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Posts Tagged ‘high-energy’


P&A Colloquium: Peter Onyisi (UT Austin)

October 15th, 2021 by geurts

Date: Wednesday October 6, 2021  at 4pm
Location: 101 Brockman Hall, Rice University

Title: Understanding the Higgs Boson and Top Quark at the LHC
Speaker:Peter Onyisi (UT Austin)
Abstract: The two heaviest known fundamental particles are the Higgs boson and the top quark, and their behaviour and properties are intimately intertwined – the Higgs boson gives the top quark its mass, and the top quark determines the potential energy of the Higgs field that fills space. Understanding the relationship of the two is critical for understanding fundamental particle physics both now and right after the Big Bang. The Large Hadron Collider is the first accelerator that produces both particles in sufficiently copious quantities for us to study their interactions directly in a lab setting, and we now have a sufficiently large dataset to begin study. I will outline the latest results from the ATLAS and CMS experiments at the LHC exploring this sector of fundamental physics.

NPP Seminar by Melissa Quinnan (UCSB)

October 15th, 2021 by geurts

Date: Tuesday Oct. 19, 2021 at 4pm
Location: online

Title: Standard Model Four-Top Production with the CMS Detector (Run II)
Speaker: Melissa Quinnan (UCSB)

Abstract

Standard model four top quark production is a rare process with great potential to reveal new physics. Measurement of the cross section is not only a direct probe of the top quark Yukawa coupling with the Higgs, but an enhancement of this cross section is predicted by several beyond the standard model (BSM) theories. This process is studied in fully-hadronic proton-proton collision events collected during Run II of the CERN LHC by the CMS detector, which corresponded to an integrated luminosity of 137fb−1 and a center of mass energy of 13TeV. In order to optimize signal sensitivity with respect to significant and challenging backgrounds, several novel machine-learning based tools are applied in a multi-step and data-driven approach. Boosted decision tree (BDT) and deep neural net (DNN) based hadronic top taggers are used to identify hadronically decaying top quark candidates with moderate and high transverse momenta, respectively, in order to suppress backgrounds and categorize events by the multiplicity of reconstructed top tags, and an event-level kinematic BDT distribution is subsequently used to extract the signal. Control regions inspired by the “ABCD” method are used to obtain a data-driven estimate of the background, and data distributions in these control regions are given as inputs to a DNN in order to estimate the event-level BDT discriminant distributions of the major backgrounds. In combination with searches in other decay modes the expected significance of this analysis is estimated to reach at least 3 standard deviations, corresponding to the “evidence” of standard model four top production.

NPP Seminar by Taylor Faucett (UCI)

January 25th, 2021 by geurts

Date: Wednesday January  27, 2021 at 2pm
Location: online (YouTube)

Title: Physicists Learning from Machines Learning
Speaker: Taylor Faucett (UCI)

Abstract

Machine Learning methods are extremely powerful but often function as black-box problem solvers, providing improved performance at the expense of clarity. Our work describes a new machine learning approach which translates the strategy of a deep neural network into simple functions that are meaningful and intelligible to the physicist, without sacrificing performance improvements. We apply this approach to benchmark high-energy problems of fat-jet classification and electron identification. In each case, we find simple new observables which provide additional classification power and novel insights into the nature of the problem.

NPP Seminar by Sven Dildick (Texas A&M)

May 17th, 2019 by geurts

Date: Thursday May 23, 2019 at 10am
Location: 223 Herman Brown Hall, Rice University

Title: Searching for pair production of new light bosons decaying to muons with the CMS detector
Speaker: Sven Dildick (Texas A&M)

Abstract

Searches for new light bosons can offer insights into the nature of the Higgs boson and dark matter. These particles are introduced in many extensions of the standard model, such as supersymmetry and models with hidden sectors. In this seminar I present a search for pair production of new light bosons with the CMS detector at the LHC. The search is uniquely sensitive to signatures with multi-muon final states and is designed to be model independent. The results of the analysis using 13 TeV collision data set are interpreted in the context of two relevant benchmark models. I will also discuss the phase-2 upgrade of the CMS detector and how it can improve the sensitivity in these searches.

NPP Seminar by Stefania Gori (University of Cincinnati)

November 8th, 2018 by geurts

Date: Thursday Nov. 15, 2018  at 4pm
Location: 223 Herman Brown Hall, Rice University

Title: Dark Sectors at High Energy and at High Intensity Experiments
Speaker: Stefania Gori (University of Cincinnati )

Abstract

Dark sector models are a compelling framework for Dark Matter (DM) theories. In this talk, after a brief introduction of dark sector physics, I will focus on models based on a new U(1)_Lmu-Ltau gauge symmetry, under which Dark Matter can be charged. These models, in addition to the DM motivation, can address some of the anomalies in data, as the (g-2)_mu anomaly and the LHCb B flavor anomalies. An overview of the experimental opportunities to probe these models will be presented.

