Publications

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

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

The Astrophysical Journal 1010, 120·2026

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

The galaxy ESO 186-063 seen edge-on, with its faint stellar stream fitted as a curved track. Arrows show the stream’s curvature, and the track is shaded by how often each point rejects a trial halo potential, over that potential’s equipotential contours.

The Astrophysical Journal 1010, 120ApJ 1010, 120·2026·S. Wu^‡, N. Starkman, et al.S. Wu^‡, N. Starkman, S. Pearson, et al.S. Wu^‡, N. Starkman, S. Pearson, J. Nibauer, J. Miro-Carretero, D. Martinez-Delgado·The Astrophysical Journal 1010, 120ApJ 1010, 120·2026

The Astrophysical Journal 1010, 120ApJ 1010, 120·2026

Potamides constrains the projected flattening and orientation of dark matter halos from the curvature of 15 streams in the Stellar Stream Legacy Survey.

Potamides constrains the projected flattening and orientation of dark matter halos from the curvature of 15 streams in the Stellar Stream Legacy Survey. Streams with edge-on loops or sharp turns constrain a halo most, and great-circle-like ones barely at all. Three of the streams hint that the stellar disk shapes the inner gravitational field. Led by Sirui Wu.

student^‡ 1st | 2nd | 20262026Context ›

Software

@article{Wu+:2026:potamides-halo-shapes,
  author        = {Wu, Sirui and Starkman, Nathaniel and Pearson, Sarah and Nibauer, Jacob and Miro-Carretero, Juan and Martinez-Delgado, David},
  title         = {{Potamides: Mapping Dark Matter Halo Shapes from Stellar Stream Tracks in the Local Universe}},
  journal       = {The Astrophysical Journal},
  volume        = {1010},
  number        = {2},
  pages         = {120},
  year          = {2026},
  month         = {oct},
  doi           = {10.3847/1538-4357/aea0d4},
  eprint        = {2605.07809},
  archivePrefix = {arXiv},
  adsurl        = {https://ui.adsabs.harvard.edu/abs/2026arXiv260507809W},
  abstract      = {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.}
}

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

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

Journal of Open Source Software 11, 10712·2026

Potamides: JAX tools for curvature-based inference from stellar streamsPotamides: The SoftwarePotamides Software

Relative likelihood of a halo’s y-axis flattening, q₂, from a Potamides tutorial: a flat plateau from about 0.7 to 1.55 that contains the true value, q₂ = 1, falling to zero on either side.

Journal of Open Source Software 11, 10712JOSS 11, 10712·2026·S. Wu^‡, N. Starkman, et al.S. Wu^‡, N. Starkman, J. Nibauer, et al.S. Wu^‡, N. Starkman, J. Nibauer, S. Pearson·Journal of Open Source Software 11, 10712JOSS 11, 10712·2026

Journal of Open Source Software 11, 10712JOSS 11, 10712·2026

Potamides is a JAX package that infers a galaxy’s mass distribution from the shapes of its stellar streams.

Potamides is a JAX package that infers a galaxy’s mass distribution from the shapes of its stellar streams. Rather than simulating a stream for every trial potential, it fits the observed track with splines and compares its curvature directly to the potential’s accelerations. It covers the pipeline from annotating streams to evaluating likelihoods. Led by Sirui Wu.

student^‡ 1st | 2nd | 20262026Context ›

Software

@article{Wu+:2026:potamides-joss,
  author   = {Wu, Sirui and Starkman, Nathaniel and Nibauer, Jacob and Pearson, Sarah},
  title    = {{Potamides: JAX tools for curvature-based inference from stellar streams}},
  journal  = {Journal of Open Source Software},
  volume   = {11},
  number   = {126},
  pages    = {10712},
  year     = {2026},
  month    = {oct},
  doi      = {10.21105/joss.10712},
  abstract = {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 Nibauer et al. (2023). 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.}
}

When Streams Curve Away: a Test of Dark Matter from Extragalactic Stellar Stream Populations

