The Standard Model of particle physics is an effective theoretical model remarkably describing matter particles and forces at the fundamental level. Despite its great success, it leaves many open questions such as the nature of Dark Matter and the origin for matter-antimatter asymmetry in the Universe.
The search for New Physics is a great challenge of contemporary science, which...
GraphNeT, a deep learning framework for neutrino telescopes, provides a common library for deep learning experts and neutrino physicists in need of advanced deep learning techniques for their analyses. GraphNeT is a detector agnostic framework, making it easy and intuitive to apply deep learning techniques from one experiment to another, and to export models to the physicists that relies on...
We pursue the use of deep learning methods to improve state-of-the-art computations in theoretical high-energy physics. Planar N = 4 Super Yang-Mills theory is a close cousin to the theory that describes Higgs boson production at the Large Hadron Collider; its scattering amplitudes are large mathematical expressions containing integer coefficients. In this paper, we apply Transformers to...
Detection of neutrinos at ultra-high energies (UHE, E >$10^{17}$eV) would open a new window to the most violent phenomena in our universe. However, owing to the expected small flux of UHE neutrinos, the detection rate will be small, with just a handful of events per year, even for large future facilities like the IceCube-Gen2 neutrino observatory at the South Pole. In this contribution, I will...
In a recent paper we presented a new benchmark tool for symbolic regression and tested it with cosmology datasets. This new benchmark tool is easy to use, and we would like to spread the word and encourage other people to try it in their respective field where applicable. Out of the box it has ten different machine learning algorithms, but we also encourage the community to add their own...
Black holes hold a tremendous discovery potential, and experiments such as the Event Horizon Telescope and its next generation upgrade could provide important cues about their structure. I will present a pedagogical discussion on the most relevant features in black-hole images (e.g. photon rings) and their detectability using realistic telescope arrays. I will focus on the structure of...
We present findings from the FASTSUM collaboration on the behaviour of bottomonium particles (bound states of a bottom quark and its antiquark) at different temperatures. To analyze this, we used three methods: a Maximum Likelihood approach with a Gaussian function for the ground state, the Backus-Gilbert method, and a machine learning technique called Kernel Ridge Regression. Our study uses...
Virtually all present graph neural network (GNN) architectures blur the distinction between the definition and invocation of the message function, forcing a node to send messages to its neighbours at every layer, synchronously. When applying GNNs to learn to execute various algorithms, however, on most steps only a handful of the nodes would have meaningful updates to send. One, hence, runs...
Accurately representing the tropical sea surface temperature (SST) remains a significant challenge for general circulation models. One of the largest sources of uncertainty is the vertical turbulent mixing. To accurately represent the distribution of ocean mixed layer depths (MLD), turbulence closure schemes necessitate careful tuning. This is commonly done manually by comparing with mixed...
Symbolic regression (SR) is a machine learning technique that seeks to identify mathematical expressions that best fit a given dataset. This method is particularly beneficial in scientific research, due to its ability to generate interpretable models that provide insight into the underlying mechanisms, in sharp contrast with purely data-driven, black-box models. Interpretable machine learning...
Computer simulations aim to replicate physical phenomena as precisely as possible, but they often require extensive knowledge and rely on differential equations, which typically lack analytical solutions. The recent rise of Machine Learning, driven by large amounts of data, has offered an alternative to traditional human-designed methods. However, these data-driven approaches produce complex,...
We give an overview of the work in AI within a joint research endeavour between the UKAEA and the STFC Hartree Centre, the Fusion Computing Lab, to accelerate discovery and design of complex facilities in magnetic-confinement fusion.
We illustrate how we are enhancing the "classical" real-time control of tokamak shots with AI techniques of emulation and reinforcement learning, both for the...
Many physical problems are governed by Laplace's equation, to which there exists a unique solution when boundary constraints are fully defined. However, given only partial knowledge or discrete observation along a boundary, statistical methods are required to construct uncertainty estimates and likelihoods for possible solutions.
Within magnetism, obtaining the magnetic field or the magnetic...
