arXiv subjects

David Harvey

Publications and source records attributed to David Harvey.

At least 19 recordsLinked to original sources

arXiv2026

Multi-sensor fusion for fine-guidance and milliarcsecond-level attitude estimation of balloon-borne telescope

Balloon-borne telescopes rely on fine-guidance systems to achieve milliarcsecond image stability despite residual disturbances from the balloon environment. In these systems, the Fast Steering Mirror (FSM) stabilizes the image in two focal-plane axes, but leaves systematic, field-dependent residual motion induced by boresight roll. This effect, referred to as roll leakage, becomes more important for wider fields of view. In this work, roll leakage is characterized using data from the 2023 Superpressure Balloon-borne Imaging Telescope (SuperBIT) science flight. SuperBIT is a 0.5-m near-ultraviolet to near-infrared telescope that demonstrated milliarcsecond-level image stability during its 45-night science flight. We find that passive focal-plane star-camera measurements correlate strongly with independent roll measurements across a large set of science exposures, showing that boresight roll frequently drives residual focal-plane motion. We then develop a simulation framework combining optical ray tracing, asynchronous guide-star measurements, estimation, and FSM control to study this behavior. The framework is used to compare single-star and multi-star guidance architectures under realistic flight disturbances. For the SuperBIT geometry, we find that multi-star estimation reduces average roll-induced science-field image motion by 31.8%, increasing to 77.4% for a representative geometry of GigaBIT, SuperBIT's planned larger-aperture successor. These results motivate further investigation of multi-star fine-guidance architectures for GigaBIT.

astro-ph.IM↗

arXiv2026

Faster enumeration of primes

We describe several new algorithms for finding all prime numbers up to a given bound $N$, achieving the first ever speedup by a positive power of $\log N$ over the ancient sieve of Eratosthenes. The fastest version, which is not fully rigorous, runs in \[ N (\log \log N)^{1+o(1)} \] bit operations when analysed in the multitape Turing model. This improves on the best existing algorithms due to Pritchard (1981), Atkin--Bernstein (2004) and Sergeev (2016) by a factor of almost $\log N$. We also present a rigorous randomised (Las Vegas) variant that is slower by a factor of $(\log \log N)^{1+o(1)}$, and a rigorous deterministic variant that is slower by a factor of $(\log N)^{1/2+o(1)}$. The new algorithms make heavy use of fast polynomial arithmetic over finite fields, and also involve ideas from the theory of error-correcting codes.

math.NT↗

arXiv2026

Lensing in the Blue III: Weak Lensing Shape Catalogs of 30 Merging Galaxy Clusters

We present the weak gravitational lensing dataset from the Super-pressure Balloon-Borne Imaging Telescope (SuperBIT), which imaged 30 galaxy clusters during its 45 night flight in April to May 2023. SuperBIT is a first-of-its-kind balloon-borne imaging telescope that achieved near diffraction-limited observations in near-space conditions above 98% of the Earth's atmosphere. We use the metacalibration algorithm to obtain calibrated galaxy shapes for our target clusters and several calibration fields, enabling unbiased reconstruction of the weak-lensing signal. We employ several diagnostics throughout the pipeline, including assessments of point-spread function (PSF) modeling residuals and their impact on weak-lensing measurements, as well as tests for correlations between galaxy shapes and measured galaxy and PSF properties. To assess the multiplicative shear bias of the pipeline, we analyze a parallel set of simulated images that incorporate the real observing conditions from the flight, including measured SuperBIT PSFs, observed sky backgrounds, and detector noise, yielding a bias of $(1.1 \pm 7.8)$~per~cent.

astro-ph.CO↗

arXiv2026

An ultra-high-resolution map of (dark) matter

Ordinary matter-including particles such as protons and neutrons-accounts for only about one sixth of all matter in the Universe. The rest is dark matter, which does not emit or absorb light but plays a fundamental role in galaxy and structure evolution. Because it interacts only through gravity, one of the most direct probes is weak gravitational lensing: the deflection of light from distant galaxies by intervening mass. Here we present an extremely detailed, wide-area weak-lensing mass map, covering 0.77 deg x 0.70 deg, using high-resolution imaging from the James Webb Space Telescope (JWST) as part of the COSMOS-Web survey. By measuring the shapes of 129 galaxies per square arcminute-many independently in the F115W and F150W bands-we achieve an angular resolution of 1.00 +/- 0.01 arcmin. Our map has more than twice the resolution of earlier Hubble Space Telescope maps, revealing how dark and luminous matter co-evolve across filaments, clusters, and under-densities. It traces mass features out to z ~ 2, including the most distant structure at z ~ 1.1. The sensitivity to high-redshift lensing constrains galaxy environments at the peak of cosmic star formation and sets a high-resolution benchmark for testing theories about the nature of dark matter and the formation of large-scale cosmic structure

