NuXTract 2023 - Towards a consensus in neutrino cross sections

Session

Methods

3 Oct 2023, 13:40

222/R-001 (CERN)

Conveners

Methods

• Adi Ashkenazi (Tel Aviv University (IL))

Methods

• Adi Ashkenazi (Tel Aviv University (IL))

13. Binned log-likelihood template fitting with T2K

Nick Latham

03/10/2023, 13:40

When performing neutrino cross section extractions, it is desirable to avoid dependence on the interaction model used in Monte-Carlo generation. Log-likelihood template fitting is a method which can be used to explore the input parameter space and find the best ways of describing data. This method has several advantages, including straightforward background constraining and informative...

30. Forward folding

Lukas Koch (Johannes Gutenberg Universitaet Mainz (DE))

03/10/2023, 14:00

Statistical methods

16. Unbiased reconstruction of calorimetric variables in cross-section analyses

Katharina Lachner (University of Warwick)

03/10/2023, 14:30

Neutrino cross-sections are often extracted purely in terms of lepton kinematics. In recent years more detailed analyses have been developed that additionally make use of kinematics in the hadronic system, which has proven very successful. However, even with new detector technologies of unparalleled precision, pattern recognition and reconstruction algorithms still require particle momenta...

29. Unfolding: Machine learning approaches

Ben Nachman (Lawrence Berkeley National Lab. (US))

03/10/2023, 14:50

OmniFold: A Method to Simultaneously Unfold All Observables

6. Machine Learning-Assisted Unfolding for Neutrino Cross-section Measurements

Andrew Cudd (University of Colorado Boulder)

03/10/2023, 15:10

The choice of unfolding method for a cross-section measurement is tightly coupled to the model dependence of the efficiency correction and the overall impact of cross-section modeling uncertainties in the analysis. A key issue is the dimensionality used, as the kinematics of all outgoing particles in an event typically affects the reconstruction performance in a neutrino detector. OmniFold is...

28. Unfolding: a statistician's perspective

Mikael Kuusela (Carnegie Mellon University (US))

03/10/2023, 16:00

Unfolding: a statistician's perspective

10. Treating Detector Systematics via a Likelihood Free Inference Method

Ms Alexandra Trettin (University of Manchester)

03/10/2023, 16:25

Estimating the impact of systematic uncertainties in particle physics experiments is challenging, especially since the detector response is unknown analytically in most situations and needs to be estimated through Monte Carlo (MC) simulations. Typically, detector property variations are parameterized in ways that implicitly assume a specific physics model, which can introduce biases on...