Prospecting for New Physics through Flavor, Dark Matter, and Machine Learning

Aspen Center for Physics

Aspen Center for Physics

Aspen Center for Physics 700 West Gillespie Street Aspen, CO 81611
David Shih (Rutgers University), Mike Williams (MIT), Patrick Fox (Fermilab), Stefania Gori (UC Santa Cruz), Wolfgang Altmannshofer (UC Santa Cruz)

We have entered a new and exciting decade of particle physics. The field of Beyond the Standard Model (BSM) physics has rapidly transformed into a diverse program of new physics (NP) searches, including: high-pT searches at the LHC; precision tests of the SM, especially in the Higgs and flavor sectors; searches for new light particles at high intensity experiments; and direct and indirect searches for Dark Matter across an increasingly broad range of masses and couplings.  In addition, exciting developments in the field of machine learning are inspiring new and innovative methods to search for NP across this broad program.

The goal of this Aspen Winter Conference is to bring together theorists and experimentalists, both to discuss the latest experimental results in all of these areas and their various theoretical implications, as well as to explore novel techniques for the future exploration of BSM physics, including the prospects for NP searches at the High-Luminosity LHC and future colliders. Key topics that will be covered include: results from the first run of the Belle II flavor factory; the status of the flavor anomalies; new ideas to probe Dark Matter and dark sectors; direct and indirect searches for new physics at high energy experiments; precision measurements at small scale high-intensity experiments, e.g. g-2 and rare kaon decay experiments; and machine learning in particle physics.


Application deadline is September 30,  2022

Please complete your application at

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    • Opening Welcome Reception
    • Muon g-2
    • 9:25 AM
      Coffee Break
    • Neutrinos
    • Neutrinos
      • 8
        Signatures for New Physics in Short-Baseline Liquid Argon Neutrino Experiments
        Speakers: Ornella Palamara (Fermilab), Ornella Palamara (Fermilab)
    • LHC 1
    • 5:45 PM
      Coffee Break
    • Dark Sectors 1
      • 11
        Dark Matter Misalignment Through the Higgs Portal
        Speakers: Brian Batell (CERN), Brian Batell (Perimeter Institute), Brian Batell (University of Pittsburgh)
      • 12
        FASER and the FPF
        Speaker: Jonathan Feng (UC Irvine)
      • 13
        Spacetime fluctuations in quantum gravity and the experiment GQuEST
        Speaker: Kathryn Zurek (Berkeley Lab)
    • Dark Sectors 2
    • 9:15 AM
      Coffee Break
    • Machine Learning 1
      • 17
        Uncertainties in the era of ML
        Speaker: Aishik Ghosh
      • 18
        Machine Learning for Event Generation and Fast Simulation

        LHC run 3 has just started and in the years leading up to 2040, we will see a 20-fold increase in available data. This forthcoming dataset will have enormous potential for a deeper understanding of the Standard Model and possible physics beyond it. At the same time, the endless possibilities of new physics hiding in this dataset pose a challenge, both for our analyses and also our simulation algorithms.
        Modern machine learning has become a standard tool in our numerical tool box. In recent years, we have not only seen applications to boost the performance of existing algorithms, but also new analysis or simulation strategies. I will highlight how advancements in modern Machine Learning, especially using invertible networks also known as normalizing flows, help speed up crucial bottlenecks in event generation and detector simulation.

        Speaker: Claudius Krause (ITP Heidelberg)
      • 19
        Anomaly Detection
        Speaker: Vinicius Massami Mikuni (Universitaet Zuerich (CH))
      • 20
        Machine Learning for Triggering
        Speaker: Javier Duarte (University of California San Diego)
    • Axion Like Particles
      • 21
        ALP theory
        Speaker: Jeff Dror (UC Santa Cruz)
      • 22
        Hybrid Cosmological Collider of Axion

        If a light axion is present during inflation and becomes part of dark matter afterwards, its quantum fluctuations contribute to dark matter isocurvature. In this article, we introduce a whole new suite of cosmological observables for axion isocurvature, which could help test the presence of axions, as well as its coupling to the inflaton and other heavy spectator fields during inflation such as the radial mode of the Peccei-Quinn field. They include correlated clock signals in the curvature and isocurvature spectra, and mixed cosmological-collider non-Gaussianities involving both curvature and isocurvature fluctuations with shapes and running unconstrained by the current data. Taking into account of the existing strong constraints on axion isocurvature fluctuations from the CMB, these novel signals could still be sizable and potentially observable. In some models, the signals, if observed, could even help us significantly narrow down the range of the inflationary Hubble scale, a crucial parameter difficult to be determined in general, independent of the tensor mode.

        Speaker: Lingfeng Li (Brown U.)
      • 23
        Speakers: Nick Du (Lawrence Livermore National Labs), Nick Du (University of Washington)
    • 5:45 PM
      Coffee Break
    • Heavy Flavor Physics 1
      • 24
        Heavy Flavors at LHCb
        Speaker: Rafael Silva Coutinho (Syracuse University)
      • 25
        Heavy Flavors at ATLAS and CMS
        Speaker: Peter Onyisi (University of Texas at Austin)
      • 26
        Heavy Flavors Theory
        Speaker: Darius Faroughy (Rutgers University)
    • Dark Matter Direct Detection
    • 9:15 AM
      Coffee Break
    • Dark Matter Indirect Detection
      • 30
        Indirect Detection
        Speaker: Rebecca Leane (SLAC)
      • 31
        Astrophysical searches for particle dark matter using neural simulation-based inference

        The complexity of astrophysical data and presence of unknowable systematics poses significant challenges to robustly characterizing signatures of dark matter in many datasets using conventional methods. I will describe how overcoming these challenges will require a qualitative shift in our approach to statistical inference, bringing together several recent advances in probabilistic machine learning, differentiable programming, and simulation-based inference. I will showcase applications of these methods to the analysis of Fermi gamma-ray data, with implications for the Galactic Center Excess, and the analysis of stellar kinematics of stars bound to dwarf galaxies, aiming to uncover the latent dark matter density profiles with implications for the nature of self-interactions in the dark sector.

        Speaker: Siddharth Mishra Sharma (New York University)
      • 32
        Machine Learning for Particle Astrophysics
        Speaker: Matthew Buckley (Fermilab)
    • LHC 1
    • Public Lecture
      • 34
        Public Lecture: Casting a Wide Net for Dark Matter
        Speaker: Tim Tait (UC Irvine)