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Constraints on the population level distribution of nearby Dark Matter halo shapes with extragalactic streams
Authors:
David Chemaly,
Elisabeth Sola,
Sergey Koposov,
HanYuan Zhang,
Vasily Belokurov,
Denis Erkal
Abstract:
Stellar streams trace the gravitational potential of their host galaxies and provide a sensitive probe of dark matter halo structure. Previously, we developed, and tested on simulated data, a hierarchical Bayesian framework to infer the population level distribution of dark matter halo shapes from ensembles of extragalactic stellar streams with images only. In this work, we apply this pipeline to…
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Stellar streams trace the gravitational potential of their host galaxies and provide a sensitive probe of dark matter halo structure. Previously, we developed, and tested on simulated data, a hierarchical Bayesian framework to infer the population level distribution of dark matter halo shapes from ensembles of extragalactic stellar streams with images only. In this work, we apply this pipeline to 32 stellar streams from the STRRINGS catalogue, a curated sample of dynamically cold minor-merger streams detected in deep imaging. Each stream is forward-modelled assuming an axisymmetric halo and fitted using only the projected stream track, yielding posterior constraints on the halo flattening parameter $q$. To account for model mismatch and track systematics, we introduce an additional variance term that inflates the uncertainty on the projected stream track and use it to identify a high quality (gold) subsample of 17 streams whose tracks retain significant constraining power. We then combine the individual posteriors through importance sampling to infer the underlying population distribution of halo flattening. For the \textit{gold} subsample, we infer an oblate population with mean $μ_q \approx 0.72$ and intrinsic scatter $σ_q \approx 0.34$. Streams dominated by additional model variance yield a nearly spherical population inference. The inferred oblate population for the gold sample is broadly consistent with expectations from cosmological hydrodynamical simulations. This work provides constraints on dark matter halo flattening from stellar streams beyond the Local Group and establishes a scalable framework for forthcoming large samples from Euclid and Rubin/LSST.
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Submitted 6 July, 2026;
originally announced July 2026.
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SchwarMAX: a GPU-friendly Schwarzschild orbit-superposition modelling framework
Authors:
HanYuan Zhang,
David Chemaly,
Eugene Vasiliev,
Vasily Belokurov,
N. Wyn Evans,
Juntai Shen
Abstract:
The Schwarzschild orbit-superposition method is a highly flexible dynamical modelling tool. It constrains the mass distribution of a galaxy using line-of-sight velocity and photometric observations. However, constructing such a dynamical model of a galaxy is computationally expensive. We present SchwarMAX, a new publicly available GPU implementation of the Schwarzschild orbit-superposition method.…
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The Schwarzschild orbit-superposition method is a highly flexible dynamical modelling tool. It constrains the mass distribution of a galaxy using line-of-sight velocity and photometric observations. However, constructing such a dynamical model of a galaxy is computationally expensive. We present SchwarMAX, a new publicly available GPU implementation of the Schwarzschild orbit-superposition method. The GPU-native code is significantly faster than other implementations, with entire model construction taking around a second on GPU A100. Using SchwarMAX, we can explore the distributions of both baryonic and dark matter in a galaxy across a high-dimensional parameter space. We demonstrate its performance using mock integrated-field spectroscopic unit data generated from an N-body simulated barred galaxy. We explore the 12-dimensional space of disc, bar and halo parameters using Markov Chain Monte Carlo. The density profiles and the bar pattern speed of the galaxy are recovered with good accuracy. We show that the code can be applied to barred galaxies across a wide range of inclination angles and can be easily extended to other stellar systems, such as elliptical and dwarf galaxies.
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Submitted 30 June, 2026;
originally announced July 2026.
