Conveners
Parallels Track H: Monday I
- Tommaso Dorigo (INFN Padova)
- Phiala Shanahan (MIT)
- Sergei Gleyzer ()
- Luca Tagliacozzo (ICCUB Barcelona)
Parallels Track H: Monday II
- Phiala Shanahan (MIT)
- Sergei Gleyzer ()
- Tommaso Dorigo (INFN Padova)
- Luca Tagliacozzo (ICCUB Barcelona)
Parallels Track H: Thursday I
- Luca Tagliacozzo (ICCUB Barcelona)
- Tommaso Dorigo (INFN Padova)
- Sergei Gleyzer ()
- Phiala Shanahan (MIT)
Parallels Track H: Thursday II
- Luca Tagliacozzo (ICCUB Barcelona)
- Tommaso Dorigo (INFN Padova)
- Sergei Gleyzer ()
- Phiala Shanahan (MIT)
Parallels Track H: Friday I
- Tommaso Dorigo (INFN Padova)
- Sergei Gleyzer ()
- Luca Tagliacozzo (ICCUB Barcelona)
- Phiala Shanahan (MIT)
Parallels Track H: Friday II
- Phiala Shanahan (MIT)
- Luca Tagliacozzo (ICCUB Barcelona)
- Sergei Gleyzer ()
- Tommaso Dorigo (INFN Padova)
Parallels Track H: Tuesday II
- Luca Tagliacozzo (ICCUB Barcelona)
- Sergei Gleyzer ()
- Tommaso Dorigo (INFN Padova)
- Phiala Shanahan (MIT)
Parallels Track H: Tuesday I
- Sergei Gleyzer ()
- Phiala Shanahan (MIT)
- Tommaso Dorigo (INFN Padova)
- Luca Tagliacozzo (ICCUB Barcelona)
Finding order parameters for the detection of critical phenomena and self-similar behavior in and out of equilibrium is a challenging endeavour in non-Abelian gauge theories. Tailored to detect topological structures in noisy data and accompanied by stability and limit theorems, persistent homology allows for the construction of sensible and sensitive observables. Based on state-of-the-art...
Recently, the introduction of relevant physical information into neural network architectures has become a widely used and successful strategy for improving the network's performances. In lattice field theories, such information can be identified with gauge symmetries, which are incorporated into the network layers of our recently proposed Lattice Gauge Equivariant Convolutional Neural...
The identification of universal properties from minimally processed data sets is one goal of machine learning techniques applied to statistical physics. Here, we study how the minimum number of variables needed to accurately describe the important features of a data set - the intrinsic dimension (Id) - behaves in the vicinity of phase transitions. We employ state-of-the-art nearest...
The design of optimal test statistics is a key task in frequentist statistics and for a number of scenarios optimal test statistics such as the profile-likelihood ratio are known. By turning this argument around we can find the profile likelihood ratio even in likelihood-free cases, where only samples from a simulator are available, by optimizing a test statistic within those scenarios. We...
I will discuss how to use random quantum circuits to sample the average energy---as well as other observables---of a desired Hamiltonian away from the ground state. Then, using those samples, how to estimate the values of observables at low energy by extrapolation.
A simple clustering algorithm based on the euclidean distance among tracks is proposed to find and reconstruct the vertices from where the tracks are emerging. This technique uses the Variational Quantum Eigensolver to find the best combinatorial track to vertex association. The study uses the IBM Quantum Computing simulation framework qiskit to simulate the VQE algorithm. Two vertices have...
The recent MODE whitepaper*, proposes an end-to-end differential pipeline for the optimisation of detector designs directly with respect to the end goal of the experiment, rather than intermediate proxy targets. The TomOpt python package is the first concrete endeavour in attempting to realise such a pipeline, and aims to allow the optimisation of detectors for the purpose of muon tomography...
Nuclear and particle physics research relies on accurate models which generate samples from conditional densities. An implicit quantile network (IQN) is a simple neural network-based machine learning model that has the ability to generate accurate samples from conditional, joint probability density functions. In this talk, we illustrate the capabilities of IQNs for simple generative tasks, as...
The ability to accurately observe two or more particles within a very small time window has always been a great challenge in modern physics, while holding great potential. It opens the possibility for correlation experiments, as for example the ground-breaking Hanbury Brown-Twiss experiment, that can lead to physical insights. For low-energy electrons, one possibility is to use a micro-channel...
