arXiv subjects

Christopher Lester

Publications and source records attributed to Christopher Lester.

12 recordsLinked to original sources

arXiv2026

Calibration and Performance of proANUBIS: A proof-of-concept detector for the ANUBIS experiment

Long-lived particles with lifetimes $\tau>10$~ps are predicted by many extensions of the Standard Model with viable dark matter candidates. The ANUBIS experiment proposes to extend the experimental sensitivity to long-lived particles by instrumenting the ceiling of the ATLAS cavern with Resistive Plate Chamber detectors in order to reconstruct vertices from long-lived particle decays in the air-filled volume above the ATLAS detector. The proANUBIS detector has been installed in the ATLAS cavern to validate the detector technology planned for ANUBIS and to take in-situ measurements of muon and hadron fluxes inside the ATLAS cavern using $pp$ collision data from the LHC. In this paper, the data collected, reconstruction techniques used, and performance of the \proanubis detector are discussed. The detection efficiency and timing resolution are found to be consistent with expectations and to meet the performance requirements of ANUBIS.

hep-ex↗

arXiv2025

Commissioning of proANUBIS: A proof-of-concept detector for the ANUBIS experiment

Long-lived particles (LLPs), predicted by various extensions of the Standard Model (SM), have become a key focus of the contemporary search programme for physics beyond the SM. To enhance LLP discovery potential at the LHC, the ANUBIS experiment has been proposed to instrument the ceiling of the ATLAS experiment's underground cavern with dedicated tracking detectors. This report summarises recent progress towards realising ANUBIS. Specifically, a key milestone has been achieved with the installation and commissioning of proANUBIS, a prototype that serves as a proof-of-concept for ANUBIS. We describe the proANUBIS setup, including its remotely-operated data acquisition system and automatic signal processing chain. The proANUBIS demonstrator is used to evaluate the detector performance under realistic conditions in the UX1 ATLAS experimental cavern, including readout synchronisation with the ATLAS experiment. Furthermore, proANUBIS allows for the direct measurement of relevant background processes in a representative location within the ATLAS cavern, providing input for the simulation of such processes for the future ANUBIS detector. The paper concludes with an update on the current status of the ANUBIS project and its roadmap toward a full-scale implementation in the ATLAS cavern.

hep-ex↗

arXiv2025

Construction of proANUBIS: A proof-of-concept detector for the ANUBIS experiment

The ANUBIS experiment aims to search for long-lived particles at the Large Hadron Collider (LHC) at CERN. To assess the feasibility of the project, a prototype detector, proANUBIS, was designed, constructed, and prepared for installation in the UX1 ATLAS experimental cavern at the LHC. The primary physics goals of proANUBIS are to determine the technical limitations of the detector technology and to explore the ANUBIS detector concept through in-situ measurements of muon and hadron fluxes inside the ATLAS cavern, which can be used to refine Monte Carlo simulations of such fluxes further. This report describes the design and construction of the proANUBIS experimental setup using Resistive Plate Chambers (RPCs), highlighting the possible future use case of the technology for ANUBIS. Details on the RPC technology, construction processes, quality control measures, and performance studies are discussed. Furthermore, the RPC front-end on-detector electronics and data acquisition components of proANUBIS are presented.

hep-ex↗

arXiv2020

Efficiently simulating discrete-state models with binary decision trees

Stochastic simulation algorithms (SSAs) are widely used to numerically investigate the properties of stochastic, discrete-state models. The Gillespie Direct Method is the pre-eminent SSA, and is widely used to generate sample paths of so-called agent-based or individual-based models. However, the simplicity of the Gillespie Direct Method often renders it impractical where large-scale models are to be analysed in detail. In this work, we carefully modify the Gillespie Direct Method so that it uses a customised binary decision tree to trace out sample paths of the model of interest. We show that a decision tree can be constructed to exploit the specific features of the chosen model. Specifically, the events that underpin the model are placed in carefully-chosen leaves of the decision tree in order to minimise the work required to keep the tree up-to-date. The computational efficencies that we realise can provide the apparatus necessary for the investigation of large-scale, discrete-state models that would otherwise be intractable. Two case studies are presented to demonstrate the efficiency of the method.