N. Starkman, J. Nibauer, S. Pearson, S. Wu^‡, L. Necib

The Astrophysical Journal·2026submitted

When Streams Curve Away: a Test of Dark Matter from Extragalactic Stellar Stream PopulationsWhen Streams Curve Awaysubmitted

$Likelihood ratio between CDM and SIDM plotted against the observed fraction of streams with a convex segment, for catalogs of 100, 1,000 and 10,000 streams. The curves steepen with catalog size; at 10,000 streams, either predicted rate is strongly favored.$

The Astrophysical JournalApJ·2026submitted·N. Starkman, J. Nibauer, et al.N. Starkman, J. Nibauer, S. Pearson, et al.N. Starkman, J. Nibauer, S. Pearson, S. Wu^‡, L. Necib·The Astrophysical JournalApJ·2026submitted

The Astrophysical JournalApJ·2026submitted

How often stellar streams curve away from their host galaxy’s center can tell cold from self-interacting dark matter at up to 5σ, with catalogs the size Euclid, Rubin and Roman will provide.

How often stellar streams curve away from their host galaxy’s center can tell cold from self-interacting dark matter at up to 5σ, with catalogs the size Euclid, Rubin and Roman will provide. In a spherical halo a stream cannot curve this way in projection; in a triaxial halo, which cold dark matter predicts, it can. The test needs only the stream’s projected track and the host’s center: no distances, velocities or model of the stream’s progenitor.

1st |

student^‡ 4th | 20262026Details ›

Software

@misc{Starkman+:2026:stream-convexity-rate,
  author        = {Starkman, Nathaniel and Nibauer, Jacob and Pearson, Sarah and Wu, Sirui and Necib, Lina},
  title         = {{When Streams Curve Away: a Test of Dark Matter from Extragalactic Stellar Stream Populations}},
  journal       = {The Astrophysical Journal},
  year          = {2026},
  month         = {sep},
  eprint        = {2609.40057},
  archivePrefix = {arXiv},
  primaryClass  = {astro-ph.GA},
  adsurl        = {https://ui.adsabs.harvard.edu/abs/2026arXiv260940057S},
  abstract      = {The nature of dark matter sets the shape of galactic halos: collisionless cold dark matter (CDM) predicts triaxial halos, while scattering in self-interacting dark matter (SIDM) rounds them toward sphericity. We show that a large catalog of projected stellar stream tracks can statistically constrain halo shapes, and therefore the particle nature of dark matter, without detailed modeling of any individual stream or gravitational potential. We develop a new geometric diagnostic for stellar streams: if the curvature of any segment along a stream points away from the host galaxy's projected center of mass, then that stream falsifies broad classes of potentials, including spherical ones. We call such a segment convex. The diagnostic needs only a projected stream track and a host-center estimate, requiring no kinematics, distances, progenitor model, or fit to individual host potentials. Halo triaxiality sets the rate at which streams show convex segments, making this rate an observable of the halo-shape distribution and the underlying dark-matter model. We forecast that upcoming survey-scale stream morphology catalogs can discriminate between CDM and SIDM at up to $5\sigma$.},
  note          = {submitted}
}

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

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

Astronomy & Astrophysics·2026submitted

Euclid Quick Data Release (Q1): The geometry of dark matter halos from extragalactic streamsHalo Geometry from Euclid StreamsEuclid Extragalactic Streamssubmitted

Stellar stream tracks from 13 Euclid galaxies, drawn as magenta curves and stacked over one host galaxy, as if every stream orbits a common center.

Astronomy & AstrophysicsA&A·2026submitted·Euclid Collaboration, N. Starkman, J. Nibauer, et al.Euclid Collaboration, N. Starkman, J. Nibauer, S. Pearson, et al.Euclid Collaboration, N. Starkman, J. Nibauer, S. Pearson, S. Wu^‡, M. Walmsley, L. Necib, J. Bovy, F. R. Marleau, et al.·Astronomy & AstrophysicsA&A·2026submitted

Astronomy & AstrophysicsA&A·2026submitted

Stellar streams around 13 galaxies in Euclid’s first quick data release give a joint measurement of their dark matter halos’ shape: consistent with spherical, with a mild preference for flattening, q = 0.95 (+0.05, −0.10), in line with ΛCDM.

