arXiv subjects

Matthew J Penn

Publications and source records attributed to Matthew J Penn.

5 recordsLinked to original sources

arXiv2026

Structure and Dynamics of the Inner Corona Measured from the DEB Initiative 2024 Eclipse Image Sequence

The Dynamic Eclipse Broadcast (DEB) Initiative citizen science program observed coronal visible continuum brightness during the 2024 April 8 total eclipse from locations across North America. We present results from 11 DEB sites spanning 2700 km of distance and showing 49 minutes of evolution. The coronal brightness radial profiles from these telescopes are tightly correlated from 1.2 to 4.0 solar radii and comparable to published photometric coronal intensities. The coronal flattening parameter is measured from 1.4 to 2.8 solar radii. A Ludendorff index of 0.0761 +/- 0.0007 is computed but the extrapolation techniques used by some to calculate this index are shown to disagree with this direct measurement, and alternate structure parameters are suggested. Measured radial velocities are compared with an MHD model of the corona during the eclipse from Y. Li et al. (2026). A polar downflow is measured with an average radial velocity of -37 +/- 3 km s-1 and a deceleration of 14 +/- 3 m s-2 at a speed and position which agrees with the model. The predicted mixture of outflows and downflows at low heights is seen, as well as outflows in two western regions of the corona. The fastest observed outflow has a radial speed of 105 km s-1 and is likely associated with a transient event not predicted by the model. Future DEB Initiative eclipse experiments can more tightly constrain models of the inner corona by using both coronal intensity and radial velocity measurements.

astro-ph.SR↗

arXiv2025

Dynamic Graph-Based Forecasts of Bookmakers' Odds in Professional Tennis

Bookmakers' odds consistently provide one of the most accurate methods for predicting the results of professional tennis matches. However, these odds usually only become available shortly before a match takes place, limiting their usefulness as an analysis tool. To ameliorate this issue, we introduce a novel dynamic graph-based model which aims to forecast bookmaker odds for any match on any surface, allowing effective and detailed pre-tournament predictions to be made. By leveraging the high-quality information contained in the odds, our model can keep pace with new innovations in tennis modelling. By analysing major tennis championships from 2024 and 2025, we show that our model achieves comparable accuracy both to the bookmakers and other models in the literature, while significantly outperforming rankings-based predictions.

stat.AP↗

arXiv2025

phylo2vec: a library for vector-based phylogenetic tree manipulation

Phylogenetics is a fundamental component of evolutionary analysis frameworks in biology and linguistics. Recently, the advent of large-scale genomics and the SARS-CoV-2 pandemic has highlighted the necessity for phylogenetic software to handle large datasets. While significant efforts have focused on scaling optimisation algorithms, visualization, and lineage identification, an emerging body of research has been dedicated to efficient representations of data for genomes and phylogenetic trees. Compared to the traditional Newick format which represents trees using strings of nested parentheses, modern tree representations utilize integer vectors to define the tree topology traversal. This approach offers several advantages, including easier manipulation, increased memory efficiency, and applicability to machine learning. Here, we present the latest release of phylo2vec (or Phylo2Vec), a high-performance software package for encoding, manipulating, and analysing binary phylogenetic trees. At its core, the package is based on the phylo2vec representation of binary trees, and is designed to enable fast sampling and tree comparison. This release features a core implementation in Rust for improved performance and memory efficiency, with wrappers in R and Python (superseding the original release), making it accessible to a broad audience in the bioinformatics community.

q-bio.PE↗

arXiv2025

Phylo2Vec: a vector representation for binary trees

Binary phylogenetic trees inferred from biological data are central to understanding the shared history among evolutionary units. However, inferring the placement of latent nodes in a tree is computationally expensive. State-of-the-art methods rely on carefully designed heuristics for tree search, using different data structures for easy manipulation (e.g., classes in object-oriented programming languages) and readable representation of trees (e.g., Newick-format strings). Here, we present Phylo2Vec, a parsimonious encoding for phylogenetic trees that serves as a unified approach for both manipulating and representing phylogenetic trees. Phylo2Vec maps any binary tree with $n$ leaves to a unique integer vector of length $n-1$. The advantages of Phylo2Vec are fourfold: i) fast tree sampling, (ii) compressed tree representation compared to a Newick string, iii) quick and unambiguous verification if two binary trees are identical topologically, and iv) systematic ability to traverse tree space in very large or small jumps. As a proof of concept, we use Phylo2Vec for maximum likelihood inference on five real-world datasets and show that a simple hill-climbing-based optimisation scheme can efficiently traverse the vastness of tree space from a random to an optimal tree.

q-bio.PE↗

arXiv2024

Leaping through tree space: continuous phylogenetic inference for rooted and unrooted trees

Phylogenetics is now fundamental in life sciences, providing insights into the earliest branches of life and the origins and spread of epidemics. However, finding suitable phylogenies from the vast space of possible trees remains challenging. To address this problem, for the first time, we perform both tree exploration and inference in a continuous space where the computation of gradients is possible. This continuous relaxation allows for major leaps across tree space in both rooted and unrooted trees, and is less susceptible to convergence to local minima. Our approach outperforms the current best methods for inference on unrooted trees and, in simulation, accurately infers the tree and root in ultrametric cases. The approach is effective in cases of empirical data with negligible amounts of data, which we demonstrate on the phylogeny of jawed vertebrates. Indeed, only a few genes with an ultrametric signal were generally sufficient for resolving the major lineages of vertebrates. Optimisation is possible via automatic differentiation and our method presents an effective way forwards for exploring the most difficult, data-deficient phylogenetic questions.

q-bio.PE↗