The evolution of neural networks from their early conceptual
roots in the 1940s to their modern-day applications are presented.
Starting with McCulloch and Pitts' model of neurons, we explore key
milestones like Rosenblatt’s perceptron, the multiple rediscovery of backpropagation,
the Hopfield networks and boltzmann machines,
and the advent of deep neural networks. The discussion...
EuCAIF is an European initiative for advancing the use of Artificial Intelligence (AI) in Fundamental Physics. Members are working on particle physics, astroparticle physics, nuclear physics, gravitational wave physics, cosmology, theoretical physics as well as simulation and computational infrastructure. The objective of the coalition is to federate on various AI-related aspects (knowledge,...
Ion beam therapy is the most advanced form of radiotherapy, requiring fast, precise, and accurate treatment planning. Speed is a critical factor for cancer patients, as timely interventions can significantly impact outcomes. While Monte Carlo simulations offer high-quality dose calculations, they are too slow for routine clinical workflows. In contrast, analytical pencil beam algorithms...
Particle therapy provides a significantly more precise dose distribution within the patient compared to conventional photon radiotherapy, reducing radiation in healthy tissue but increasing the need for dose verification. A mixed beam of helium and carbon ions, recently first produced at GSI, can enable online range monitoring and simultaneous imaging. In the case of lung cancer, the measured...
The precision of carbon ion therapy offers great potential for more targeted lung cancer therapy. To use the steep dose gradients to target moving tumors requires motion mitigation strategies that rely on real-time motion information acquired, e.g., with optical tracking systems. To utilize the information most effectively requires to predict motion variations. We developed an LSTM model to be...
Using the recent advances in machine learning (ML) algorithms, especially in the area of image recognition, we plan to develop new ML particle identification algorithms for the PANDA Barrel DIRC and compare the results to conventional reconstruction methods.
First network implementations show a performance comparable to conventional methods on a limited phase space. As a next step, we are...
The calibration of detectors in heavy-ion experiments is key to the measurement of several physics observables. Corrections must be applied to the expected detector signal in order to account for changes in environmental or hardware conditions over time and in different spatial regions. The use of neural network-based corrections trained on clean input signals allows this to be performed with...
LISA project aims to develop an array of five 5 × 5 grids of thin single-crystal diamond detectors, to measure otherwise inaccessible lifetimes in exotic nuclei. Determining reaction positions in the target is a pivotal component of the project and we are developing ML algorithms for characterization.We used ANN and Random Forest Algorithm on the simulated data. RF algorithm has shown the most...
Non-destructive Schottky detectors can be used to accurately determine the masses and lifetimes of exotic nuclear species and/or their isomeric states in storage rings. The analysis requires time-resolved spectra to undergo particle identification and determination of frequency spread ∆f and decay time. Often times corrections for rigidity, drift, etc. need to be applied, or spectra need to be...
Real-time calibrations for future detectors at FAIR
The online data processing of the next generation of experiments conducted at FAIR requires a reliable reconstruction of event topologies and, therefore, will depend heavily on in-situ calibration procedures. In this study we present a neural network-based tool designed to provide real-time predictions of calibration constants, which rely...
The $\textbf{H}$igh $\textbf{A}$cceptance $\textbf{D}$i$\textbf{E}$lectron $\textbf{S}$pectrometer (HADES) is a versatile magnetic spectrometer aimed at studying dielectron production in pion, proton and heavy-ion induced collisions. The conventional reconstruction algorithm for the HADES RICH is based upon the Hough Transform (HT) method for ring finding. This method fails to efficiently...
In this contribution we present weak decay topology recognition utilizing an artifial Neural Network. This approach significantly improved the precision of the analysis of Λ hyperons and K0s mesons and enabled us to measure more rare probes like the Ξ hyperon or Hypernuclei for the first time in heavy-ion collisions with HADES. We highlight the choice of input parameters as well as the applied...
LISE quick safety setting simulation using ML
The LISE tool from MSU is widely use to prepare settings for nuclear physics spectrometers around the world. LISE is a very versatile and potent tool including all nuclear physics models needed for precise calculation. The main drawback is a standard setting calculation for a Super-FRS setting, used to check the total particle rate at each focal...
The ARTIFACT project leverages AI to enhance tuning, calibration, beam focusing, and anomaly detection in accelerator systems. By developing an advanced AI framework, the project aims to improve the efficiency and precision of accelerator operations across various scientific applications. A key feature is the creation of a unified metadata schema to structure and separate metadata from...
Accelerator laboratories across the globe are investigating numerous techniques to achieve this goal, including classical optimization, Bayesian optimization (BO), and reinforcement learning. This presentation will provide an overview of recent activities in these domains at GSI. The implementation of the Generic Optimization Framework & Frontend (GOFF) at GSI, supported by the EURO-Labs...
CRYRNG@ESR is a low-energy ion storage ring and Swedish in-kind contributon to FAIR. It is operational since 2019 serving atomic, nuclear and material physics experiments. Besides this purpose, it serves as a test bench for new FAIR concepts such as novel detectors and control system features. In this context, the "device-automator" application provides an end-user application for implementing...
To improve beam quality and beam intensity at the Cooler Synchrotron COSY several machine learning and artificial intelligence projects were carried out. Highlights include:
- automated beam line control with Reinforcement Learning
- automated intensity optimisation with Bayesian Optimization for Arbitrary Targets
- improved agreement between machine and model with Genetic Optimization
A...
