Date: Wednesday October 6, 2021 at 4pm
Location: 101 Brockman Hall, Rice University
Date: Wednesday October 6, 2021 at 4pm
Location: 101 Brockman Hall, Rice University
Date: Tuesday Oct. 19, 2021 at 4pm
Location: online
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.
Date: Wednesday January 27, 2021 at 2pm
Location: online (YouTube)
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.
Date: Thursday May 23, 2019 at 10am
Location: 223 Herman Brown Hall, Rice University
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.
Date: Thursday Nov. 15, 2018 at 4pm
Location: 223 Herman Brown Hall, Rice University
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.
Date: Friday Sept. 21, 2018 at noon
Location: 223 Herman Brown Hall, Rice University
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.
Date: Thursday April 26, 2018 at 4pm
Location: 223 Herman Brown Hall, Rice University
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.
Date: Thursday April 12, 2018 at 4pm
Location: 223 Herman Brown Hall, Rice 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.
Date: Friday February 23, 2018 at 3pm
Location: 223 Herman Brown Hall, Rice University
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.
Date: Monday November 13, 2017 at 4pm
Location: 223 Herman Brown Hall, Rice University