This Week at Physics

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This Week at Physics

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Events During the Week of January 28th through February 4th, 2018

Monday, January 29th, 2018

NPAC (Nuclear/Particle/Astro/Cosmo) Forum
Top quarks & beyond at the Large Hadron Collider
Time: 3:30 pm
Place: 4274 Chamberlin Hall
Speaker: Louise Skinnari, Cornell
Abstract: The first run of the Large Hadron Collider (LHC) has been a great success, most notably with the discovery of the Higgs boson. Despite the continued triumph of the Standard Model, critical questions remain unanswered about how nature works on small scales. As the most massive of all known elementary particles, the top quark plays a central role in many proposed extensions to the Standard Model that address some of these questions. A precise understanding of top quarks and their production and properties is therefore critical. In this talk, I will discuss the motivation and status of top quark physics at the LHC, presenting recent results from the CMS experiment. Additionally, I will give an outlook to the future upgrade of the LHC to higher luminosities (HL-LHC), and its resulting potential and challenges for the CMS experiment.
Host: Sridhara Dasu
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Council Meeting
Council Meeting-CANCELLED
Time: 4:00 pm
Place: 2314 Chamberlin Hall
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Tuesday, January 30th, 2018

Chaos & Complex Systems Seminar
Stochastic optimization: Making complex design, planning, and operation decisions in the face of uncertainty
Time: 12:05 pm
Place: 4274 Chamberlin (Refreshments will be served)
Speaker: Jim Luedtke, UW Department of Industrial Engineering
Abstract: Stochastic optimization is a branch of mathematical optimization concerned with helping make design, planning, and operation decisions in the face of uncertain outcomes or data. Example applications of stochastic optimization include: planning power generation in systems with uncertainty in wind outputs and rainfall (which effects hydro-reservoir levels); deciding order quantities at a retailer with uncertain customer demands; and making financial investments without knowing the returns the different investment options will yield. I will provide an overview of the field stochastic optimization, with a bias towards topics related to my research. I will focus on discussing different types of models and when they might be useful, and, time permitting, will overview some of the solution approaches.
Host: Clint Sprott
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"Physics Today" Undergrad Colloquium (Physics 301)
Neutrino oscillation experiments
Time: 1:20 pm
Place: 2241 Chamberlin Hall
Speaker: Jennifer A Thomas, UW Madison Department of Physics
Host: Wesley Smith
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Wednesday, January 31st, 2018

No events scheduled

Thursday, February 1st, 2018

R. G. Herb Condensed Matter Seminar
Supercurrent in the quantum Hall regime
Time: 10:00 am
Place: chamberlin 5310
Speaker: Gleb Finkelstein, Duke University
Abstract: One of the promising routes towards creating novel topological states and excitations is to combine superconductivity and quantum Hall (QH) effect. However, signatures of superconductivity in the QH regime remain scarce, and a superconducting current through a QH weak link has so far eluded experimental observation. By utilizing high mobility graphene/boron nitride heterostructures we demonstrate the existence of a novel type of supercurrent-carrying states in a QH regime at magnetic fields as high as 2 Tesla. At low magnetic fields, devices demonstrate the Fraunhoffer pattern and Fabri-Perot oscillations, confirming their uniformity and ballisticity. In the QH regime, when Landau quantization is fully developed, regions of superconductivity can be observed on top of the conventional QH fan diagram. The measured supercurrent is very small, on a few nA scale, and periodic in magnetic field. We discuss possible mechanisms that could mediate supercurrent along the QH edge states.
Host: Alex Levchenko
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Friday, February 2nd, 2018

Physics Department Colloquium
Playing Newton: Learning equations of motion from data
Time: 3:30 pm
Place: 2241 Chamberlin Hall
Speaker: Ilya Nemenman, Emory University
Abstract: Arguably, science' goal of understanding nature can be formulated as inferring mathematical laws that govern natural systems from experimental data. With the fast growth of power of modern computers and of artificial intelligence algorithms, there has been a recent surge in attempts to automate this goal and to design, to some extent, an “artificial scientist.” I will discuss this emerging field, but will focus primarily on our own approach to it. I will introduce an algorithm that we have recently developed, which allows one to infer the underlying dynamical equations behind a noisy time series, even if the dynamics are nonlinear, and only a few of the relevant variables are measured. I will illustrate the method on applications to toy problems, including inferring the iconic Newton’s law of universal gravitation, as well as a few biochemical reaction networks. I will end with applications to experimental biological data: modeling the landscape of possible behavioral states underlying reflexive escape from pain in a roundworm and (if time permits) modeling insulin secretion in pancreatic beta cells.
Host: Maxim Vavilov
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