Publications

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Euclid Quick Data Release (Q1): The geometry of dark matter halos from extragalactic streamssubmitted

Euclid Collaboration, N. Starkman, J. Nibauer, S. Pearson, S. Wu, M. Walmsley, L. Necib, J. Bovy, F. R. Marleau, et al.

Astronomy & Astrophysics·2026

Wide-field surveys like Euclid mark a new era of extragalactic stellar stream studies. With a large number of streams, it is now possible to constrain the dark matter halos of galaxies in a cosmological volume and draw comparisons to theoretical expectations for the geometry of dark matter halos. This study combines Euclid imaging with visual detection and segmentation annotations to analyse streams. We use projected stream morphologies to constrain the shape and centre-of-mass position (CoM) of each host galaxy's potential, jointly probing baryonic and dark matter distributions. These inferences complement weak lensing methods, with sensitivity to halo profile and geometry on sub-virial scales. The method enables both stacked, population-level constraints on halo flattening and CoM position, and constraints on these quantities for individual halos. We also present a novel method for transforming segmentation maps of stellar streams into smooth, curvature-preserving tracks optimised for fast and robust dynamical inference. This approach enables rapid modelling of stream morphology, supports a statistically rigorous combination of constraints across multiple streams within a single galaxy, and enables joint inference across galactic hosts. From our study of 13 galaxies with prominent tidal streams, we find agreement with spherical halos, albeit a mild preference for flattening with q=0.95−0.10+0.05 at 68\% confidence. This is promising early agreement with ΛCDM predictions. With thousands more discovered streams expected across Euclid 's mission, our programme will enable precise measurements of halo shapes and CoM positions across large samples and redshifts, offering constraints on the geometry of dark matter halos.

Physics-Informed Neural Networks for Modeling Galactic Gravitational Potentialssubmitted

C. Myers, N. Starkman, L. Necib

Monthly Notices of the Royal Astronomical Society·2026

The gravitational potential of a galaxy encodes its mass distribution, formation history, and dark matter halo structure. Accurate potential models are therefore critical for interpreting stellar kinematics, orbital dynamics, and the influence of satellite systems like the Large Magellanic Cloud. Analytic potential models offer interpretability and efficiency but struggle to capture complex, non-axisymmetric structure and time-dependent perturbations. Neural network-based methods can capture this complexity but offer little interpretability. We introduce a physics-informed neural network (PINN) framework that combines data-driven learning with embedded physical constraints, available as the open-source package GalactoPINNS. Trained on acceleration measurements, the framework captures complex, small-scale features while preserving global physical consistency. We test on systems of increasing complexity, from controlled analytic halos to cosmological simulations of Milky Way-like galaxies, achieving sub-percent acceleration errors with orbit reconstruction that consistently outperforms analytic baselines. Additionally, we implement a Bayesian neural network to provide spatially calibrated uncertainty estimates, and a time-dependent extension to capture smooth temporal evolution. By treating an analytic model as a structured prior and learning corrections on top of it, the method retains physical interpretability while gaining the flexibility to represent realistic galactic potentials, making it well suited for Milky Way modeling and dynamical inference in the era of current and upcoming large-scale surveys.

Galactic Amnesia: The Information Washout of the Milky Way Merger Historysubmitted