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Constraining the population of dark matter halo shapes using hierarchical inference with extragalactic stellar streams
Authors:
David Chemaly,
Elisabeth Sola,
Vasily Belokurov,
Sergey Koposov,
GyuChul Meyong,
HanYuan Zhang,
Denis Erkal
Abstract:
Stellar streams, the debris of tidally disrupted satellites, trace their host's gravitational potential and thus probe dark matter halo structure. While six-dimensional phase-space data of Galactic streams enable precise dark matter halo modelling in the Milky Way, streams around external galaxies are typically available only as low surface brightness features without kinematics (i.e. two-dimensio…
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Stellar streams, the debris of tidally disrupted satellites, trace their host's gravitational potential and thus probe dark matter halo structure. While six-dimensional phase-space data of Galactic streams enable precise dark matter halo modelling in the Milky Way, streams around external galaxies are typically available only as low surface brightness features without kinematics (i.e. two-dimensional photometric data), providing only weak constraints when considered individually. We present a hierarchical Bayesian framework that infers the population distribution of halo flattening using only projected stream tracks. Streams are forward-modelled in StreaMAX, a new JAX-accelerated particle-spray package that achieves orders of magnitude faster stream generation when compared to traditional methods. For each stream we fit an axisymmetric dark matter halo model and obtain a posterior on the flattening. These posteriors are then combined through hierarchical reweighting to constrain the population distribution. Using mock data, we show that individual fits recover the correct flattening with modest precision and exhibit projection-induced multi-modalities. Nevertheless, aggregating these fits yields accurate and confident constraints on the underlying population distribution of dark matter halo morphologies, clearly distinguishing between oblate, spherical, and prolate populations. The total computational cost scales linearly with sample size. Our results demonstrate that ensembles of purely photometric streams carry sufficient information to constrain dark matter halo shapes in external galaxies at the population level. With the forthcoming samples from Euclid and Rubin/LSST, this approach offers a practical path to population-level inferences of halo morphology without any kinematic measurements.
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Submitted 12 July, 2026; v1 submitted 21 January, 2026;
originally announced January 2026.
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Orbital migration and heating history of the Galactic disc: a transition between the bimodal discs
Authors:
HanYuan Zhang,
Vasily Belokurov,
Jason L. Sanders,
N. Wyn Evans,
David Chemaly,
Daisuke Kawata,
Natsuki Funakoshi,
Neige Frankel,
Sarah G. Kane,
Sergey E. Koposov
Abstract:
Stellar orbits in the Galactic disc evolve from their birth to the current shape through both radial migration and dynamical heating. The history of their secular evolution is imprinted in the current kinematics and age-metallicity distribution. We construct a chrono-chemo-dynamical model of the disc, incorporating inside-out growth, metallicity evolution, radial migration, and heating to fit the…
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Stellar orbits in the Galactic disc evolve from their birth to the current shape through both radial migration and dynamical heating. The history of their secular evolution is imprinted in the current kinematics and age-metallicity distribution. We construct a chrono-chemo-dynamical model of the disc, incorporating inside-out growth, metallicity evolution, radial migration, and heating to fit the observed age-metallicity-kinematics distribution of LAMOST subgiant stars in both the low and high-$α$ disc. By modelling all distribution parameters with spline fitting, we present the first non-parametric stellar migration and heating history of the Galaxy. We determine the heating-to-migration ratio, the ratio of the root-mean-square changes in radial/vertical and azimuthal actions, to be $\approx0.075$ for radial to azimuthal actions and $\approx0.015$ for vertical to azimuthal actions, implying a highly anisotropic diffusion in the action space. Furthermore, we identify a transition in radial migration efficiency coinciding with the transition moment of the bimodal disc, for which the radial migration was more efficient for the high-$α$ disc than for the low-$α$ disc. This transition may be attributed to two correlated scenarios: 1) a bar formation epoch accompanied by violent outward migration, and 2) a drop in the gas mass fraction in the disc when the low-$α$ disc began to form. These findings offer further constraints on the formation mechanisms of bimodal discs, favouring the downsizing scenario. We also briefly discuss the connection between our results and recent high-redshift observations. In addition to the secular evolution history, our model maps the Milky Way ISM metallicity gradient at different lookback times, which we find has only varied a little (in the range of $-0.07~\rm to~-0.10~dex/kpc$) since disc formation.
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Submitted 10 December, 2025;
originally announced December 2025.
