Automatic setup of 18 MeV electron beamline using machine learning

To improve the performance-critical stability and brightness of the electron bunch at injection into the proton-driven plasma wakefield at the AWAKE CERN experiment, automation approaches based on unsupervised Machine Learning (ML) were developed and deployed. Numerical optimisers were tested together with different model-free reinforcement learning agents. In order to avoid any bias, reinforcement learning agents have been trained also using a completely unsupervised state encoding using auto-encoders. To aid hyper-parameter selection, a full synthetic model of the beamline was constructed using a variational auto-encoder trained to generate surrogate data from equipment settings. This paper describes the novel approaches based on deep learning and reinforcement learning to aid the automatic setup of a low energy line, as the one used to deliver beam to the AWAKE facility. The results obtained with the different ML approaches, including automatic unsupervised feature extraction from images using computer vision are presented. The prospects for operational deployment and wider applicability are discussed

Predicting the Trajectory of a Relativistic Electron Beam for External Injection in Plasma Wakefields

We use beam position measurements over the first part of the AWAKE electron beamline, together with beamline modeling, to deduce the beam average momentum and to predict the beam position in the second part of the beamline. Results show that using only the first five beam position monitors leads to much larger differences between predicted and measured positions at the last two monitors than when using the first eight beam position monitors. These last two positions can in principle be used with ballistic calculations to predict the parameters of closest approach of the electron bunch with the proton beam. In external injection experiments of the electron bunch into plasma wakefields driven by the proton bunch, only the first five beam position monitors measurements remain un-affected by the presence of the much higher charge proton bunch. Results with eight beam position monitors show the prediction method works in principle to determine electron and proton beams closest approach within the wakefields width ($<$1\,mm), corresponding to injection of electrons into the wakefields. Using five beam position monitors is not sufficient.Comment: seven pages, five figures, submitted for EAAC 2019 Proceeding

Association of kidney disease measures with risk of renal function worsening in patients with type 1 diabetes

Background: Albuminuria has been classically considered a marker of kidney damage progression in diabetic patients and it is routinely assessed to monitor kidney function. However, the role of a mild GFR reduction on the development of stage 653 CKD has been less explored in type 1 diabetes mellitus (T1DM) patients. Aim of the present study was to evaluate the prognostic role of kidney disease measures, namely albuminuria and reduced GFR, on the development of stage 653 CKD in a large cohort of patients affected by T1DM. Methods: A total of 4284 patients affected by T1DM followed-up at 76 diabetes centers participating to the Italian Association of Clinical Diabetologists (Associazione Medici Diabetologi, AMD) initiative constitutes the study population. Urinary albumin excretion (ACR) and estimated GFR (eGFR) were retrieved and analyzed. The incidence of stage 653 CKD (eGFR &lt; 60 mL/min/1.73 m2) or eGFR reduction &gt; 30% from baseline was evaluated. Results: The mean estimated GFR was 98 \ub1 17 mL/min/1.73m2 and the proportion of patients with albuminuria was 15.3% (n = 654) at baseline. About 8% (n = 337) of patients developed one of the two renal endpoints during the 4-year follow-up period. Age, albuminuria (micro or macro) and baseline eGFR &lt; 90 ml/min/m2 were independent risk factors for stage 653 CKD and renal function worsening. When compared to patients with eGFR &gt; 90 ml/min/1.73m2 and normoalbuminuria, those with albuminuria at baseline had a 1.69 greater risk of reaching stage 3 CKD, while patients with mild eGFR reduction (i.e. eGFR between 90 and 60 mL/min/1.73 m2) show a 3.81 greater risk that rose to 8.24 for those patients with albuminuria and mild eGFR reduction at baseline. Conclusions: Albuminuria and eGFR reduction represent independent risk factors for incident stage 653 CKD in T1DM patients. The simultaneous occurrence of reduced eGFR and albuminuria have a synergistic effect on renal function worsening

Higher brightness beams from the SPS for the HL-LHC era

The need to push the LHC beyond its limits and increase the deliverable luminosity to the experiments by about one order of magnitude has driven the ongoing injector and HL-LHC upgrades. The higher luminosity requires to increase the beam brightness, which directly translates in the need to adapt the different machine protection systems. Among all the foreseen upgrades, the transfer line collimators (TCDI) and the LHC injection protection systems will be revised. In particular, the guaranteed protection is evaluated in this Ph D work, together with the specification for the minimum shielded aperture in case of injection failures. A detailed model is also developed which insures a more reliable and efficient procedure for the validation of the TCDI setup within the required accuracy. The physics beyond colliders will also be pushed over its current limits in the HL-LHC era. SHiP, a new proposed fixed target experiment served by the SPS is under study. The unprecedented level of requested protons on target per year needs an assessment of the present SPS slow extraction. The main performance limitation of this technique is the activation of the area surrounding the extraction elements due to losses. The possibilities to optimise the present slow extraction as well as new ideas are investigated in order to preserve today’s performances while reducing the extraction losses

