arXiv subjects

Connor Stone

Publications and source records attributed to Connor Stone.

At least 19 recordsLinked to original sources

arXiv2025

Neural Deprojection of Galaxy Stellar Mass Profiles

We introduce a neural approach to dynamical modeling of galaxies that replaces traditional imaging-based deprojections with a differentiable mapping. Specifically, we train a neural network to translate Nuker profile parameters into analytically deprojectable Multi Gaussian Expansion components, enabling physically realistic stellar mass models without requiring optical observations. We integrate this model into SuperMAGE, a differentiable dynamical modelling pipeline for Bayesian inference of supermassive black hole masses. Applied to ALMA data, our approach finds results consistent with state-of-the-art models while extending applicability to dust-obscured and active galaxies where optical data analysis is challenging.

astro-ph.GA↗

arXiv2025

Pixellated Posterior Sampling of Point Spread Functions in Astronomical Images

We introduce a novel framework for upsampled Point Spread Function (PSF) modeling using pixel-level Bayesian inference. Accurate PSF characterization is critical for precision measurements in many fields including: weak lensing, astrometry, and photometry. Our method defines the posterior distribution of the pixelized PSF model through the combination of an analytic Gaussian likelihood and a highly expressive generative diffusion model prior, trained on a library of HST ePSF templates. Compared to traditional methods (parametric Moffat, ePSF template-based, and regularized likelihood), we demonstrate that our PSF models achieve orders of magnitude higher likelihood and residuals consistent with noise, all while remaining visually realistic. Further, the method applies even for faint and heavily masked point sources, merely producing a broader posterior. By recovering a realistic, pixel-level posterior distribution, our technique enables the first meaningful propagation of detailed PSF morphological uncertainty in downstream analysis. An implementation of our posterior sampling procedure is available on GitHub.

astro-ph.IM↗

arXiv2025

Mind the Information Gap: Unveiling Detailed Morphologies of z 0.5-1.0 Galaxies with SLACS Strong Lenses and Data-Driven Analysis

We present new state-of-the-art lens models for strong gravitational lensing systems from the Sloan Lens ACS (SLACS) survey, developed within a Bayesian framework that employs high-dimensional (pixellated), data-driven priors for the background source, foreground lens light, and point-spread function (PSF). Unlike conventional methods, our approach delivers high-resolution reconstructions of all major physical components of the lensing system and substantially reduces model-data residuals compared to previous work. For the majority of 30 lensing systems analyzed, we also provide posterior samples capturing the full uncertainty of each physical model parameter. The reconstructions of the background sources reveal high significance morphological structures as small as 200 parsecs in galaxies at redshifts of z 0.5-1.0, demonstrating the power of strong lensing and the analysis method to be used as a cosmic telescope to study the high redshift universe. This study marks the first application of data-driven generative priors to modeling real strong-lensing data and establishes a new benchmark for strong lensing precision modeling in the era of large-scale imaging surveys.

astro-ph.GA↗

arXiv2025

Blind Strong Gravitational Lensing Inversion: Joint Inference of Source and Lens Mass with Score-Based Models

Score-based models can serve as expressive, data-driven priors for scientific inverse problems. In strong gravitational lensing, they enable posterior inference of a background galaxy from its distorted, multiply-imaged observation. Previous work, however, assumes that the lens mass distribution (and thus the forward operator) is known. We relax this assumption by jointly inferring the source and a parametric lens-mass profile, using a sampler based on GibbsDDRM but operating in continuous time. The resulting reconstructions yield residuals consistent with the observational noise, and the marginal posteriors of the lens parameters recover true values without systematic bias. To our knowledge, this is the first successful demonstration of joint source-and-lens inference with a score-based prior.