Stellar streams around 13 galaxies in Euclid’s first quick data release give a joint measurement of their dark matter halos’ shape: consistent with spherical, with a mild preference for flattening, q = 0.95 (+0.05, −0.10), in line with ΛCDM. A stream’s shape on the sky constrains the shape and center of its host’s halo, complementing weak lensing. These galaxies are seen up to about 4 billion years in the past, and Euclid is expected to find thousands more streams.

1st |

student^‡ 4th | 20262026Context ›

Software

@misc{Euclid+Starkman+:2026:stream-halo-geometry,
  author        = {{Euclid Collaboration} and Starkman, Nathaniel and Nibauer, Jacob and Pearson, S. and Wu, Sirui and Walmsley, M. and Necib, Lina and Bovy, Jo and Marleau, F. R. and Sola, E. and Scott, D.},
  title         = {{Euclid Quick Data Release (Q1): The geometry of dark matter halos from extragalactic streams}},
  journal       = {Astronomy \& Astrophysics},
  year          = {2026},
  month         = {jun},
  doi           = {10.48550/arXiv.2606.21774},
  eprint        = {2606.21774},
  archivePrefix = {arXiv},
  primaryClass  = {astro-ph.GA},
  adsurl        = {https://ui.adsabs.harvard.edu/abs/2026arXiv260621774E},
  abstract      = {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 $q = 0.95^{+0.05}_{-0.10}$ at 68\% confidence. This is promising early agreement with $Λ$CDM 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.},
  note          = {submitted}
}

Reconstructing Galactic Gravitational Potentials from Stellar Kinematics with Physics-Informed Neural Networks

C. Myers^†, N. Starkman, L. Necib

The Astrophysical Journal·2026submitted

Reconstructing Galactic Gravitational Potentials from Stellar Kinematics with Physics-Informed Neural NetworksPINNs for Galactic PotentialsGalactic PINNssubmitted

Orbits of a test particle in a Milky Way–LMC system, integrated in the true potential, a near-true analytic model, and three neural-network models. The most complete neural-network models track the true orbit almost exactly, while the near-true analytic model drifts away from it.

The Astrophysical JournalApJ·2026submitted·C. Myers^†, N. Starkman, et al.C. Myers^†, N. Starkman, L. NecibC. Myers^†, N. Starkman, L. Necib·The Astrophysical JournalApJ·2026submitted

The Astrophysical JournalApJ·2026submitted

A physics-informed neural network learns only the corrections to an analytic galactic potential, reaching sub-percent acceleration errors while staying interpretable.

A physics-informed neural network learns only the corrections to an analytic galactic potential, reaching sub-percent acceleration errors while staying interpretable. Its orbits are more faithful than the analytic model’s alone, and Bayesian and time-dependent versions add uncertainties and follow a potential as it changes. Led by undergraduate Charlotte Myers; code in galactoPINNs, first shown at NeurIPS 2025.

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Software

@misc{Myers+:2026:galactic-pinns,
  author        = {Myers, Charlotte and Starkman, Nathaniel and Necib, Lina},
  title         = {{Reconstructing Galactic Gravitational Potentials from Stellar Kinematics with Physics-Informed Neural Networks}},
  journal       = {The Astrophysical Journal},
  year          = {2026},
  month         = {jun},
  eprint        = {2606.18386},
  archivePrefix = {arXiv},
  adsurl        = {https://ui.adsabs.harvard.edu/abs/2026arXiv260618386M},
  abstract      = {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.},
  note          = {submitted}
}

Physics-Informed Neural Networks for Modeling Galactic Gravitational Potentials

NeurIPS 2025 Workshop on Machine Learning and the Physical SciencesNeurIPS ML4PS·2025·C. Myers^†, N. Starkman, et al.C. Myers^†, N. Starkman, L. NecibC. Myers^†, N. Starkman, L. Necib·NeurIPS 2025 Workshop on Machine Learning and the Physical SciencesNeurIPS ML4PS·2025

