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Amy Roberts

Publications and source records attributed to Amy Roberts.

11 recordsLinked to original sources
arXiv2026

Single Parameter Model for Galaxy Rotation Curves

One key piece of evidence for dark matter is the rotation-curve problem: the disagreement between measured galactic rotation curves and their luminous mass. A novel solution to this problem is presented here, in a model that predicts observed Doppler-shifted spectra based only on the luminous matter estimates and one free model parameter. This model is applied to fit the rotation curves of the SPARC sample of 175 galaxies, yielding mass-to-light ratios, goodness of fit measurements, and the free parameter. The model's average reduced chi square compares favorably with the dark matter model for the same data, and more galaxies are successfully fit by this model. The model provides a useful formulation linking luminous matter to the observed rotation curves, with the dark matter contribution to galaxies encoded in two transformation terms of the luminous mass. It also offers a lower-parameter characterization of the rotation curve problem, and a power law relationship between the model's free parameter and galactic photometric quantities is observed, potentially removing the need for the free parameter.

astro-ph.GA↗
arXiv2026

Machine Learning on Heterogeneous, Edge, and Quantum Hardware for Particle Physics (ML-HEQUPP)

The next generation of particle physics experiments will face a new era of challenges in data acquisition, due to unprecedented data rates and volumes along with extreme environments and operational constraints. Harnessing this data for scientific discovery demands real-time inference and decision-making, intelligent data reduction, and efficient processing architectures beyond current capabilities. Crucial to the success of this experimental paradigm are several emerging technologies, such as artificial intelligence and machine learning (AI/ML), silicon microelectronics, and the advent of quantum algorithms and processing. Their intersection includes areas of research such as low-power and low-latency devices for edge computing, heterogeneous accelerator systems, reconfigurable hardware, novel codesign and synthesis strategies, readout for cryogenic or high-radiation environments, and analog computing. This white paper presents a community-driven vision to identify and prioritize research and development opportunities in hardware-based ML systems and corresponding physics applications, contributing towards a successful transition to the new data frontier of fundamental science.

physics.ins-det↗
arXiv2025

Grounding Large Language Models in Clinical Evidence: A Retrieval-Augmented Generation System for Querying UK NICE Clinical Guidelines

This paper presents the development and evaluation of a Retrieval-Augmented Generation (RAG) system for querying the United Kingdom's National Institute for Health and Care Excellence (NICE) clinical guidelines using Large Language Models (LLMs). The extensive length and volume of these guidelines can impede their utilisation within a time-constrained healthcare system, a challenge this project addresses through the creation of a system capable of providing users with precisely matched information in response to natural language queries. The system's retrieval architecture, composed of a hybrid embedding mechanism, was evaluated against a corpus of 10,195 text chunks derived from three hundred guidelines. It demonstrates high performance, with a Mean Reciprocal Rank (MRR) of 0.814, a Recall of 81% at the first chunk and of 99.1% within the top ten retrieved chunks, when evaluated on 7901 queries. The most significant impact of the RAG system was observed during the generation phase. When evaluated on a manually curated dataset of seventy question-answer pairs, RAG-enhanced models showed substantial gains in performance. Faithfulness, the measure of whether an answer is supported by the source text, was increased by 64.7 percentage points to 99.5% for the RAG-enhanced O4-Mini model and significantly outperformed the medical-focused Meditron3-8B LLM, which scored 43%. Clinical evaluation by seven Subject Matter Experts (SMEs) further validated these findings, with GPT-4.1 achieving 98.7% accuracy while reducing unsafe responses by 67% compared to O4-Mini (from 3.0 to 1.0 per evaluator). This study thus establishes RAG as an effective, reliable, and scalable approach for applying generative AI in healthcare, enabling cost-effective access to medical guidelines.

cs.CL↗
arXiv2025

The Making of a Community Dark Matter Dataset with the National Science Data Fabric

Dark matter is believed to constitute approximately 85 percent of the universes matter, yet its fundamental nature remains elusive. Direct detection experiments, though globally deployed, generate data that is often locked within custom formats and non-reproducible software stacks, limiting interdisciplinary analysis and innovation. This paper presents a collaboration between the National Science Data Fabric (NSDF) and dark matter researchers to improve accessibility, usability, and scientific value of a calibration dataset collected with Cryogenic Dark Matter Search (CDMS) detectors at the University of Minnesota. We describe how NSDF services were used to convert data from a proprietary format into an open, multi-resolution IDX structure; develop a web-based dashboard for easily viewing signals; and release a Python-compatible CLI to support scalable workflows and machine learning applications. These contributions enable broader use of high-value dark matter datasets, lower the barrier to entry for new collaborators, and support reproducible, cross-disciplinary research.

hep-ex↗
arXiv2024

Describe Data to get Science-Data-Ready Tooling: Awkward as a Target for Kaitai Struct YAML

