First Steps Toward an Autonomous Accelerator, a Common Project Between DESY and KIT

Reinforcement Learning algorithms have risen in popularity in recent years in the accelerator physics community, showing potential in beam control and in the optimization and automation of tasks in accelerator operation. The Helmholtz AI project "Machine Learning toward Autonomous Accelerators" is a collaboration between DESY and KIT that works on investigating and developing RL applications for the automatic start-up of electron linear accelerators. The work is carried out in parallel at two similar research accelerators: ARES at DESY and FLUTE at KIT, giving the unique opportunity of transfer learning between facilities. One of the first steps of this project is the establishment of a common interface between the simulations and the machine, in order to test and apply various optimization approaches interchangeably between the two accelerators. In this paper we present the first results on the common interface and its application to beam focusing in ARES, and the idea of laser shaping with spatial light modulators at FLUTE

Machine Learning Based Spatial Light Modulator Control for the Photoinjector Laser at FLUTE

FLUTE (Ferninfrarot Linac- und Test-Experiment) at KIT is a compact linac-based test facility for novel accelerator technology and a source of intense THz radiation. FLUTE is designed to provide a wide range of electron bunch charges from the pC- to nC-range, high electric fields up to 1.2 GV/m, and ultra-short THz pulses down to the fs-timescale. The electrons are generated at the RF photoinjector, where the electron gun is driven by a commercial titanium sapphire laser. In this kind of setup the electron beam properties are determined by the photoinjector, but more importantly by the characteristics of the laser pulses. Spatial light modulators can be used to transversely and longitudinally shape the laser pulse, offering a flexible way to shape the laser beam and subsequently the electron beam, influencing the produced THz pulses. However, nonlinear effects inherent to the laser manipulation (transportation, compression, third harmonic generation) can distort the original pulse. In this paper we propose to use machine learning methods to manipulate the laser and electron bunch, aiming to generate tailor-made THz pulses. The method is demonstrated experimentally in a test setup

Neurocognitive patterns across genetic levels in behavioral variant frontotemporal dementia: a multiple single cases study

Q3Q2Pacientes con Demencia frontotemporalBackground: Behavioral variant frontotemporal dementia (bvFTD) has been related to different genetic factors. Identifying multimodal phenotypic heterogeneity triggered by various genetic influences is critical for improving diagnosis, prognosis, and treatments. However, the specific impact of different genetic levels (mutations vs. risk variants vs. sporadic presentations) on clinical and neurocognitive phenotypes is not entirely understood, specially in patites from underrepresented regions such as Colombia. Methods: Here, in a multiple single cases study, we provide systematic comparisons regarding cognitive, neuropsychiatric, brain atrophy, and gene expression-atrophy overlap in a novel cohort of FTD patients (n = 42) from Colombia with different genetic levels, including patients with known genetic influences (G-FTD) such as those with genetic mutations (GR1) in particular genes (MAPT, TARDBP, and TREM2); patients with risk variants (GR2) in genes associated with FTD (tau Haplotypes H1 and H2 and APOE variants including ε2, ε3, ε4); and sporadic FTD patients (S-FTD (GR3)). Results: We found that patients from GR1 and GR2 exhibited earlier disease onset, pervasive cognitive impairments (cognitive screening, executive functioning, ToM), and increased brain atrophy (prefrontal areas, cingulated cortices, basal ganglia, and inferior temporal gyrus) than S-FTD patients (GR3). No differences in disease duration were observed across groups. Additionally, significant neuropsychiatric symptoms were observed in the GR1. The GR1 also presented more clinical and neurocognitive compromise than GR2 patients; these groups, however, did not display differences in disease onset or duration. APOE and tau patients showed more neuropsychiatric symptoms and primary atrophy in parietal and temporal cortices than GR1 patients. The gene-atrophy overlap analysis revealed atrophy in regions with specific genetic overexpression in all G-FTD patients. A differential family presentation did not explain the results. Conclusions: Our results support the existence of genetic levels affecting the clinical, neurocognitive, and, to a lesser extent, neuropsychiatric presentation of bvFTD in the present underrepresented sample. These results support tailored assessments characterization based on the parallels of genetic levels and neurocognitive profiles in bvFTD.https://orcid.org/0000-0001-9422-3579https://orcid.org/0000-0001-6705-7157https://orcid.org/0000-0001-6529-7077Revista Internacional - IndexadaA2N

