AI Takes on Particle Physics Design

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Author: Denis Avetisyan

A new approach leverages artificial intelligence agents to automate the optimization of complex detector systems, promising faster progress in high-energy physics.

The framework optimizes a detector through a bilevel process, employing a geometry-based outer loop and a reconstruction algorithm-driven inner loop, both converging on user-defined optimization criteria to achieve a provably optimal solution.
The framework optimizes a detector through a bilevel process, employing a geometry-based outer loop and a reconstruction algorithm-driven inner loop, both converging on user-defined optimization criteria to achieve a provably optimal solution.

This review details the successful application of agentic AI and bilevel optimization to the design of a dual-readout electromagnetic calorimeter within vertically-integrated differentiable full simulations.

Optimizing complex scientific instruments often requires extensive, manual parameter sweeps and expert intuition. This work, ‘Phenomenological Detector Design and Optimization in Vertically-Integrated Differentiable Full Simulations with Agentic-AI’, introduces an automated workflow leveraging AI agents to optimize the design of high-energy physics detectors through bilevel optimization of vertically integrated simulations. We demonstrate that these agents can effectively navigate detector parameter spaces and improve performance-in this case, optimizing a dual-readout electromagnetic calorimeter-with minimal experiment-specific guidance. Could this approach unlock new efficiencies in experimental design and accelerate the pace of discovery in complex scientific fields?

The Challenge of Detector Optimization: A Pursuit of Mathematical Precision

The pursuit of increasingly precise measurements at future particle colliders necessitates the development of high-performance electromagnetic calorimeters, devices that meticulously measure the energy of photons and electrons. However, designing these detectors presents a significant computational challenge; simulating the interactions of particles within a calorimeter’s complex geometry and materials demands enormous processing power. Traditional methods struggle with the sheer number of variables-detector dimensions, material choices, electronic readout configurations-that influence performance. Consequently, innovative approaches, such as advanced algorithms and distributed computing techniques, are crucial to efficiently explore the vast design space and identify optimal configurations capable of delivering the required precision for next-generation experiments. The complexity stems not just from the intricate physics involved, but also from the need to simultaneously optimize both the physical construction and the software algorithms used to reconstruct particle energies from the raw detector signals.

The optimization of electromagnetic calorimeters, crucial components in particle physics experiments, presents a significant computational challenge due to the sheer number of variables involved. Traditional optimization methods falter when confronted with the expansive parameter space defined by detector geometry – the arrangement of absorber and scintillator materials – and the intricacies of reconstruction algorithms, which translate raw signals into measurable quantities. Each geometric parameter, such as layer thickness or cell size, and each algorithmic choice, like clustering methods or energy calibration techniques, contributes to a multidimensional landscape where finding the optimal configuration becomes computationally intractable. The combinational explosion of possibilities quickly overwhelms conventional approaches, necessitating the development of novel strategies capable of efficiently navigating this complex design space and identifying configurations that maximize detector performance.

The pursuit of peak performance in electromagnetic calorimeters isn’t simply a matter of refining physical dimensions; it demands a concurrent optimization of the algorithms used to interpret the data those dimensions capture. Historically, these hardware and software components have been treated as separate challenges, yet their interplay is fundamentally linked-a change in detector geometry necessitates a corresponding adjustment to reconstruction algorithms, and vice versa. This interwoven relationship creates a highly complex optimization landscape, where improvements to one component can be negated, or even reversed, by inadequacies in the other. Consequently, achieving genuinely optimal performance requires a holistic approach, simultaneously tuning both the physical detector and the computational processes that transform raw signals into meaningful physical quantities, a task that dramatically increases the computational burden and necessitates innovative optimization strategies.

The optimization of electromagnetic calorimeters for future particle colliders presents a significant computational challenge, traditionally hampered by the sheer complexity of the design space. This research addresses this issue by decomposing the 11-dimensional optimization problem into a series of manageable stages, implemented through an agentic workflow. Rather than attempting to simultaneously tune all parameters, the system strategically sequences the optimization process, allowing each ‘agent’ to focus on specific aspects of detector geometry and reconstruction algorithms. This staged approach not only dramatically reduces computational demands but also facilitates a more thorough exploration of the parameter space, ultimately leading to demonstrably improved detector performance and a more efficient path toward optimal design.

An agentic large language model adaptively optimized detector geometry-specifically crystal offset and width-using signal-to-noise ratio as the primary metric, successfully navigating initial fluctuations in probe points to identify the optimal parameter region.
An agentic large language model adaptively optimized detector geometry-specifically crystal offset and width-using signal-to-noise ratio as the primary metric, successfully navigating initial fluctuations in probe points to identify the optimal parameter region.

An Agentic Workflow: Intelligent Design Through Autonomous Agents

An agentic workflow for detector design and analysis utilizes artificial intelligence and machine learning (AI/ML) to automate iterative processes previously performed manually. This approach moves beyond traditional scripting by employing autonomous agents capable of planning, executing, and evaluating design options. Automation extends to tasks such as simulation setup, data analysis, and parameter variation, significantly reducing development time and enabling exploration of a wider range of design possibilities than is feasible with manual optimization techniques. The workflow is designed to systematically search the parameter space, identify optimal configurations, and accelerate the overall detector design lifecycle.