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Holovibes: real-time ultrahigh-speed digital hologram rendering and short-time analysis
Authors:
Marius Dubosc,
Maxime Boy-Arnould,
Jules Guillou,
Titouan Gragnic,
Arthur Courselle,
Gustave Hervé,
Alexis Pinson,
Etienne Senigout,
Bastien Gaulier,
Simon Riou,
Chloé Magnier,
Noé Topeza,
Oscar Morand,
Thomas Xu,
Samuel Goncalves,
Edgar Delaporte,
Adrien Langou,
Paul Duhot,
Julien Nicolle,
Sacha Bellier,
David Chemaly,
Damien Didier,
Philippe Bernet,
Eliott Bouhana,
Fabien Colmagro
, et al. (29 additional authors not shown)
Abstract:
Real-time ultrahigh-speed rendering of digital holograms from high-bitrate interferogram streams demands robust parallel computing and efficient data handling with minimal latency. We present Holovibes, a high-performance software engine that enables real-time holographic image reconstruction and short-time analysis at unprecedented throughput. Holovibes integrates spatial demodulation techniques,…
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Real-time ultrahigh-speed rendering of digital holograms from high-bitrate interferogram streams demands robust parallel computing and efficient data handling with minimal latency. We present Holovibes, a high-performance software engine that enables real-time holographic image reconstruction and short-time analysis at unprecedented throughput. Holovibes integrates spatial demodulation techniques, such as Fresnel transformations and angular spectrum propagation, with temporal analysis methods including short-time Fourier transform (STFT) and principal component analysis (PCA) in a unified pipeline. By leveraging CUDA-based GPU acceleration, multithreaded parallelism, and efficient buffering, the system achieves high-throughput, low-latency processing suitable for demanding computational imaging applications. We demonstrate sustained real-time hologram rendering of 256x256-pixel from interferograms acquired by a streaming camera at 71,400 frames per second on commodity hardware with no frame loss, while maintaining an end-to-end latency of 30 ms. The engine also supports simultaneous recording of raw or processed data, enabling high-speed acquisition workflows essential for experimental applications. This work represents a significant advance over prior digital holography systems and provides a versatile platform for ultra-high-speed, real-time computational imaging.
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Submitted 5 August, 2025;
originally announced August 2025.
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STRRINGS: STReams in Residual Images of Nearby GalaxieS
Authors:
E. Sola,
D. Chemaly,
V. Belokurov,
O. Müller,
A. Ardern-Arentsen,
E. Y. Davies,
J. Laguna-Miralles,
G. Myeong,
K. Panagiotakis,
H. Zhang,
D. Erkal,
S. E. Koposov,
D. Lang,
J. Nibauer
Abstract:
Tidal features from galaxy mergers, particularly stellar streams, offer valuable insights into galaxy assembly and dark matter halo properties. This paper aims to identify a large sample of nearby stellar streams suitable for detailed modelling and comparison with simulations to enable population-level constraints on halo properties. We visually inspect and compile a tidal feature catalogue for…
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Tidal features from galaxy mergers, particularly stellar streams, offer valuable insights into galaxy assembly and dark matter halo properties. This paper aims to identify a large sample of nearby stellar streams suitable for detailed modelling and comparison with simulations to enable population-level constraints on halo properties. We visually inspect and compile a tidal feature catalogue for $19,387$ galaxies with redshift $z \leq 0.02$ from the Siena Galaxy Atlas 2020 using original, model, and residual images from the DESI Legacy Imaging Surveys. Residual images, produced by subtracting models of all sources, enhance the detectability of faint asymmetries such as tidal features. We find that $11.9 \pm 0.2\%$ of galaxies host detectable tidal features, more frequently around early-type than late-type galaxies. The tidal feature fraction increases with stellar mass, from $2.4 \pm 0.4\%$ at $\sim10^8$M$_\odot$ to $36.5 \pm 1.2\%$ at $\sim 5\times10^{11}$M$_\odot$. From this, we present the first release of STRRINGS: STReams in Residual Images of Nearby GalaxieS, a subsample of 35 galaxies with long, narrow streams suitable for modelling. We segment these streams and derive their geometry, surface brightness, colours, and stellar masses. The median $g$-band surface brightness is 26.8 mag$\,$arcsec$^{-2}$, reaching 27.5 mag$\,$arcsec$^{-2}$ for the faintest stream. Mass ratios are consistent with minor mergers, and we identify five potential dwarf galaxy progenitors. Our streams are typically longer (median 124 kpc) than the literature, with comparable widths. Stream mass correlates with length and colour, and wider streams lie at larger galactocentric radii. STRRINGS will be expanded and used to constrain halo properties in future work.
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Submitted 25 September, 2025; v1 submitted 4 August, 2025;
originally announced August 2025.
