2016-2017 High Energy Physics Seminar

The monthly High Energy Physics seminar series at Tufts follows recent experimental theoretical developments in neutrino, collider, and other particle physics. Speakers are drawn predominantly from universities in the greater Boston area with occasional visitors from further afield.

Unless otherwise noted, all seminars are held at Collaborative Learning and Innovation Complex (CLIC) - 574 Boston Avenue in Medford.

Spring 2017

Wednesday, February 15 | 10:30am, Room 310

Inclusive and Semi-inclusive Neutrino Reactions with Nuclei
T. William Donnelly, MIT

Abstract: Theoretical issues involved in studies of inclusive charge-changing neutrino reactions with nuclei and their extensions to semi-inclusive processes will be discussed. For the former, the basic SuperScaling Approach (SuSA) will be summarized and scaling violations from meson-exchange current contributions quantified in terms of the so-called SuSAv2+MEC model. For the latter (semi-inclusive processes where, in addition to a charged lepton, final-state hadrons are detected), the concept of trajectories in the missing energy, missing momentum plane will be introduced. The importance of understanding the "nuclear landscape" in terms of these kinematic variables will be motivated.

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Wednesday, May 3 | 10:30am, Room 310

The LHCb Experiment: Results and Prospects
Mike Williams, MIT

Abstract: The LHCb experiment, located at the LHC at CERN, has been the world's premier experiment for studying processes in which quark types (flavors) change since LHC Run 1 — and produced almost 400 papers to date. I will summarize the LHCb physics program, focusing on a few intriguing recent results, and discuss future prospects for both indirect and direct searches for physics beyond the Standard Model, and studies of emergent properties of the strong nuclear force (QCD).

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Fall 2016

Thursday, September 15 | 10:30am, Room 114

"Say My Name": Neutrino physics through a quantum lens
Joseph Formaggio, MIT

Abstract: One of the most counter-intuitive aspects of quantum mechanics is the fact that the state of a particle is undetermined until it is measured. The Leggett-Garg inequality, an analogue of Bell's inequality involving correlations of measurements on a system at different times, provides a quantitative test of this unique property of quantum mechanics. The phenomenon of neutrino oscillations should adhere to quantum-mechanical predictions and provide an observable violation of the Leggett-Garg inequality. We demonstrate how oscillation phenomena can be used to test for violations of the classical bound by performing measurements on an ensemble of neutrinos at distinct energies, as opposed to a single neutrino at distinct times. A study of the MINOS experiment's data shows a greater than 6σ violation over a distance of 735 km, representing the longest distance over which either the Leggett-Garg inequality or Bell's inequality has been tested.

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Thursday, October 13 | 10:30am, Room 114

Recursive Jigsaw Reconstruction in HEP complex event topologies
Christopher Rogan, Harvard University

Abstract: At the Large Hadron Collider (LHC), many new physics signatures feature pair-production of massive particles with subsequent direct or cascading decays to weakly interacting particles, such as SUSY scenarios with conserved R-parity or Higgs decaying to two leptons and two neutrinos through W bosons, often motivated by models of new physics which attempt to mitigate the hierarchy problem in the Standard Model. While final states containing multiple weakly interacting particles represent an opportunity for discovery of new physics phenomena, they also present a unique experimental challenge; the kinematic information lost through particles escaping detection makes fully reconstructing these collision events impossible. In order to address this shortcoming special kinematic variables are used to partially reconstruct these events, providing sensitivity to properties of the particles appearing in them, including masses and even their spin correlations. We introduce a systematic prescription, Recursive Jigsaw Reconstruction, for generating a preferred kinematic basis of kinematic variables developed to study final states with weakly interacting particles at HEP experiments, specifically catered to each case of interest. Using the examples of slepton pair-production at the LHC, the motivation and derivation of these observables are described along with comparisons to previously existing approaches. Generalizations to more complicated decay topologies are also discussed, including fully leptonic top quark pair production (resonant and non-resonant), its supersymmetric analogue of stop pair-production with subsequent decays to b-quarks, leptons, and neutrinos, and several examples involving both SM and BSM Higgs decays.

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Thursday, November 17 | 10:30am, Room 114

Getting the most out of the LHC
Alan Barr, Oxford University

Abstract: Since you don't get many 27km accelerators to play with it makes sense to make good use of the one we've got. Using several case studies, I describe how new ideas in triggering, analysis and interpretation have made a real difference to what we can learn from world's leading collider.

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Thursday, December 8 | 10:30am, Room 316

From Pixels to Neutrinos in MicroBooNE
Taritree Wongjirad, MIT

Abstract: The MicroBooNE experiment consists of a liquid argon time-projection chamber (LArTPC) that sits 470 m from the origin of the Booster Neutrino beam at Fermi National Lab. The goal of the experiment is to advance our knowledge of neutrino-nucleus cross sections and shed light on the MiniBooNE low energy anomaly. The latter is one of several anomalies seen in neutrino oscillation experiments that have been interpreted as hints for non-standard neutrinos. I will discuss the status of MicroBooNE and its achievements after one year of data taking. In particular, I will focus on one effort to use convolutional neural networks (CNNs) to reconstruct and select neutrino events. CNNs, a type of machine learning algorithm, are often the state-of-the-art approach in many computer vision tasks. For example, CNNs have found applications ranging from automated human face recognition to real-time object detection for self-driving cars. I'll describe our first steps in applying CNNs to the task of analyzing neutrino events in LArTPCs.

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