Conveners
Track 3: Computations in Theoretical Physics: Techniques and Methods
- Barry Dillon (University of Heidelberg)
- Domenico Pomarico (INFN Sezione di Bari)
Track 3: Computations in Theoretical Physics: Techniques and Methods
- Daniel Maitre (University of Durham (GB))
- Marcello Maggi (Universita e INFN, Bari (IT))
Track 3: Computations in Theoretical Physics: Techniques and Methods
- Leonardo Cosmai
- Ryan Moodie (Turin University)
Track 3: Computations in Theoretical Physics: Techniques and Methods
- Anke Biekoetter (IPPP Durham)
- Domenico Elia (INFN Bari)
Track 3: Computations in Theoretical Physics: Techniques and Methods
- Domenico Pomarico (INFN Sezione di Bari)
- Joshua Davies (University of Sussex)
Track 3: Computations in Theoretical Physics: Techniques and Methods
- Andrea Valassi (CERN)
- Domenico Colella (University and INFN Bari, Italy)
Track 3: Computations in Theoretical Physics: Techniques and Methods
- Giuseppe DeLaurentis (Freiburg University)
- Andrea Wulzer (Universita e INFN, Padova (IT))
Track 3: Computations in Theoretical Physics: Techniques and Methods
- Adriano Di Florio (Politecnico e INFN, Bari)
- Juan Carlos Criado Álamo (University of Granada)
The matrix element (ME) calculation in any Monte Carlo physics event generator is an ideal fit for implementing data parallelism with lockstep processing on GPUs and on CPU vector registers. For complex physics processes where the ME calculation is the computational bottleneck of event generation workflows, this can lead to very large overall speedups by efficiently exploiting these hardware...
For more than a decade Monte Carlo (MC) event generators with the current matrix element algorithms have been used for generating hard scattering events on CPU platforms, with excellent flexibility and good efficiency.
While the HL-LHC is approaching and precision requirements are becoming more demanding, many studies have been made to solve the bottleneck in the current MC event generator...
For more than a decade the current generation of fully automated, matrix element generators has provided hard scattering events with excellent flexibility and good efficiency.
However, as recent studies have shown, they are a major bottleneck in the established Monte Carlo event generator toolchains. With the advent of the HL-LHC and ever rising precision requirements, future developments...
High-precision calculations are an indispensable ingredient to the success of the LHC physics programme, yet their poor computing efficiency has been a growing cause for concern, threatening to become a paralysing bottleneck in the coming years. We present solutions to eliminate the apprehension by focussing on two major components of general purpose Monte Carlo event generators: The...
Hadronization is a non-perturbative process, which theoretical description can not be deduced from first principles. Modeling hadron formation requires several assumptions and various phenomenological approaches. Utilizing state-of-the-art Computer Vision and Deep Learning algorithms, it is eventually possible to train neural networks to learn non-linear and non-perturbative features of the...
For many years, the matrix element method has been considered the perfect approach to LHC inference. We show how conditional invertible neural networks can be used to unfold detector effects and initial-state QCD radiation, to provide the hard-scattering information for this method. We illustrate our approach for the CP-violating phase of the top Yukawa coupling in associated Higgs and...
Learning tasks are implemented via mappings of the sampled data set, including both the classical and the quantum framework. The quantum-inspired approach mimics the support vector machine mapping in a high-dimensional feature space, yielded by the qubit encoding. In our application such scheme is framed in the formulation of a least-squares problem for the minimization of the mean squared...
The computation of loop integrals is required in high energy physics to account for higher-order corrections of the interaction cross section in perturbative quantum field theory. Depending on internal masses and external momenta, loop integrals may suffer from singularities where the integrand denominator vanishes at the boundaries, and/or in the interior of the integration domain (for...
Evaluating loop amplitudes is a time-consuming part of LHC event generation. For di-photon production with jets we show that simple, Bayesian networks can learn such amplitudes and model their uncertainties reliably. A boosted training of the Bayesian network further improves the uncertainty estimate and the network precision in critical phase space regions. In general, boosted network...
Evaluation of one-loop matrix elements is computationally expensive and makes up a large proportion of time during event generation. We present a neural network emulator that builds in the factorisation properties of matrix elements which accurately reproduces the NLO k-factors for electron-position annihilation into up to 5 jets.
We show that our emulator retains good performance for high...
In this talk I will give an overview of our recent progress in developing anomaly detection methods for finding new physics at the LHC. I will discuss how we define anomalies in this context, and the deep learning tools that we can use to find them. I will also discuss how self-supervised representation learning techniques can be used to enhance anomaly detection methods.
Local Unitarity provides an order-by-order representation of perturbative cross-sections that realises at the local level the cancellation of final-state collinear and soft singularities predicted by the KLN theorem. The representation is obtained by manipulating the real and virtual interference diagrams contributing to transition probabilities using general local identities. As a...
Tensor Networks (TN) are approximations of high-dimensional tensors designed to represent locally entangled quantum many-body systems efficiently. In this talk, we will discuss how to use TN to connect quantum mechanical concepts to machine learning techniques, thereby facilitating the improved interpretability of neural networks. As an application, we will use top jet classification against...
The potential exponential speed-up of quantum computing compared to classical computing makes it to a promising method for High Energy Physics (HEP) simulations at the LHC at CERN.