astro-ph.IM↗

arXiv2025

UNIONS: The Ultraviolet Near-Infrared Optical Northern Survey

The Ultraviolet Near-Infrared Optical Northern Survey (UNIONS) is a "collaboration of collaborations" that is using the Canada-France-Hawai'i Telescope, the Pan-STARRS telescopes, and the Subaru Observatory to obtain $ugriz$ images of a core survey region of 6250 deg$^2$ of the northern sky. The $10\sigma$ point source depth of the data, as measured within a 2-arcsecond diameter aperture, are $[u,g,r,i,z] = [23.7, 24.5, 24.2, 23.8, 23.3]$\ in AB magnitudes. UNIONS is addressing some of the most fundamental questions in astronomy, including the properties of dark matter, the growth of structure in the Universe from the very smallest galaxies to large-scale structure, and the assembly of the Milky Way. It is set to become the major ground-based legacy survey for the northern hemisphere for the next decade and provides an essential northern complement to the static-sky science of the Vera C. Rubin Observatory's Legacy Survey of Space and Time. UNIONS supports the core science mission of the {\it Euclid} space mission by providing the data necessary in the northern hemisphere for the calibration of the wavelength dependence of the {\it Euclid} point-spread function and derivation of photometric redshifts in the North Galactic Cap. This region contains the highest quality sky for {\it Euclid}, with low backgrounds from the zodiacal light, stellar density, extinction, and emission from Galactic cirrus. Here, we describe the UNIONS survey components, science goals, data products, and the current status of the overall program.

astro-ph.GA↗

arXiv2024

The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data

We present the MULTIMODAL UNIVERSE, a large-scale multimodal dataset of scientific astronomical data, compiled specifically to facilitate machine learning research. Overall, the MULTIMODAL UNIVERSE contains hundreds of millions of astronomical observations, constituting 100\,TB of multi-channel and hyper-spectral images, spectra, multivariate time series, as well as a wide variety of associated scientific measurements and "metadata". In addition, we include a range of benchmark tasks representative of standard practices for machine learning methods in astrophysics. This massive dataset will enable the development of large multi-modal models specifically targeted towards scientific applications. All codes used to compile the MULTIMODAL UNIVERSE and a description of how to access the data is available at https://github.com/MultimodalUniverse/MultimodalUniverse

astro-ph.IM↗

arXiv2024

The Intrinsic Flattening of Galaxy Disks

Highly inclined (edge-on) disk galaxies offer the unique perspective to constrain their intrinsic flattening, $c/a$, where $c$ and $a$ are respectively the vertical and long radial axes of the disk measured at suitable stellar densities. The ratio $c/a$ is a necessary quantity in the assessment of galaxy inclinations, three-dimensional structural reconstructions, total masses, as well as a constraint to galaxy formation models. 3.6 micron maps of 133 edge-on spiral galaxies from the Spitzer Survey of Stellar Structure in Galaxies (S4G) and its early-type galaxy extension are used to revisit the assessment of $c/a$ free from dust extinction and away from the influence of a stellar bulge. We present a simple definition of $c/a$ and explore trends with other galactic physical parameters: total stellar mass, concentration index, total HI mass, mass of the central mass concentration, circular velocity, model-dependent scales, as well as Hubble type. Other than a dependence on early/late Hubble types, and a related trend with light concentration, no other parameters were found to correlate with the intrinsic flattening of spiral galaxies. The latter is mostly constant with $\langle c/a \rangle$ = 0.124 $\pm$ 0.001 (stat) $\pm$ 0.033 (intrinsic/systematic) and greater for earlier types.

astro-ph.GA↗

arXiv2024

Tackling the Problem of Distributional Shifts: Correcting Misspecified, High-Dimensional Data-Driven Priors for Inverse Problems

Bayesian inference for inverse problems hinges critically on the choice of priors. In the absence of specific prior information, population-level distributions can serve as effective priors for parameters of interest. With the advent of machine learning, the use of data-driven population-level distributions (encoded, e.g., in a trained deep neural network) as priors is emerging as an appealing alternative to simple parametric priors in a variety of inverse problems. However, in many astrophysical applications, it is often difficult or even impossible to acquire independent and identically distributed samples from the underlying data-generating process of interest to train these models. In these cases, corrupted data or a surrogate, e.g. a simulator, is often used to produce training samples, meaning that there is a risk of obtaining misspecified priors. This, in turn, can bias the inferred posteriors in ways that are difficult to quantify, which limits the potential applicability of these models in real-world scenarios. In this work, we propose addressing this issue by iteratively updating the population-level distributions by retraining the model with posterior samples from different sets of observations, and we showcase the potential of this method on the problem of background image reconstruction in strong gravitational lensing when score-based models are used as data-driven priors. We show that, starting from a misspecified prior distribution, the updated distribution becomes progressively closer to the underlying population-level distribution, and the resulting posterior samples exhibit reduced bias after several updates.

