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Patrick Owen

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hep-ex hep-ph Artificial Intelligence Machine Learning physics.ins-det

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preprint2026arXiv

Joint Treatment Effect Estimation from Incomplete Healthcare Data: Temporal Causal Normalizing Flows with LLM-driven Evolutionary MNAR Imputation

Target trial emulation (TTE) enables causal questions to be studied with observational data when randomized controlled trials (RCTs) are infeasible. Yet treatment-effect methods often address causal estimation, missingness, and temporal structure separately, limiting their robustness in electronic health records (EHRs), where time-varying confounding and missing-not-at-random (MNAR) biomarkers can reach 50%--80%. We propose a two-stage pipeline for treatment effect estimation from incomplete longitudinal EHRs. First, CausalFlow-T, a directed acyclic graph (DAG)-constrained normalizing flow with long short-term memory (LSTM)-encoded patient history, performs exact invertible counterfactual inference, avoiding approximation errors from variational inference and separating confounding through explicit causal structure. Ablations on four synthetic and one semi-synthetic benchmark with known counterfactuals show that DAG constraints and exact inference address distinct failure modes: neither compensates for the other. Second, because CausalFlow-T requires completed inputs, we introduce an LLM-driven evolutionary imputer that proposes executable imputation operators rather than individual entries, and evaluate it with three large language model (LLM) backends, including two open-source models. Across 30%--80% MNAR missingness, this imputer achieves the best pooled rank over biomarker and causal metrics, leading in point-wise accuracy and temporal extrapolation while preserving average treatment effect (ATE) recovery as statistical baselines degrade. On Swiss primary-care EHRs from adults with type 2 diabetes initiating a GLP-1 receptor agonist or SGLT-2 inhibitor, the pipeline estimates a per-protocol weight-loss difference of -0.98 kg [95% CI -1.01, -0.96] favoring GLP-1 receptor agonists, consistent with randomized evidence and obtained from realistically incomplete real-world EHRs.

preprint2026arXiv

Large Language Models for Physics Instrument Design

We study the use of large language models (LLMs) for physics instrument design and compare their performance to reinforcement learning (RL). Using only prompting, LLMs are given task constraints and summaries of prior high-scoring designs and propose complete detector configurations, which we evaluate with the same simulators and reward functions used in RL-based optimization. Although RL yields stronger final designs, we find that modern LLMs consistently generate valid, resource-aware, and physically meaningful configurations that draw on broad pretrained knowledge of detector design principles and particle--matter interactions, despite having no task-specific training. Based on this result, as a first step toward hybrid design workflows, we explore pairing the LLMs with a dedicated trust region optimizer, serving as a precursor to future pipelines in which LLMs propose and structure design hypotheses while RL performs reward-driven optimization. Based on these experiments, we argue that LLMs are well suited as meta-planners: they can design and orchestrate RL-based optimization studies, define search strategies, and coordinate multiple interacting components within a unified workflow. In doing so, they point toward automated, closed-loop instrument design in which much of the human effort required to structure and supervise optimization can be reduced.

preprint2026arXiv

Towards replacing detector simulation with heterogeneous GNNs in flavour physics analyses

Driven by the increasing volume of recorded data, the demand for simulation from experiments based at the Large Hadron Collider will rise sharply in the coming years. Addressing this demand solely with existing computationally intensive workflows is not feasible. This paper introduces a new fast simulation tool designed to address this demand at the LHCb experiment. This tool emulates the detector response to arbitrary multibody decay topologies at LHCb. Rather than memorising specific decay channels, the model learns generalisable patterns within the response, allowing it to interpolate to channels not present in the training data. Novel heterogeneous graph neural network architectures are employed that are designed to embed the physical characteristics of the task directly into the network structure. We demonstrate the performance of the tool across a range of decay topologies, showing the networks can correctly model the relationships between complex variables. The architectures and methods presented are generic and could readily be adapted to emulate workflows at other simulation-intensive particle physics experiments.

preprint2021arXiv

A general effective field theory description of $b \to s l^+ l^-$ lepton universality ratios

