arXiv subjects

Connor Stone

Publications and source records attributed to Connor Stone.

26 records · Page 2Linked to original sources

arXiv2022

NIHAO-LG: The uniqueness of Local Group dwarf galaxies

Recent observational and theoretical studies of the Local Group (LG) dwarf galaxies have highlighted their unique star-formation history, stellar metallicity, gas content, and kinematics. We investigate the commonality of these features by comparing constrained LG and field central dwarf halo simulations in the Numerical Investigation of a Hundred Astrophysical Objects (NIHAO) project. Our simulations, performed with NIHAO-like hydrodynamics which track the evolution of the Milky Way (MW) and M31 along with ~100 dwarfs in the LG, reveal the total gas mass and stellar properties (velocity dispersion, evolution history, etc.) of present-day LG dwarfs to be similar to field systems. However, relative to field galaxies, LG dwarfs have more cold gas in their central parts and more metal-rich gas in the halo stemming from interactions with other dwarfs living in a high-density environment like the LG. Interestingly, the direct impact of massive MW/M31 analogues on the metallicity evolution of LG dwarfs is minimal; LG dwarfs accrete high-metallicity gas mostly from other dwarfs at late times. We have also tested for the impact of metal diffusion on the chemical evolution of LG dwarfs, and found that it does not affect the stellar or gaseous content of LG dwarfs. Our simulations suggest that the stellar components of LG dwarfs offer a unique and unbiased local laboratory for galaxy-formation tests and comparisons, especially against the overall dwarf population in the Universe.

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arXiv2022

The diversity of spiral galaxies explained

An extensive catalog of spatially-resolved galaxy rotation curves and multi-band optical light profiles for 1752 observed spiral galaxies is assembled to explore the drivers of diversity in galaxy structural parameters, rotation curve shapes, and stellar mass profiles. Similar data were extracted from the NIHAO galaxy simulations to identify any differences between observations and simulations. Several parameters, including the inner slope "S" of a rotation curve (RC), were tested for diversity. Two distinct populations are found in observed and simulated galaxies; (i) blue, low mass spirals with stellar mass M* < 10^9.3 Msol and roughly constant "S", and (ii) redder, more massive and more diverse spirals with rapidly increasing "S". In all cases, the value of "S" seems equally contributed by the baryonic and non-baryonic (dark) matter. Diversity is shown to increase mildly with mass. Numerical simulations reproduce well most baryon-dominated galaxy parameter distributions, such as the inner stellar mass profile slope and baryonic scaling relations, but they struggle to match the full diversity of observed galaxy rotation curves (through "S") and most dark-matter-dominated parameters. To reproduce observations, the error broadening of the simulation's intrinsic spread of RC metrics would have to be tripled. The differences in various projections of observed and simulated scaling relations may reflect limitations of current sub-grid physics models to fully capture the complex nature of galaxies. For instance, AGNs are shown to have a significant effect on the shapes of simulated RCs. The inclusion of AGN feedback brings simulated and observed inner RC shapes into closer agreement.

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arXiv2022

Realistic galaxy image simulation via score-based generative models

We show that a Denoising Diffusion Probabalistic Model (DDPM), a class of score-based generative model, can be used to produce realistic mock images that mimic observations of galaxies. Our method is tested with Dark Energy Spectroscopic Instrument (DESI) grz imaging of galaxies from the Photometry and Rotation curve OBservations from Extragalactic Surveys (PROBES) sample and galaxies selected from the Sloan Digital Sky Survey. Subjectively, the generated galaxies are highly realistic when compared with samples from the real dataset. We quantify the similarity by borrowing from the deep generative learning literature, using the `Fréchet Inception Distance' to test for subjective and morphological similarity. We also introduce the `Synthetic Galaxy Distance' metric to compare the emergent physical properties (such as total magnitude, colour and half light radius) of a ground truth parent and synthesised child dataset. We argue that the DDPM approach produces sharper and more realistic images than other generative methods such as Adversarial Networks (with the downside of more costly inference), and could be used to produce large samples of synthetic observations tailored to a specific imaging survey. We demonstrate two potential uses of the DDPM: (1) accurate in-painting of occluded data, such as satellite trails, and (2) domain transfer, where new input images can be processed to mimic the properties of the DDPM training set. Here we `DESI-fy' cartoon images as a proof of concept for domain transfer. Finally, we suggest potential applications for score-based approaches that could motivate further research on this topic within the astronomical community.