astro-ph.IM↗

arXiv2024

Caustics: A Python Package for Accelerated Strong Gravitational Lensing Simulations

Gravitational lensing is the deflection of light rays due to the gravity of intervening masses. This phenomenon is observed in a variety of scales and configurations, involving any non-uniform mass such as planets, stars, galaxies, clusters of galaxies, and even the large scale structure of the universe. Strong lensing occurs when the distortions are significant and multiple images of the background source are observed. The lens objects must align on the sky of order ~1 arcsecond for galaxy-galaxy lensing, or 10's of arcseonds for cluster-galaxy lensing. As the discovery of lens systems has grown to the low thousands, these systems have become pivotal for precision measurements and addressing critical questions in astrophysics. Notably, they facilitate the measurement of the Universe's expansion rate, dark matter, supernovae, quasars, and the first stars among other topics. With future surveys expected to discover hundreds of thousands of lensing systems, the modelling and simulation of such systems must occur at orders of magnitude larger scale then ever before. Here we present `caustics`, a Python package designed to handle the extensive computational demands of modeling such a vast number of lensing systems.

astro-ph.IM↗

arXiv2024

The Nearly Universal Disk Galaxy Rotation Curve

The Universal Rotation Curve (URC) of disk galaxies was originally proposed to predict the shape and amplitude of any rotation curve (RC) based solely on photometric data. Here, the URC is investigated with an extensive set of spatially-resolved rotation curves drawn from the PROBES-I, PROBES-II, and MaNGA data bases with matching multi-band surface brightness profiles from the DESI-LIS and WISE surveys for 3,846 disk galaxies. Common URC formulations fail to achieve an adequate level of accuracy to qualify as truly universal over fully sampled RCs. We develop neural network (NN) equivalents for the proposed URCs which predict RCs with higher accuracy, showing that URC inaccuracies are not due to insufficient data but rather non-optimal formulations or sampling effects. This conclusion remains even if the total RC sample is pruned for symmetry. The latest URC prescriptions and their NN equivalents trained on our sub-sample of 579 disk galaxies with symmetric RCs perform similarly to the URC/NN trained on the complete data sample. We conclude that a URC with an acceptable level of accuracy ($\Delta V_{\rm circ} \lesssim15$ per cent) at all radii would require a detailed modelling of a galaxy's central regions and outskirts (e.g., for baryonic effects leading to contraction or expansion of any dark-matter-only halo).

astro-ph.GA↗

arXiv2024

The dark balance: quantifying the inner halo response to active galactic nuclei feedback in galaxies

This paper presents a study of the impact of supermassive black hole (SMBH) feedback on dark matter (DM) halos in numerical NIHAO simulations of galaxies. In particular, the amount of DM displaced via active galactic nuclei (AGN) feedback and the physical scale over which AGN feedback affects the DM halo are quantified by comparing NIHAO simulations with and without AGN feedback. NIHAO galaxies with $\log(M_*/M_{\rm \odot})\geq 10.0$ show a growing central DM suppression of 0.2 dex (~40%) from z = 1.5 to the present relative to noAGN feedback simulations. The growth of the DM suppression is related to the mass evolution of the SMBH and the gas mass in the central regions. For the most massive NIHAO galaxies with $\log(M_*/M_{\rm \odot}) > 10.5$, partially affected by numerical resolution, the central DM suppression peaks at z = 0.5, after which halo contraction overpowers AGN feedback due a shortage of gas and, thus, SMBH growth. The spatial scale, or ``sphere of influence,'' over which AGN feedback affects the DM distribution decreases as a function of time for MW-mass galaxies (from ~16 kpc at z = 1.5 to ~7.8 kpc at z = 0) as a result of halo contraction due to stellar growth. For the most massive NIHAO galaxies, the size of the sphere of influence remains constant (~16 kpc) for z > 0.5 owing to the balance between AGN feedback and halo contraction.