astro-ph.CO↗

arXiv2026

Deterministic methods for finding elements of large multiplicative order

We revisit the problem of rigorously and deterministically finding elements of large order in the multiplicative group of integers modulo a natural number $N$. Solving this problem is an essential step in several recent deterministic algorithms for factoring $N$, including the currently fastest ones. In 2018, the second author gave an algorithm that for a given target order $D \geq N^{2/5}$, finds either an element of order exceeding $D$, or a nontrivial divisor of $N$, or proves that $N$ is prime. The running time was \[ O\left(\frac{D^{1/2}}{(\log \log D)^{1/2}} \log^2 N \right) \] bit operations, asymptotically the same as the cost of computing the order of a single element using Sutherland's optimisation of the classical babystep-giantstep method. Subsequent work by several authors weakened the hypothesis $D \geq N^{2/5}$ to $D \geq N^{1/6}$. In this paper, we show that the hypothesis may be dropped altogether. Moreover, if $N$ is prime, we can guarantee returning an element of order exceeding $D$, rather than a proof that $N$ is prime.

math.NT↗

arXiv2026

Vision Foundation Models for Domain Generalisable Cross-View Localisation in Planetary Ground-Aerial Robotic Teams

Accurate localisation in planetary robotics enables the advanced autonomy required to support the increased scale and scope of future missions. The successes of the Ingenuity helicopter and multiple planetary orbiters lay the groundwork for future missions that use ground-aerial robotic teams. In this paper, we consider rovers using machine learning to localise themselves in a local aerial map using limited field-of-view monocular ground-view RGB images as input. A key consideration for machine learning methods is that real space data with ground-truth position labels suitable for training is scarce. In this work, we propose a novel method of localising rovers in an aerial map using cross-view-localising dual-encoder deep neural networks. We leverage semantic segmentation with vision foundation models and high volume synthetic data to bridge the domain gap to real images. We also contribute a new cross-view dataset of real-world rover trajectories with corresponding ground-truth localisation data captured in a planetary analogue facility, plus a high volume dataset of analogous synthetic image pairs. Using particle filters for state estimation with the cross-view networks allows accurate position estimation over simple and complex trajectories based on sequences of ground-view images.

cs.CV↗

arXiv2025

Measuring the Dark Matter Self-Interaction Cross-Section with Deep Compact Clustering for Robust Machine Learning Inference

We have developed a machine learning algorithm capable of detecting ``out-of-domain data'' for trustworthy cosmological inference. By using data from two separate suites of cosmological simulations, we show that our algorithm is able to determine whether ``observed'' data is consistent with its training domain, returning confidence estimates as well as accurate parameter estimations. We apply our algorithm to two-dimensional images of galaxy clusters from the BAHAMAS-SIDM and DARKSKIES simulations with the aim to measure the self-interaction cross-section of dark matter. Through deep compact clustering we construct an informative latent space where galaxy clusters are mapped to the latent space forming ``latent-clusters'' for each simulation, with the location of the latent-cluster corresponding to the macroscopic parameters, such as the cross-section, $\sigma_{\rm DM}/m$. We then pass through mock observations, where the location of the observed latent-cluster informs us of which properties are shared with the training data. If the observed latent-cluster shares no similarities with latent-clusters from the known simulations, we can conclude that our simulations do not represent the observations and discard any parameter estimations, thus providing us with a method to measure machine learning confidence. This method serves as a blueprint for transparent and robust inference that is in demand in scientific machine learning.

astro-ph.CO↗

arXiv2025

DARKSKIES: A suite of super-sampled zoom-in simulations of galaxy clusters with self-interacting dark matter