L. Necib, D. Folsom, E. Y. Davies, N. Starkman, A. Thoyas

arXiv:2605.04138·2026

The merger history of a galaxy leaves imprints on its present-day stellar chemodynamics, yet dynamical processes progressively erase this record. We ask: how far back in time, and from which observables, can a galaxy's assembly history still be recovered? We provide a quantitative framework to address this question, using Mutual Information normalized by Shannon entropy to measure how much present-day stellar chemodynamics retains about each past merger's stellar mass M⋆ and infall time tinfall. This framework is applied to TNG50 Milky Way -- like galaxies, with comparison to FIRE-2. We find that the gravitational potential and total energy are the most informative and longest-lived tracers of merger properties, highlighting the need for accurately measuring the Milky Way's potential. The information carried by the radial velocity decays to the noise floor within ∼5 Gyr, angular momentum carries low information overall with a mass-dependent decay, and chemical abundances retain a flat, low information floor. Information washout depends on three key factors: (1) radial position -- stars in the inner galaxy lose information faster due to shorter orbital times; (2) infall time -- old mergers are largely phase-mixed; and (3) merger mass -- larger mergers sink to the bottom of the potential well via dynamical friction, inducing violent relaxation that erases dynamical information. At each galactocentric radius, we map the observational horizon in the (M⋆,tinfall) plane beyond which past mergers can no longer be recovered from that observable. By recasting merger reconstruction into this quantitative, observable-by-observable map of what is and is not recoverable, our results provide a foundation for interpreting chemodynamical signatures of past mergers and for guiding surveys and modeling toward the observables that maximize merger information recovery.

Potamides: Mapping Dark Matter Halo Shapes from Stellar Stream Tracks in the Local Universesubmitted

S. Wu, N. Starkman, S. Pearson, J. Nibauer, J. Miro-Carretero, D. Martinez-Delgado

The Astrophysical Journal·2026

Stellar streams trace the gravitational potential of their host galaxies and offer a direct probe of dark matter halo geometry. Cosmological simulations predict that halo shapes depend on both baryonic physics and the nature of dark matter, yet observational constraints on halo flattening and orientation remain limited, especially for individual galaxies. We present Potamides, which utilizes the curvature of extragalactic stellar streams to derive constraints on halo shapes. We apply Potamides to 15 stellar streams from the Stellar Stream Legacy Survey to infer the projected axis ratios and orientation of their host halos. We find that some streams in our sample exclude large regions of halo flattenings and halo orientations. Systems with edge-on wrapping loops or sharp turning points yield the strongest constraints, whereas great circle-like streams remain largely uninformative. All streams in our sample support a spherical halo for a given flattening direction. These results demonstrate that stream morphology can provide halo shape constraints for individual external galaxies. With upcoming surveys (such as Euclid, Rubin, Roman, and ARRAKIHS) expected to discover large numbers of stellar streams, this curvature-based technique will enable rapid statistical tests of dark matter and baryonic physics through the shapes and alignments of halos and disks across cosmic time.

phasecurvefit: A High-Performance JAX Framework for Phase-Space Walks with Pluggable Metrics and Strategiessubmitted

N. Starkman, J. Nibauer

Journal of Open Source Software·2026

Filamentary structures are ubiquitous in the physical sciences, ranging from coherent streams of stars called stellar streams to elongated structures in turbulent fluids, plasmas, and the interstellar medium. In the context of stellar streams, a common preprocessing step is ordering observational or simulation data to derive the mean path of the stream's trajectory through phase-space. phasecurvefit is an open-source Python package for constructing such orderings and paths by walking along the local phase-space flow using JAX. There are two core components: the first is walk_local_flow which builds an approximate ordering and trajectory but which might miss some of the data; and the second is a fast-to-train autoencoder that imputes the full ordering and trajectory. walk_local_flow is very modular and can be customized to use any: 1. distance metric that scores candidate next steps in phase space, and 2. query strategy, like brute-force or KD-trees, that proposes which candidates to consider. This design makes the Nearest Neighbors with Momentum (NN+p) method from one particular configuration (via phasecurvefit.metrics.AlignedMomentumDistanceMetric), while enabling alternative metrics and search strategies better matched to different data scenarios and performance constraints. In addition to the walk itself, phasecurvefit packages a neural-network gap-filling component which assigns a continuous ordering parameter to data skipped during the walk and reconstructs the spatial mean path of the structure. This encoder develops upon the one in: speeding up different components of training by between 1 and 3 orders of magnitude; adding an intermediate decoder-only training that quarters the epochs necessary for training the full autoencoder, halving the overall training time; and adding stabilization of the loss function across training phases.

