arXiv subjects

Henry Day-Hall

Publications and source records attributed to Henry Day-Hall.

8 recordsLinked to original sources

arXiv2026

Transferable Fast Calorimeter Shower Generation via Multi-Geometry Pre-training

Detailed Geant4 simulation of calorimeter showers dominates the computing budget of high-energy physics experiments. Deep generative surrogates reduce this cost, but they have remained tied to the detector they were trained on, so each new geometry needs a large in-domain dataset. We study whether a single point cloud shower generator can be pre-trained on multiple detectors and transferred to unseen calorimeters. The pre-training geometries come from synthetic geometric variation rather than real-detector data. We introduce SimpleBox, a family of $10^4$ box calorimeters spanning the plane of sampling fraction and longitudinal segmentation, and benchmark it against pre-training on realistic detectors. On a calorimeter unseen in pre-training, with $10^3$ target showers for fine-tuning, the two priors reduce the aggregated sliced Wasserstein distance to Geant4 by factors of 5.2 (synthetic) and 8.0 (realistic) relative to training from scratch. At larger target sizes the synthetic prior performs better than the realistic one. Geometric diversity alone is therefore a practical way to pre-train a transferable shower generator.

physics.ins-det↗

arXiv2026

AllShowers: One model for all calorimeter showers

Accurate and efficient detector simulation is essential for modern collider experiments. To reduce the high computational cost, various fast machine learning surrogate models have been proposed. Traditional surrogate models for calorimeter shower modeling train separate networks for each particle species, limiting scalability and reuse. We introduce AllShowers, a unified generative model that simulates calorimeter showers across multiple particle types using a single generative model. AllShowers is a continuous normalizing flow model with a Transformer architecture, enabling it to generate complex spatial and energy correlations in variable-length point cloud representations of showers. Trained on a diverse dataset of simulated showers in the highly granular ILD detector, the model demonstrates the ability to generate realistic showers for electrons, photons, and charged and neutral hadrons across a wide range of incident energies and angles without retraining. In addition to unifying shower generation for multiple particle types, AllShowers surpasses the fidelity of previous single-particle-type models for hadronic showers. Key innovations include the use of a layer embedding, allowing the model to learn all relevant calorimeter layer properties; a custom attention masking scheme to reduce computational demands and introduce a helpful inductive bias; and a shower- and layer-wise optimal transport mapping to improve training convergence and sample quality. AllShowers marks a significant step towards a universal model for calorimeter shower simulations in collider experiments.

physics.ins-det↗

arXiv2025

A First Full Physics Benchmark for Highly Granular Calorimeter Surrogates

The physics programs of current and future collider experiments necessitate the development of surrogate simulators for calorimeter showers. While much progress has been made in the development of generative models for this task, they have typically been evaluated in simplified scenarios and for single particles. This is particularly true for the challenging task of highly granular calorimeter simulation. For the first time, this work studies the use of highly granular generative calorimeter surrogates in a realistic simulation application. We introduce DDML, a generic library which enables the combination of generative calorimeter surrogates with realistic detectors implemented using the DD4hep toolkit. We compare two different generative models - one operating on a regular grid representation, and the other using a less common point cloud approach. In order to disentangle methodological details from model performance, we provide comparisons to idealized simulators which directly sample representations of different resolutions from the full simulation ground-truth. We then systematically evaluate model performance on post-reconstruction benchmarks for electromagnetic shower simulation. Beginning with a typical single particle study, we introduce a first multi-particle benchmark based on di-photon separations, before studying a first full-physics benchmark based on hadronic decays of the tau lepton. Our results indicate that models operating on a point cloud can achieve a favorable balance between speed and accuracy for highly granular calorimeter simulation compared to those which operate on a regular grid representation.

hep-ex↗

arXiv2025

CaloClouds3: Ultra-Fast Geometry-Independent Highly-Granular Calorimeter Simulation

We present CaloClouds3, a model for the fast simulation of photon showers in the barrel of a high granularity detector. This iteration demonstrates for the first time how a pointcloud model can employ angular conditioning to replicate photons at all incident angles. Showers produced by this model can be used across the whole detector barrel, due to specially produced position agnostic training data. With this flexibility, the model is usable in a full simulation and reconstruction chain, which offers a further handle for evaluating physics performance of the model. As inference time is a crucial consideration for a generative model, the pre-processing and hyperparameters are aggressively optimised, achieving a speed up factor of two orders of magnitude over Geant4 at inference.

