Automating the Hunt for New Physics

Author: Denis Avetisyan

A new AI framework streamlines the complex process of particle physics research, from theoretical models to observable predictions.

The ColliderAgent architecture anticipates eventual systemic failure by embracing a decoupled, hierarchical design wherein a cognitive engine distributes phenomenological tasks to specialized agents-each possessing dedicated skills-and unifies execution via a standardized command-line interface to underlying high-energy physics tools, effectively translating natural language and physics notation into outputs like kinematic distributions and exclusion limits as a transient state before inevitable fragmentation.

This paper introduces ColliderAgent, an agent-based AI system designed to automate end-to-end collider phenomenology workflows and enhance reproducibility in high-energy physics.

The increasing complexity of high-energy physics analyses often clashes with the demand for reproducible and scalable research. This tension motivates the development presented in ‘An End-to-end Architecture for Collider Physics and Beyond’, which introduces ColliderAgent, an agent-based system automating end-to-end collider phenomenology-from theoretical $\mathcal{L}$ agrangian input to final phenomenological outputs-without relying on bespoke code. Validated across scenarios including leptoquark searches and parameter scans, this decoupled architecture demonstrates a pathway toward more efficient and reliable investigations of fundamental physics. Could such an automated framework fundamentally reshape the landscape of scientific discovery, extending beyond collider physics to other data-intensive fields?

The Inevitable Bridge

Collider phenomenology forms the crucial bridge between the abstract world of theoretical particle physics and the tangible data produced by experiments at accelerators like the Large Hadron Collider. It’s the painstaking process of translating complex theoretical frameworks – such as the Standard Model and its potential extensions – into concrete, testable predictions about the outcomes of high-energy collisions. These predictions aren’t simply direct calculations; they involve modeling the entire detector response, accounting for backgrounds from known processes, and carefully estimating uncertainties. The success of endeavors like searching for the Higgs boson, or probing for evidence of supersymmetry, fundamentally relies on the accuracy and precision of these theoretical predictions, making collider phenomenology an indispensable component of modern high-energy physics. Without it, experimental results would be difficult, if not impossible, to interpret and the quest to understand the fundamental building blocks of the universe would be severely hampered.

The interpretation of data from high-energy particle colliders historically relies on painstakingly detailed theoretical calculations. These are not single-step processes; instead, physicists must navigate a labyrinth of intermediate steps, each requiring careful consideration of quantum mechanics, relativity, and complex mathematical frameworks. Accurately predicting collision outcomes demands expertise in Feynman diagrams, loop integrals, and renormalization techniques – skills honed over years of dedicated study. Furthermore, error analysis is crucial, as even minor uncertainties in these calculations can significantly impact the ability to confirm or refute new physics beyond the Standard Model. The manual nature of these calculations makes them exceptionally time-consuming and prone to human error, representing a substantial bottleneck in the process of discovery.

The pursuit of higher energy frontiers in particle physics necessitates the design of increasingly sophisticated colliders. These future machines, such as proposed facilities exploring muon or proton collisions at unprecedented energies, will generate interactions far exceeding the complexity of those currently studied at the Large Hadron Collider. This escalation in event topologies and particle multiplicities demands a paradigm shift in how physicists analyze data; manual calculations and simulations become computationally prohibitive and prone to error. Consequently, the field is actively developing automated tools – sophisticated algorithms and machine learning techniques – to streamline the process of theoretical prediction, event generation, and data interpretation, ensuring that the full potential of these next-generation colliders can be realized and new physics discoveries aren’t obscured by computational bottlenecks.

ColliderAgent successfully reproduces representative literature results-including <span class="katex-eq" data-katex-display="false">p\,p \to \text{LQ} \to e\,j</span> distributions, normalized missing transverse energy distributions for <span class="katex-eq" data-katex-display="false">p\,p \to aW^{\pm}\gamma</span>, and <span class="katex-eq" data-katex-display="false">2\sigma</span> exclusion contours for Z' and U1 LQs-validating its ability to replicate established high-energy physics analyses. — ColliderAgent successfully reproduces representative literature results-including $p\,p \to \text{LQ} \to e\,j$ distributions, normalized missing transverse energy distributions for $p\,p \to aW^{\pm}\gamma$ , and $2\sigma$ exclusion contours for Z’ and U1 LQs-validating its ability to replicate established high-energy physics analyses.