In some fields, scientific data formats differ across experiments due to specialized hardware and data acquisition systems. Researchers need to develop, document, and maintain experiment-specific analysis software to interact with these data formats. These software are often tightly coupled with a particular data format. This proliferation of custom data formats has been a prominent challenge for small to mid-scale experiments. The widespread adoption of ROOT has largely mitigated this problem for the Large Hadron Collider experiments. However, many smaller experiments continue to use custom data formats to meet specific research needs. Therefore, simplifying the process of accessing a unique data format for analysis holds immense value for scientific communities within HEP. We have added Awkward Arrays as a target language for Kaitai Struct for this purpose. Researchers can describe their custom data format in the Kaitai Struct YAML (KSY) language. The Kaitai Struct Compiler generates C++ code to fill the LayoutBuilder buffers using the KSY format. In a few steps, the Kaitai Struct Awkward Runtime API can convert the generated C++ code into a compiled Python module. Finally, the raw data can be passed to the module to produce Awkward Arrays. This paper introduces the Awkward Target for the Kaitai Struct Compiler and the Kaitai Struct Awkward Runtime API. It also demonstrates the conversion of a given KSY for a specific custom file format to Awkward Arrays.

cs.PL↗
arXiv2023

Rotation Curve Fitting Model

One key piece of evidence for dark matter is the flat rotation curve problem: the disagreement between measured galactic rotation curves and their luminous mass. A novel solution to this problem is presented here. A model of relativistic frame effects on Doppler shifts due to the slightly curved frames of an emitting galaxy and the Milky Way is derived. This model predicts observed Doppler shifted spectra (in excess of the luminous mass) based only on the observed luminous matter profile and one free model parameter. Fits to the 175 galaxies reported in the SPARC database of galactic rotation profiles and accurate photometry measurements are compared between this novel model and dark matter and MOND (RAR) models. We find on the SPARC sample of 175 galaxies; that MOND-RAR has an average reduced chisquare of $\chi^2_r = 4.22$ for 175 galaxies fitted, the isothermal dark matter model has $\chi^2_r = 1.90$ for 165 galaxies fitted, and the new model we present has $\chi^2_r = 2.39$ for 172 galaxies fitted. Implications of this model are discussed.

astro-ph.GA↗
arXiv2022

CompF5: End User Analysis Topical Group Report

This report summarizes the work of the Computational Frontier topical group on end user analysis for Snowmass 2021. End User Analysis refers to the extraction of physics results from reconstructed and simulated experimental data. High energy physics experiments produce systems that perform common reconstruction, calibration, and simulation tasks, resulting in shared data samples. These detailed data samples are then reduced to create a range of analysis samples, often optimized for a particular physics topic by a trigger or data object selection. End users (analyzers) then analyze those samples to produce physics results. Community discussions converged on categorizing the end user analysis ensemble into Analysis Ecosystems, Analysis Models, Dataset Bookkeeping and Formats, Collaborative Software, and Training. We present findings and recommendations based on these key areas that impact end user analysis.

physics.comp-ph↗
arXiv2022

Dark-matter And Neutrino Computation Explored (DANCE) Community Input to Snowmass

This paper summarizes the needs of the dark matter and neutrino communities as it relates to computation. The scope includes data acquisition, triggers, data management and processing, data preservation, simulation, machine learning, data analysis, software engineering, career development, and equity and inclusion. Beyond identifying our community needs, we propose actions that can be taken to strengthen this community and to work together to overcome common challenges.

hep-ex↗
arXiv2022

A Search for Low-mass Dark Matter via Bremsstrahlung Radiation and the Migdal Effect in SuperCDMS

In this paper, we present a re-analysis of SuperCDMS data using a profile likelihood approach to search for sub-GeV dark matter particles (DM) through two inelastic scattering channels: bremsstrahlung radiation and the Migdal effect. By considering possible inelastic scattering channels, experimental sensitivity can be extended to DM masses that would otherwise be undetectable through the DM-nucleon elastic scattering channel, given the energy threshold of current experiments. We exclude DM masses down to $220~\textrm{MeV}/c^2$ at $2.7 \times 10^{-30}~\textrm{cm}^2$ via the bremsstrahlung channel. The Migdal channel search excludes DM masses down to $30~\textrm{MeV}/c^2$ at $5.0 \times 10^{-30}~\textrm{cm}^2$.

hep-ex↗
arXiv2018

Energy Loss Due to Defect Formation from $^{206}$Pb Recoils in SuperCDMS Germanium Detectors

The Super Cryogenic Dark Matter Search experiment (SuperCDMS) at the Soudan Underground Laboratory studied energy loss associated with Frenkel defect formation in germanium crystals at mK temperatures using in situ $^{210}$Pb sources. We examine the spectrum of $^{206}$Pb nuclear recoils near its expected 103 keV endpoint energy and determine an energy loss of $\left(6.08\pm0.18\right)$ %, which we attribute to defect formation. From this result and using TRIM simulations, we extract the first experimentally determined average displacement threshold energy of $\left(19.7^{+0.6}_{-0.5}\right)$ eV for germanium. This has implications for the analysis thresholds of future germanium-based dark matter searches.

physics.ins-det↗