LEAPS – the League of European Accelerator-based Photon Sources – is a strategic consortium initiated by the Directors of the Synchrotron Radiation and Free Electron Laser user facilities in Europe. Its primary goal is to actively and constructively ensure and promote the quality and impact of fundamental, applied and industrial research carried out at each facility to the greater benefit of...
Maxwell, GPUs and the future of AI computing in the DESY compute center
Pelagic imaging, the capture of images of plankton and particles in the open-water zones of the oceans, is central for understanding plankton diversity, distribution, and dynamics on a large scale.The integration of deep learning (DL) into pelagic imaging workflows offers the potential to improve the precision and scalability of image-based analyses in plankton research.
This talk will first...
Foundation models are multi-dataset and multi-task machine learning methods that once pre-trained can be fine-tuned for a large variety of downstream applications. The successful development of such general-purpose models for physics data would be a major breakthrough as they could improve the achievable physics performance while at the same time drastically reduce the required amount of...
- first prototype of our fully local OpenAI alternative
- first integrations
- what we plan next
Based on the LLM (Large Language Model) and RAG (Retrieval-Augmented Generation) technologies, the FS-EC group has developed a technical documentation and code retrieval Q&A AI Agent. This Agent will be further enhanced by integrating historical Q&A information from the ticket system, aiming to co-develop a professional Q&A AI Agent for beam scientists and users.
This talk presents applications of Large Language Model (LLM)-powered tools for enhancing daily accelerator operation. First, an overview of LLM tools that utilize Retrieval Augmented Generation (RAG) techniques is provided, demonstrating how existing knowledge bases, such as electronic logbooks, can be leveraged. Additionally, an advanced ReAct prompting approach (Reasoning and Action) is...
Over the last year a group of imaging enthusiasts has met to discuss
current issues of phase contrast imaging and tomography at PETRA III. I
will give a short introduction to the experimental modality and
introduce some of the challenges we investigate currently.
In near-field imaging, accurate phase retrieval is crucial for reconstructing complex wavefronts, with applications in optics, microscopy, and X-ray imaging. The beamlines at PETRA III, DESY, like many advanced imaging facilities, involve various inverse problems, including computed tomography, phase retrieval, and image deblurring. Among these, phase retrieval stands out as a non-linear,...
In this talk, we're exploring possible data-driven ML methods to make quality estimation of retrieved phases in the context of near-field holograpy.
Near-field holography imaging is essential in science and industry for high-resolution imaging at nanostructures and microscopic scales, but it is highly sensitive to noise, which varies depending on both the detector type and the exposure time. This study introduces a machine learning based denoising method using dilated convolutional neural networks (DnCNN), which effectively reduces noise...
We present a deep learning approach based on the Noise2Noise framework to denoise multidimensional photoemission spectroscopy (MPES) data obtained with a time-of-flight momentum microscope. Specifically, a 3D U-Net architecture is trained using low- and high-count noisy data, enabling the model to learn noise characteristics without requiring clean images. Our approach excels at reconstructing...
As part of our correlative characterisation studies of biodegradable metal bone implants we have performed both synchrotron-radiation microtomography (SR-µCT) and histology sequentially on the same samples and regions of interest. Histological staining is still the gold standard for tissue visualisation yet requires multiple time-consuming sample preparation steps (fixing, embedding,...
In materials science research, digital volume correlation (DVC) analysis is commonly used to track deformations and strains to elucidate morphology-function relationships. Recently, we proposed the neural network, VolRAFT, which estimated the 3D displacement vector between the reference volume and the deformed volume by extending the state-of-the-art optical flow network from 2D images to 3D...
With the high brilliance and ultrashort pulses of X-ray Free Electron Lasers, Serial Femtosecond Crystallography (SFX) achieved atomic-resolution for micro and nano protein crystals. Throughout the data collection the beam is prone to fluctuations caused by the self-amplified spontaneous emission process which generates the beam and is intrinsically a stochastic phenomenon. These fluctuations...
