[A My Name Is Anthony Gonsalves Movie Online
Christel Malden
Big data and machine learning techniques have the potential to revolutionize research on intense laser-matter interaction and laser-plasma acceleration. With many groups adopting these technologies, we propose to survey and potentially consolidate on-going development efforts. The LPA online workshop will feature plenary talks, invited contributions and discussion sessions regarding specific implementations of control systems, data acquisition and machine learning in laser accelerator laboratories. Furthermore, we invite submissions for a limited virtual poster presentations and a limited number of contributed talks. Also, as a unique feature of the online format, the workshop will be preceded by a number of seminar talks and lectures as part of the LPA Online Seminars.
Scientists use the LCLS X-Ray free electron laser to take crisp pictures of atomic motions, watch chemical reactions unfold, probe the properties of materials and explore fundamental processes in living things. In the fall of 2022 the LCLS-II superconducting linac will be commissioned and the X-Ray shot rate will increase from 120Hz to 1MHz. Correspondingly, the raw data volume will increase from 2GB/s to 200GB/s. We will discuss the data acquisition and analysis techniques used to handle this, as well as real-time data reduction techniques used to lower the recorded data volume to 20GB/s.
[1] Tango controls, -controls.org/
[2] ElliOOs, monitoring and control library based on a distributed multi-client and multi-server architecture used by Amplitude Laser
[3] An Ada-Based Framework to Develop Web-Based Applications, -web-server
[4] Grafana, open source monitoring and analytics platform
[5] CNRS data management plan,link Recommendations for Services in a FAIR data ecosystem
The control, management and analysis of data arising from laser-plasma experiments are key issues that, when well addressed, enable rapid insights into underlying physics and time-critical decision making for a given investigation. This issue now demands more attention if we are to take full advantage of developments in high-repetition rate, high-power laser technology.
Over the past decade, at Strathclyde and supported by work at the Central Laser Facility, we have developed a series of data control and analysis libraries (DARB, LPI-Py and BISHOP) which have supported a number of experiments. We introduce and review those developments and highlight potential future directions for these projects including advancements in data analysis, visualisation and machine-learning guided experiments.
Gemini, at the Central Laser Facility, provides access for academic and industrial users to perform a variety of cutting-edge experiments. As well as two beams, each with 12 J in 40 fs, we provide mechanical and electrical services to support experiments. These now include systems to support data acquisition, analysis, and storage, as well as experiment control. This has allowed us to conduct experiments involving active feedback.
In this talk I will describe development of the BELLA Center control system, from requirements to implementation on all of our beamlines. The control system, named GEECS (Generalized Equipment and Experiment Control System), monitors, logs, and controls equipment distributed across a network. It was designed to be a complete software package that is straightforward to install, use, and develop. It is modular and scalable, and built using LabView graphical object oriented programming (GOOP). It has been the control system used for the experiments at the BELLA center for over 10 years. I will explain why we went down the path we did, give my perspectives on what has been successful and what changing requirements mean for the future of controls at the BELLA center.
Laser-driven energetic proton accelerators have the potential to provide compact sources of MeV energy, low emittance, sub-picosecond duration proton beams for a variety of applications. The primary impediment to their wider adoption is the challenge of shot-to-shot reproducibility and tuning of the parameters to optimize desirable proton beam qualities in a multi-dimensional parameter space. Recent developments in laser technology and control systems, making available multi-Hz delivery of joule-class, relativistically-intense laser pulses with automated control, combined with online diagnostics have enabled the automated scanning of parameters space, quantification of uncertainty and use of feedback loops for optimization of desirable outputs (e.g. proton beam maximum energy). Bayesian optimization has already demonstrated impressive gain in x-ray generation when used in conjunction with a laser wakefield accelerator [1]. Here, we discuss the preliminary results from experiments expanding this tool to laser-driven proton acceleration and challenges facing the adaption of this gaussian-process-regression-based Bayesian optimizer to the sharply varying parameter space of laser-solid interactions.
