig (Université catholique de Louvain (UCL))24/05/2021, 18:00Tools
SModelS is an automatic, public python tool providing a fast reinterpretation of simplified model results from the LHC within any model of new physics respecting a Z_2 symmetry. We present the recently released v2.0 that includes a major revision of the decomposition algorithm. It introduces the particles’ decay widths and their quantum numbers as additional parameters of the simplified model...
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Max Fieg24/05/2021, 18:15Tools
Lately, interest has grown in forward, high $\eta$ physics with experiments like FASER and FORMOSA at the LHC. However, particle physics event generators like Pythia have primarily been calibrated to make predictions in the central, low $\eta$ regions. As such, predictions in the forward region are in tension with current forward physics data. It is imperative to obtain accurate particle...
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Prasanth Shyamsundar (Fermi National Accelerator Laboratory)24/05/2021, 18:30BSM
In this talk, we will introduce a technique to train neural networks into being good event variables, which are useful to an analysis over a range of values for the unknown parameters of a model.
We will use our technique to learn event variables for several common event topologies studied in colliders. We will demonstrate that the networks trained using our technique can mimic powerful,...
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