The Necessary Infrastructure

Calculations in high-energy physics rely on the framework of Quantum Field Theory (QFT), necessitating the use of specialized software tools for managing the complex mathematical calculations. Specifically, Feynman diagrams, which visually represent particle interactions, are generated using packages like FeynArts. These diagrams then require analytical or numerical evaluation to obtain scattering amplitudes and cross-sections; this is commonly performed with FeynCalc. To interface these calculations with Monte Carlo event generators, FeynRules automates the process of deriving model files from the Lagrangian, ensuring consistency between theoretical predictions and simulation input. These tools collectively provide a computational infrastructure for translating theoretical models into quantifiable predictions for experimental verification.

Monte Carlo event generators are crucial for simulating high-energy particle collisions and the subsequent development of particle showers, a process known as hadronization. Programs like MadGraph handle the initial collision, generating unweighted events based on the calculated cross-sections from perturbative Quantum Chromodynamics (QCD) and electroweak interactions. These events are then passed to programs such as Pythia, which model the non-perturbative aspects of hadronization – the fragmentation of quarks and gluons into observable hadrons. Pythia utilizes algorithms based on probabilistic branching to simulate the evolution of the shower, accounting for phenomena like soft gluon emission and multiple parton interactions. The output of these generators is a set of simulated events, each representing a potential collision outcome, which can then be compared to experimental data to test theoretical predictions.

Detector simulations, such as those performed with Delphes, are crucial for bridging the gap between theoretical predictions and experimental measurements. These simulations model the complex interactions of particles with the various components of a detector – including tracking systems, calorimeters, and muon chambers – to predict the signals that would be observed. Delphes, specifically, is a parameterized detector response simulation, allowing users to rapidly assess detector effects without requiring a full, detailed Geant4-based simulation. The output of these simulations provides an estimation of quantities like particle momentum, energy, and identification, which are then compared to the results of actual experiments to validate theoretical models and search for new physics.

ColliderAgent successfully reproduces key results from existing literature, including production cross sections for <span class="katex-eq" data-katex-display="false">pp \to \mu^{\pm}N</span> at the LHC as a function of <span class="katex-eq" data-katex-display="false">m_N</span>, dilepton production for a <span class="katex-eq" data-katex-display="false">Z^{\prime}</span> benchmark, resonant <span class="katex-eq" data-katex-display="false">e^{+}e^{-} \to \mu^{+}\mu^{-}</span> cross sections in the Randall-Sundrum model, and exclusion/discovery contours for a <span class="katex-eq" data-katex-display="false">U_1</span> leptoquark benchmark at a muon collider, as detailed in Refs. [11, 24, 27, 49]. — ColliderAgent successfully reproduces key results from existing literature, including production cross sections for $pp \to \mu^{\pm}N$ at the LHC as a function of $m_N$ , dilepton production for a $Z^{\prime}$ benchmark, resonant $e^{+}e^{-} \to \mu^{+}\mu^{-}$ cross sections in the Randall-Sundrum model, and exclusion/discovery contours for a $U_1$ leptoquark benchmark at a muon collider, as detailed in Refs. [11, 24, 27, 49].

The Inevitable Automation

ColliderAgent is an autonomous agent system engineered to perform complete collider phenomenology analyses without manual intervention. This encompasses the entire workflow, beginning with the definition of theoretical particle physics models, proceeding through simulation of particle interactions and detector responses, and culminating in the generation of observable signatures. Automation is achieved through the integration of specialized tools and algorithms, allowing for end-to-end task completion without requiring user direction at each step. The system’s autonomy facilitates efficient exploration of parameter spaces and systematic variation of model assumptions, enabling comprehensive studies of potential collider physics.

ColliderAgent utilizes Magnus, a unified execution backend, to streamline and manage the complex workflow of collider phenomenology tasks. Magnus functions as an orchestrator, coordinating the execution of diverse tools – including model generation, event simulation, and detector response calculations – within a single framework. This centralized approach facilitates efficient resource allocation, enabling parallelization and optimized utilization of computational resources such as CPU time and memory. Furthermore, Magnus provides a standardized interface for tool integration and data transfer, reducing the overhead associated with managing multiple independent software packages and ensuring reproducibility of results.

