arXiv subjects

W. Hopkins

Publications and source records attributed to W. Hopkins.

9 recordsLinked to original sources

arXiv2025

Design and development of optical modules for the BUTTON-30 detector

BUTTON-30 is a neutrino detector demonstrator located in the STFC Boulby underground facility in the north-east of England. The main goal of the project is to deploy and test the performance of the gadolinium-loaded water-based liquid scintillator for neutrino detection in an underground environment. This will pave the way for a future large-volume neutrino observatory that can also perform remote monitoring of nuclear reactors for nonproliferation. This paper describes the design and construction of the watertight optical modules of the experiment.

physics.ins-det↗

arXiv2025

The BUTTON-30 detector at Boulby

The BUTTON-30 detector is a 30-tonne technology demonstrator designed to evaluate the potential of hybrid event detection, simultaneously exploiting both Cherenkov and scintillation light to detect particles produced in neutrino interactions. The detector is installed at a depth of 1.1 km in the Boulby Underground Laboratory allowing to test the performance of this new technology underground in a low background environment. This paper describes the design and construction of the experiment.

physics.ins-det↗

arXiv2025

ArgoLOOM: agentic AI for fundamental physics from quarks to cosmos

Progress in modern physics has been supported by a steadily expanding corpus of numerical analyses and computational frameworks, which in turn form the basis for precision calculations and baseline predictions in experimental programs. These tools play a central role in navigating a complex landscape of theoretical models and current and potential observables to identify and understand fundamental interactions in physics. In addition, efforts to search for new fundamental interactions increasingly have a cross-disciplinary nature, such that understanding and leveraging interoperabilities among computational tools may be a significant enhancement. This work presents a new agentic AI framework, which we call ArgoLOOM, designed to bridge methodologies and computational analyses across cosmology, collider physics, and nuclear science. We describe the system contours, key internal aspects, and outline its potential for unifying scientific discovery pipelines. In the process, we demonstrate the use of ArgoLOOM on two small-scale problems to illustrate its conceptual foundations and potential for extensibility into a steadily growing agentic framework for fundamental physics.

hep-ph↗

arXiv2022

The Future of High Energy Physics Software and Computing

Software and Computing (S&C) are essential to all High Energy Physics (HEP) experiments and many theoretical studies. The size and complexity of S&C are now commensurate with that of experimental instruments, playing a critical role in experimental design, data acquisition/instrumental control, reconstruction, and analysis. Furthermore, S&C often plays a leading role in driving the precision of theoretical calculations and simulations. Within this central role in HEP, S&C has been immensely successful over the last decade. This report looks forward to the next decade and beyond, in the context of the 2021 Particle Physics Community Planning Exercise ("Snowmass") organized by the Division of Particles and Fields (DPF) of the American Physical Society.

hep-ex↗

arXiv2022

Snowmass Computational Frontier: Topical Group Report on Experimental Algorithm Parallelization

The substantial increase in data volume and complexity expected from future experiments will require significant investment to prepare experimental algorithms. These algorithms include physics object reconstruction, calibrations, and processing of observational data. In addition, the changing computing architecture landscape, which will be primarily composed of heterogeneous resources, will continue to pose major challenges with regard to algorithmic migration. Portable tools need to be developed that can be shared among the frontiers (e.g., for code execution on different platforms) and opportunities, such as forums or cross-experimental working groups, need to be provided where experiences and lessons learned can be shared between experiments and frontiers. At the same time, individual experiments also need to invest considerable resources to develop algorithms unique to their needs (e.g., for facilities dedicated to the experiment), and ensure that their specific algorithms will be able to efficiently exploit external heterogeneous computing facilities. Common software tools represent a cost-effective solution, providing ready-to-use software solutions as well as a platform for R\&D work. These are particularly important for small experiments which typically do not have dedicated resources needed to face the challenges imposed by the evolving computing technologies. Workforce development is a key concern across frontiers and experiments, and additional support is needed to provide career opportunities for researchers working in the field of experimental algorithm development. Finally, cross-discipline collaborations going beyond high-energy physics are a key ingredient to address the challenges ahead and more support for such collaborations needs to be created. This report targets future experiments, observations and experimental algorithm development for the next 10-15 years.