REMOTE: Applications of computer vision and forecasting to the CERN accelerators

Abstract: ​​​​​​​The recent progress in computing and ad-hoc software has significantly simplified the access to machine learning techniques and numerical optimisation. In the LHC and its injector complex, a very diverse and inhomogeneous set of problems present the right observables types to be addressed with the classic or most cutting edge machine learning algorithms. In this talk, we introduce a set of techniques that have been applied to the CERN accelerator complex to solve problems that would have been otherwise impossible or very complicated to solve classically. Specifically, we will introduce the usage of computer vision techniques, time series analysis and physics aware neural networks. For all these algorithms and principles, we will present real applications to the LHC and its injectors. Short bio Francesco Maria Velotti Francesco obtained his MSc at Universita' del Sannio in Electronic engineering and his PhD at Ecole Polytechnique Fédérale de Lausanne in 2017 in accelerator physics with studies regarding the HL-LHC injection system and crystal-shadowing slow extraction. He is now a CERN staff member in the SY department and ABT group. Since 2018 he is one of the SPS supervisors and directly involved in the operation of the SPS. His research topics include slow extraction losses and spill quality optimisation, as well as machine learning applications to ABT and accelerator systems in general.&nbsp;</p

Electron beam trajectory reconstruction for transfer lines

The goal of the AWAKE Run 2a experiment is to study the electron-seeding of the self-modulation of a proton bunch propagating in plasma. To study the dependence of the electron-seeding on the relative alignment between the beams, a method to measure this alignment has been developed. The electron and proton beamlines are brought together into a common line before they are injected into the plasma. The BPMs within the common line cannot be used for electron measurements in the presence of a proton beam as the signal from the proton beam dominates. In this case, the electron beam trajectory through the common line needs reconstructing, event-by-event, using beam trajectory measurements from the first part of the electron line. Here we describe the use of Physics-Guided Neural Networks to propagate the trajectory of the electron beam through the common line to the entrance of the AWAKE plasma cell

Construction of TT10-SPS Injection Region Model and Following Aperture Studies

The main objective of this project was to model the injection region going from the transfer line TT10 into the SPS. This was done using MADX, and this model was then used to study the effect of various machine and beam parameters as well as upcoming or potential updates to the existing system. Scaling down the voltage on the MKP-L using the so called rebalancing factor was shown to be an issue with regards to MSI8 aperture, however adding an injection bump shifted the aperture constraint to the MKP-A but still allowed a clean injection. An additional MKP-S injection kicker was unable to be properly integrated under realistic conditions, but could be tested further, possibly in conjunction with an additional septa. The planned LSS1 dog-leg removal proved to not pose a critical problem to the acceptance of the injected beam, and the additional shield inserts required on the MKP-L for the high luminosity upgrade were also shown to not pose a issue to the vertical aperture of the beam, or to the horizontal good field region. Many other potential modifications to the machine could also tested with this model in the future

TT20 unsplit beam optics for dedicated ECN3 operation

The Transfer Tunnel 20 (TT20) in the CERN North Area (NA) contains transfer lines TT21-25 to transport beam extracted from the Super Proton Synchrotron (SPS). The beam is shared between three primary production targets simultaneously using two sets of Lamberston septa magnets. Proposals for a future facility in the ECN3 underground cavern might require new optics in the TT20 transfer lines to provide high-intensity, `unsplit' beam directly to future NA experiment(s). Here, we present an optics to transmit an unsplit beam through the splitter magnets without collimation and through the transfer lines without losses. The T4 target is unsuitable for high beam intensity and a closed magnetic orbit bump is proposed to bypass the target

MD#4164: Separatrix folding with octupoles during slow extraction at SPS

The powering of the SPS main octupole circuit (LOF) during third-integer slow extraction was tested to reduce the density of the beam impinging the wires of the electrostatic extraction septum (ZS). This note briefly summarises the results of the slow extraction tests carried out with 400 GeV protons, including the demonstration of a 40% reduction of the beam loss during extraction at the ZS in Long Straight Section (LSS) 2

TT20 Transport and Splitting of Beams Extracted Using Crystal Shadowing in LSS2 of the SPS

The CERN Super Proton Synchrotron (SPS) is employed to supply slow extracted beams to fixed target experiments located in CERN’s North Area. In order to reduce beam loss at extraction, several techniques have been proposed for implementation in the SPS. One of those techniques, which was tested with beam in 2018, uses a bent silicon crystal in Long Straight Section 2 (LSS2) to shadow the blade of the extraction electrostatic septum [7]. This approach alters the shape of the extracted particle distribution that needs to be transported and split in Transfer Tunnel 20 (TT20). This report studies the impact on the transmission and splitting efficiency with the crystal aligned in channelling and provides a solution for start-up after LS2 without hardware changes by modifying the transfer line optics. The main simulation code and files are available at https://gitlab.cern.ch/parrutia/beamlet_lss2