Our machine is based on Tango control system, currently operating at 10Hz, it was designed to control the machine (magnets, laser, RF, diags etc.), and perform dedicated physical measurements. This architecture allows us to easily integrate ML methods and tools to measure, predict and improve the transported electron beam quality.
We discuss the tuning of the machine with use of surrogate model based on numerical simulations completed with the experimental data.
We present a procedure for optimization of a laser pulse duration to the shortest possible value using a feedback control loop between the FC SPIDER from APE and DAZZLER from Fastlite. New SPIDER software was developed in collaboration with APE. It was integrated into the laser control system and it enables real-time measurement running on real-time hardware with pulse reconstruction time of less than 25 ms. SPIDER measurement is published live using EPICS 3.14. This solution uses Channel Access protocol and is also linked to an archiver. Data is read from EPICS using a custom developed LabVIEW library (LabIOC - developed in collaboration with Observatory Sciences). A combination of Gradient Descent and a Differential Genetic Algorithm provides an optimization by changing three DAZZLER parameters: GDD, TOD and FOD. The optimization algorithm is written as a function in Python and then implemented into LabVIEW code through a LabVIEW Python node. Optimization steps are performed at a laser repetition rate 3.3 Hz and new values of the aforementioned three parameters are saved to a text file that are uploaded to DAZZLER every shot. Although complete implementation is not yet fully tested, simulations show several problems with algorithm speed and convergence.
Tango Controls has been adopted as main system for supervisory control and data acquisition at the Center for Advanced Laser Applications (CALA) in recent years. As an open-source, free and software independent toolkit it is highly customizable and applicable for almost any measurement device. In its current implementation at CALA the main laser system, as well as each experimental cave, has independently operating database servers that supervise the measurement instruments in a decentralized manner.
The developed Tango Controls architecture allows communication between experimental devices and laser instruments, while enabling the inclusion of security features. Such security features help prevent the destruction of experimental equipment and laser components. Furthermore, Tango Controls allows for a simple and streamlined integration process of new measurement instruments.
The Centre for Advanced Laser Applications (CALA) in Munich is home to the ATLAS-3000 high power laser dedicated to research on laser particle acceleration and applications thereof. The laser and each experimental area are running control systems based on Tango controls. In addition to the hardware control, this is used to record experimental data in an automated fashion with every laser shot. As the laser shots are executed via software, the system emits a software trigger to acquire data on slow diagnostics, as well as an electrical trigger for hardware-triggered devices. In this poster, the current design of this data archiving system including file formats, call hierarchy, timings and some example diagnostics will be presented.
In recent research, reinforcement learning algorithms have been shown capable of solving complex control tasks, also showing potential for beam control, and in the optimization and automation of tasks in accelerator operation.
As part of the Helmholtz AI project "Machine Learning Toward Autonomous Accelerators" -- a collaboration between DESY and KIT -- reinforcement learning applications for the automatic control of an electron linear accelerators are investigated. In this contribution, we present first steps taken toward developing a framework for training reinforcement learning agents in simulation environments on specific tasks and applying these agents on an actual particle accelerator. In the future, this framework will allow for fast application of reinforcement learning to a multitude of optimization tasks on particle accelerators, eventually enabling autonomous operation to improve reproducibility and machine availability.
Laser-plasma acceleration (LPA) promises compact sources of high-brightness electron beams for science and industry. However, transforming LPA into a technology to drive real-world applications remains a challenge. In this talk, we discuss how the design and operational principles of the LUX experiment allow us to adopt data-driven approaches to understanding and improving the performance of laser-plasma accelerators. The basis of this development is the deep integration of the machine into a control system that enables real-time monitoring and active stabilization at 1 Hz. In consequence, stable and reproducible conditions can be maintained over many hours of operation and thousands of individual events, which opens the path for applying machine learning techniques to analyze and control the experiment. Featured results include the use of Bayesian optimization to autonomously tune the accelerator to improve the electron bunch quality, and the demonstration of a predictive model that precisely links the LPA stability to fluctuations of the drive laser pulse. Our findings provide guidelines for the development of the KALDERA laser system and highlight the potential of active stabilization at kHz repetition rates.
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