Name: Mini-workshop on lattice gauge theory and related topics for high-energy physics
Start: 2025-05-14T09:00:00+08:00
End: 2025-05-14T17:30:00+08:00
Location: NYCU Beimen campus

Mini-workshop on lattice gauge theory and related topics for high-energy physics

Wednesday, 14 May 2025 - 09:00

Monday, 12 May 2025
Tuesday, 13 May 2025
Wednesday, 14 May 2025
09:30 Opening - C.-J. David Lin (National Yang Ming Chiao Tung University)
Opening
- C.-J. David Lin (National Yang Ming Chiao Tung University)
09:30 - 09:45
Room: Lecture room 6
09:45 Machine Learning for Parton Distribution Functions - Luigi Del Debbio (University of Edinburgh)
Machine Learning for Parton Distribution Functions
- Luigi Del Debbio (University of Edinburgh)
09:45 - 10:30
Room: Lecture room 6 The NNPDF collaboration has been using Machine Learning techniques to solve the inverse problem of extracting Parton Distribution Functions from finite sets of experimental data for almost two decades. With the increased precision of the LHC measurements, It has become mandatory to understand the robustness of the error bars and of the correlations in the results of PDFs fit. We review the fitting procedure from a Bayesian perspective and discuss the training of neural networks in a Bayesian framework.
10:30 Coffee
Coffee
10:30 - 11:00
Room: Lecture room 6
11:00 TMD soft function on the lattice using complex directional Wilson lines - Wayne Morris (National Yang Ming Chiao Tung University)
TMD soft function on the lattice using complex directional Wilson lines
- Wayne Morris (National Yang Ming Chiao Tung University)
11:00 - 11:45
Room: Lecture room 6
11:45 Tackling the Signal to Noise problem with Stochastic Automatic Differentiation - Guilherme Catumba (University of Milano)
Tackling the Signal to Noise problem with Stochastic Automatic Differentiation
- Guilherme Catumba (University of Milano)
11:45 - 12:30
Room: Lecture room 6 Lattice field theory computations of two-point functions are generally affected by the so called signal to noise problem, wherein the signal of the Euclidean time correlator decays faster than the variance. In this talk we propose a different perspective on the origin of this problem. Following this, we argue that by writing correlators as derivatives with respect to sources and evaluating these derivatives using techniques of stochastic automatic differentiation we can eliminate completely the signal to noise problem. Results in a four dimensional scalar theory confirm the expected behavior.
12:30

12:30 - 14:00

14:00 TBA
TBA
14:00 - 14:45
Room: Lecture room 6

14:45 TBA
TBA
14:45 - 15:30
Room: Lecture room 6

15:30 Coffee
Coffee
15:30 - 16:00
Room: Lecture room 6

16:00 TBA
TBA
16:00 - 16:45
Room: Lecture room 6

16:45 Discussion
Discussion
16:45 - 17:30
Room: Lecture room 6