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Galaxies OBserved as Low-luminosity Identified Nebulae (GOBLIN): a catalog of 43,000 high-probability dwarf galaxy candidates in the UNIONS survey
Authors:
Nick Heesters,
David Chemaly,
Oliver Müller,
Elisabeth Sola,
Sébastien Fabbro,
Ashley Ferreira,
Alan W. McConnachie,
Eugene Magnier,
Michael J. Hudson,
Kenneth Chambers,
François Hammer,
Ruben Sanchez-Janssen
Abstract:
The detection of low surface brightness galaxies beyond the Local Group poses significant observational challenges, yet these faint systems are fundamental to our understanding of dark matter, hierarchical galaxy formation, and cosmic structure. Their abundance and distribution provide crucial tests for cosmological models, particularly regarding the small-scale predictions of $Λ$CDM. We present a…
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The detection of low surface brightness galaxies beyond the Local Group poses significant observational challenges, yet these faint systems are fundamental to our understanding of dark matter, hierarchical galaxy formation, and cosmic structure. Their abundance and distribution provide crucial tests for cosmological models, particularly regarding the small-scale predictions of $Λ$CDM. We present a systematic detection framework for dwarf galaxy candidates in Ultraviolet Near Infrared Optical Northern Survey (UNIONS) data covering 4,861 deg$^{2}$. Our pipeline preprocesses UNIONS gri-band data through binning, artifact removal, and stellar masking, then employs MTObjects (MTO) for low surface brightness detection. After parameter cuts and cross-matching, we obtain $\sim$360 candidates per deg$^{2}$, totaling $\sim$1.5 million candidates forming our GOBLIN (Galaxies OBserved as Low-luminosity Identified Nebulae) catalog. We fine-tuned the deep learning model Zoobot, pre-trained on Galaxy Zoo labels, for classification. Training data came from visual inspection of literature candidates with probability labels from expert assessments, capturing consensus and uncertainty. Applied to all MTO objects, our method identifies 42,965 dwarf candidates with probability $>$ 0.8, including 23,072 with probability $>$ 0.9. High-probability candidates correlate spatially with massive galaxies (log$(M_{*}/M_{\odot}) \geq$ 10) within 120 Mpc. While some of these objects may have been previously identified in other surveys, we present this extensive catalog of candidates, including their positions, structural parameter estimates, and classification probabilities, as a resource for the community to enable studies of galaxy formation, evolution, and the distribution of dwarf galaxies in different environments.
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Submitted 23 May, 2025;
originally announced May 2025.
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UNIONS: The Ultraviolet Near-Infrared Optical Northern Survey
Authors:
Stephen Gwyn,
Alan W. McConnachie,
Jean-Charles Cuillandre,
Ken C. Chambers,
Eugene A. Magnier,
Michael J. Hudson,
Masamune Oguri,
Hisanori Furusawa,
Hendrik Hildebrandt,
Raymond Carlberg,
Sara L. Ellison,
Junko Furusawa,
Raphaël Gavazzi,
Rodrigo Ibata,
Yannick Mellier,
Ken Osato,
H. Aussel,
Lucie Baumont,
Manuel Bayer,
Olivier Boulade,
Patrick Côté,
David Chemaly,
Cail Daley,
Pierre-Alain Duc,
A. Ellien
, et al. (64 additional authors not shown)
Abstract:
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 $10σ$ point source depth of the data, as measured within a 2-arcsecond diameter aperture, are…
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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 $10σ$ 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.
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Submitted 17 March, 2025;
originally announced March 2025.
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The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data
Authors:
The Multimodal Universe Collaboration,
Jeroen Audenaert,
Micah Bowles,
Benjamin M. Boyd,
David Chemaly,
Brian Cherinka,
Ioana Ciucă,
Miles Cranmer,
Aaron Do,
Matthew Grayling,
Erin E. Hayes,
Tom Hehir,
Shirley Ho,
Marc Huertas-Company,
Kartheik G. Iyer,
Maja Jablonska,
Francois Lanusse,
Henry W. Leung,
Kaisey Mandel,
Juan Rafael Martínez-Galarza,
Peter Melchior,
Lucas Meyer,
Liam H. Parker,
Helen Qu,
Jeff Shen
, et al. (4 additional authors not shown)
Abstract:
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 sc…
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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
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Submitted 3 December, 2024;
originally announced December 2024.