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arXiv2021

AutoProf -- I. An automated non-parametric light profile pipeline for modern galaxy surveys

We present an automated non-parametric light profile extraction pipeline called AutoProf. All steps for extracting surface brightness (SB) profiles are included in AutoProf, allowing streamlined analyses of galaxy images. AutoProf improves upon previous non-parametric ellipse fitting implementations with fit-stabilization procedures adapted from machine learning techniques. Additional advanced analysis methods are included in the flexible pipeline for the extraction of alternative brightness profiles (along radial or axial slices), smooth axisymmetric models, and the implementation of decision trees for arbitrarily complex pipelines. Detailed comparisons with widely used photometry algorithms (photutils, XVISTA, and GALFIT) are presented. These comparisons rely on a large collection of late-type galaxy images from the PROBES survey. The direct comparison of SB profiles shows that AutoProf can reliably extract fainter isophotes than other methods on the same images, typically by >2 mag/arcsec^2. Contrasting non-parametric elliptical isophote fitting with simple parametric models also shows that two-component fits (e.g., Sérsic plus exponential) are insufficient to describe late-type galaxies with high fidelity. It is established that elliptical isophote fitting, and in particular AutoProf, is ideally suited for a broad range of automated isophotal analysis tasks. AutoProf is freely available to the community at: https://github.com/ConnorStoneAstro/AutoProf

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arXiv2021

MaNGA galaxy properties -- I. An extensive optical, mid-infrared photometric, and environmental catalogue

We present an extensive catalog of non-parametric structural properties derived from optical and mid-infrared imaging for 4585 galaxies from the MaNGA survey. DESI and WISE imaging are used to extract surface brightness profiles in the g, r, z, W1, W2 photometric bands. Our optical photometry takes advantage of the automated algorithm AutoProf and probes surface brightnesses that typically reach below 29 mag/arcsec^2 in the r band, while our WISE photometry achieves 28 mag/arcsec^2 in the W1 band. Neighbour density measures and central/satellite classifications are also provided for a large sub-sample of the MaNGA galaxies. Highlights of our analysis of galaxy light profiles include: (i) an extensive comparison of galaxian structural properties that illustrates the robustness of non-parametric extraction of light profiles over parametric methods; (ii) the ubiquity of bimodal structural properties suggesting the existence of galaxy families in multiple dimensions; and, (iii) an appreciation that structural properties measured relative to total light, regardless of the fractional level, are uncertain. We study galaxy scaling relations based on photometric parameters, and present detailed comparisons with literature and theory. Salient features of this analysis include the near-constancy of the slope and scatter of the size-luminosity and size-stellar mass relations for late-type galaxies with wavelength, and the saturation of the central surface density, measured within 1 kpc, for elliptical galaxies with M* > 10.7 Msol (corresponding to Sigma_1 ~ 10^{10} Msol/kpc^2). The multi-band photometry, environmental parameters, and structural scaling relations presented are useful constraints for stellar population and galaxy formation models.