NPP Seminar by Alexander Monin (Univ of Geneva)

September 14th, 2018 by geurts

Date: Friday Sept. 21, 2018  at noon
Location: 223 Herman Brown Hall, Rice University

Title: Hadronic decay of a light Higgs-like scalar
Speaker: Alexander Monin (Univ of Geneva)

Abstract: A number of extensions of the Standard Model predicts Higgs sector with additional light scalars. Currently operating and planned Intensity Frontier experiments will probe for the existence of such particles, while theoretical computations are plagued by uncertainties. I revisit the question of hadronic decays of a GeV-mass Higgs-like scalar. To this end I’ll provide a physically motivated fitting ansatz for the decay width that reproduces the previous non-perturbative numerical analysis. I describe systematic uncertainties of the non-perturbative method and provide explicit examples of the influence of extra resonances above 1.4 GeV onto the total decay width.

NPP Seminar by Andrew Hart (OSU)

April 6th, 2018 by geurts

Date: Thursday April 26, 2018  at 4pm
Location: 223 Herman Brown Hall, Rice University

Title: Search for disappearing tracks at CMS
Speaker: Andrew Hart (OSU)

Abstract

As the experiments at the LHC accumulate larger and larger data sets with dozens of searches showing no signs of physics beyond the standard model, it becomes imperative to examine the assumptions made in these searches. One of the most ubiquitous assumptions is that new particles will have short lifetimes and leave decay products that originate from the proton-proton interaction point. If the new particles are instead long-lived, they may produce experimental signatures that are completely missed by these more conventional searches. One particularly challenging signature of long-lived particles is the so-called “disappearing track,” where a new long-lived charged particle decays in the middle of the tracker of a collider detector to invisible decay products. In this talk, I will discuss the search for disappearing tracks in the 13 TeV data collected by the CMS detector, and how this search fits into the broader search for new physics at the LHC.

NPP Seminar by Satya Nandi (Oklahoma State University)

April 1st, 2018 by geurts

Date: Thursday April 12, 2018  at 4pm
Location: 223 Herman Brown Hall, Rice University

Title: A new model connecting the intensity and the energy frontier
Speaker: Satya Nandi (Oklahoma State University)

Abstract

A new model for the generation of the neutrino mass will be presented. The model has a triply charged Higgs boson whose mass is naturally at the TeV scale. This can be pair produced at the LHC, and its decay give rise to same sign trileptons in the final state. Depending on the parameter space, its decay can also produce displaced vertex. These signals will be within reach of the current or future runs of the LHC. The model also has interesting implications for the dark matter.

NPP Seminar by Daniel Whiteson (University of Chicago)

February 14th, 2018 by geurts

Date: Friday February 23, 2018  at 3pm
Location: 223 Herman Brown Hall, Rice University

Title: Deep Learning in High Energy Physics
Speaker: Daniel Whiteson (University of Chicago)

Abstract:Recent advances in artificial intelligence offer opportunities to disrupt the traditional techniques for data analysis in high energy physics. I will describe the new machine learning techniques, explain why they are particularly well suited for particle physics, and present selected results that demonstrate their new capabilities.

NPP Seminar by Michela Paginini (Yale)

October 31st, 2017 by geurts

Date: Monday November 13, 2017  at 4pm
Location: 223 Herman Brown Hall, Rice University

Title: Accelerating Science with Deep Learning
Speaker:  Michela Paginini (Yale)
Abstract: With a rate of approximately 1 billion proton-proton collisions per second at an energy of 13 TeV, data sets from high energy physics collected at the Large Hadron Collider (LHC) are ideal for the application of machine learning. As new particles are created and detected, they produce high-dimensional, multi-modal streams of information that can be cast as sequential, image-based, causal learning tasks. In this talk, I will explore applications of computer vision techniques to improve generative and discriminative capabilities at the LHC. Specifically, I will outline the methodologies in a recent contribution where we introduced a deep generative model to enable high-fidelity, fast, detector simulation and achieved preliminary speed-up factors of up to 100,000x. Although there are still open challenges, this work represents a significant stepping stone toward a full neural network-based simulator that could save significant computing time and enable many analyses at the LHC and beyond. I will conclude with applications of deep learning to analysis scenarios and ideas for future machine learning powered solutions in high energy physics.