We present the "DARKSKIES" suite of one hundred, zoom-in hydrodynamic simulations of massive ($M_{200}>5\times10^{14}{\rm M}_\odot)$ galaxy clusters with self-interacting dark matter (SIDM). We super-sample the simulations such that $m_{\rm DM}/m_{\rm gas}\sim0.1$, enabling us to simulate a dark matter particle mass of $m=0.68\times10^{8}M_\odot$ an order of magnitude faster, whilst exploring SIDM in the core of clusters at extremely high resolution. We calibrate the baryonic feedback to produce observationally consistent and realistic galaxy clusters across all simulations and simulate five models of velocity-independent SIDM targeting the expected sensitivity of future telescopes - $\sigma_{\rm DM}/m=0.,0.01,0.05,0.1,0.2$ cm$^2$/g. We find the density profiles exhibit the characteristic core even in the smallest of cross-sections, with cores developing only at late times ($z<0.5$). We investigate the dynamics of the brightest cluster galaxy inside the dark matter halo and find in SIDM cosmologies there exists a so-called wobbling not observed in collisionless dark matter. We find this wobble is driven by accreting mass on to a cored density profile with the signal peaking at $z=0.25$ and dropping thereafter. This finding is further supported by the existence of an anti-correlation between the offset between the BCG and the dark matter halo and its relative velocity in SIDM only, a hallmark of harmonic oscillation.

astro-ph.CO↗

arXiv2025

Impact of line-of-sight structure on weak lensing observables of galaxy clusters

Weak gravitational lensing observations of galaxy clusters are sensitive to all mass along the line-of-sight, introducing systematic and additional statistical uncertainties due to intervening structures. We quantify their impact on the recovery of mass density profile parameters using 967 clusters from the highest-resolution FLAMINGO simulation. We construct mock weak lensing maps, including both single source plane mocks and Euclid-like mocks with a realistic source redshift distribution. Applying Bayesian inference with Nautilus, we fit spherical and elliptical Navarro-Frenk-White models to recover the cluster mass, concentration, axis ratio, and centre, which we use to measure the brightest cluster galaxy (BCG) offset from the potential centre, or `BCG wobble'. We find that the spherical model fits clusters along under-dense sight-lines better than those along over-dense ones. This introduces a positive skew in the relative error distributions for mass and concentration, which increases with source redshift. In Euclid-like mocks, this results in a mean mass bias of $+5.3\pm1.4$% (significant at $3.5\sigma$) when assuming a spherical NFW model. We also detect a mean axis ratio bias of $-2.0\pm0.7$% ($2.9\sigma$), with no significant bias in concentration. We measure a BCG wobble of ~14 kpc in our Euclid-like mocks, with negligible contribution from line-of-sight structure. Furthermore, we predict the scatter in mass estimates from future weak lensing surveys that have mean source redshifts $z_\text s \gtrsim 1.2$ (such as the Nancy Grace Roman Space Telescope), will be dominated by line-of-sight structure and hence assuming a diagonal covariance matrix will lead to overestimating the precision. We conclude that cluster weak lensing pipelines should be calibrated on simulations with lightcone data in order to properly account for the significant impact of line-of-sight structure.

astro-ph.CO↗

arXiv2025

Integer multiplication is at least as hard as matrix transposition

Working in the multitape Turing model, we show how to reduce the problem of matrix transposition to the problem of integer multiplication. If transposing an $n \times n$ binary matrix requires $\Omega(n^2 \log n)$ steps on a Turing machine, then our reduction implies that multiplying $n$-bit integers requires $\Omega(n \log n)$ steps. In other words, if matrix transposition is as hard as expected, then integer multiplication is also as hard as expected.

cs.CC↗

arXiv2024

SuperBIT Superpressure Flight Instrument Overview and Performance: Near diffraction-limited Astronomical Imaging from the Stratosphere

SuperBIT was a 0.5-meter near-ultraviolet to near-infrared wide-field telescope that launched on a NASA superpressure balloon into the stratosphere from New Zealand for a 45-night flight. SuperBIT acquired multi-band images of galaxy clusters to study the properties of dark matter using weak gravitational lensing. We provide an overview of the instrument and its various subsystems. We then present the instrument performance from the flight, including the telescope and image stabilization system, the optical system, the power system, and the thermal system. SuperBIT successfully met the instrument's technical requirements, achieving a telescope pointing stability of 0.34 +/- 0.10 arcseconds, a focal plane image stability of 0.055 +/- 0.027 arcseconds, and a PSF FWHM of ~ 0.35 arcseconds over 5-minute exposures throughout the 45-night flight. The telescope achieved a near-diffraction limited point-spread function in all three science bands (u, b, and g). SuperBIT served as a pathfinder to the GigaBIT observatory, which will be a 1.34-meter near-ultraviolet to near-infrared balloon-borne telescope.