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arXiv2021

Pix2Prof: fast extraction of sequential information from galaxy imagery via a deep natural language 'captioning' model

We present 'Pix2Prof', a deep learning model that can eliminate any manual steps taken when extracting galaxy profiles. We argue that a galaxy profile of any sort is conceptually similar to a natural language image caption. This idea allows us to leverage image captioning methods from the field of natural language processing, and so we design Pix2Prof as a float sequence 'captioning' model suitable for galaxy profile inference. We demonstrate the technique by approximating a galaxy surface brightness (SB) profile fitting method that contains several manual steps. Pix2Prof processes $\sim$1 image per second on an Intel Xeon E5 2650 v3 CPU, improving on the speed of the manual interactive method by more than two orders of magnitude. Crucially, Pix2Prof requires no manual interaction, and since galaxy profile estimation is an embarrassingly parallel problem, we can further increase the throughput by running many Pix2Prof instances simultaneously. In perspective, Pix2Prof would take under an hour to infer profiles for $10^5$ galaxies on a single NVIDIA DGX-2 system. A single human expert would take approximately two years to complete the same task. Automated methodology such as this will accelerate the analysis of the next generation of large area sky surveys expected to yield hundreds of millions of targets. In such instances, all manual approaches -- even those involving a large number of experts -- will be impractical.

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arXiv2021

The Intrinsic Scatter of Galaxy Scaling Relations

We present a compendium of disk galaxy scaling relations and a detailed characterization of their intrinsic scatter. Observed scaling relations are typically characterized by their slope, intercept, and scatter; however, these parameters are a mixture of observational errors and astrophysical processes. We introduce a novel Bayesian framework for computing the intrinsic scatter of scaling relations that accounts for nonlinear error propagation and covariant uncertainties. Bayesian intrinsic scatters are ~25percent more accurate than those obtained with a first-order classical method, which systematically underestimates the true intrinsic scatter. Structural galaxy scaling relations based on velocity (V23.5), size (R23.5), luminosity (L23.5), colour (g-z), central stellar surface density (Sigma1), stellar mass (M*), dynamical mass (Mdyn), stellar angular momentum (j*), and dynamical angular momentum (jdyn), are examined to demonstrate the power and importance of the Bayesian formalism. Our analysis is based on a diverse selection of over 1000 late-type galaxies from the Photometry and Rotation Curve Observations from Extragalactic Surveys compilation with deep optical photometry and extended rotation curves. We determine the tightest relation for each parameter by intrinsic orthogonal scatter, finding M*-V23.5, R23.5-j*, and L23.5-jdyn to be especially tight. The scatter of the R23.5-L23.5, V23.5-(g-z), and R23.5-jdyn relations is mostly intrinsic, making them ideal for galaxy formation and evolutionary studies. Our code to compute the Bayesian intrinsic scatter of any scaling relation is also presented. We quantify the correlated nature of many uncertainties in galaxy scaling relations and scrutinize the uncertain nature of disk inclination corrections and their effect on scatter estimates.

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arXiv2019

The Intrinsic Scatter of the Radial Acceleration Relation

We present a detailed Monte Carlo model of observational errors in observed galaxy scaling relations to recover the intrinsic (cosmic) scatter driven by galaxy formation and evolution processes. We apply our method to the stellar radial acceleration relation (RAR) which compares the local observed radial acceleration to the local Newtonian radial acceleration computed from the stellar mass distribution. The stellar and baryonic RAR are known to exhibit similar scatter. Lelli+2017 (L17) studied the baryonic RAR using a sample of 153 spiral galaxies and inferred a negligible intrinsic scatter. If true, a small scatter might challenge the LCDM galaxy formation paradigm, possibly favoring a modified Newtonian dynamics interpretation. The intrinsic scatter of the baryonic RAR is predicted by modern LCDM simulations to be ~0.06-0.08 dex, contrasting with the null value reported by L17. We have assembled a catalog of structural properties with over 2500 spiral galaxies from six deep imaging and spectroscopic surveys (called PROBES for the "Photometry and Rotation curve OBservations from Extragalactic Surveys") to quantify the intrinsic scatter of the stellar RAR and other scaling relations. The stellar RAR for our full sample has a median observed scatter of 0.17 dex. We use our Monte Carlo method, which accounts for all major sources of measurement uncertainty, to infer a contribution of 0.12 dex from the observational errors. The intrinsic scatter of the stellar RAR is thus estimated to be 0.11$\pm$0.02 dex, in agreement with, though slightly greater than, current LCDM predictions.

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