arXiv subjects

Patrick Brown

Publications and source records attributed to Patrick Brown.

At least 19 recordsLinked to original sources

arXiv2026

Spatio-Temporal Disaggregation with Changing Areal Boundaries

Small area estimation and disease mapping increasingly rely on areal data where reporting boundaries change over time. We develop a computationally efficient spatio-temporal disaggregation method to recover high-resolution risk surfaces from observed counts under changing boundaries. Our approach extends the spatially aggregated log-Gaussian Cox process and uses the Extended Latent Gaussian Model framework for fast approximate posterior inference. We replace standard lognormal polygon-specific effects with gamma-distributed overdispersion which yields a marginal negative binomial likelihood, and removes one latent variable per polygon-time pair. We illustrate the approach by mapping mortality risk across shifting NUTS-3 boundaries in Belgium and the Netherlands. For the purpose of dissemination we use Codex to leverage the methodology presented in this paper for the analysis of a separate data set concerning the city of Manchester. The methodology is implemented in the open-source R package DAST.

stat.ME↗

arXiv2025

Spaceflight KID Readout Electronics for PRIMA

We present the design and testing of a prototype multiplexing kinetic inductance detector (KID) readout electronics for the PRobe far-Infrared Mission for Astrophysics (PRIMA) space mission. PRIMA is a Probe-class astrophysics mission concept that will answer fundamental questions about the formation of planetary systems, the co-evolution of stars and supermassive black holes in galaxies, and the rise of heavy elements and dust over cosmic time. The readout electronics for PRIMA must be compatible with operation at Earth-Sun L2 and capable of multiplexing more than 1000 detectors over 2.5 GHz bandwidth while consuming around 30 W per readout chain. The electronics must also be capable of switching between the two instruments, which have different readout bands: the hyperspectral imager (PRIMAger, 2.6-4.9 GHz) and the spectrometer (FIRESS, 0.4-2.4 GHz). The PRIMA readout electronics use high-heritage SpaceCube digital electronics with a build-to-print SpaceCube Mini v3.0 board using a radiation-tolerant Kintex KU060 field programmable gate array (FPGA) and a custom high-speed digitizer board, along with RF electronics that provide filtering and power conditioning. We present the driving requirements for the system, as well as the hardware, firmware, software, and system-level design that meets those requirements.

astro-ph.IM↗

arXiv2025

Candidate Dark Galaxy-2: Validation and Analysis of an Almost Dark Galaxy in the Perseus Cluster

Candidate Dark Galaxy-2 (CDG-2) is a potential dark galaxy consisting of four globular clusters (GCs) in the Perseus cluster, first identified in Li et al. (2025) through a sophisticated statistical method. The method searched for over-densities of GCs from a \textit{Hubble Space Telescope} (\textit{HST}) survey targeting Perseus. Using the same \textit{HST} images and the new imaging data from the \textit{Euclid} survey, we report the detection of extremely faint but significant diffuse emission around the four GCs of CDG-2. We thus have exceptionally strong evidence that CDG-2 is a galaxy. This is the first galaxy detected purely through its GC population. Under the conservative assumption that the four GCs make up the entire GC population, preliminary analysis shows that CDG-2 has a total luminosity of $L_{V, \mathrm{gal}}= 6.2\pm{3.0} \times 10^6 L_{\odot}$ and a minimum GC luminosity of $L_{V, \mathrm{GC}}= 1.03\pm{0.2}\times 10^6 L_{\odot}$. Our results indicate that CDG-2 is one of the faintest galaxies having associated GCs, while at least $\sim 16.6\%$ of its light is contained in its GC population. This ratio is likely to be much higher ($\sim 33\%$) if CDG-2 has a canonical GC luminosity function (GCLF). In addition, if the previously observed GC-to-halo mass relations apply to CDG-2, it would have a minimum dark matter halo mass fraction of $99.94\%$ to $99.98\%$. If it has a canonical GCLF, then the dark matter halo mass fraction is $\gtrsim 99.99\%$. Therefore, CDG-2 may be the most GC dominated galaxy and potentially one of the most dark matter dominated galaxies ever discovered.

astro-ph.GA↗

arXiv2024

Discovery of Two Ultra-Diffuse Galaxies with Unusually Bright Globular Cluster Luminosity Functions via a Mark-Dependently Thinned Point Process (MATHPOP)