ColliderAgent’s automated workflow significantly accelerates the investigation of diverse theoretical physics models and parameter spaces. This capability is crucial for the planning and optimization of future collider experiments, including the Circular Electron-Positron Collider (CEPC), the Future Circular Collider (FCC), and proposed Muon Collider designs. Validation of the system has been demonstrated through successful reproduction of published results from existing collider phenomenology studies, confirming its accuracy and reliability in simulating complex particle physics processes and detector responses.

The Looming Horizon

ColliderAgent streamlines the investigation of physics beyond the Standard Model, offering a powerful platform to explore theoretical landscapes currently beyond experimental reach. The system allows researchers to efficiently probe models such as the Randall-Sundrum framework – which posits the existence of extra dimensions – and actively search for new particles, including the U1 Leptoquark, a hypothetical particle potentially mediating interactions between quarks and leptons. By automating key steps in the analysis process, ColliderAgent facilitates a more comprehensive and rapid assessment of these complex scenarios, effectively accelerating the pace of discovery in high-energy physics and opening new avenues for understanding the fundamental constituents of the universe.

The advent of automated analysis workflows is reshaping the landscape of particle physics research, dramatically curtailing the traditionally laborious process of data scrutiny. Previously, physicists devoted substantial time to meticulously sifting through collision events, a task now largely handled by sophisticated algorithms. This shift isn’t simply about speed; it frees researchers from repetitive computational burdens, enabling a heightened focus on the nuanced interpretation of findings and the formulation of new theoretical hypotheses. By automating the initial stages of analysis, ColliderAgent and similar tools allow for more comprehensive explorations of potential signals, ultimately accelerating the pace of discovery and maximizing the scientific yield from high-energy collider experiments.

The potential for discovery at high-energy colliders remains immense, yet fully exploiting the data they produce demands increasingly sophisticated analytical techniques. This automated workflow addresses this need by dramatically accelerating the process of hypothesis testing and signal searching, allowing physicists to efficiently scan through vast parameter spaces for evidence of new physics. Validation of the system’s efficacy is evidenced by its successful reproduction of published results across established benchmarks, confirming its reliability and accuracy. Consequently, this capability isn’t simply about faster computation; it represents a fundamental shift in how collider data is analyzed, promising to maximize the scientific yield from current experiments like the LHC and paving the way for even more ambitious explorations with future colliders, ultimately driving the boundaries of fundamental understanding.

The pursuit of automated workflows, as demonstrated by ColliderAgent, inevitably reveals the inherent limitations of any system built to predict the unpredictable. It’s a prophecy of failure, meticulously coded and deployed. As Søren Kierkegaard observed, “Life can only be understood backwards; but it must be lived forwards.” ColliderAgent attempts to impose order on the chaotic realm of particle physics, yet the very act of building such a framework acknowledges the fundamental unknowability at the heart of the endeavor. The system grows, adapting to new data and refining its models, but each iteration is merely a temporary respite from the inevitable entropy of incomplete information. It’s not about building a solution, but cultivating a resilient ecosystem capable of navigating ongoing uncertainty.

What Lies Beyond?

The pursuit of automated phenomenology, as exemplified by ColliderAgent, is not a quest for frictionless science. It is, rather, the construction of a more elaborate failure mode. Each layer of abstraction, each automated workflow, simply pushes the inevitable point of breakage further down the stack, and into more obscure corners. The true measure of this work will not be speed of calculation, but the speed with which unforeseen consequences – the subtle biases in Lagrangian choices, the unanticipated edge cases in simulation – are revealed.

The promise of scalable reproducibility is a siren song. Reproducibility isn’t achieved by automating the process of science, but by cultivating an ecosystem of critical review. This framework, like all frameworks, will accrue dependencies – not just on software, but on specific versions of algorithms, training datasets, and, ultimately, the tacit knowledge of those who maintain it. The challenge isn’t building a system that can run itself, but one that makes its own limitations transparent, allowing for controlled dismantling when the inevitable entropy sets in.

The path forward isn’t more automation, but a deeper understanding of the irreducible complexity of the questions being asked. This means investing not in tools to answer them faster, but in tools that help physicists articulate better questions – questions that are less susceptible to the biases inherent in any computational model. Order, after all, is merely a temporary cache between failures.

Original article: https://arxiv.org/pdf/2603.14553.pdf

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

The Inevitable Bridge

The Necessary Infrastructure

The Inevitable Automation

The Looming Horizon

What Lies Beyond?

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