arXiv subjects

Chris Tennant

Publications and source records attributed to Chris Tennant.

12 recordsLinked to original sources

arXiv2026

Machine-State Embeddings as an Operational Coordinate System for Accelerator Operation

We demonstrate that graph neural network (GNN) embeddings of injector configurations provide a practical operational coordinate system for the Continuous Electron Beam Accelerator Facility (CEBAF) injector at Jefferson Lab. Using 137,389 snapshots spanning January 2022 through March 2023, we show that injector operation occupies a small number of persistent, well-separated neighborhoods in a 16-dimensional learned state space rather than a featureless continuum. Density-based clustering identifies ten recurring operating regimes with strong operational run alignment, and regime persistence statistics confirm that these regimes are stable over timescales of hours to weeks. Large relocations between neighborhoods are rare and episodic; 99.6% of one-hour operating windows fall within an empirically derived jitter baseline. Geometric outlier screening narrows a year-long dataset to a small set of intervals warranting operational review, and nearest-neighbor retrieval enables case-based reasoning over the historical archive. A controlled beam study validates that deliberate injector reconfiguration traces coherent, interpretable trajectories in embedding space. Together these capabilities demonstrate that machine-state embeddings support holistic operational monitoring in ways that single-channel inspection cannot.

physics.acc-ph↗

arXiv2026

Toward a Fully Autonomous, AI-Native Particle Accelerator

This position paper presents a vision for self-driving particle accelerators that operate autonomously with minimal human intervention. We propose that future facilities be designed through artificial intelligence (AI) co-design, where AI jointly optimizes the accelerator lattice, diagnostics, and science application from inception to maximize performance while enabling autonomous operation. Rather than retrofitting AI onto human-centric systems, we envision facilities designed from the ground up as AI-native platforms. We outline nine critical research thrusts spanning agentic control architectures, knowledge integration, adaptive learning, digital twins, health monitoring, safety frameworks, modular hardware design, multimodal data fusion, and cross-domain collaboration. This roadmap aims to guide the accelerator community toward a future where AI-driven design and operation deliver unprecedented science output and reliability.

physics.acc-ph↗

arXiv2025

From Natural Language to Control Signals: A Conceptual Framework for Semantic Channel Finding in Complex Experimental Infrastructure

Modern experimental platforms such as particle accelerators, fusion devices, telescopes, and industrial process control systems expose tens to hundreds of thousands of control and diagnostic channels accumulated over decades of evolution. Operators and AI systems rely on informal expert knowledge, inconsistent naming conventions, and fragmented documentation to locate signals for monitoring, troubleshooting, and automated control, creating a persistent bottleneck for reliability, scalability, and language-model-driven interfaces. We formalize semantic channel finding-mapping natural-language intent to concrete control-system signals-as a general problem in complex experimental infrastructure, and introduce a four-paradigm framework to guide architecture selection across facility-specific data regimes. The paradigms span (i) direct in-context lookup over curated channel dictionaries, (ii) constrained hierarchical navigation through structured trees, (iii) interactive agent exploration using iterative reasoning and tool-based database queries, and (iv) ontology-grounded semantic search that decouples channel meaning from facility-specific naming conventions. We demonstrate each paradigm through proof-of-concept implementations at four operational facilities spanning two orders of magnitude in scale-from compact free-electron lasers to large synchrotron light sources-and diverse control-system architectures, from clean hierarchies to legacy environments. These implementations achieve 90-97% accuracy on expert-curated operational queries.

cs.CL↗

arXiv2025

A Core Ontology for Particle Accelerators: Interoperable Data and Workflows Across Facilities

We propose a small, shared core ontology for particle accelerators that provides a semantic backbone for interoperable data and workflows across facilities. The ontology names key device types, signals, parameters, and regions, and relates them through explicit properties (e.g., hasSetpoint, hasReadback, partOf). Each site contributes a lightweight facility bundle, a profile that maps local conventions into the shared vocabulary plus data slices that instantiate those mappings, without renaming channel addresses or changing existing systems. Using standard W3C technologies, the approach supports both sparse and rich descriptions. We demonstrate the idea on two beamline segments at different laboratories. A single semantic query is expressed once and evaluated against both knowledge bases, returning the locally correct PVs. The ontology thereby enables not only portable workflows but also interoperable data, since measurements and catalogs are annotated with shared semantics rather than facility-specific names. The framework complements, rather than replaces, existing middle layers and lattice/data standards, and it creates a stable foundation for reusable tools and agentic workflows.