q-bio.QM↗

arXiv2018

Multi-level Approximate Bayesian Computation

Approximate Bayesian Computation is widely used to infer the parameters of discrete-state continuous-time Markov networks. In this work, we focus on models that are governed by the Chemical Master Equation (the CME). Whilst originally designed to model biochemical reactions, CME-based models are now frequently used to describe a wide range of biological phenomena mathematically. We describe and implement an efficient multi-level ABC method for investigating model parameters. In short, we generate sample paths of CME-based models with varying time resolutions. We start by generating low-resolution sample paths, which require only limited computational resources to construct. Those sample paths that compare well with experimental data are selected, and the temporal resolutions of the chosen sample paths are recursively increased. Those sample paths unlikely to aid in parameter inference are discarded at an early stage, leading to an optimal use of computational resources. The efficacy of the multi-level ABC is demonstrated through two case studies.

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arXiv2017

Efficient simulation techniques for biochemical reaction networks

Discrete-state, continuous-time Markov models are becoming commonplace in the modelling of biochemical processes. The mathematical formulations that such models lead to are opaque, and, due to their complexity, are often considered analytically intractable. As such, a variety of Monte Carlo simulation algorithms have been developed to explore model dynamics empirically. Whilst well-known methods, such as the Gillespie Algorithm, can be implemented to investigate a given model, the computational demands of traditional simulation techniques remain a significant barrier to modern research. In order to further develop and explore biologically relevant stochastic models, new and efficient computational methods are required. In this thesis, high-performance simulation algorithms are developed to estimate summary statistics that characterise a chosen reaction network. The algorithms make use of variance reduction techniques, which exploit statistical properties of the model dynamics, to improve performance. The multi-level method is an example of a variance reduction technique. The method estimates summary statistics of well-mixed, spatially homogeneous models by using estimates from multiple ensembles of sample paths of different accuracies. In this thesis, the multi-level method is developed in three directions: firstly, a nuanced implementation framework is described; secondly, a reformulated method is applied to stiff reaction systems; and, finally, different approaches to variance reduction are implemented and compared. The variance reduction methods that underpin the multi-level method are then re-purposed to understand how the dynamics of a spatially-extended Markov model are affected by changes in its input parameters. By exploiting the inherent dynamics of spatially-extended models, an efficient finite difference scheme is used to estimate parametric sensitivities robustly.

q-bio.QM↗

arXiv2017

Robustly simulating biochemical reaction kinetics using multi-level Monte Carlo approaches

In this work, we consider the problem of estimating summary statistics to characterise biochemical reaction networks of interest. Such networks are often described using the framework of the Chemical Master Equation (CME). For physically-realistic models, the CME is widely considered to be analytically intractable. A variety of Monte Carlo algorithms have therefore been developed to explore the dynamics of such networks empirically. Amongst them is the multi-level method, which uses estimates from multiple ensembles of sample paths of different accuracies to estimate a summary statistic of interest. {In this work, we develop the multi-level method in two directions: (1) to increase the robustness, reliability and performance of the multi-level method, we implement an improved variance reduction method for generating the sample paths of each ensemble; and (2) to improve computational performance, we demonstrate the successful use of a different mechanism for choosing which ensembles should be included in the multi-level algorithm.

q-bio.QM↗

arXiv2016

Efficient parameter sensitivity computation for spatially-extended reaction networks

Reaction-diffusion models are widely used to study spatially-extended chemical reaction systems. In order to understand how the dynamics of a reaction-diffusion model are affected by changes in its input parameters, efficient methods for computing parametric sensitivities are required. In this work, we focus on stochastic models of spatially-extended chemical reaction systems that involve partitioning the computational domain into voxels. Parametric sensitivities are often calculated using Monte Carlo techniques that are typically computationally expensive; however, variance reduction techniques can decrease the number of Monte Carlo simulations required. By exploiting the characteristic dynamics of spatially-extended reaction networks, we are able to adapt existing finite difference schemes to robustly estimate parametric sensitivities in a spatially-extended network. We show that algorithmic performance depends on the dynamics of the given network and the choice of summary statistics. We then describe a hybrid technique that dynamically chooses the most appropriate simulation method for the network of interest. Our method is tested for functionality and accuracy in a range of different scenarios.

