Machine-enhanced CP-asymmetries in the electroweak sector
Noah Clarke Hall, Isaac Criddle, Archie Crossland, Christoph Englert,, Patrick Forbes, Robert Hankache, Andrew D. Pilkington

TL;DR
This paper explores the potential of machine learning-enhanced observables to improve the detection of CP violation in electroweak processes at the LHC, aiming to better understand matter-antimatter asymmetry.
Contribution
It introduces machine-learning constructed CP-sensitive observables that significantly enhance sensitivity to CP-violating effects in electroweak processes.
Findings
ML observables improve sensitivity by up to five times
Inclusive Wγ and Zjj processes can set strong constraints on CP-odd operators
Study covers multiple electroweak processes relevant for CP violation detection
Abstract
The violation of charge conjugation (C) and parity (P) symmetries are a requirement for the observed dominance of matter over antimatter in the Universe. As an established effect of beyond the Standard Model physics, this could point towards additional CP violation in the Higgs-gauge sector. The phenomenological footprint of the associated anomalous couplings can be small, and designing measurement strategies with the highest sensitivity is therefore of the utmost importance in order to maximise the discovery potential of the Large Hadron Collider (LHC). There are, however, very few measurements of CP-sensitive observables in processes that probe the weak-boson self-interactions. In this article, we study the sensitivity to new sources of CP violation for a range of experimentally-accessible electroweak processes, including production, production via photon fusion,…
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Taxonomy
TopicsParticle physics theoretical and experimental studies · Computational Physics and Python Applications · Dark Matter and Cosmic Phenomena
