arXiv subjects

Matthew Craigie

Publications and source records attributed to Matthew Craigie.

6 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

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↗

arXiv2025

Learning Balanced Field Summaries of the Large-Scale Structure with the Neural Field Scattering Transform

We present a cosmology analysis of simulated weak lensing convergence maps using the Neural Field Scattering Transform (NFST) to constrain cosmological parameters. The NFST extends the Wavelet Scattering Transform (WST) by incorporating trainable neural field filters while preserving rotational and translational symmetries. This setup balances flexibility with robustness, ideal for learning in limited training data regimes. We apply the NFST to 500 simulations from the CosmoGrid suite, each providing a total of 1000 square degrees of noiseless weak lensing convergence maps. We use the resulting learned field compression to model the posterior over $\Omega_m$, $\sigma_8$, and $w$ in a $w$CDM cosmology. The NFST consistently outperforms the WST benchmark, achieving a 16% increase in the average posterior probability density assigned to test data. Further, the NFST improves direct parameter prediction precision on $\sigma_8$ by 6% and $w$ by 11%. We also introduce a new visualization technique to interpret the learned filters in physical space and show that the NFST adapts its feature extraction to capture task-specific information. These results establish the NFST as a promising tool for extracting maximal cosmological information from the non-Gaussian information in upcoming large-scale structure surveys, without requiring large simulated training datasets.

astro-ph.CO↗

arXiv2025

Learning Intrinsic Alignments from Local Galaxy Environments

We present DELTA (Data-Empiric Learned Tidal Alignments), a deep learning model that isolates galaxy intrinsic alignments (IAs) from weak lensing distortions using only observational data. The model uses an Equivariant Graph Neural Network backbone suitable for capturing information from the local galaxy environment, in conjunction with a probabilistic orientation output. Unlike parametric models, DELTA flexibly learns the relationship between galaxy shapes and their local environments, without assuming an explicit IA form or relying on simulations. When applied to mock catalogs with realistic noisy IAs injected, it accurately reconstructs the noise-free, pure IA signal. Mapping these alignments provides a direct visualization of IA patterns in the mock catalogs. Combining DELTA with deep learning interpretation techniques provides further insights into the physics driving tidal relationships between galaxies. This new approach to understanding and controlling IAs is suitable for application to joint photometric and spectroscopic surveys such as the combination of upcoming Euclid, Rubin, and DESI datasets.

astro-ph.CO↗

arXiv2024

Unsupervised Searches for Cosmological Parity Violation: Improving Detection Power with the Neural Field Scattering Transform

Recent studies using four-point correlations suggest a parity violation in the galaxy distribution, though the significance of these detections is sensitive to the choice of simulation used to model the noise properties of the galaxy distribution. In a recent paper, we introduce an unsupervised learning approach which offers an alternative method that avoids the dependence on mock catalogs, by learning parity violation directly from observational data. However, the Convolutional Neural Network (CNN) model utilized by our previous unsupervised approach struggles to extend to more realistic scenarios where data is limited. We propose a novel method, the Neural Field Scattering Transform (NFST), which enhances the Wavelet Scattering Transform (WST) technique by adding trainable filters, parameterized as a neural field. We first tune the NFST model to detect parity violation in a simplified dataset, then compare its performance against WST and CNN benchmarks across varied training set sizes. We find the NFST can detect parity violation with $4\times$ less data than the CNN and $32\times$ less than the WST. Furthermore, in cases with limited data the NFST can detect parity violation with up to $6\sigma$ confidence, where the WST and CNN fail to make any detection. We identify that the added flexibility of the NFST, and particularly the ability to learn asymmetric filters, as well as the specific symmetries built into the NFST architecture, contribute to its improved performance over the benchmark models. We further demonstrate that the NFST is readily interpretable, which is valuable for physical applications such as the detection of parity violation.

astro-ph.IM↗

arXiv2023

Unsupervised Searches for Cosmological Parity-Violation: An Investigation with Convolutional Neural Networks

Recent measurements of the $4$-point correlation functions (4PCF) from spectroscopic surveys provide evidence for parity-violations in the large-scale structure of the Universe. If physical in origin, this could point to exotic physics during the epoch of inflation. However, searching for parity-violations in the 4PCF signal relies on a large suite of simulations to perform a rank test, or an accurate model of the 4PCF covariance to claim a detection, and this approach is incapable of extracting parity information from the higher-order $N$-point functions. In this work we present an unsupervised method which overcomes these issues, before demonstrating the approach is capable of detecting parity-violations in a few toy models using convolutional neural networks. This technique is complementary to the 4-point method and could be used to discover parity-violations in several upcoming surveys including DESI, Euclid and Roman.

astro-ph.CO↗