Physics-Informed Neural Networks for Modeling Galactic Gravitational Potentials

C. Myers, N. Starkman, L. Necib

Advances in Neural Information Processing Systems (NeurIPS 2025)·2025

We introduce a physics-informed neural framework for modeling static and time-dependent galactic gravitational potentials. The method combines data-driven learning with embedded physical constraints to capture complex, small-scale features while preserving global physical consistency. We quantify predictive uncertainty through a Bayesian framework, and model time evolution using a neural ODE approach. Applied to mock systems of varying complexity, the model achieves reconstruction errors at the sub-percent level (0.14\% mean acceleration error) and improves dynamical consistency compared to analytic baselines. This method complements existing analytic methods, enabling physics-informed baseline potentials to be combined with neural residual fields to achieve both interpretable and accurate potential models.

Potamides: JAX tools for curvature-based inference from stellar streamssubmitted

S. Wu, N. Starkman, J. Nibauer, S. Pearson

Journal of Open Source Software·2025

Potamides is a Python package for inferring the mass distribution of galaxies from the projected shapes of stellar streams in imaging data. Stellar streams are elongated structures produced when star clusters or dwarf galaxies are tidally disrupted by their host. Because their projected tracks carry information about the host's gravitational field, the local curvature of a stream can constrain the underlying potential. The package implements and extends the curvature-based likelihood framework of. Rather than generating a full dynamical realization of a stellar stream for each trial model, Potamides represents observed stream tracks with JAX-based splines. It evaluates gravitational accelerations in candidate potentials and compares them directly to the local stream geometry. This provides a lower-cost inference workflow that complements traditional forward-modeling approaches. Potamides supports the complete analysis pipeline for a galaxy, from annotating stream ridge-lines to evaluating likelihoods across many potential models.

StreamSculptor: Hamiltonian Perturbation Theory for Stellar Streams in Flexible Potentials with Differentiable Simulations

J. Nibauer, N. Starkman, K. Johnston

The Astrophysical Journal 983, 68·2025

Stellar streams retain a memory of their gravitational interactions with small-scale perturbations. While perturbative models for streams have been formulated in action-angle coordinates, a direct transformation to these coordinates is only available for static and typically axisymmetric models for the galaxy. The real Milky Way potential is in a state of disequilibrium, complicating the application of perturbative methods around an equilibrium system. Here, we utilize a combination of differentiable simulations and Hamiltonian perturbation theory to model the leading-order effect of dark matter subhalos on stream observables. To obtain a perturbative description of streams, we develop a direct and efficient forward mode differentiation of Hamilton's equations of motion. Our model operates in observable coordinates, allowing us to treat the effects of arbitrary subhalo potentials on streams perturbatively, while simultaneously capturing non-linear effects due to other substructures like the infalling LMC or the rotating bar. The model predicts the velocity dispersion of streams as a function of subhalo statistics, allowing us to constrain the low-mass range of subhalos down to 10^5~M_. We forecast the velocity dispersion of the GD-1 stream, and find that observations are in agreement with a CDM subhalo population, with a slight preference for more dense subhalos. The method provides a new approach to characterize streams in the presence of substructure, with significantly more modeling flexibility compared to previous works.

unxt: A Python package for unit-aware computing with JAX

N. Starkman, A. M. Price-Whelan, J. Nibauer

Journal of Open Source Software 10, 7771·2025

unxt is a Python package for unit-aware computing with JAX. unxt is built on top of quax, which provides a framework for building array-like objects that can be used with JAX. unxt extends quax to provide support for unit-aware computing using the astropy.units package as a units backend. unxt provides seamless integration of physical units into high performance numerical computations, significantly enhancing the capabilities of JAX for scientific applications.

Stream Members Only: Data-Driven Characterization of Stellar Streams with Mixture Density Networks

