arXiv subjects

Rupert Tombs

Publications and source records attributed to Rupert Tombs.

3 recordsLinked to original sources

arXiv2022

Hunting for vampires and other unlikely forms of parity violation at the Large Hadron Collider

Non-Standard-Model parity violation may be occurring in LHC collisions. Any such violation would go unseen, however, as searches are for it are not currently performed. One barrier to searches for parity violation is the lack of model-independent methods sensitive to all of its forms. We remove this barrier by demonstrating an effective and model-independent way to search for parity-violating physics at the LHC. The method is data-driven and makes no reference to any particular parity-violating model. Instead, it inspects data to construct sensitive parity-odd event variables (using machine learning tools), and uses these variables to test for parity asymmetry in independent data. We demonstrate the efficacy of this method by testing it on data simulated from the Standard Model and from a non-standard parity-violating model. This result enables the possibility of investigating a variety of previously unexplored forms of parity violation in particle physics. Data and software are shared at https://zenodo.org/record/6827724

hep-ph↗

arXiv2021

A method to challenge symmetries in data with self-supervised learning

Symmetries are key properties of physical models and of experimental designs, but any proposed symmetry may or may not be realized in nature. In this paper, we introduce a practical and general method to test such suspected symmetries in data, with minimal external input. Self-supervision, which derives learning objectives from data without external labelling, is used to train models to predict 'which is real?' between real data and symmetrically transformed alternatives. If these models make successful predictions in independent tests, then they challenge the targeted symmetries. Crucially, our method handles filtered data, which often arise from inefficiencies or deliberate selections, and which could give the illusion of asymmetry if mistreated. We use examples to demonstrate how the method works and how the models' predictions can be interpreted. Code and data are available at https://zenodo.org/record/6861702.

hep-ph↗

arXiv2021

Using unsupervised learning to detect broken symmetries, with relevance to searches for parity violation in nature. (Previously: "Stressed GANs snag desserts")

Testing whether data breaks symmetries of interest can be important to many fields. This paper describes a simple way that machine learning algorithms (whose outputs have been appropriately symmetrised) can be used to detect symmetry breaking. The original motivation for the paper was an important question in Particle Physics: "Is parity violated at the LHC in some way that no-one has anticipated?" and so we illustrate the main idea with an example strongly related to that question. However, in order that the key ideas be accessible to readers who are not particle physicists but who are interesting in symmetry breaking, we choose to illustrate the method/approach with a 'toy' example which places a simple discrete source of symmetry breaking (the handedness of human handwriting) within a idealised particle-physics-like context. Readers interested in seeing extensions to continuous symmetries, non-ideal environments or more realistic particle-physics contexts are provided with links to separate papers which delve into such details.

hep-ph↗