Leo W. H. Fung

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 2023 April–May. 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 (1.54% ± 6.00%).

Characterizing the population and internal structure of sub-galactic haloes is critical for constraining the nature of dark matter. These haloes can be detected near galaxies that act as strong gravitational lenses with extended arcs, as they perturb the shapes of the arcs. However, this method is subject to false-positive detections and systematic uncertainties, particularly degeneracies between an individual halo and larger scale asymmetries in the distribution of lens mass. We present a new free-form lens modelling code, developed within the framework of the open-source software pyautolens, to address these challenges. Our method models mass perturbations that cannot be captured by parametric models as pixelized potential corrections and suppresses unphysical solutions via a Matérn regularization scheme that is inspired by Gaussian process regression. This approach enables the recovery of diverse mass perturbations, including subhaloes, line-of-sight haloes, external shear, and multipole components that represent the complex angular mass distribution of the lens galaxy, such as boxiness/disciness. Additionally, our fully Bayesian framework objectively infers hyperparameters associated with the regularization of pixelized sources and potential corrections, eliminating the need for manual fine-tuning. By applying our code to the well-known ‘Jackpot’ lens system, SLACS0946+1006, we robustly detect a highly concentrated subhalo that challenges the standard cold dark matter model. This study represents the first attempt to independently reveal the mass distribution of a subhalo using a fully free-form approach.

A distinct power-law break is apparent at m _AB ∼ 21 in the deep near-infrared PEARLS-JWST galaxy counts. The break becomes more pronounced at longer wavelengths, with the slope flattening smoothly with apparent magnitude in the shortest band used at 0.9 μm, and trending toward an increasingly broken slope by the longest wavelength passband of JWST’s Near Infrared Camera, 4.4 μm. This behaviour is remarkably well predicted by the GALFORM semi-analytical model of galaxy formation. We use the model to diagnose the origin of this behaviour. The features that are responsible for the break are (1) the inherent break in the luminosity function (LF); (2) the change in the volume element with redshift; (3) the redshift-dependent nature of the k-correction (with 1 contributing to the existence of the break and 2–3 contributing to its shape). We study the contribution to these effects by their morphology using the bulge-to-total stellar mass ratio. The way in which bulge-dominated galaxies populate the bright end of the LF while disk-dominated galaxies dominate the faint end is preserved in the galaxy number counts, with a characteristic stellar mass at a break of ∼10¹⁰ M _⊙. The shape of the number counts is mainly driven by galaxies with relatively low redshift (z ≲ 2) for a limit of m _AB ≲ 28. We give a comprehensive description of why the galaxy number counts in the near-infrared PEARLS-JWST observation look the way they do and which population of galaxies is dominant at each apparent magnitude.

Our understanding of galaxy properties and evolution is contingent on knowing the initial mass function (IMF), and yet to date the IMF is constrained only to local galaxies. Individual stars are now becoming routinely detected at cosmological distances, where luminous stars such as supergiants in background galaxies strongly lensed by galaxy clusters are temporarily further magnified by huge factors (up to 10⁴) by intracluster stars, thus being detected as transients. The detection rate of these events depends on the abundance of luminous stars in the background galaxy and is thus sensitive to the IMF and the star formation history (SFH), especially for the blue supergiants detected as transients in the rest-frame ultraviolet/optical filters. As a proof of concept, we use simple SFH and IMF models constrained by spectral energy distributions (SEDs) to see how well we can predict the Hubble Space Telescope and James Webb Space Telescope transient detection rate in a lensed arc dubbed “Spock” (z = 1.0054). We find that demanding a simultaneous fit of the SED and the transient detection rate places constraints on the IMF, independent of the assumed simple SFH model. We conclude that our likelihood analysis indicates that the data definitively prefers the “Spock” galaxy to have a Salpeter IMF (α = 2.35) rather than a top-heavy IMF (α = 1)—which is thought to be the case in the early universe—with no clear excess of supergiants above the standard IMF.

In ultra-fast astronomical observations featuring fast transients on sub-μs time scales, the conventional signal-to-noise ratio (SNR) threshold, often fixed at 5σ, becomes inadequate as observational window timescales shorten, leading to unsustainably high false alarm rates (FAR). We provide a basic statistical framework that captures the essential noise generation processes relevant to the analysis of time series data from photon-counting detectors. In particular, we establish a protocol of defining detection limits in astronomical photon-counting experiments, such that a FAR-based criterion is preferred over the traditional SNR-based threshold scheme. We developed statistical models that account for noise sources such as dark counts, sky background, and crosstalk and established a probabilistic detection criterion applicable to high-speed detectors. The model is tested against the on-site data obtained in the Single-Photon Imager for Nanosecond Astrophysics (SPINA) experiment, and consistency is confirmed. We compare the performance of several detector technologies, including photon-counting CMOS/CCDs, SPADs, SiPMs, and PMTs, in detecting faint astronomical signals. These findings offer insights into optimizing detector choice for future ultra-fast astronomical instruments and suggest pathways for improving detection fidelity under rapid observational conditions.