N. Starkman, J. Nibauer, J. Bovy, J. Webb, K. Tavangar, A. Price-Whelan, A. Bonaca

The Astrophysical Journal 980, 253·2025

Stellar streams are sensitive probes of the Milky Way’s gravitational potential. The mean track of a stream constrains global properties of the potential, while its fine-grained surface density constrains galactic substructure. A precise characterization of streams from potentially noisy data marks a crucial step in inferring galactic structure, including the dark matter, across orders of magnitude in mass scales. Here we present a new method for constructing a smooth probability density model of stellar streams using all of the available astrometric and photometric data. To characterize a stream’s morphology and kinematics, we utilize mixture density networks to represent its on-sky track, width, stellar number density, and kinematic distribution. We model the photometry for each stream as a single-stellar population, with a distance track that is simultaneously estimated from the stream’s inferred distance modulus (using photometry) and parallax distribution (using astrometry). We use normalizing flows to characterize the distribution of background stars. We apply the method to the stream GD-1, and the tidal tails of Palomar 5. For both streams we obtain a catalog of stellar membership probabilities that are made publicly available. Importantly, our model is capable of handling data with incomplete phase-space observations, making our method applicable to the growing census of Milky Way stellar streams.

Angular Correlations of Cosmic Microwave Background Spectrum Distortions from Photon Diffusion

N. Starkman, A. Kosowsky, G. Starkman

Monthly Notices of the Royal Astronomical Society 529, 2274·2024

During cosmic recombination, charged particles bind into neutral atoms and the mean free path of photons rapidly increases, resulting in the familiar diffusion damping of primordial radiation temperature variations. An additional effect is a small photon spectrum distortion, because photons arriving from a particular sky direction were originally in thermal equilibrium at various spatial locations with different temperatures; the combination of these different blackbody temperature distributions results in a spectrum with a Compton y-distortion. Using the approximation that photons had zero mean free path prior to their second-to-last scattering, we derive an expression for the resulting y-distortion, and compute the angular correlation function of the diffusion y-distortion and its cross-correlation with the square of the photon temperature fluctuation. Detection of the cross-correlation is within reach of existing arcminute-resolution microwave background experiments such as the Atacama Cosmology Telescope and the South Pole Telescope.

On the fast track: Rapid construction of stellar stream paths

N. Starkman, J. Bovy, J. Webb, D. Calvetti, E. Somersalo

Monthly Notices of the Royal Astronomical Society 522, 5022·2023

Stellar streams are sensitive probes of the Galactic potential. The likelihood of a stream model given stream data is often assessed using simulations. However, comparing to simulations is challenging when even the stream paths can be hard to quantify. Here we present a novel application of self-organizing maps and first-order Kalman filters to reconstruct a stream’s path, propagating measurement errors and data sparsity into the stream path uncertainty. The technique is Galactic-model independent, non-parametric, and works on phase-wrapped streams. With this technique, we can uniformly analyse and compare data with simulations, enabling both comparison of simulation techniques and ensemble analysis with stream tracks of many stellar streams. Our method is implemented in the public Python package TrackStream, available at https://github.com/nstarman/trackstream.

The Astropy Project: Sustaining and Growing a Community-oriented Open-source Project and the Latest Major Release (v5.0) of the Core Package

The Astropy Collaboration, A. M. Price-Whelan, P. L. Lim, N. Earl, N. Starkman, L. Bradley, D. L. Shupe, A. A. Patil, L. Corrales, et al.

The Astrophysical Journal 935, 167·2022

The Astropy Project supports and fosters the development of open-source and openly-developed Python packages that provide commonly needed functionality to the astronomical community. A key element of the Astropy Project is the core package astropy, which serves as the foundation for more specialized projects and packages. In this article, we summarize key features in the core package as of the recent major release, version 5.0, and provide major updates for the Project. We then discuss supporting a broader ecosystem of interoperable packages, including connections with several astronomical observatories and missions. We also revisit the future outlook of the Astropy Project and the current status of Learn Astropy. We conclude by raising and discussing the current and future challenges facing the Project.

