2022 Future Collider Monthly Meeting


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High-precision measurement of the W boson mass with the CDF II detector

2022 May 31, 19:00~20:50

Speaker: Ashutosh Kotwal (Duke University)

Abstract: The mass of the W boson, a mediator of the weak force between elementary particles, is tightly constrained by the symmetries of the standard model of particle physics. The Higgs boson was the last missing component of the model. After observation of the Higgs boson, a measurement of the W boson mass provides a stringent test of the model. We measure the W boson mass, Mᴡ, using data corresponding to 8.8 inversefemtobarns of integrated luminosity collected in proton-antiproton collisions at a 1.96 TeV center-of-mass energy with the CDF II detector at theFermilab Tevatron collider. A sample of approximately 4 million W boson candidates is used to obtain Mᴡ=80,433.5±6.4stat±6.9syst=80,433.5±9.4 MeV/c², the precision of which exceeds that of all previous measurements combined. This measurement is in significant tension with the standard model expectation.


Completely Quantum Neural Networks

2022 June 9, 16:00~17:30

Speaker: Steven Abel (Durham University)

Abstract: Artificial neural networks are at the heart of modern deep learning algorithms.  We describe how to embed and train a general neural network in a quantum annealer without introducing any classical element in training.  To implement the network on a state-of-the-art quantum annealer, we develop three crucial ingredients: binary encoding the free parameters of the network, polynomial approximation of the activation function, and reduction of binary higher-order polynomials into quadratic ones.  Together, these ideas allow encoding the loss function as an Ising model Hamiltonian.  The quantum annealer then trains the network by finding the ground state.  We implement this for an elementary network and illustrate the advantages of quantum training: its consistency in finding the global minimum of the loss function and the fact that the network training converges in a single annealing step, which leads to short training times while maintaining a high classification performance.  Our approach opens a novel avenue for the quantum training of general machine learning models.


[Tentative] Towards excluding a light Z' explanation of b → s ℓ⁺ℓ⁻

2022 June 16, 16:00~17:30

Speaker: Claudio Andrea Manzari (Universität Zürich)

Abstract: TBA


[Tentative] Creating Simple, Interpretable Anomaly Detectors for New Physics in Jet Substructure

2022 June 23, 10:00~11:30

Speaker: Spencer Chang (University of Oregon)

Abstract: TBA


[Tentative] SymmetryGAN: Symmetry Discovery with Deep Learning

2022 June 30, 10:00~11:30

Speaker: Krish Desai(University of California, Berkeley)

Abstract: TBA