hep-ex↗

arXiv2021

Event-based anomaly detection for new physics searches at the LHC using machine learning

This paper discusses model-agnostic searches for new physics at the Large Hadron Collider (LHC) using anomaly-detection techniques for the identification of event signatures that deviate from the Standard Model (SM). We investigate anomaly detection in the context of machine-learning approaches using autoencoders, and illustrate expected shapes of invariant masses in the outlier region using Monte Carlo simulations. Challenges and conceptual limitations of this approach are discussed.

hep-ph↗

arXiv2020

Automated detector simulation and reconstruction parametrization using machine learning

Rapidly applying the effects of detector response to physics objects (e.g. electrons, muons, showers of particles) is essential in high energy physics. Currently available tools for the transformation from truth-level physics objects to reconstructed detector-level physics objects involve manually defining resolution functions. These resolution functions are typically derived in bins of variables that are correlated with the resolution (e.g. pseudorapidity and transverse momentum). This process is time consuming, requires manual updates when detector conditions change, and can miss important correlations. Machine learning offers a way to automate the process of building these truth-to-reconstructed object transformations and can capture complex correlation for any given set of input variables. Such machine learning algorithms, with sufficient optimization, could have a wide range of applications: improving phenomenological studies by using a better detector representation, allowing for more efficient production of Geant4 simulation by only simulating events within an interesting part of phase space, and studies on future experimental sensitivity to new physics.

physics.data-an↗

arXiv2014

Vertical beam size measurement in the CESR-TA $e^+e^-$ storage ring using x-rays from synchrotron radiation

We describe the construction and operation of an x-ray beam size monitor (xBSM), a device measuring $e^+$ and $e^-$ beam sizes in the CESR-TA storage ring using synchrotron radiation. The device can measure vertical beam sizes of $10-100~μ$m on a turn-by-turn, bunch-by-bunch basis at $e^\pm$ beam energies of $\sim2~$GeV. At such beam energies the xBSM images x-rays of $ε\approx$1-10$~$keV ($λ\approx 0.1-1$ nm) that emerge from a hard-bend magnet through a single- or multiple-slit (coded aperture) optical element onto an array of 32 InGaAs photodiodes with 50$~μ$m pitch. Beamlines and detectors are entirely in-vacuum, enabling single-shot beam size measurement down to below 0.1$~$mA ($2.5\times10^9$ particles) per bunch and inter-bunch spacing of as little as 4$~$ns. At $E_{\rm b}=2.1 $GeV, systematic precision of $\sim 1~μ$m is achieved for a beam size of $\sim12~μ$m; this is expected to scale as $\propto 1/σ_{\rm b}$ and $\propto 1/E_{\rm b}$. Achieving this precision requires comprehensive alignment and calibration of the detector, optical elements, and x-ray beam. Data from the xBSM have been used to extract characteristics of beam oscillations on long and short timescales, and to make detailed studies of low-emittance tuning, intra-beam scattering, electron cloud effects, and multi-bunch instabilities.

physics.ins-det↗

arXiv2007

Finding the Charge of the top quark in the Dilepton Channel

There is a question about the identity of the top quark. Is it the top quark of the Standard Model (SM) with electric charge 2/3 or is it an exotic quark with charge -4/3? An exotic quark has been proposed by D. Chang et al.\cite{hep-ph/9810531, hep-ph/9805273}. This analysis will use the standard CDF run II dilepton sample. The key ingredients of this analysis are the correct pairing of the lepton and b-jet, the determination of the charge of the b-jet. The analysis proceeds by using a binomial distribution and is formulated so that rejecting one hypothesis means support for the other hypothesis.

hep-ex↗