We present \textsc{Mathpop}, a novel method to infer the globular cluster (GC) counts in ultra-diffuse galaxies (UDGs) and low-surface brightness galaxies (LSBGs). Many known UDGs have a surprisingly high ratio of GC number to surface brightness. However, standard methods to infer GC counts in UDGs face various challenges, such as photometric measurement uncertainties, GC membership uncertainties, and assumptions about the GC luminosity functions (GCLFs). \textsc{Mathpop} tackles these challenges using the mark-dependent thinned point process, enabling joint inference of the spatial and magnitude distributions of GCs. In doing so, \textsc{Mathpop} allows us to infer and quantify the uncertainties in both GC counts and GCLFs with minimal assumptions. As a precursor to \textsc{Mathpop}, we also address the data uncertainties coming from the selection process of GC candidates: we obtain probabilistic GC candidates instead of the traditional binary classification based on the color--magnitude diagram. We apply \textsc{Mathpop} to 40 LSBGs in the Perseus cluster using GC catalogs from a \textit{Hubble Space Telescope} imaging program. We then compare our results to those from an independent study using the standard method. We further calibrate and validate our approach through extensive simulations. Our approach reveals two LSBGs having GCLF turnover points much brighter than the canonical value with Bayes' factor being $\sim4.5$ and $\sim2.5$, respectively. An additional crude maximum-likelihood estimation shows that their GCLF TO points are approximately $0.9$~mag and $1.1$~mag brighter than the canonical value, with $p$-value $\sim 10^{-8}$ and $\sim 10^{-5}$, respectively.

astro-ph.GA↗

arXiv2024

A Review of Stable, Traversable Wormholes in f (R) Gravity Theories

It has been proven that in standard Einstein gravity, exotic matter is required to stabilize traversable wormholes. Quantum field theory permits these violations due to the quantum coherent effects found in any quantum field. Even reasonable classical scalar fields violate the energy conditions. In the case of the Casimir effect and squeezed vacuum states, these violations have been experimentally proven. It is advantageous to investigate methods to minimize the use of exotic matter. One such area of interest is extended theories of Einstein gravity. It has been claimed that in some extended theories, stable traversable wormholes solutions can be found without the use of exotic matter. There are many extended theories of gravity, and in this review paper, we first explore modified gravity theories and then explore some wormhole solutions in such theories, including Lovelock gravity and Einstein Dilaton Gauss Bonnet gravity. For completeness, we have also reviewed other wormholes such as Casimir wormholes, dark matter halo wormholes, thin-shell wormholes, and Nonlocal Gravity wormholes, where alternative techniques are used to either avoid or reduce the amount of exotic matter that is required.

gr-qc↗

arXiv2024

An Intrinsic Coordinate Reference Frame Procedure I: Tensorial Canonical Weyl Scalars

Canonical quantization of gravity in general relativity is greatly simplified by the artificial decomposition of space and time into a 3+1 formalism. Such a simplification may appear to come at the cost of general covariance. This requires tangential and perpendicular infinitesimal diffeomorphisms generated by the symmetry group under the Legendre transformation of the given action. This gauge generator, along with the fact that Weyl curvature scalars may act as ``intrinsic coordinates" (or a dynamical reference frame) which depend only on the spatial metric $g_{ab}$ and the conjugate momenta $p^{cd}$, allow for an alternative approach to canonical quantization of gravity. In this paper we present the tensorial solution of the set of Weyl scalars in terms of canonical phase-space variables.

gr-qc↗

arXiv2024

Case-crossover designs and overdispersion with application in air pollution epidemiology

Over the last three decades, case-crossover designs have found many applications in health sciences, especially in air pollution epidemiology. They are typically used, in combination with partial likelihood techniques, to define a conditional logistic model for the responses, usually health outcomes, conditional on the exposures. Despite the fact that conditional logistic models have been shown equivalent, in typical air pollution epidemiology setups, to specific instances of the well-known Poisson time series model, it is often claimed that they cannot allow for overdispersion. This paper clarifies the relationship between case-crossover designs, the models that ensue from their use, and overdispersion. In particular, we propose to relax the assumption of independence between individuals traditionally made in case-crossover analyses, in order to explicitly introduce overdispersion in the conditional logistic model. As we show, the resulting overdispersed conditional logistic model coincides with the overdispersed, conditional Poisson model, in the sense that their likelihoods are simple re-expressions of one another. We further provide the technical details of a Bayesian implementation of the proposed case-crossover model, which we use to demonstrate, by means of a large simulation study, that standard case-crossover models can lead to dramatically underestimated coverage probabilities, while the proposed models do not. We also perform an illustrative analysis of the association between air pollution and morbidity in Toronto, Canada, which shows that the proposed models are more robust than standard ones to outliers such as those associated with public holidays.