astro-ph.IM↗

arXiv2024

From SuperBIT to GigaBIT: Informing next-generation balloon-borne telescope design with Fine Guidance System flight data

The Super-pressure Balloon-borne Imaging Telescope (SuperBIT) is a near-diffraction-limited 0.5m telescope that launched via NASA's super-pressure balloon technology on April 16, 2023. SuperBIT achieved precise pointing control through the use of three nested frames in conjunction with an optical Fine Guidance System (FGS), resulting in an average image stability of 0.055" over 300-second exposures. The SuperBIT FGS includes a tip-tilt fast-steering mirror that corrects for jitter on a pair of focal plane star cameras. In this paper, we leverage the empirical data from SuperBIT's successful 45-night stratospheric mission to inform the FGS design for the next-generation balloon-borne telescope. The Gigapixel Balloon-borne Imaging Telescope (GigaBIT) is designed to be a 1.35m wide-field, high resolution imaging telescope, with specifications to extend the scale and capabilities beyond those of its predecessor SuperBIT. A description and analysis of the SuperBIT FGS will be presented along with methodologies for extrapolating this data to enhance GigaBIT's FGS design and fine pointing control algorithm. We employ a systems engineering approach to outline and formalize the design constraints and specifications for GigaBIT's FGS. GigaBIT, building on the SuperBIT legacy, is set to enhance high-resolution astronomical imaging, marking a significant advancement in the field of balloon-borne telescopes.

astro-ph.IM↗

arXiv2024

A deep-learning algorithm to disentangle self-interacting dark matter and AGN feedback models

Different models of dark matter can alter the distribution of mass in galaxy clusters in a variety of ways. However, so can uncertain astrophysical feedback mechanisms. Here we present a Machine Learning method that ''learns'' how the impact of dark matter self-interactions differs from that of astrophysical feedback in order to break this degeneracy and make inferences on dark matter. We train a Convolutional Neural Network on images of galaxy clusters from hydro-dynamic simulations. In the idealised case our algorithm is 80% accurate at identifying if a galaxy cluster harbours collisionless dark matter, dark matter with ${\sigma}_{\rm DM}/m = 0.1$cm$^2/$g or with ${\sigma}_{DM}/m = 1$cm$^2$/g. Whilst we find adding X-ray emissivity maps does not improve the performance in differentiating collisional dark matter, it does improve the ability to disentangle different models of astrophysical feedback. We include noise to resemble data expected from Euclid and Chandra and find our model has a statistical error of < 0.01cm$^2$/g and that our algorithm is insensitive to shape measurement bias and photometric redshift errors. This method represents a new way to analyse data from upcoming telescopes that is an order of magnitude more precise and many orders faster, enabling us to explore the dark matter parameter space like never before.

astro-ph.CO↗

arXiv2024

Hydrodynamical simulations of merging galaxy clusters: giant dark matter particle colliders, powered by gravity

Terrestrial particle accelerators collide charged particles, then watch the trajectory of outgoing debris - but they cannot manipulate dark matter. Fortunately, dark matter is the main component of galaxy clusters, which are continuously pulled together by gravity. We show that galaxy cluster mergers can be exploited as enormous, natural dark matter colliders. We analyse hydrodynamical simulations of a universe containing self-interacting dark matter (SIDM) in which all particles interact via gravity, and dark matter particles can also scatter off each other via a massive mediator. During cluster collisions, SIDM spreads out and lags behind cluster member galaxies. Individual systems can have quirky dynamics that makes them difficult to interpret. Statistically, however, we find that the mean or median of dark matter's spatial offset in many collisions can be robustly modelled, and is independent of our viewing angle and halo mass even in collisions between unequal-mass systems. If the SIDM cross-section were sigma/m = 0.1cm^2/g = 0.18 barn/GeV, the 'bulleticity' lag would be ~5 percent that of gas due to ram pressure, and could be detected at 95 percent confidence in weak lensing observations of ~100 well-chosen clusters.