physics.acc-ph↗

arXiv2025

eLog analysis for accelerators: status and future outlook

This work demonstrates electronic logbook (eLog) systems leveraging modern AI-driven information retrieval capabilities at the accelerator facilities of Fermilab, Jefferson Lab, Lawrence Berkeley National Laboratory (LBNL), SLAC National Accelerator Laboratory. We evaluate contemporary tools and methodologies for information retrieval with Retrieval Augmented Generation (RAGs), focusing on operational insights and integration with existing accelerator control systems. The study addresses challenges and proposes solutions for state-of-the-art eLog analysis through practical implementations, demonstrating applications and limitations. We present a framework for enhancing accelerator facility operations through improved information accessibility and knowledge management, which could potentially lead to more efficient operations.

hep-ex↗

arXiv2024

Data-Driven Gradient Optimization for Field Emission Management in a Superconducting Radio-Frequency Linac

Field emission can cause significant problems in superconducting radio-frequency linear accelerators (linacs). When cavity gradients are pushed higher, radiation levels within the linacs may rise exponentially, causing degradation of many nearby systems. This research aims to utilize machine learning with uncertainty quantification to predict radiation levels at multiple locations throughout the linacs and ultimately optimize cavity gradients to reduce field emission induced radiation while maintaining the total linac energy gain necessary for the experimental physics program. The optimized solutions show over 40% reductions for both neutron and gamma radiation from the standard operational settings.

physics.acc-ph↗

arXiv2024

Accelerating Cavity Fault Prediction Using Deep Learning at Jefferson Laboratory

Accelerating cavities are an integral part of the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Laboratory. When any of the over 400 cavities in CEBAF experiences a fault, it disrupts beam delivery to experimental user halls. In this study, we propose the use of a deep learning model to predict slowly developing cavity faults. By utilizing pre-fault signals, we train a LSTM-CNN binary classifier to distinguish between radio-frequency (RF) signals during normal operation and RF signals indicative of impending faults. We optimize the model by adjusting the fault confidence threshold and implementing a multiple consecutive window criterion to identify fault events, ensuring a low false positive rate. Results obtained from analysis of a real dataset collected from the accelerating cavities simulating a deployed scenario demonstrate the model's ability to identify normal signals with 99.99% accuracy and correctly predict 80% of slowly developing faults. Notably, these achievements were achieved in the context of a highly imbalanced dataset, and fault predictions were made several hundred milliseconds before the onset of the fault. Anticipating faults enables preemptive measures to improve operational efficiency by preventing or mitigating their occurrence.

physics.acc-ph↗

arXiv2022

The Development of Energy-Recovery Linacs

Energy-recovery linacs (ERLs) have been emphasised by the recent (2020) update of the European Strategy for Particle Physics as one of the most promising technologies for the accelerator base of future high-energy physics. The current paper has been written as a base document to support and specify details of the recently published European roadmap for the development of energy-recovery linacs. The paper summarises the previous achievements on ERLs and the status of the field and its basic technology items. The main possible future contributions and applications of ERLs to particle and nuclear physics as well as industrial developments are presented. The paper includes a vision for the further future, beyond 2030, as well as a comparative data base for the main existing and forthcoming ERL facilities. A series of continuous innovations, such as on intense electron sources or high-quality superconducting cavity technology, will massively contribute to the development of accelerator physics at large. Industrial applications are potentially revolutionary and may carry the development of ERLs much further, establishing another shining example of the impact of particle physics on society and its technical foundation with a special view on sustaining nature.

physics.acc-ph↗

arXiv2020

Superconducting radio-frequency cavity fault classification using machine learning at Jefferson Laboratory

