arXiv subjects

Adam Onus

Publications and source records attributed to Adam Onus.

6 recordsLinked to original sources

arXiv2026

Learning the Graphical Nature of Symmetries

Finite groups are rigid algebraic objects, whose Cayley graphs expose a rich network geometry through which group-theoretic structure can be measured, compared, and learned. In this paper, a dataset of $131{,}406$ Cayley graphs is constructed, covering all groups of order at most $767$ except order $512$, recording exact algebraic labels for group properties together with a broad collection of graph, cycle, distance, and spectral statistics. This census aims to provide novel benchmarks for studying how finite-group properties are reflected in Cayley graph observables. It also yields new enumerative contributions: alongside recovering known OEIS sequences for standard group classes, new sequences for monolithic groups and for groups generated by at most three, four, and five elements are contributed to the OEIS. The accompanying network analysis identifies several empirical regularities and formulates testable conjectures, including relationships involving square clustering, Cayley graph diameter, average graph disorder, and spectral eigengaps of nilpotent groups. Finally, a comparison between classical models, an MLP, and graph neural network architectures is performed for predicting algebraic group properties directly from Cayley graph data. The results show that engineered graph statistics are highly informative, while GNNs, especially GIN and in some fixed-order settings GCN, can recover substantial structural signal directly from the graph. Such that graph-aware architectures show phases of optimality on these group-theoretic graph representations.

stat.ML↗

arXiv2025

Persistent Local Systems of Periodic Spaces

The topology of periodic spaces has attracted a lot of interest in recent years in order to study and classify crystalline structures and other large homogeneous data sets, such as the distribution of galaxies in cosmology. In practice, these objects are studied by taking a finite sample and introducing periodic boundary conditions, however this introduces and removes many subtle homological features. Here, build on the work of Onus and Robins (2022) and Onus and Skraba (2023) to investigate whether one can recover the (persistent) homology of a periodic cell complex $K$ from a finite quotient space $G$ of equivalence classes under translations. In particular, we search for a computationally friendly method to identify all ''toroidal cycles'' of $G$ which do not lift to cycles in $K$. We show that all toroidal and non-toroidal cycles of $G$ of arbitrary homology degree can be completely classified for $K$ of arbitrary periodicity using the recently developed machinery of bisheaves and persistent local systems. In doing so, we also introduce a framework for a computationally viable persistence theory of periodic spaces. Finally, we outline algorithms for how to apply our results to real data, including a polynomial time algorithm for calculating the canonical persistent local system attributed to a given bisheaf.

math.AT↗

arXiv2024

Shoving tubes through shapes gives a sufficient and efficient shape statistic

The Persistent Homology Transform (PHT) was introduced in the field of Topological Data Analysis about 10 years ago, and has since been proven to be a very powerful descriptor of Euclidean shapes. The PHT consists of scanning a shape from all possible directions $v\in S^{n-1}$ and then computing the persistent homology of sublevel set filtrations of the respective height functions $h_v$; this results in a sufficient and continuous descriptor of Euclidean shapes. We introduce a generalisation of the PHT in which we consider arbitrary parameter spaces and sublevel sets with respect to any function. In particular, we study transforms, defined on the Grassmannian $\mathbb{A}\mathbb{G}(m,n)$ of affine subspaces of $\mathbb{R}^n$, that allow to scan a shape by probing it with all possible affine $m$-dimensional subspaces $P\subset \mathbb{R}^n$, for fixed dimension $m$, and by computing persistent homology of sublevel set filtrations of the function $\mathrm{dist}(\cdot, P)$ encoding the distance from the flat $P$. We call such transforms "distance-from-flat" PHTs. We show that these transforms are injective and continuous and that they provide computational advantages over the classical PHT. In particular, we show that it is enough to compute homology only in degrees up to $m-1$ to obtain injectivity; for $m=1$ this provides a very powerful and computationally advantageous tool for examining shapes, which in a previous work by a subset of the authors has proven to significantly outperform state-of-the-art neural networks for shape classification tasks.

math.AT↗

arXiv2023

Computing 1-Periodic Persistent Homology with Finite Windows

Let $K$ be a periodic cell complex endowed with a covering $q:K\to G$ where $G$ is a finite quotient space of equivalence classes under translations acting on $K$. We assume $G$ is embedded in a space whose homotopy type is a $d$-torus for some $d$, which introduces "toroidal cycles" in $G$ which do not lift to cycles in $K$ by $q$ . We study the behaviour of toroidal and non-toroidal cycles for the case $K$ is 1-periodic, i.e. $G=K/\mathbb{Z}$ for some free action of $\mathbb{Z}$ on $K$. We show that toroidal cycles can be entirely classified by endomorphisms on the homology of unit cells of $K$, and moreover that toroidal cycles have a sense of unimodality when studying the persistent homology of $G$.

math.AT↗

arXiv2022

Quantifying the homology of periodic cell complexes

A periodic cell complex, $K$, has a finite representation as the quotient space, $q(K)$, consisting of equivalence classes of cells identified under the translation group acting on $K$. We study how the Betti numbers and cycles of $K$ are related to those of $q(K)$, first for the case that $K$ is a graph, and then higher-dimensional cell complexes. When $K$ is a $d$-periodic graph, it is possible to define $\mathbb{Z}^d$-weights on the edges of the quotient graph and this information permits full recovery of homology generators for $K$. The situation for higher-dimensional cell complexes is more subtle and studied in detail using the Mayer-Vietoris spectral sequence.

math.AT↗

arXiv2018

Numerical Calibration of the HCN$-$Star Formation Correlation

HCN(1$-$0) emission traces dense gas and correlates very strongly with star formation rates (SFRs) on scales from small Milky Way clouds to whole galaxies. The observed correlation offers strong constraints on the efficiency of star formation in dense gas, but quantitative interpretation of this constraint requires a mapping from HCN emission to gas mass and density. In this paper we provide the required calibration by postprocessing high-resolution simulations of dense, star-forming clouds to calculate their HCN emission ($L_{\rm HCN}$) and to determine how that emission is related to the underlying gas density distribution and star formation efficiency. We find that HCN emission traces gas with a luminosity-weighted mean number density of $0.8-1.7 \times 10^4\,{\rm cm}^{-3}$ and that HCN luminosity is related to mass of dense gas of $\gtrsim 10^4\,{\rm cm}^{-3}$ with a conversion factor of $α_{\rm HCN} \approx 14\,\rm M_{\odot}/(K\,km\,s^{-1}\,{pc}^2)$. We also measure a new empirical relationship between the SFR per global mean freefall time ($ε_{\rm ff}$) and the SFR$-$HCN relationship, ${\rm SFR}/L_{\rm HCN} = 2.0 \times 10^{-7}\,(ε_{\rm ff}/0.01)^{1.1}\,\rm M_{\odot}\,{yr}^{-1}/(K\,km\,s^{-1}\,{pc}^2)$. The observed SFR$-$HCN correlation strongly constrains $ε_{\rm ff} \approx 1\%$ with a factor of $\sim 3$ systematic uncertainty. The scatter in $ε_{\rm ff}$ from cloud to cloud within the Milky Way is a factor of a few. We conclude that $L_{\rm HCN}$ is an effective tracer of dense gas and that the IR$-$HCN correlation is a significant diagnostic of the microphysics of star formation in dense gas.

astro-ph.GA↗