The SciFi framework manages the detector design optimization process through the implementation of autonomous agents. These agents operate by decomposing a high-level design goal into a series of executable tasks, iteratively refining the design based on the results of each task. SciFi facilitates this iteration by providing tools for task planning, code execution, data analysis, and result interpretation, all managed within a closed-loop system. This agentic approach enables automated exploration of the design space, allowing the system to independently propose, evaluate, and refine detector configurations without requiring constant human intervention, ultimately accelerating the optimization workflow.

The task plan development within the agentic workflow is driven by Claude Code Opus 4.6, a commercially available large language model from Anthropic. This LLM is specifically utilized to decompose high-level design goals into a series of executable steps, defining the necessary operations and their sequence for detector optimization. Claude Code Opus 4.6’s capabilities in code generation and reasoning are central to translating design requirements into a functional plan that can be executed by other components of the SciFi framework, facilitating automated iteration and analysis. The model’s use ensures a structured and reproducible approach to the optimization process, enabling systematic exploration of the detector parameter space.

Automated optimization, as implemented within the SciFi framework, enables a comprehensive and methodical examination of detector design parameters that is impractical with manual approaches. Traditional optimization relies on human intuition and limited iterative cycles, restricting the scope of the explored parameter space. In contrast, the agentic workflow systematically varies input parameters, evaluates resultant detector performance metrics, and iteratively refines the design based on defined objectives. This process, driven by Claude Code Opus 4.6, facilitates the investigation of a significantly larger and more complex parameter space, increasing the probability of discovering optimal or near-optimal detector configurations beyond the reach of human-driven experimentation.

Optimization using signal-to-noise ratio (SNR) as a metric revealed that crystal offset has minimal impact on SNR, leading to the selection of an offset of 5 cm for further investigation with agentic AI.
Optimization using signal-to-noise ratio (SNR) as a metric revealed that crystal offset has minimal impact on SNR, leading to the selection of an offset of 5 cm for further investigation with agentic AI.

Bilevel Optimization and Simulation Fidelity: A Foundation for Accurate Results

The bilevel optimization framework employs a two-level approach to enhance detector performance. The outer level adjusts detector geometrical parameters – dimensions, material choices, and component placement – while the inner level optimizes reconstruction algorithms and associated parameters, such as clustering thresholds and calibration coefficients. This simultaneous optimization, rather than sequential tuning, allows for synergistic improvements; a detector geometry optimized for a specific reconstruction approach, and vice versa. The objective function driving this process directly correlates geometrical and reconstruction choices with key physics performance metrics, enabling the identification of configurations that maximize signal discrimination and minimize systematic uncertainties. This process is particularly valuable for complex detector designs where interactions between geometry and reconstruction are non-trivial and a holistic optimization strategy is required.

The detector optimization framework employs DD4HEP for detector description and Geant4 for comprehensive simulation of particle interactions. DD4HEP facilitates the creation of a hierarchical detector geometry, allowing for flexible and scalable detector designs. Geant4 then propagates particles through this geometry, simulating energy deposition and secondary particle production with detailed physics models. This full simulation approach accurately models detector response to physics events, enabling realistic performance evaluation of different detector configurations and reconstruction algorithms before physical construction and experimentation. The combination of these tools provides a high-fidelity representation of the detector environment, critical for optimizing performance metrics and predicting detector capabilities.

The optimization process relies on quantifiable performance metrics to evaluate detector designs; Signal-to-Noise Ratio (SN Ratio) assesses the clarity of detected signals, while Sphoton Error quantifies inaccuracies in photon reconstruction. ADC Bit Depth, representing the precision of analog-to-digital conversion, impacts the granularity of energy measurements. Through iterative refinement guided by these metrics, the detector configuration achieves a hadronic jet energy resolution of 25% and an electromagnetic energy resolution of 3%/[latex]E[/latex], where [latex]E[/latex] represents the energy of the electromagnetic shower.

The ROOT format is utilized as the primary data storage mechanism for simulation outputs due to its capabilities in handling large datasets common in high energy physics. ROOT’s data structures, specifically TNtuples and Trees, allow for efficient storage and retrieval of particle trajectories, calorimeter hits, and reconstructed objects. This facilitates rapid I/O operations, reducing processing time and enabling iterative optimization cycles within the bilevel optimization framework. Furthermore, ROOT’s built-in histogramming and analysis tools are directly compatible with the simulation data, simplifying the evaluation of key performance metrics and streamlining the overall workflow from simulation to performance assessment.

Optimization of digitization parameters using [latex]\SphotonS\\_{\\mathrm{photon}}[/latex] error as the primary metric demonstrates a trade-off with ADC cost, as indicated by the dashed line.
Optimization of digitization parameters using [latex]\SphotonS\\_{\\mathrm{photon}}[/latex] error as the primary metric demonstrates a trade-off with ADC cost, as indicated by the dashed line.

