Machine learning classification: case of Higgs boson CP state in H to tau tau decay at LHC

Machine Learning (ML) techniques are rapidly finding a place among the methods of High Energy Physics data analysis. Different approaches are explored concerning how much effort should be put into building high-level variables based on physics insight into the problem, and when it is enough to rely on low-level ones, allowing ML methods to find patterns without explicit physics model. In this paper we continue the discussion of previous publications on the CP state of the Higgs boson measurement of the H to tau tau decay channel with the consecutive tau^pm to rho^pm nu; rho^pm to pi^pm pi^0 and tau^pm to a_1^pm nu; a_1^pm to rho^0 pi^pm to 3 pi^pm cascade decays. The discrimination of the Higgs boson CP state is studied as a binary classification problem between CP-even (scalar) and CP-odd (pseudoscalar), using Deep Neural Network (DNN). Improvements on the classification from the constraints on directly non-measurable outgoing neutrinos are discussed. We find, that once added, they enhance the sensitivity sizably, even if only imperfect information is provided. In addition to DNN we also evaluate and compare other ML methods: Boosted Trees (BT), Random Forest (RF) and Support Vector Machine (SVN).Comment: 1+20 pages, 9 figures, 6 tables, extended content and improved readabilit

Non-Invasive Beam Diagnostics with Schottky Signals and Cherenkov Diffraction Radiation

This dissertation reports on the developments made regarding two techniques used for non-invasive beam diagnostics: the analysis of Schottky signals and the observation of Cherenkov Diffraction Radiation (ChDR). The Schottky signal manifests itself in the beam intensity in the form of statistical noise, which is typically more conveniently analysed in frequency domain. The analysis of Schottky signals is a fundamental technique for estimating various physical parameters of unbunched hadron beams, but the task is far more challenging in the case of beams accelerated with RF systems, i.e. bunched beams. A new approach based on simulating Schottky signals and comparing them with the exper- imentally obtained ones is proposed and developed herein. The relevant beam characteristics serve as input parameters for the simulations, and with the help of optmisation routines one obtains the values which fit best to the measured Schottky signals. The proposed approach has been applied to the data acquired by the LHC Schottky Monitor and benchmarked against the results of alterna- tive measurement techniques. The theory of Schottky signals in the presence of bunched beams is revisited as well, and a formal derivation of the relation between the width of the synchro-betatron sidebands in the Schottky spectrum and the value of chromaticity is presented. The second part of this dissertation discusses the application of Cherenkov Diffraction Radiation in beam diagnostics. The concept is relatively new and still requires extensive studies from both theoretical and practical points of view. A brief presentation of the theoretical models used so far for describing ChDR emission in simple geometries is followed by a systematic study of how relevant beam parameters express themselves in the properties of the emitted radiation. This is supplemented with the development of a new semi-analytical framework, which allows for calculation of the expected radiation yield in the case of a more complex multilayered radiator, and fills the gap between simplified theoretical models and detailed numerical simulations, which often require large computing resources. The performance of the new framework is illustrated with studies on the impact of thin coatings deposited on the radiator surface, either for signal enhancement or to mitigate undesirable effects, such as the creation of electron clouds. In addition, the feasibility of using ChDR-based diagnostic devices in the Large Hadron Collider is preliminarily assessed. This part of the thesis is concluded with the results of a dedicated experiment carried out in 2021 on the CLEAR accelerator at CERN, whose aim was to understand the range of validity of the currently used theoretical models. The obtained results do not confirm the investigated theoretical models and may constitute a starting point for further research on the use of ChDR in beam diagnostics

Estimation of longitudinal bunch characteristics in the LHC using Schottky-based diagnostics

The Large Hadron Collider (LHC) Schottky monitors have been designed to measure various parameters of relevance to beam quality, namely tune, momentum spread, and chromaticity. In this work, we present how this instrument can be used to estimate longitudinal bunch characteristics, such as longitudinal bunch profile or synchrotron frequency distribution. Under the assumption of bunched beams with no intrabunch coherent motion, we start by deriving the relation between the distribution of synchrotron amplitudes within the bunch population and the longitudinal bunch profile from probabilistic principles. Subsequently, we fit the cumulative power density of acquired Schottky spectra with the underlying distribution of synchrotron amplitudes. Finally, the result of this fit is used to reconstruct the bunch profile using the derived model. The results obtained with this method are verified by comparison with longitudinal profile measurements from the LHC wall current monitors