stat.ME↗

arXiv2023

Efficient Modeling of Quasi-Periodic Data with Seasonal Gaussian Process

Quasi-periodicity refers to a pattern in a function where it appears periodic but has evolving amplitudes over time. This is often the case in practical settings such as the modeling of case counts of infectious disease or the carbon dioxide (CO2) concentration over time. In this paper, we introduce a class of Gaussian processes, called seasonal Gaussian Processes (sGP), for model-based inference of such quasi-periodic behavior. We illustrate that the exact sGP can be efficiently fit within $O(n)$ time using its state space representation for equally spaced locations. However, for large datasets with irregular spacing, the exact approach becomes computationally inefficient and unstable. To address this, we develop a continuous finite dimensional approximation for sGP using the seasonal B-spline (sB-spline) basis constructed by damping B-splines with sinusoidal functions. We prove that the proposed approximation converges in distribution to the true sGP as the number of basis functions increases, and show its superior approximation quality through numerical studies. We also provide a unified and interpretable way to define priors for the sGP, based on the notion of predictive standard deviation (PSD). Finally, we implement the proposed inference method on several real data examples to illustrate its practical usage.

stat.ME↗

arXiv2023

Profile likelihoods for parameters in Gaussian geostatistical models

Profile likelihoods are rarely used in geostatistical models due to the computational burden imposed by repeated decompositions of large variance matrices. Accounting for uncertainty in covariance parameters can be highly consequential in geostatistical models as some covariance parameters are poorly identified, the problem is severe enough that the differentiability parameter of the Matern correlation function is typically treated as fixed. The problem is compounded with anisotropic spatial models as there are two additional parameters to consider. In this paper, we make the following contributions: 1, A methodology is created for profile likelihoods for Gaussian spatial models with Mat\'ern family of correlation functions, including anisotropic models. This methodology adopts a novel reparametrization for generation of representative points, and uses GPUs for parallel profile likelihoods computation in software implementation. 2, We show the profile likelihood of the Mat\'ern shape parameter is often quite flat but still identifiable, it can usually rule out very small values. 3, Simulation studies and applications on real data examples show that profile-based confidence intervals of covariance parameters and regression parameters have superior coverage to the traditional standard Wald type confidence intervals.

stat.ME↗

arXiv2023

Model-based Smoothing with Integrated Wiener Processes and Overlapping Splines

In many applications that involve the inference of an unknown smooth function, the inference of its derivatives will often be just as important as that of the function itself. To make joint inferences of the function and its derivatives, a class of Gaussian processes called $p^{\text{th}}$ order Integrated Wiener's Process (IWP), is considered. Methods for constructing a finite element (FEM) approximation of an IWP exist but have focused only on the order $p = 2$ case which does not allow appropriate inference for derivatives, and their computational feasibility relies on additional approximation to the FEM itself. In this article, we propose an alternative FEM approximation, called overlapping splines (O-spline), which pursues computational feasibility directly through the choice of test functions, and mirrors the construction of an IWP as the Ospline results from the multiple integrations of these same test functions. The O-spline approximation applies for any order $p \in \mathbb{Z}^+$, is computationally efficient and provides consistent inference for all derivatives up to order $p-1$. It is shown both theoretically, and empirically through simulation, that the O-spline approximation converges to the true IWP as the number of knots increases. We further provide a unified and interpretable way to define priors for the smoothing parameter based on the notion of predictive standard deviation (PSD), which is invariant to the order $p$ and the placement of the knot. Finally, we demonstrate the practical use of the O-spline approximation through simulation studies and an analysis of COVID death rates where the inference is carried on both the function and its derivatives where the latter has an important interpretation in terms of the course of the pandemic.