astro-ph.GA↗

arXiv2024

PQMass: Probabilistic Assessment of the Quality of Generative Models using Probability Mass Estimation

We propose a likelihood-free method for comparing two distributions given samples from each, with the goal of assessing the quality of generative models. The proposed approach, PQMass, provides a statistically rigorous method for assessing the performance of a single generative model or the comparison of multiple competing models. PQMass divides the sample space into non-overlapping regions and applies chi-squared tests to the number of data samples that fall within each region, giving a p-value that measures the probability that the bin counts derived from two sets of samples are drawn from the same multinomial distribution. PQMass does not depend on assumptions regarding the density of the true distribution, nor does it rely on training or fitting any auxiliary models. We evaluate PQMass on data of various modalities and dimensions, demonstrating its effectiveness in assessing the quality, novelty, and diversity of generated samples. We further show that PQMass scales well to moderately high-dimensional data and thus obviates the need for feature extraction in practical applications.

stat.ML↗

arXiv2023

Echoes in the Noise: Posterior Samples of Faint Galaxy Surface Brightness Profiles with Score-Based Likelihoods and Priors

Examining the detailed structure of galaxy populations provides valuable insights into their formation and evolution mechanisms. Significant barriers to such analysis are the non-trivial noise properties of real astronomical images and the point spread function (PSF) which blurs structure. Here we present a framework which combines recent advances in score-based likelihood characterization and diffusion model priors to perform a Bayesian analysis of image deconvolution. The method, when applied to minimally processed \emph{Hubble Space Telescope} (\emph{HST}) data, recovers structures which have otherwise only become visible in next-generation \emph{James Webb Space Telescope} (\emph{JWST}) imaging.

astro-ph.IM↗

arXiv2023

AstroPhot: Fitting Everything Everywhere All at Once in Astronomical Images

We present AstroPhot, a fast, powerful, and user-friendly Python based astronomical image photometry solver. AstroPhot incorporates automatic differentiation and GPU (or parallel CPU) acceleration, powered by the machine learning library PyTorch. Everything: AstroPhot can fit models for sky, stars, galaxies, PSFs, and more in a principled Chi^2 forward optimization, recovering Bayesian posterior information and covariance of all parameters. Everywhere: AstroPhot can optimize forward models on CPU or GPU; across images that are large, multi-band, multi-epoch, rotated, dithered, and more. All at once: The models are optimized together, thus handling overlapping objects and including the covariance between parameters (including PSF and galaxy parameters). A number of optimization algorithms are available including Levenberg-Marquardt, Gradient descent, and No-U-Turn MCMC sampling. With an object-oriented user interface, AstroPhot makes it easy to quickly extract detailed information from complex astronomical data for individual images or large survey programs. This paper outlines novel features of the AstroPhot code and compares it to other popular astronomical image modeling software. AstroPhot is open-source, fully Python based, and freely accessible here: https://github.com/Autostronomy/AstroPhot

astro-ph.IM↗

arXiv2023

MaNGA galaxy properties -- II. A detailed comparison of observed and simulated spiral galaxy scaling relations