We construct an expression for a general lepton flavour universality (LFU) ratio, $R_{X}$ , in $b\to s l^+ l^-$ decays in terms of a series of hadronic quantities which can be treated as nuisance parameters. This expression allows to include any LFU ratio in global fits of $b\to s l^+ l^-$ short-distance parameters, even in the absence of a precise knowledge of the corresponding hadronic structure. The absence of sizeable LFU violation and the approximate left-handed structure of the Standard Model amplitude imply that only a very limited set of hadronic parameters hamper the sensitivity of $R_X$ to a possible LFU violation of short-distance origin. A global $b\to s l^+ l^-$ combination is performed including the measurement of $R_{pK}$ for the first time, resulting in a significance of new physics of $4.2\,σ$ . In light of this, we evaluate the impact on the global significance of new physics using a set of experimentally promising non-exclusive $R_X$ measurements that LHCb can perform, and find that they can significantly increase the discovery potential of the experiment.

preprint2021arXiv

Isospin extrapolation as a method to study inclusive $\bar{B} \to X_{s} \ell^{+}\ell^{-}$ decays

A novel approach to reconstruct inclusive $\bar{B} \to X_{s} \ell^{+}\ell^{-}$ decays is presented. The method relies on isopsin symmetry to extrapolate the semi-inclusive signature $X_{b}\to K^{+} \ell^{+}\ell^{-} X$ to the fully inclusive rate in $B^{+}$ and $B^{0}$ decays. We investigate the possibility to measure branching fractions and other observables such as lepton universality ratios and $CP$ asymmetries. As a proof of concept, fast simulation is used to compare the $X_{b}\to K^{+} \ell^{+}\ell^{-} X$ signature with a fully inclusive approach. Several experimental advantages are seen which have the potential to make measurements of inclusive $\bar{B} \to X_{s} \ell^{+}\ell^{-}$ decays tractable at a hadron collider.

preprint2020arXiv

Probing effects of new physics in $Λ^0_{b}\toΛ^+_{c}μ^{-}\barν_μ$ decays

We present, for the first time, the six-fold differential decay density expression for $Λ^0_b\toΛ^+_{c} l^- \barν_{l}$ , taking into account the polarisation of the $Λ^0_b$ baryon and a complete basis of new physics operators. Using the expected yield in the current dataset collected at the LHCb experiment, we present sensitivity studies to determine the experimental precision on the Wilson coefficients of the new physics operators with $Λ^0_{b}\toΛ^+_{c}μ^{-}\barν_μ$ decays in two scenarios. In the first case, unpolarised $Λ^0_{b}\toΛ^+_{c}μ^{-}\barν_μ$ decays with $Λ^+_c\to p K^+ π^-$ are considered, whereas polarised $Λ^0_{b}\toΛ^+_{c}μ^{-}\barν_μ$ decays with $Λ^+_c \to p K^0_S$ are studied in the second. For the latter scenario, the experimental precision that can be achieved on the determination of $Λ^0_b$ polarisation and $Λ^+_c$ weak decay asymmetry parameter is also presented.

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Patrick Owen

Research overview

Is this your work?

6 publications

Joint Treatment Effect Estimation from Incomplete Healthcare Data: Temporal Causal Normalizing Flows with LLM-driven Evolutionary MNAR Imputation

Large Language Models for Physics Instrument Design

Towards replacing detector simulation with heterogeneous GNNs in flavour physics analyses

A general effective field theory description of $b \to s l^+ l^-$ lepton universality ratios

Isospin extrapolation as a method to study inclusive $\bar{B} \to X_{s} \ell^{+}\ell^{-}$ decays

Probing effects of new physics in $Λ^0_{b}\toΛ^+_{c}μ^{-}\barν_μ$ decays

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Patrick Owen

Research overview

Is this your work?

6 publications

Joint Treatment Effect Estimation from Incomplete Healthcare Data: Temporal Causal Normalizing Flows with LLM-driven Evolutionary MNAR Imputation

Large Language Models for Physics Instrument Design

Towards replacing detector simulation with heterogeneous GNNs in flavour physics analyses

A general effective field theory description of b→sl+l−b \to s l^+ l^-b→sl+l− lepton universality ratios

Isospin extrapolation as a method to study inclusive Bˉ→Xsℓ+ℓ−\bar{B} \to X_{s} \ell^{+}\ell^{-}Bˉ→Xs​ℓ+ℓ− decays

Probing effects of new physics in Λb0→Λc+μ−νˉμΛ^0_{b}\toΛ^+_{c}μ^{-}\barν_μΛb0​→Λc+​μ−νˉμ​ decays

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A general effective field theory description of $b \to s l^+ l^-$ lepton universality ratios

Isospin extrapolation as a method to study inclusive $\bar{B} \to X_{s} \ell^{+}\ell^{-}$ decays

Probing effects of new physics in $Λ^0_{b}\toΛ^+_{c}μ^{-}\barν_μ$ decays