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France-Berkeley PHYSTAT Conference on Unfolding

10–13 Jun 2024
Europe/Paris timezone

Contribution List

29 out of 29 displayed
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  1. 1. Welcome and Logistics
    Anja Butter (Centre National de la Recherche Scientifique (FR)), Ben Nachman (Lawrence Berkeley National Lab. (US)), Lydia Brenner (Nikhef National institute for subatomic physics (NL))
    10/06/2024, 13:30
  2. 3. Statistics overview (30'+15')
    Mikael Kuusela (Carnegie Mellon University (US))
    10/06/2024, 13:45
  3. 2. HEP overview (30'+15')
    Philippe Gras (Université Paris-Saclay (FR))
    10/06/2024, 14:30
  4. 4. ML overview (30'+15')
    Tilman Plehn (Heidelberg University)
    10/06/2024, 15:45
  5. 6. Unbinned Discriminative ML methods overview (20'+20')
    Mariel Pettee (Lawrence Berkeley National Lab. (US))
    10/06/2024, 16:30
  6. 7. Unbinned Generative ML methods overview (20'+20')
    Nathan Huetsch
    10/06/2024, 17:10
  7. 5. Binned ML methods overview (20'+20')
    Jingjing Pan (Yale University (US))
    11/06/2024, 09:00
  8. 8. Performance / benchmarking with regularisation choice (20'+20')
    Lydia Brenner (Nikhef National institute for subatomic physics (NL))
    11/06/2024, 09:40
  9. 20. Simplified Template Cross Sections (STXS) (20+20)
    Rahul Balasubramanian (Centre National de la Recherche Scientifique (FR))
    11/06/2024, 10:50
  10. 29. Likelihood-based unfolding with the CMS Higgs combination tool (20+20)
    Alessandro Tarabini (ETH Zurich (CH))
    11/06/2024, 11:30

    The CMS Higgs combination tool is the software package used for statistical analyses by the CMS Collaboration. The package, originally designed to perform searches for a Higgs boson and the combined analysis of those searches, has evolved to become the statistical analysis tool presently used in the majority of measurements and searches performed by the CMS Collaboration. Since Combine has...

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  11. 25. Unfolding is not unsmearing (20+20)
    Michael Schmelling (Max Planck Society (DE))
    11/06/2024, 13:30

    In particle physics unfolding methods are employed when the basis used to represent an estimate of the truth is not the basis with statistically independent expansion coefficients. For the discrete unfolding problem the latter is given by the eigenvectors of the Fisher information matrix, which measures the amount of information carried by the data about the truth. In typical cases it is...

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  12. 9. Unfolding in direct BSM search-sensitive regions of phase space (20'+20')
    Sarah Louise Williams (University of Cambridge (GB))
    11/06/2024, 14:10
  13. 28. Using unfolded data in global QCD analyses (20+20)
    Oleksandr Zenaiev (Hamburg University)
    11/06/2024, 15:20
  14. 24. Unfolding in the context of a heavy ion analysis (20+20)
    Molly Park (Massachusetts Inst. of Technology (US))
    11/06/2024, 16:00

    In this study, the process of unfolding is studied in the context of a heavy ion photon-tagged jet analysis. The SVD and D'Agostini unfolding algorithms are compared, and an application of using the MSE to choose the regularization strength is shown. Additionally, the investigation looks into the bias associated with unfolding in relation to prior choice. The performance is evaluated with...

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  15. 17. Response Matrix Estimation in Unfolding Differential Cross Sections (20+20)
    Richard Zhu (Carnegie Mellon University)
    11/06/2024, 16:40

    In unfolding problem, the response matrix is the forward operator which models the detector response. In practice, the response matrix is not known analytically. Instead, it needs to be estimated using Monte Carlo simulation, which introduces statistical uncertainty into the unfolding procedure. This raises the question of how to estimate the response matrix in a sensible way. In most analyses...

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  16. 18. Dealing with Uncertainties (20+20)
    Kyle Cormier (University of Zurich (CH))
    12/06/2024, 09:00
  17. 23. Unfolding in the context of g-2 (20+20)
    Laurent Lellouch (CNRS and Aix-Marseille U.)
    12/06/2024, 09:40
  18. 27. Profile likelihood unfolding with large number of bins (20+20)
    David Walter (CERN)
    12/06/2024, 10:50

    In this talk we will discuss previous measurements using binned maximum likelihood unfolding focusing on analyses with large numbers of bins and nuisance parameters. We will outline the technical implementation and highlight the challenges and limitations. Furthermore, we showcase the newly-developed approach of "linearized binned likelihood unfolding",
    a modified formalism that has a better...

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  19. 26. Moment Unfolding (20+20)
    Krish Desai
    12/06/2024, 11:30
  20. 19. QUnfold: Quantum Annealing for Distributions Unfolding in High-Energy Physics (20+20)
    Dr Gianluca Bianco (Universita e INFN, Bologna (IT)), Simone Gasperini (Universita e INFN, Bologna (IT))
    13/06/2024, 09:30

    In High-Energy Physics (HEP) experiments, each measurement apparatus exhibit a unique signature in terms of detection efficiency, resolution, and geometric acceptance. The overall effect is that the distribution of each observable measured in a given physical process could be smeared and biased. Unfolding is the statistical technique employed to correct for this distortion and restore the...

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  21. 22. Unfolding using Denoising Diffusion (20+20)
    Camila Pazos (Tufts University (US))
    13/06/2024, 10:10

    Unfolding detector distortions in experimental data is critical for enabling precision measurements in high-energy physics (HEP). However, traditional unfolding methods face challenges in scalability, flexibility, and dependence on simulations. We introduce a novel unfolding approach using conditional denoising diffusion probabilistic models (cDDPM). By modeling the conditional probability...

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  22. 16. Full Event Particle-Level Unfolding with Variable Length Latent Variational Diffusion (20+20)
    Alexander Shmakov (University of California Irvine (US)), Kevin Thomas Greif (University of California Irvine (US))
    13/06/2024, 11:20

    Collisions at the Large Hadron Collider (LHC) provide information about the values of parameters in theories of fundamental physics. Extracting measurements of these parameters requires accounting for effects introduced by the particle detector used to observe the collisions. The typical approach is to use a high-fidelity simulation of the detector to generate synthetic datasets that can then...

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  23. 12. A 24-dimensional Cross-section Measurement with ATLAS (15+15)
    Mariel Pettee (Lawrence Berkeley National Lab. (US))
    13/06/2024, 13:30
  24. 13. Multidimensional cross-section measurements with H1 (15+15)
    Fernando Torales Acosta (Lawrence Berkeley National Lab. (US))
    13/06/2024, 14:00
  25. 21. Open discussion on challenges and possible solutions in unfolding
    Bogdan Malaescu (LPNHE-Paris CNRS/IN2P3 (FR))
    13/06/2024, 14:30
  26. 14. Closing of the meeting and summary
    Ben Nachman (Lawrence Berkeley National Lab. (US))
    13/06/2024, 15:00
  27. 10. Contributed talks
  28. 11. HEP summary
  29. 15. Welcome reception