astro-ph.CO↗

arXiv2023

Data downloaded via parachute from a NASA super-pressure balloon

In April to May 2023, the superBIT telescope was lifted to the Earth's stratosphere by a helium-filled super-pressure balloon, to acquire astronomical imaging from above (99.5% of) the Earth's atmosphere. It was launched from New Zealand then, for 40 days, circumnavigated the globe five times at a latitude 40 to 50 degrees South. Attached to the telescope were four 'DRS' (Data Recovery System) capsules containing 5 TB solid state data storage, plus a GNSS receiver, Iridium transmitter, and parachute. Data from the telescope were copied to these, and two were dropped over Argentina. They drifted 61 km horizontally while they descended 32 km, but we predicted their descent vectors within 2.4 km: in this location, the discrepancy appears irreducible below 2 km because of high speed, gusty winds and local topography. The capsules then reported their own locations to within a few metres. We recovered the capsules and successfully retrieved all of superBIT's data - despite the telescope itself being later destroyed on landing.

astro-ph.IM↗

arXiv2023

Beyond the Ultra-deep Frontier Fields And Legacy Observations (BUFFALO): a high-resolution strong + weak-lensing view of Abell 370

The HST treasury program BUFFALO provides extended wide-field imaging of the six Hubble Frontier Fields galaxy clusters. Here we present the combined strong and weak-lensing analysis of Abell 370, a massive cluster at z=0.375. From the reconstructed total projected mass distribution in the 6arcmin x 6arcmin BUFFALO field-of-view, we obtain the distribution of massive substructures outside the cluster core and report the presence of a total of seven candidates, each with mass $\sim 5 \times 10^{13}M_{\odot}$. Combining the total mass distribution derived from lensing with multi-wavelength data, we evaluate the physical significance of each candidate substructure, and conclude that 5 out of the 7 substructure candidates seem reliable, and that the mass distribution in Abell 370 is extended along the North-West and South-East directions. While this finding is in general agreement with previous studies, our detailed spatial reconstruction provides new insights into the complex mass distribution at large cluster-centric radius. We explore the impact of the extended mass reconstruction on the model of the cluster core and in particular, we attempt to physically explain the presence of an important external shear component, necessary to obtain a low root-mean-square separation between the model-predicted and observed positions of the multiple images in the cluster core. The substructures can only account for up to half the amplitude of the external shear, suggesting that more effort is needed to fully replace it by more physically motivated mass components. We provide public access to all the lensing data used as well as the different lens models.

astro-ph.CO↗

arXiv2023

Federated Neural Radiance Fields

The ability of neural radiance fields or NeRFs to conduct accurate 3D modelling has motivated application of the technique to scene representation. Previous approaches have mainly followed a centralised learning paradigm, which assumes that all training images are available on one compute node for training. In this paper, we consider training NeRFs in a federated manner, whereby multiple compute nodes, each having acquired a distinct set of observations of the overall scene, learn a common NeRF in parallel. This supports the scenario of cooperatively modelling a scene using multiple agents. Our contribution is the first federated learning algorithm for NeRF, which splits the training effort across multiple compute nodes and obviates the need to pool the images at a central node. A technique based on low-rank decomposition of NeRF layers is introduced to reduce bandwidth consumption to transmit the model parameters for aggregation. Transferring compressed models instead of the raw data also contributes to the privacy of the data collecting agents.

cs.CV↗

arXiv2023

Strong gravitational lensing's 'external shear' is not shear

The distribution of mass in galaxy-scale strong gravitational lenses is often modelled as an elliptical power law plus 'external shear', which notionally accounts for neighbouring galaxies and cosmic shear. We show that it does not. Except in a handful of rare systems, the best-fit values of external shear do not correlate with independent measurements of shear: from weak lensing in 45 Hubble Space Telescope images, or in 50 mock images of lenses with complex distributions of mass. Instead, the best-fit shear is aligned with the major or minor axis of 88% of lens galaxies; and the amplitude of the external shear increases if that galaxy is disky. We conclude that 'external shear' attached to a power law model is not physically meaningful, but a fudge to compensate for lack of model complexity. Since it biases other model parameters that are interpreted as physically meaningful in several science analyses (e.g. measuring galaxy evolution, dark matter physics or cosmological parameters), we recommend that future studies of galaxy-scale strong lensing should employ more flexible mass models.

astro-ph.CO↗