France-Berkeley PHYSTAT Conference on Unfolding

10 Jun 2024, 09:00 → 13 Jun 2024, 18:00 Europe/Paris

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))

A central task in differential cross section measurements in particle-, nuclear-, and astrophysics is unfolding: the removal of detector distortions, also called deblurring or deconvolution.  Unfolding is a challenging inverse, simulation-based inference task.  

The goal of this conference is to bring together method developers and practioners to discuss the state-of-the-art in unfolding.  One key aspect of the conference will be machine learning-based unfolding methods, which have enabled new possibilities (e.g. unbinned and high-dimensional measurements).

The conference will be held at the LPNHE in Paris from June 10 - 13, 2024. There will be a zoom connection for remote participation as well.

Organizing Committee:

Olaf Behnke
Lydia Brenner
Anja Butter
Louis Lyons

Bogdan Malaescu
Ben Nachman

 

Acknowledgements: We are grateful to the France-Berkeley Fund for sponsorship and to PHYSTAT for logitistcal support.

   

Monday, 10 June

11:30 → 13:30 Registration

13:30 → 13:45 Welcome and Logistics

Speakers: 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))

13:45 → 14:30 HEP overview (30'+15')

14:30 → 15:15 Statistics overview (30'+15')

Speaker: Mikael Kuusela (Carnegie Mellon University (US))

15:15 → 16:00 ML overview (30'+15')

Speaker: Tilman Plehn (Heidelberg University)

16:00 → 17:30 Welcome reception

Tuesday, 11 June

09:30 → 10:10 Binned ML methods overview (20'+20')

10:10 → 10:50 Unbinned Discriminative ML methods overview (20'+20')

10:50 → 11:20 Coffee

11:20 → 12:00 Unbinned Generative ML methods overview (20'+20')

Speakers: Javier Marino, Nathan Hutsch

12:00 → 13:30 Lunch

13:30 → 14:10 Performance / benchmarking (20'+20')

Speakers: Dr Carsten Burgard (Technische Universitaet Dortmund (DE)), Lydia Brenner (Nikhef National institute for subatomic physics (NL))

14:10 → 14:50 How to pick the regularization (20'+20')

Speaker: Lydia Brenner (Nikhef National institute for subatomic physics (NL))

14:50 → 15:20 Coffee

15:20 → 16:50 Contributed talks

Wednesday, 12 June

09:30 → 10:50 Challenges

09:30 Dealing with Uncertainties

Speaker: Kyle Cormier (University of Zurich (CH))

10:10 Response Matrix Estimation in Unfolding Differential Cross Sections

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 at the LHC, this is done by binning the events and counting the corresponding numbers of events from bins to bins. However, this approach can suffer from undersmoothing, especially with a small sample size. To address this issue, we propose a two-step approach to response matrix estimation. First, we estimate the response kernel on the unbinned space. Second, we propagate the estimated response kernel into an integral equation to obtain an estimate for the response matrix.

Speaker: Richard Zhu (Carnegie Mellon University)

10:50 → 11:20 Coffee

11:20 → 12:00 Challenges

11:20 Open discussion on challenges and possible solutions in unfolding

Speaker: Bogdan Malaescu (LPNHE-Paris CNRS/IN2P3 (FR))

12:00 → 13:30 Lunch

13:30 → 17:30 Tutorials

Conveners: Dr Carsten Burgard (Technische Universitaet Dortmund (DE)), Javier Marino, Lydia Brenner (Nikhef National institute for subatomic physics (NL)), Nathan Hutsch, Vincent Alexander Croft (Nikhef National institute for subatomic physics (NL))

18:30 → 20:30 Conference dinner

Thursday, 13 June

09:30 → 10:50 New/related methods

09:30 Simplified Template Cross Sections (STXS)

Speaker: Rahul Balasubramanian (Centre National de la Recherche Scientifique (FR))

10:10 QUnfold: Quantum Annealing for Distributions Unfolding in High-Energy Physics

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 original distribution. This process is essential to make effective comparisons between the outcomes obtained from different experiments and the theoretical predictions.
The emerging technology of Quantum Computing represents an enticing opportunity to enhance the unfolding performance and potentially yield more accurate results.
This work introduces QUnfold, a simple Python module designed to address the unfolding challenge by harnessing the capabilities of quantum annealing. In particular, the regularized log-likelihood minimization formulation of the unfolding problem is translated to a Quantum Unconstrained Binary Optimization (QUBO) problem, solvable by using quantum annealing systems. The algorithm is validated on a simulated sample of particles collisions data generated combining the Madgraph Monte Carlo event generator and the Delphes simulation software to model the detector response. A variety of fundamental kinematic distributions are unfolded and the results are compared with conventional unfolding algorithms commonly adopted in precision measurements at the Large Hadron Collider (LHC) at CERN.
The implementation of the quantum unfolding model relies on the D-Wave Ocean software and the algorithm is run by heuristic classical solvers as well as the physical D-Wave Advantage quantum annealer boasting 5000+ qubits.

Speakers: Dr Gianluca Bianco (Universita e INFN, Bologna (IT)), Simone Gasperini (Universita e INFN, Bologna (IT))

10:50 → 11:20 Coffee

11:20 → 12:00 New/related methods

11:20 Full Event Particle-Level Unfolding with Variable Length Latent Variational Diffusion

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 be compared directly with experimental data. However, these simulations are often proprietary and computationally expensive. An alternative approach, unfolding, statistically adjusts the experimental data for detector effects. Traditional unfolding algorithms require binning data in a small set of pre-selected dimensions. Recent methods using generative machine learning models have shown promise for performing un-binned unfolding in high dimensions, allowing later computation of many observables. However, all current generative approaches are limited to unfolding a fixed set of observables, making them unable to perform full-event unfolding in the variable dimensional environment of collider data. A novel modification to the variational latent diffusion model (VLD) approach to generative unfolding is presented, which allows for unfolding of high- and variable-dimensional feature spaces. The performance of this method is evaluated in the context of semi-leptonic $t\bar{t}$ production at the LHC. Additionally, the dependence of the unfolding on the training data prior is assessed by evaluating the model on datasets with alternative priors.

Speakers: Alexander Shmakov (University of California Irvine (US)), Kevin Thomas Greif (University of California Irvine (US))

12:00 → 13:30 Lunch

13:30 → 14:00 HEP summary

14:00 → 14:30 Statistics summary

14:30 → 15:00 ML summary

15:00 → 15:30 Closing of the meeting