We present a catalogue of dynamical properties for 2368 late-type galaxies from the MaNGA survey. The latter complements the catalogue of photometric properties for the same sample based on deep optical DESI photometry processed with AutoProf. Rotation curves (RCs), extracted by model fitting H$\alpha$ velocity maps from the MaNGA Data Analysis Pipeline, extend out to 1.4 (1.9) R$_{e}$ for the primary (secondary) MaNGA samples. The RCs and ancillary MaNGA Pipe3D data products were used to construct various fundamental galaxy scaling relations that are also compared uniformly with similar relations from NIHAO zoom-in simulations. Simulated NIHAO galaxies were found to broadly reproduce the observed MaNGA galaxy population for $\log (M_*/{\rm M_{\odot}) > 8.5}$. Some discrepancies remain, such as those pertaining to central stellar densities and the diversity of RCs due to strong feedback schemes. Also presented are spatially-resolved scatters for the velocity-size-stellar mass (VRM$_*$) structural relations using MaNGA and NIHAO samples. The scatter for these relations in the galaxian interiors is a consequence of the diversity of inner RC shapes, while scatter in the outskirts is dictated by the large range of stellar surface densities which itself is driven by sporadic star formation. The detailed spatially-resolved scatter analysis highlights the complex interplay between local and global astrophysical processes and provides a strong constraint to numerical simulations.

astro-ph.GA↗

arXiv2022

PROBES-I: A Compendium of Deep Rotation Curves and Matched multiband Photometry

We present the Photometry and Rotation Curve Observations from Extragalactic Surveys (PROBES) compendium of extended rotation curves for 3163 late-type spirals, with matching homogeneous multiband photometry for 1677 of them. PROBES rotation curves originally extracted from Halpha long-slit spectra and aperture synthesis HI (21cm) velocity maps typically extend out to a median 2R_e (or 1R_{23.5, r}). Our uniform photometry takes advantage of GALEX, DESI-LIS, and WISE images and the software AutoProf to yield multiband azimuthally averaged surface brightness profiles that achieve depths greater than 25 mag/arcsec^2 (FUV, NUV), 27 mag/arcsec^2 (g, r), and 26 mag/arcsec^2 (z, W1, W2). With its library of spatially resolved profiles and an extensive table of structural parameters, the versatile PROBES data set will benefit studies of galaxy structure and formation.

astro-ph.GA↗

arXiv2022

Population-Level Inference of Strong Gravitational Lenses with Neural Network-Based Selection Correction

A new generation of sky surveys is poised to provide unprecedented volumes of data containing hundreds of thousands of new strong lensing systems in the coming years. Convolutional neural networks are currently the only state-of-the-art method that can handle the onslaught of data to discover and infer the parameters of individual systems. However, many important measurements that involve strong lensing require population-level inference of these systems. In this work, we propose a hierarchical inference framework that uses the inference of individual lensing systems in combination with the selection function to estimate population-level parameters. In particular, we show that it is possible to model the selection function of a CNN-based lens finder with a neural network classifier, enabling fast inference of population-level parameters without the need for expensive Monte Carlo simulations.

astro-ph.IM↗

arXiv2022

The Spectroscopy and H-band Imaging of Virgo cluster galaxies (SHIVir) Survey: Data Catalogue and Kinematic Profiles

The ``Spectroscopy and H-band Imaging of Virgo cluster galaxies'' (SHIVir) survey is an optical and near-infrared survey which combines SDSS photometry, deep H-band photometry, and long-slit optical spectroscopy for 190 Virgo cluster galaxies (VCGs) covering all morphological types over the stellar mass range log (M_*/M_Sun) = 7.8-11.5$. We present the spectroscopic sample selection, data reduction, and analysis for this SHIVir sample. We have used and optimised the \texttt{pPXF} routine to extract stellar kinematics from our data. Ultimately, resolved kinematic profiles (rotation curves and velocity dispersion profiles) are available for 133 SHIVir galaxies. A comprehensive database of photometric and kinematic parameters for the SHIVir sample is presented with: grizH magnitudes, effective surface brightnesses, effective and isophotal radii, rotational velocities, velocity dispersions, and stellar and dynamical masses. Parameter distributions highlight some bimodal distributions and possible sample biases. A qualitative study of resolved extended velocity dispersion profiles suggests a link between the so-called ``sigma-drop'' kinematic profile and the presence of rings in lenticular S0 galaxies. Rising dispersion profiles are linked to early-type spirals or dwarf ellipticals for which a rotational component is significant, whereas peaked profiles are tied to featureless giant ellipticals.

astro-ph.GA↗