physics.ins-det↗

arXiv2023

Fat $b$-Jet Analyses Using Old and New Clustering Algorithms in New Higgs Boson Searches at the LHC

We compare different jet-clustering algorithms in establishing fully hadronic final states stemming from the chain decay of a heavy Higgs state into a pair of the 125 GeV Higgs boson that decays into bottom-antibottom quark pairs. Such 4$b$ events typically give rise to boosted topologies, wherein bottom-antibottom quark pairs emerging from each 125 GeV Higgs boson tend to merge into a single, fat $b$-jet. Assuming Large Hadron Collider (LHC) settings, we illustrate how both the efficiency of selecting the multi-jet final state and the ability to reconstruct from it the masses of all Higgs bosons depend on the choice of jet-clustering algorithm and its parameter settings. We indicate the optimal choice of clustering method for the purpose of establishing such a ubiquitous Beyond the SM (BSM) signal, illustrated via a Type-II 2-Higgs Doublet Model (2HDM).

hep-ph↗

arXiv2022

Re-evaluating Jet Reconstruction Techniques for New Higgs Boson Searches

The ultimate motivation of our study is to look for signs of physics beyond the Standard Model (BSM). We investigate whether different jet clustering techniques might be more or less suited to the particular final states of interest. In particular, we are interested in fully hadronic final states emerging from the decay chain of the Standard Model like Higgs boson into pairs of light Higgs states, the latter in turn decaying into bottom-anti bottom pairs. We show that, the ability of selecting the multi-jet final state and to reconstruct invariant masses of the Higgs bosons from it depend strongly on the choice of acceptance cuts, resolution parameters and reconstruction algorithm as well as its settings. Hence, we indicate the optimal choice of the latter for the purpose of establishing such a benchmark as a BSM signal. We then repeat the exercise for a heavy Higgs boson cascading into two SM-like Higgs states, obtaining similar results.

hep-ph↗

arXiv2022

Revisiting Jet Clustering Algorithms for New Higgs Boson Searches in Hadronic Final States

We assess the performance of different jet-clustering algorithms, in the presence of different resolution parameters and reconstruction procedures, in resolving fully hadronic final states emerging from the chain decay of the discovered Higgs boson into pairs of new identical Higgs states, the latter in turn decaying into bottom-antibottom quark pairs. We show that, at the Large Hadron Collider (LHC), both the efficiency of selecting the multi-jet final state and the ability to reconstruct from it the masses of the Higgs bosons (potentially) present in an event sample depend strongly on the choice of acceptance cuts, jet-clustering algorithm as well as its settings. Hence, we indicate the optimal choice of the latter for the purpose of establishing such a benchmark Beyond the SM (BSM) signal.

hep-ph↗

arXiv2020

Mapping $pp\to A\to ZH\to l^+l^-b\bar b$ and $pp\to H\to ZA\to l^+l^-b\bar b$ Current and Future Searches onto 2HDM Parameter Spaces

By borrowing the results from a Large Hadron Collider (LHC) analysis performed with $36.1~\text{fb}^{-1}$ of Run 2 data intended to search for $A$ production followed by $ZH$ decay in turn yielding $l^+l^-b\bar b$ ($l=e,μ$) final states in the context of the standard four Yukawa types of the 2-Higgs Doublet Model (2HDM), we recast it in terms of sensitivity reaches for the similar process $pp\to H\to ZA\to l^+l^-b\bar b$. This simple exercise across the two processes, which is possible because the only kinematic difference between these are different widths for the Higgs bosons, in turn affecting minimally the efficiency of an experimental selection, enables us to expand the region of parameter space that can be tested to the case when $m_H\ge m_A+m_Z$. Furthermore, we extrapolate our results to full Run 3 data samples. We conclude that, while the high energy and luminosity stage of the LHC can afford one with increased sensitivity to the 2HDM in general, the recast analysis does not add anything to what already probed through the actual one.

hep-ph↗