q-bio.QM↗

arXiv2014

Extending the multi-level method for the simulation of stochastic biological systems

The multi-level method for discrete state systems, first introduced by Anderson and Higham [Multiscale Model. Simul. 10:146--179, 2012], is a highly efficient simulation technique that can be used to elucidate statistical characteristics of biochemical reaction networks. A single point estimator is produced in a cost-effective manner by combining a number of estimators of differing accuracy in a telescoping sum, and, as such, the method has the potential to revolutionise the field of stochastic simulation. The first term in the sum is calculated using an approximate simulation algorithm, and can be calculated quickly but is of significant bias. Subsequent terms successively correct this bias by combining estimators from approximate stochastic simulations algorithms of increasing accuracy, until a desired level of accuracy is reached. In this paper we present several refinements of the multi-level method which render it easier to understand and implement, and also more efficient. Given the substantial and complex nature of the multi-level method, the first part of this work (Sections 2 - 5) is written as a tutorial, with the aim of providing a practical guide to its use. The second part (Sections 6 - 8) takes on a form akin to a research article, thereby providing the means for a deft implementation of the technique, and concludes with a discussion of a number of open problems.

q-bio.QM↗

arXiv2014

An adaptive multi-level simulation algorithm for stochastic biological systems

Discrete-state, continuous-time Markov models are widely used in the modeling of biochemical reaction networks. Their complexity often precludes analytic solution, and we rely on stochastic simulation algorithms to estimate system statistics. The Gillespie algorithm is exact, but computationally costly as it simulates every single reaction. As such, approximate stochastic simulation algorithms such as the tau-leap algorithm are often used. Potentially computationally more efficient, the system statistics generated suffer from significant bias unless tau is relatively small, in which case the computational time can be comparable to that of the Gillespie algorithm. The multi-level method (Anderson and Higham, Multiscale Model. Simul. 2012) tackles this problem. A base estimator is computed using many (cheap) sample paths at low accuracy. The bias inherent in this estimator is then reduced using a number of corrections. Each correction term is estimated using a collection of paired sample paths where one path of each pair is generated at a higher accuracy compared to the other (and so more expensive). By sharing random variables between these paired paths the variance of each correction estimator can be reduced. This renders the multi-level method very efficient as only a relatively small number of paired paths are required to calculate each correction term. In the original multi-level method, each sample path is simulated using the tau-leap algorithm with a fixed value of $\tau$. This approach can result in poor performance when the reaction activity of a system changes substantially over the timescale of interest. By introducing a novel, adaptive time-stepping approach where $\tau$ is chosen according to the stochastic behaviour of each sample path we extend the applicability of the multi-level method to such cases. We demonstrate the efficiency of our method using a number of examples.

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arXiv2009

MTGEN : Mass scale measurements in pair-production at colliders

We introduce a new kinematic event variable MTGEN which can provide information relating to the mass scales of particles pair-produced at hadronic and leptonic colliders. The variable is of particular use in events with a large number of particles in the final state when some of those particles are massive and not detected, such as may arise in R-parity-conserving supersymmetry.

hep-ph↗

arXiv2003

m_T2 : the truth behind the glamour

We present the kinematic variable, m_T2, which is in some ways similar to the more familiar `transverse-mass', but which can be used in events where two or more particles have escaped detection. We define this variable and describe the event topologies to which it applies, then present some of its mathematical properties. We then briefly discuss two case studies which show how m_T2 is vital when reconstructing the masses of supersymmetric particles in mSUGRA-like and AMSB-like scenarios at the Large Hadron Collider.

hep-ph↗