stat.ME↗

arXiv2022

clrng: A tool set for parallel random numbergeneration on GPUs in R

We introduce the R package clrng which leverages the gpuR package and is able to generate random numbers in parallel on a Graphics Processing Unit (GPU) with the clRNG (OpenCL) library. Parallel processing with GPU's can speed up computationally intensive tasks, which when combined with R, it can largely improve R's downsides in terms of slow speed, memory usage and computation mode. clrng enables reproducible research by setting random initial seeds for streams on GPU and CPU, and can thus accelerate several types of statistical simulation and modelling. The random number generator in clrng guarantees independent parallel samples even when R is used interactively in an ad-hoc manner, with sessions being interrupted and restored. This package is portable and flexible, developers can use its random number generation kernel for various other purposes and applications.

stat.CO↗

arXiv2021

Fast, Scalable Approximations to Posterior Distributions in Extended Latent Gaussian Models

We define a novel class of additive models, called Extended Latent Gaussian Models, that allow for a wide range of response distributions and flexible relationships between the additive predictor and mean response. The new class covers a broad range of interesting models including multi-resolution spatial processes, partial likelihood-based survival models, and multivariate measurement error models. Because computation of the exact posterior distribution is infeasible, we develop a fast, scalable approximate Bayesian inference methodology for this class based on nested Gaussian, Laplace, and adaptive quadrature approximations. We prove that the error in these approximate posteriors is op(1) under standard conditions, and provide numerical evidence suggesting that our method runs faster and scales to larger datasets than methods based on Integrated Nested Laplace Approximations and Markov Chain Monte Carlo, with comparable accuracy. We apply the new method to the mapping of malaria incidence rates in continuous space using aggregated data, mapping leukaemia survival hazards using a Cox Proportional-Hazards model with a continuously-varying spatial process, and estimating the mass of the Milky Way Galaxy using noisy multivariate measurements of the positions and velocities of star clusters in its orbit.

stat.ME↗

arXiv2020

Red Light Green Light Method for Solving Large Markov Chains

Discrete-time discrete-state finite Markov chains are versatile mathematical models for a wide range of real-life stochastic processes. One of most common tasks in studies of Markov chains is computation of the stationary distribution. Without loss of generality, and drawing our motivation from applications to large networks, we interpret this problem as one of computing the stationary distribution of a random walk on a graph. We propose a new controlled, easily distributed algorithm for this task, briefly summarized as follows: at the beginning, each node receives a fixed amount of cash (positive or negative), and at each iteration, some nodes receive `green light' to distribute their wealth or debt proportionally to the transition probabilities of the Markov chain; the stationary probability of a node is computed as a ratio of the cash distributed by this node to the total cash distributed by all nodes together. Our method includes as special cases a wide range of known, very different, and previously disconnected methods including power iterations, Gauss-Southwell, and online distributed algorithms. We prove exponential convergence of our method, demonstrate its high efficiency, and derive scheduling strategies for the green-light, that achieve convergence rate faster than state-of-the-art algorithms.

math.NA↗

arXiv2020

Fully Bayesian inference for spatiotemporal data with the multi-resolution approximation

Large spatiotemporal datasets are a challenge for conventional Bayesian models because of the cubic computational complexity of the algorithms for obtaining the Cholesky decomposition of the covariance matrix in the multivariate normal density. Moreover, standard numerical algorithms for posterior estimation, such as Markov Chain Monte Carlo (MCMC), are intractable in this context, as they require thousands, if not millions, of costly likelihood evaluations. To overcome those limitations, we propose IS-MRA (Importance sampling - Multi-Resolution Approximation), which takes advantage of the sparse inverse covariance structure produced by the Multi-Resolution Approximation (MRA) approach. IS-MRA is fully Bayesian and facilitates the approximation of the hyperparameter marginal posterior distributions. We apply IS-MRA to large MODIS Level 3 Land Surface Temperature (LST) datasets, sampled between May 18 and May 31, 2012 in the western part of the state of Maharashtra, India. We find that IS-MRA can produce realistic prediction surfaces over regions where concentrated missingness, caused by sizable cloud cover, is observed. Through a validation analysis and simulation study, we also find that predictions tend to be very accurate.

stat.CO↗

arXiv2019

Geographically-dependent individual-level models for infectious diseases transmission

