A Start To End Machine Learning Approach To Maximize Scientific Throughput From The LCLS-II-HE
- URL: http://arxiv.org/abs/2505.23858v1
- Date: Thu, 29 May 2025 07:49:39 GMT
- Title: A Start To End Machine Learning Approach To Maximize Scientific Throughput From The LCLS-II-HE
- Authors: Aashwin Mishra, Matt Seaberg, Ryan Roussel, Fred Poitevin, Jana Thayer, Daniel Ratner, Auralee Edelen, Apurva Mehta,
- Abstract summary: In this article, we outline the strategy we are developing at SLAC to implement Machine Learning driven optimization, automation and real-time knowledge extraction.<n>This strategy will be implemented at the start of the electron accelerator, to the multidimensional X-ray optical systems till the experimental endstations and the high readout rate, multi-megapixel detectors at L, and to deliver the design performance to the users.
- Score: 1.0579853652793991
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: With the increasing brightness of Light sources, including the Diffraction-Limited brightness upgrade of APS and the high-repetition-rate upgrade of LCLS, the proposed experiments therein are becoming increasingly complex. For instance, experiments at LCLS-II-HE will require the X-ray beam to be within a fraction of a micron in diameter, with pointing stability of a few nanoradians, at the end of a kilometer-long electron accelerator, a hundred-meter-long undulator section, and tens of meters long X-ray optics. This enhancement of brightness will increase the data production rate to rival the largest data generators in the world. Without real-time active feedback control and an optimized pipeline to transform measurements to scientific information and insights, researchers will drown in a deluge of mostly useless data, and fail to extract the highly sophisticated insights that the recent brightness upgrades promise. In this article, we outline the strategy we are developing at SLAC to implement Machine Learning driven optimization, automation and real-time knowledge extraction from the electron-injector at the start of the electron accelerator, to the multidimensional X-ray optical systems, and till the experimental endstations and the high readout rate, multi-megapixel detectors at LCLS to deliver the design performance to the users. This is illustrated via examples from Accelerator, Optics and End User applications.
Related papers
- Resource-Efficient Beam Prediction in mmWave Communications with Multimodal Realistic Simulation Framework [57.994965436344195]
Beamforming is a key technology in millimeter-wave (mmWave) communications that improves signal transmission by optimizing directionality and intensity.<n> multimodal sensing-aided beam prediction has gained significant attention, using various sensing data to predict user locations or network conditions.<n>Despite its promising potential, the adoption of multimodal sensing-aided beam prediction is hindered by high computational complexity, high costs, and limited datasets.
arXiv Detail & Related papers (2025-04-07T15:38:25Z) - Lumina-Next: Making Lumina-T2X Stronger and Faster with Next-DiT [120.39362661689333]
We present an improved version of Lumina-T2X, showcasing stronger generation performance with increased training and inference efficiency.
Thanks to these improvements, Lumina-Next not only improves the quality and efficiency of basic text-to-image generation but also demonstrates superior resolution extrapolation capabilities.
arXiv Detail & Related papers (2024-06-05T17:53:26Z) - Automated Anomaly Detection on European XFEL Klystrons [1.9389881806157316]
High-power multi-beam klystrons represent a key component to amplify RF at European XFEL.
We conducted experiments to determine various operational modes and conduct feature extraction and dimensionality reduction.
We recognized the most promising components that might help us better understand klystron operational states and identify early on possible faults or anomalies.
arXiv Detail & Related papers (2024-05-20T21:59:07Z) - Multi-Modal Data-Efficient 3D Scene Understanding for Autonomous Driving [58.16024314532443]
We introduce LaserMix++, a framework that integrates laser beam manipulations from disparate LiDAR scans and incorporates LiDAR-camera correspondences to assist data-efficient learning.<n>Results demonstrate that LaserMix++ outperforms fully supervised alternatives, achieving comparable accuracy with five times fewer annotations.<n>This substantial advancement underscores the potential of semi-supervised approaches in reducing the reliance on extensive labeled data in LiDAR-based 3D scene understanding systems.
arXiv Detail & Related papers (2024-05-08T17:59:53Z) - Closing the loop: Autonomous experiments enabled by
machine-learning-based online data analysis in synchrotron beamline
environments [80.49514665620008]
Machine learning can be used to enhance research involving large or rapidly generated datasets.
In this study, we describe the incorporation of ML into a closed-loop workflow for X-ray reflectometry (XRR)
We present solutions that provide an elementary data analysis in real time during the experiment without introducing the additional software dependencies in the beamline control software environment.
arXiv Detail & Related papers (2023-06-20T21:21:19Z) - Ultra-High-Definition Low-Light Image Enhancement: A Benchmark and
Transformer-Based Method [51.30748775681917]
We consider the task of low-light image enhancement (LLIE) and introduce a large-scale database consisting of images at 4K and 8K resolution.
We conduct systematic benchmarking studies and provide a comparison of current LLIE algorithms.
As a second contribution, we introduce LLFormer, a transformer-based low-light enhancement method.
arXiv Detail & Related papers (2022-12-22T09:05:07Z) - Multipoint-BAX: A New Approach for Efficiently Tuning Particle
Accelerator Emittance via Virtual Objectives [47.52324722637079]
We propose a new information-theoretic algorithm, Multipoint-BAX, for black-box optimization on multipoint queries.
We use Multipoint-BAX to minimize emittance at the Linac Coherent Light Source (LCLS) and the Facility for Advanced Accelerator Experimental Tests II (FACET-II)
arXiv Detail & Related papers (2022-09-10T04:01:23Z) - Mixed Diagnostics for Longitudinal Properties of Electron Bunches in a
Free-Electron Laser [0.0]
Longitudinal properties of electron bunches are critical for the performance of a wide range of scientific facilities.
We leverage the power of artificial intelligence to build a neural network model using experimental data.
We propose a way to significantly improve the CTR spectrometer online measurement by combining the predicted and measured spectra.
arXiv Detail & Related papers (2022-01-15T06:32:48Z) - Scaling and Acceleration of Three-dimensional Structure Determination
for Single-Particle Imaging Experiments with SpiniFEL [0.0]
We present SpiniFEL, an application used for structure determination of proteins from single-particle imaging (SPI) experiments.
SpiniFEL is being developed to run on supercomputers in near real-time while an experiment is taking place, so that the feedback about the data can guide the data collection strategy.
arXiv Detail & Related papers (2021-09-11T18:49:54Z) - Accurate and confident prediction of electron beam longitudinal
properties using spectral virtual diagnostics [0.0]
Longitudinal phase space (LPS) provides a critical information about electron beam dynamics for various scientific applications.
We present a machine learning-based Virtual Diagnostic (VD) tool to accurately predict the LPS for every shot using non-destructively from the radiation of relativistic electron beam.
arXiv Detail & Related papers (2020-09-27T13:02:33Z) - Transfer Learning for Motor Imagery Based Brain-Computer Interfaces: A
Complete Pipeline [54.73337667795997]
Transfer learning (TL) has been widely used in motor imagery (MI) based brain-computer interfaces (BCIs) to reduce the calibration effort for a new subject.
This paper proposes that TL could be considered in all three components (spatial filtering, feature engineering, and classification) of MI-based BCIs.
arXiv Detail & Related papers (2020-07-03T23:44:21Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.