Generative modeling is a promising task for near-term quantum devices, the probabilistic nature of quantum mechanics allows us to exploit a new class of generative models: quantum circuit Born machine (QCBM)....
Accurate molecular force fields are of paramount importance for the efficient implementation of molecular dynamics techniques at large scales. In the last decade, machine learning methods have demonstrated impressive performances in predicting accurate values for energy and forces when trained on finite size ensembles generated with ab initio techniques. At the same time, quantum computers...
High-multiplicity loop-level amplitude computations involve significant algebraic complexity, which is usually sidestepped by employing numerical routines. Yet, when available, final analytical expressions can display improved numerical stability and reduced evaluation times. It has been shown that significant insights into the analytic structure of the results can be obtained by tailored...
I will discuss the analytic calculation of two-loop five-point helicity amplitudes in massless QCD. In our workflow, we perform the bulk of the computation using finite field arithmetic, avoiding the precision-loss problems of floating-point representation. The integrals are provided by the pentagon functions. We use numerical reconstruction techniques to bypass intermediate complexity and...
The search of New Physics through Dark Sectors is an exciting possibility to explain, among others, the origin of Dark Matter (DM). Within this context, the sensitivity study of a given experiment is a key point in estimating its potential for discovery. In this contribution we present the fully GEANT4-compatible Monte Carlo simulation package for production and propagation of DM particles,...
The Belle II is an experiment taking data from 2019 at the asymmetric e+e- SuperKEKB collider, a second generation B-factory, at Tsukuba, Japan. Its goal is to perform high precision measurements of flavor physics observables One of the many challenges of the experiment is to have a Monte Carlo simulation with very accurate modeling of the detector, including any variation occurring during...
The generation of unit-weight events for complex scattering processes presents a severe challenge to modern Monte Carlo event generators. Even when using sophisticated phase-space sampling techniques adapted to the underlying transition matrix elements, the efficiency for generating unit-weight events from weighted samples can become a limiting factor in practical applications. Here we present...
In Lattice Field Theory, one of the key drawbacks of the Markov Chain Monte Carlo(MCMC) simulation is the critical slowing down problem. Generative machine learning methods, such as normalizing flows, offer a promising solution to speed up MCMC simulations, especially in the critical region. However, training these models for different parameter values of the lattice theory is inefficient. We...
A decade of data-taking from the LHC has seen great progress in ruling out archetypal new-physics models up to high direct-production energy scales, but few persistent deviations from the SM have been seen. So as we head into the new data-taking era, it is of paramount importance to look beyond such archetypes and consider general BSM models that exhibit multiple phenomenological signatures....
We introduce a restricted version of the Riemann-Theta Boltzmann machine, a generalization of the Boltzmann machine with continuous visible and discrete integer valued hidden states. Though the normalizing higher dimensional Riemann-Theta function does not factorize, the restricted version can be trained efficiently with the method of score matching, which is based on the Fisher divergence. At...
Continuously comparing theory predictions to experimental data is a common task in analysis of particle physics such as fitting parton distribution functions (PDFs). However, typically, both the computation of scattering amplitudes and the evolution of candidate PDFs from the fitting scale to the process scale are non-trivial, computing intesive tasks. We develop a new stack of software tools...
In this presentation I will show how one can perform parametric integrations using a neural network. This could be applied for example to perform the integration over the auxiliary parameters in the integrals that result from the sector decomposition of multi-loop integrals.
Quantum annealing provides an optimization framework with the potential to outperform classical algorithms in finding the global minimum of non-convex functions. The availability of quantum annealers with thousands of qubits makes it possible today to tackle real-world problems using this technology. In this talk, I will review the quantum annealing paradigm and its use in the minimization of...
Over the last 20 years, thanks to the development of quantum technologies, it has been
possible to deploy quantum algorithms and applications, that before were only
accessible through simulation, on real quantum hardware. The current devices available are often refereed to as noisy intermediate-scale quantum (NISQ) computers and they require
calibration routines in order to obtain...
The variational quantum eigensolver (VQE) is an algorithm to compute ground and excited state energy of quantum many-body systems. A key component of the algorithm and an active research area is the construction of a parametrized trial wavefunction – a so called variational ansatz. The wavefunction parametrization should be expressive enough, i.e. represent the true eigenstate of a quantum...
The present work is based on the research within the framework of cooperation between Intel Labs and Deggendorf Institute of Technology, since the Intel® Quantum SDK (Software Development Kit) has recently released. Transport phenomena e.g. heat transfer and mass transfer are nowadays the most challenging unsolved problems in computational physics due to the inherent nature of fluid...
In Solar Power Plants, temperatures sufficient for chemical processes or the generation of electrical power are created by reflecting sunlight with thousands of mirrors ("heliostats") to a surface ("the receiver"). In operation, the temperature distribution on the receiver is critical for the performance and must be optimized. The heliostats are never perfectly flat as due to budget...
Physics-informed neural networks (PINNs) have emerged as a coherent framework to build predictive models that combine statistical patterns with domain knowledge. The underlying notion is to enrich the optimization loss function with known equations to constrain the space of possible model solutions. Successful applications cover a variety of areas, including physics, astronomy and...