Straight Lightning as a Signature of Macroscopic Dark Matter

N. Starkman, J. S. Sidhu, H. Winch, G. Starkman

Physical Review D 103, 063024·2020

Macroscopic dark matter (macros) is a broad class of alternative candidates to particle dark matter. These candidates would transfer energy to matter primarily through elastic scattering. A sufficiently large macro passing through the atmosphere would produce a straight channel of ionized plasma. If the cross-section of the macro is σ_x 6 10^-9 cm^2, then under atmospheric conditions conducive to lightning (eg. a thunderstorm) the plasma channel would be sufficient to seed a lightning strike with a single leader. This is entirely unlike ordinary bolt lightning in which a long sequence of hundreds or thousands of few-meter-long leaders are strung together. This macro-induced lightning would be extremely straight, and thus highly distinctive. Neither wind shear nor magnetohydrodynamic instabilities would markedly spoil its straightness. The only photographically documented case of a straight lightning bolt is probably not straight enough to have been macro-induced. We estimate the region of macro parameter space that could be probed by a search for straight lightning from the number of thunderstorms happening on Earth at any time. We also estimate the parameter space that can be probed by carefully monitoring Jupiter, e.g. using the Hubble Space Telescope. All code and data is available at https://github.com/cwru-pat/macro_lightning.

An extended Pal 5 stream in Gaia DR2

N. Starkman, J. Bovy, J. Webb

Monthly Notices of the Royal Astronomical Society·2020

We present the results of a detailed search for members of the Pal 5 tidal tail system in Gaia Data Release 2 (DR2). Tidal tails provide a sensitive method for measuring the current and past gravitational potential of their host galaxy as well as for testing predictions for the abundance of dark matter subhaloes. The Pal 5 globular cluster and its associated tails are an excellent candidate for such analysis; however, only ∼23° of arc are currently known, with in particular the leading tail much shorter than the trailing. Using Gaia DR2 and its precise astrometry, we extend the known extent of the Pal 5 tail to ∼30°, 7° of which are newly detected along the leading arm. The detected leading and trailing arms are symmetric in length and remain near constant width. This detection constrains proposed models in which the Galactic bar truncates Pal 5’s leading arm. Follow-up spectroscopic observations are necessary to verify the candidate stream stars are consistent with the known tidal tails. If confirmed, this Pal 5 stream extension opens up new possibilities to constrain the Galactic potential.

A constant characteristic volume density of dark matter haloes from SPARC rotation curve fits

P. Li, F. Lelli, S. S. McGaugh, N. Starkman, J. M. Schombert

Monthly Notices of the Royal Astronomical Society·2018

We study the scaling relations between dark matter (DM) haloes and galaxy discs using 175 galaxies from the SPARC database. We explore two cosmologically motivated DM halo profiles: the Einasto profile from DM-only simulations and the DC14 profile from hydrodynamic simulations. We fit the observed rotation curves using a Markov Chain Monte Carlo method and break the disc-halo degeneracy using near-infrared photometry and ΛCDM-motivated priors. We find that the characteristic volume density ρ_ s of DM haloes is nearly constant over 5 decades in galaxy luminosity. The scale radius r_s and the characteristic surface density ρ_s r_s, instead, correlate with galaxy luminosity. These scaling relations provide an empirical benchmark to cosmological simulations of galaxy formation.

A new algorithm to quantify maximum discs in galaxies

N. Starkman, F. Lelli, S. S. McGaugh, J. Schombert

Monthly Notices of the Royal Astronomical Society 480, 2292·2018

Maximum disc decompositions of rotation curves place a dynamical upper limit to the mass attributable to stars in galaxies. The precise definition of this term, however, can be vague and varies in usage. We develop an algorithm to robustly quantify maximum-disc mass models and apply it to 153 galaxies from the SPARC database. Our automatic procedure recovers classic results from manual decompositions. High-mass, high-surface-brightness galaxies have mean maximum-disc mass-to-light ratios of 0.7 \;M_/L_ in the Spitzer 3.6 μm band, which are close to the expectations from stellar population models, suggesting that these galaxies are nearly maximal. Low-mass, low-surface-brightness galaxies have very high maximum-disc mass-to-light ratios (up to 10 M_/L_), which are unphysical for standard stellar population models, confirming they are sub-maximal. The maximum-disc mass-to-light ratios are more closely correlated with surface brightness than luminosity. The mean ratio between baryonic and observed velocity at the peak of the baryonic contribution is V_bar/V_p 0.88, but correlates with surface brightness, so it is unwise to use this mean value to define the maximum disc concept. Our algorithm requires no manual intervention and could be applied to large galaxy samples from future HI surveys with Apertif, Askap, and SKA.