Active deep learning for nonlinear optics design of a vertical FFA accelerator

Vertical Fixed-Field Alternating Gradient (vFFA) accelerators exhibit particle orbits which move vertically during acceleration. This recently rediscovered circular accelerator type has several advantages over conventional ring accelerators, such as zero momentum compaction factor. At the same time, inherently non-planar orbits and a unique transverse coupling make controlling the beam dynamics a complex task. In general, betatron tune adjustment is crucial to avoid resonances, particularly when space charge effects are present. Due to highly nonlinear magnetic fields in the vFFA, it remains a challenging task to determine an optimal lattice design in terms of maximising the dynamic aperture. This contribution describes a deep learning based algorithm which strongly improves on regular grid scans and random search to find an optimal lattice: a surrogate model is built iteratively from simulations with varying lattice parameters to predict the dynamic aperture. The training of the model follows an active learning paradigm, which thus considerably reduces the number of samples needed from the computationally expensive simulations

Learning to Do or Learning While Doing: Reinforcement Learning and Bayesian Optimisation for Online Continuous Tuning

Online tuning of real-world plants is a complex optimisation problem that continues to require manual intervention by experienced human operators. Autonomous tuning is a rapidly expanding field of research, where learning-based methods, such as Reinforcement Learning-trained Optimisation (RLO) and Bayesian optimisation (BO), hold great promise for achieving outstanding plant performance and reducing tuning times. Which algorithm to choose in different scenarios, however, remains an open question. Here we present a comparative study using a routine task in a real particle accelerator as an example, showing that RLO generally outperforms BO, but is not always the best choice. Based on the study's results, we provide a clear set of criteria to guide the choice of algorithm for a given tuning task. These can ease the adoption of learning-based autonomous tuning solutions to the operation of complex real-world plants, ultimately improving the availability and pushing the limits of operability of these facilities, thereby enabling scientific and engineering advancements.Comment: 17 pages, 8 figures, 2 table

Online fit of an analytical response matrix model for orbit correction and optical function measurement

At the Karlsruhe Research Accelerator (KARA), an analytical online model of the orbit response matrix (ORM) has been developed and tested. The model, called the bilinear-exponential model with dispersion (BE+d model), is derived from the Mais-Ripken formalism describing coupled betatron motion. Compared to the standard approach of measuring the ORM, this method continuously adapts to changing beam optics without a dedicated measurement. It is especially useful for storage rings without turn-by-turn capable beam position monitors (BPMs) as the online model also gives access to estimates of the coupled optical functions. In the following, experimental orbit correction results and a comparison of fitted and simulated optical functions are presented

Prediction of Beam Losses during Crab Cavity Quenches at the HL-LHC

Studies of the crab cavities at KEKB revealed that the RF phase could shift by up to 50o within ~50 us during a quench; while the cavity voltage is still at approximately 75% of its nominal amplitude. If such a failure were to occur on the HL-LHC crab cavities, it is likely that the machine would sustain substantial damage to the beam line and surrounding infrastructure due to uncontrolled beam loss before the machine protection system could dump the beam. We have developed a low-level RF system model, including detuning mechanisms and beam loading, and use this to simulate the behaviour of a crab cavity during a quench, modeling the low-level RF system, detuning mechanisms and beam loading. We supplement this with measurement data of the actual RF response of the proof of principle Double-Quarter Wave Crab Cravity during a quench. Extrapolating these measurements to the HL-LHC, we show that Lorentz Force detuning is the dominant effect leading to phase shifts in the crab cavity during quenches; rather than pressure detuning which is expected to be dominant for the KEKB crab cavities. The total frequency shift for the HL-LHC crab cavities during quenches is expected to be about 460 Hz, leading to a phase shift of no more than 3o. The results of the quench model are read into a particle tracking simulation, SixTrack, and used to determine the effect of quenches on the HL-LHC beam. The quench model has been benchmarked against the KEKB experimental measurements. In this paper we present the results of the simulations on a crab cavity failure for HL-LHC as well as for the SPS and show that beam loss is negligible when using a realistic low-level RF response.Comment: 21 Pages, 22 figures, Submitted to PRA

A low-latency feedback system for the control of horizontal betatron oscillations

Reinforcement learning (RL) algorithms are investigated at KIT as an option to control the beam dynamics at storage rings. These methods require specialized hardware to satisfy throughput and latency constraints dictated by the timescale of the relevant phenomena. The KINGFISHER platform, based on the novel Xilinx Versal Adaptive Compute and Acceleration Platform, is an ideal candidate to deploy RL-on-a-chip thanks to its ability to execute computationally intensive and low latency feedback loops in the order of tens of microseconds. In this publication, we will present the integration of the KINGFISHER system at the Karlsruhe Research Accelerator (KARA), as a proof-of-principle turn-by-turn control feedback loop, to control induced transversal oscillations of an electron beam