Infectious disease models can be of great use for understanding the underlying mechanisms that influence the spread of diseases and predicting future disease progression. Modeling has been increasingly used to evaluate the potential impact of different control measures and to guide public health policy decisions. In recent years, there has been rapid progress in developing spatio-temporal modeling of infectious diseases and an example of such recent developments is the discrete time individual-level models (ILMs). These models are well developed and provide a common framework for modeling many disease systems, however, they assume the probability of disease transmission between two individuals depends only on their spatial separation and not on their spatial locations. In cases where spatial location itself is important for understanding the spread of emerging infectious diseases and identifying their causes, it would be beneficial to incorporate the effect of spatial location in the model. In this study, we thus generalize the ILMs to a new class of geographically-dependent ILMs (GD-ILMs), to allow for the evaluation of the effect of spatially varying risk factors (e.g., education, environmental), as well as unobserved spatial structure, upon the transmission of infectious disease. Specifically, we consider a conditional autoregressive model to capture the effects of unobserved spatially structured latent covariates or measurement error. This results in flexible infectious disease models that can be used for formulating etiological hypotheses and identifying geographical regions of unusually high risk to formulate preventive action. The reliability of these models are investigated on a combination of simulated epidemic data and Alberta seasonal influenza outbreak data (2009). This new class of models is fitted to data within a Bayesian statistical framework using MCMC methods.

stat.AP↗

arXiv2016

A Partial Proof of a Conjecture of Dris

Euler showed that if an odd perfect number $N$ exists, it must consist of two parts $N=q^k n^2$, with $q$ prime, $q \equiv k \equiv 1 \pmod{4}$, and gcd$(q,n)=1$. Dris conjectured that $q^k < n$. We first show that $q<n$ for all odd perfect numbers. Afterwards, we show $q^k < n$ holds in many cases.

math.NT↗

arXiv2015

More on the Nonexistence of Odd Perfect Numbers of a Certain Form

Euler showed that if an odd perfect number exists, it must be of the form $N = p^\alpha q_{1}^{2\beta_{1}}$ $\ldots$ $q_{k}^{2\beta_{k}}$, where $p, q_{1}, \ldots, q_k$ are distinct odd primes, $\alpha$, $\beta_{i} \geq 1$, for $1 \leq i \leq k$, with $p \equiv \alpha \equiv 1 \pmod{4}$. In 2005, Evans and Pearlman showed that $N$ is not perfect, if $3|N$ or $7|N$ and each $\beta_{i} \equiv 2 \pmod{5}$. We improve on this result by removing the hypothesis that $3|N$ or $7|N$ and show that $N$ is not perfect, simply, if each $\beta_{i} \equiv 2 \pmod{5}$.

math.NT↗

arXiv2012

OSS (Outer Solar System): A fundamental and planetary physics mission to Neptune, Triton and the Kuiper Belt

The present OSS mission continues a long and bright tradition by associating the communities of fundamental physics and planetary sciences in a single mission with ambitious goals in both domains. OSS is an M-class mission to explore the Neptune system almost half a century after flyby of the Voyager 2 spacecraft. Several discoveries were made by Voyager 2, including the Great Dark Spot (which has now disappeared) and Triton's geysers. Voyager 2 revealed the dynamics of Neptune's atmosphere and found four rings and evidence of ring arcs above Neptune. Benefiting from a greatly improved instrumentation, it will result in a striking advance in the study of the farthest planet of the Solar System. Furthermore, OSS will provide a unique opportunity to visit a selected Kuiper Belt object subsequent to the passage of the Neptunian system. It will consolidate the hypothesis of the origin of Triton as a KBO captured by Neptune, and improve our knowledge on the formation of the Solar system. The probe will embark instruments allowing precise tracking of the probe during cruise. It allows to perform the best controlled experiment for testing, in deep space, the General Relativity, on which is based all the models of Solar system formation. OSS is proposed as an international cooperation between ESA and NASA, giving the capability for ESA to launch an M-class mission towards the farthest planet of the Solar system, and to a Kuiper Belt object. The proposed mission profile would allow to deliver a 500 kg class spacecraft. The design of the probe is mainly constrained by the deep space gravity test in order to minimise the perturbation of the accelerometer measurement.

gr-qc↗