X-ray--induced Coulomb explosion imaging is one promising method to perform single-particle molecular imaging on a femtosecond timescale. By firing an intense ultrashort XFEL pulse at single molecules, it gets strongly ionized and violently dissociates into atomic fragments that are measured in coincidence. However, due to the finite detection efficiency in the experiment, the collected data...
Virtual diagnostics can provide complementary diagnostics, by combining information from several sources, thereby profiting from the advantages of each one. To this end, we present the Virtual Spectrometer, which maps data from a low-resolution time-of-flight spectrometer to a high-resolution one. While the low-resolution spectrometer is non-invasive, can operate at 4.5 MHz and has complex...
Plasma-based accelerators hold the potential to achieve mulit-giga-volt-per-metre accelerating gradients, offering a promising route to more compact and cost-effective accelerators for future light sources and colliders. However, plasma wakefield acceleration (PWFA) is often a nonlinear, high-dimensional process that is sensitive to jitters in multiple input parameters, making the setup,...
In machine learning, the ability to make reliable predictions is paramount. Yet, standard ML models and pipelines provide only point predictions without accounting for model confidence (or the lack thereof). Uncertainty in model outputs, especially when faced with out-of-distribution (OOD) data, is essential when deploying models in production. This talk serves as an introduction to the...
The European XFEL is a complex machine building on hundreds of subsystems, which might require frequent calibration. The automation of the latter frees operators’ time and potentially increases the exploitation of allotted beamtime.
Three use-cases are shown in this presentation. A first use-case takes advantage of Bayesian Optimization to spatially align an optical laser to a camera. A...
The talk focuses on an on-going effort that aims to predict the x-ray pulse properties from machine settings and available diagnostics via a surrogate model. While still at an early stage, preliminary results already provide useful insights into the correlation between electron bunches and x-ray spectral properties at MHz repetition rates. The goal of the program is not only to provide a...
Virtual diagnostic tools leveraging readily available input data offer a non-invasive way to optimize Free-Electron Lasers (FEL) operation and delivery, especially when limitations with conventional diagnostics arise. This work presents a novel approach using an artificial neural network to online predict photon pulse pointing at MHz level for both soft and hard x-rays. The model input is...
At the LHC, collision data events are produced every 25 ns. To handle these large data streams, the CMS trigger system filters events in real time. The first stage of that system, the Level-1 trigger, is implemented in hardware using FPGAs. We present a novel ML-based anomaly detection algorithm that has been integrated in the Level-1 Trigger and successfully taken data during the 2024 pp...
The likelihood ratio (LR) plays an important role in statistics and many domains of science. The Neyman-Pearson lemma states that it is the most powerful test statistic for simple statistical hypothesis testing problems [1] or binary classification problems. Likelihood ratios are also key to Monte Carlo importance sampling techniques [2]. Unfortunately, in many areas of study the probability...
A normalising flow is a stochastic tool that can be used for generative modelling and reconstruction. While not the lightest models in the toolbox, normalising flows are often very accurate and their bi-directionality can be uniquely advantageous. Literature that guides architecture and design choice for users of these models is focused on non-HEP applications, and optimal results in HEP...
Searches for physics beyond the Standard Model at the Large Hadron collider usually rely on phenomena that affect leptons, photons or jets with high transverse momenta (> 15 GeV).
Alongside these hard physics objects, proton-proton collisions produce a multitude of soft ones, which are known as the underlying event. This work focuses on the search of anomalies among the soft physics objects,...
Ever-increasing collision rates place significant computational stress on the simulation of future experiments in high energy physics. Generative machine learning (ML) models have been found to speed up and augment the most computationally intensive part of the traditional simulation chain: the calorimeter simulation. Many previous studies relied on fixed grid-like data representation of...
Monte Carlo (MC) simulations are essential for collider experiments, enabling comparisons of experimental findings and theoretical predictions. However, these simulations are computationally demanding, and future developments, like increased event rates, are expected to exceed available computational resources. Generative modeling can substantially cut computing costs by augmenting MC...