Precision Through Digitization Parameters: Unlocking Optimal Detector Resolution

A bilevel optimization framework provides a systematic approach to maximizing detector resolution by precisely adjusting digitization parameters like sampling rate and Analog-to-Digital Converter (ADC) bit depth. This method transcends traditional manual tuning, allowing for the exploration of a vast parameter space to identify configurations that yield optimal performance. The framework functions by iteratively refining these parameters – essentially, how frequently and with what precision the detector records signals – to minimize distortions and ensure accurate energy measurements. By intelligently linking the parameter settings to the resulting detector response, the system can pinpoint the ideal balance, ultimately enhancing the detector’s ability to discern subtle physical phenomena and improve the potential for groundbreaking discoveries at future collider experiments.

Maintaining signal fidelity is paramount in high-energy physics, and meticulous optimization of digitization parameters directly addresses potential sources of degradation. Subtle adjustments to settings like sampling rate and analog-to-digital converter (ADC) bit depth can dramatically reduce noise and distortion, thereby preserving the integrity of the energy measurements derived from detector signals. This careful calibration isn’t merely about achieving higher resolution; it ensures the accurate reconstruction of particle interactions and minimizes systematic uncertainties. Consequently, a robust optimization process yields data with improved statistical power, allowing physicists to probe more subtle phenomena and confidently identify new particles or effects at the energy frontier – a necessity for maximizing the discovery potential of future collider experiments.

Advancements in detector technology, specifically through refined digitization parameters, promise substantial gains in physics performance at future colliders. Optimization efforts have yielded a significantly improved crystal granularity, now ranging from 0.5 to 2 centimeters – a marked improvement over initial trial runs employing a granularity of 1 to 10 centimeters. This finer granularity directly translates to enhanced spatial resolution, allowing for more precise tracking of particle trajectories and a reduction in the ambiguity of energy measurements. Consequently, researchers anticipate a heightened capacity to observe rare decay processes and subtle signals indicative of new physics, ultimately broadening the scope for discovery and furthering understanding of the fundamental constituents of the universe.

A comprehensive exploration of digitization parameters, achieved through a bilevel optimization framework, reveals detector configurations previously unattainable via conventional, manual adjustments. This systematic approach transcends the limitations of trial-and-error tuning, allowing researchers to navigate a complex parameter space – encompassing variables like sampling rate and ADC bit depth – with unprecedented precision. The resulting optimized settings not only minimize signal degradation and enhance energy measurement accuracy, but also unlock detector performance levels that were formerly hidden, offering the potential for significant advancements in physics research and improved discovery capabilities at future collider experiments, notably through refined crystal granularity ranging from 0.5-2 cm.

The pursuit of optimized detector design, as demonstrated within this work, aligns with a fundamental principle of mathematical elegance. The agentic-AI workflow detailed here isn’t merely about achieving functional results; it’s about discovering solutions exhibiting inherent scalability and provable efficiency. As Sergey Sobolev once stated, “The most beautiful code is the one that is closest to the mathematical truth.” This sentiment perfectly encapsulates the approach taken in optimizing the dual-readout electromagnetic calorimeter. The emphasis isn’t simply on a detector that works, but one whose performance characteristics are demonstrably superior due to a foundation rooted in rigorous optimization and scalable algorithmic design, moving beyond empirical testing towards a provably correct solution.

Beyond the Readout

The successful demonstration of agentic optimization within a full simulation framework, while encouraging, merely shifts the locus of difficulty. The current approach excels at navigating a predefined parameter space for a specific detector configuration. However, the true challenge lies not in refining existing designs, but in discovering genuinely novel architectures. The agent, presently, is a sophisticated local optimizer – a tireless climber on a known peak. To truly advance, the system must develop the capacity for conceptual leaps, for proposing geometries and readout schemes that transcend current intuition. This demands a move beyond bilevel optimization toward a more generative paradigm, one where the agent can formulate and test fundamental design principles.

Furthermore, the reliance on full simulations, while providing fidelity, introduces a computational bottleneck. The cost of evaluating each proposed design remains substantial, limiting the exploration of truly vast design spaces. A fruitful avenue for future research lies in the development of surrogate models – analytically tractable representations of detector performance that can be rapidly evaluated. Such models, if sufficiently accurate, would allow the agent to explore a far greater number of possibilities, potentially uncovering designs that would remain hidden within the confines of brute-force simulation. The goal is not simply to find a good detector, but to prove that a given design is, in some absolute sense, optimal.

Ultimately, the question is not whether AI can automate detector design, but whether it can illuminate the underlying mathematical principles that govern optimal signal reconstruction. The current work represents a step towards that goal, but the path forward demands a commitment to elegance – to solutions that are not merely effective, but demonstrably correct.

Original article: https://arxiv.org/pdf/2604.21804.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2026-04-24 09:04