Identification of LHC beam loss mechanism : a deterministic treatment of loss patterns

CERN's Large Hadron Collider (LHC) is the largest machine ever built, with a total circumference of 26.7 km; and it is the most powerful accelerator ever, both in beam energy and beam intensity. The main magnets are superconducting, keeping the particles into two counter circulating beams, which collide in four interaction points. CERN and the LHC will be described in chap. 1. The superconducting magnets of the LHC have to be protected against particle losses. Depending on the number of lost particles, the coils of the magnets will become normal conducting and/or will be damaged. To avoid these events a beam loss monitoring (BLM) system was installed to measure the particle loss rates. If the predefined safe thresholds of loss rates are exceeded, the beams are directed out of the accelerator ring towards the beam dump. The detectors of the BLM system are mainly ionization chambers located outside of the cryostats. In total, about 3500 ionisation chambers are installed. Further challenges include the high dynamical range of losses (chamber currents ranging between 2 pA and 1 mA). The BLM system will be further described in chap. 2. The subject of this thesis is to study the loss patterns and find the origin of the losses in a deterministic way, by comparing measured losses to well understood loss scenarios. This is done through a case study: different techniques were used on a restrained set of loss scenarios, as a proof of concept of the possibility to extract information from a loss profile. Finding the origin of the losses should allow acting in response. A justification of the doctoral work will be given at the end of chap. 2. Then, this thesis will focus on the theoretical understanding and the implementation of the decomposition of a measured loss profile as a linear combination of the reference scenarios; and the evaluation of the error on the recomposition and its correctness. The principles of vector decomposition are developed in chap. 3. An ensemble of well controlled loss scenarios (such as vertical and horizontal blow-up of the beams or momentum offset during collimator loss maps) has been gathered, in order to allow the study and creation of reference vectors. To achieve the Vector Decomposition, linear algebra (matrix inversion) is used with the numeric algorithm for the Singular Value Decomposition. Additionally, a specific code for vector projection on a non-orthogonal basis of a hyperplane was developed. The implementation of the vector decomposition on the LHC data is described in chap. 4. After this, the use of the decomposition tools systematically on the time evolution of the losses will be described: first as a study of the variations second by second, then by comparison to a calculated default loss profile. The different ways to evaluate the variation are studied, and are presented in chap. 5. The next chapter (6) describes the gathering of decomposition results applied to beam losses of 2011. The vector decomposition is applied on every second of the ''stable beans'' periods, as a study of the spatial distribution of the loss. Several comparisons of the results given by the decompositions with measurements from other LHC instruments allowed different validations. Eventually, a global conclusion on the interest of the vector decomposition is given. Then, the extra chapter in Appendix A describes the code which was developed to access the BLM data, to represent them in a meaningful way, and to store them. This included connecting to different databases. The whole instrument uses ROOT objects to send SQL queries to the databases, as well as java API, and is coded in Python. A short glossary of the acronyms used here can be found at the end, before the bibliography