We report on the development of machine learning models for classifying C100 superconducting radio-frequency (SRF) cavity faults in the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. CEBAF is a continuous-wave recirculating linac utilizing 418 SRF cavities to accelerate electrons up to 12 GeV through 5-passes. Of these, 96 cavities (12 cryomodules) are designed with a digital low-level RF system configured such that a cavity fault triggers waveform recordings of 17 RF signals for each of the 8 cavities in the cryomodule. Subject matter experts (SME) are able to analyze the collected time-series data and identify which of the eight cavities faulted first and classify the type of fault. This information is used to find trends and strategically deploy mitigations to problematic cryomodules. However manually labeling the data is laborious and time-consuming. By leveraging machine learning, near real-time (rather than post-mortem) identification of the offending cavity and classification of the fault type has been implemented. We discuss performance of the ML models during a recent physics run. Results show the cavity identification and fault classification models have accuracies of 84.9% and 78.2%, respectively.

physics.acc-ph↗

arXiv2015

Linear Vlasov solver for microbunching gain estimation with inclusion of CSR, LSC and linac geometric impedances

As is known, microbunching instability (MBI) has been one of the most challenging issues in designs of magnetic chicanes for short-wavelength free-electron lasers or linear colliders, as well as those of transport lines for recirculating or energy recovery linac machines. To more accurately quantify MBI in a single-pass system and for more complete analyses, we further extend and continue to increase the capabilities of our previously developed linear Vlasov solver [1] to incorporate more relevant impedance models into the code, including transient and steady-state free-space and/or shielding coherent synchrotron radiation (CSR) impedances, the longitudinal space charge (LSC) impedances, and the linac geometric impedances with extension of the existing formulation to include beam acceleration [2]. Then, we directly solve the linearized Vlasov equation numerically for microbunching gain amplification factor. In this study we apply this code to a beamline lattice of transport arc [3] following an upstream linac section. The resultant gain functions and spectra are presented here, and some results are compared with particle tracking simulation by ELEGANT [4]. We also discuss some underlying physics with inclusion of these collective effects and the limitation of the existing formulation. It is anticipated that this more thorough analysis can further improve the understanding of MBI mechanisms and shed light on how to suppress or compensate MBI effects in lattice designs.

physics.acc-ph↗

arXiv2015

Multistage CSR microbunching gain development in transport or recirculation arcs

Coherent synchrotron radiation (CSR) induced microbunching instability has been one of the most challenging issues in the design of modern accelerators. A linear Vlasov solver has been developed [1] and applied to investigate the physical processes of microbunching gain amplification for several example lattices [2]. In this paper, by further extending the concept of stage gain as proposed by Huang and Kim [3], we develop a method to characterize the microbunching development in terms of stage orders that allow the quantitative comparison of optics impacts on microbunching gain for different lattices. We find that the microbunching instability in our demonstrated arcs has a distinguishing feature of multistage amplification (e.g, up to 6th stage amplification for our example transport arcs, in contrast to two-stage amplification for a typical 4-dipole bunch compressor chicane). We also try to connect lattice optics pattern with the obtained stage gain functions by a physical interpretation. This Vlasov analysis is validated by ELEGANT [4] tracking results with excellent agreement.

physics.acc-ph↗

arXiv2015

CSR induced microbunching gain estimation including transient effects in transport and recirculation arcs

The coherent synchrotron radiation (CSR) of a high brightness electron beam traversing a series of dipoles, such as transport or recirculation arcs, may result in the microbunching instability (μBI). To accurately quantify the direct consequence of this effect, we further extend our previously developed semi-analytical Vlasov solver [C. -Y. Tsai et al., FEL Conference 2014 (THP022)] to include more relevant coherent radiation models than the steady-state free-space CSR impedance, such as the entrance and exit transient effects derived from upstream beam entering to and exiting from individual dipoles. The resultant microbunching gain functions and spectra for our example lattices are presented and compared with particle tracking simulation. Some underlying physics with inclusion of these effects are also discussed.

physics.acc-ph↗