NeurIPS 2025 Workshop on Machine Learning and the Physical SciencesNeurIPS ML4PS·2025

student^† 1st | 2nd | 20252025

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

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

arXiv:2605.04138·2026submitted

Galactic Amnesia: The Information Washout of the Milky Way Merger HistoryGalactic Amnesiasubmitted

Heat map of how much a merger’s present-day orbital energy reveals about its infall time, by infall time and galactocentric radius. Memory is highest for recent mergers in the outer galaxy and fades for older mergers and toward the center, following dashed lines of constant orbit count.

arXiv:2605.04138·2026submitted·L. Necib, …, N. Starkman, et al.L. Necib, …, N. Starkman, et al.L. Necib, D. Folsom, E. Y. Davies, N. Starkman, A. Thoyas·arXiv:2605.04138·2026submitted

arXiv:2605.04138·2026submitted

In 98 simulated Milky Ways, a merger’s imprint on stellar orbital energy lasts more than 10 Gyr, while its imprint on radial velocity fades within about 5 Gyr.

In 98 simulated Milky Ways, a merger’s imprint on stellar orbital energy lasts more than 10 Gyr, while its imprint on radial velocity fades within about 5 Gyr. Orbits mix the stars a merger brings in, so the record of how a galaxy was assembled fades with time. Reading it from energies requires knowing the Milky Way’s gravitational potential.

nth | 20262026Context ›

@misc{Necib+:2026:galactic-amnesia,
  author        = {Necib, Lina and Folsom, Dylan and Davies, Elliot Y. and Starkman, Nathaniel and Thoyas, Andreas},
  title         = {{Galactic Amnesia: The Information Washout of the Milky Way Merger History}},
  year          = {2026},
  month         = {may},
  eprint        = {2605.04138},
  archivePrefix = {arXiv},
  primaryClass  = {astro-ph.GA},
  adsurl        = {https://ui.adsabs.harvard.edu/abs/2026arXiv260504138N},
  abstract      = {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 $M_\star$ and infall time $t_{\rm infall}$. 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 $\sim$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 $(M_\star,\; t_{\rm infall})$ 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.},
  note          = {submitted}
}

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

N. Starkman, J. Nibauer

Journal of Open Source Software·2026submitted

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

Journal of Open Source SoftwareJOSS·2026submitted·N. Starkman, J. NibauerN. Starkman, J. NibauerN. Starkman, J. Nibauer·Journal of Open Source SoftwareJOSS·2026submitted

Journal of Open Source SoftwareJOSS·2026submitted

A stellar stream is observed as an unordered cloud of stars, so fitting one starts with putting them in order along it.

A stellar stream is observed as an unordered cloud of stars, so fitting one starts with putting them in order along it. phasecurvefit does this by walking from star to star through phase space, with a choice of distance metrics and search strategies, then trains a neural network to place any stars the walk skipped and trace the stream’s smooth central path. Built on JAX, it is fast and differentiable: each step takes seconds.

1st | 20262026

Software

phasecurvefit

@misc{Starkman+Nibauer:2026:phasecurvefit,
  author   = {Starkman, Nathaniel and Nibauer, Jacob},
  title    = {{phasecurvefit: A High-Performance JAX Framework for Phase-Space Walks with Pluggable Metrics and Strategies}},
  journal  = {Journal of Open Source Software},
  year     = {2026},
  month    = {jan},
  abstract = {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.},
  note     = {submitted}
}

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

J. Nibauer, A. Bonaca, D. N. Spergel, A. M. Price-Whelan, J. E. Greene, N. Starkman, K. V. Johnston

The Astrophysical Journal 983, 68·2025

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

Sketch of the method: a black base orbit with a fan of blue orbits beside it, each bent a little further as the perturbation strength grows from 0.01 to 0.04. Red arrows along the base orbit show the derivatives that predict that bending.

The Astrophysical Journal 983, 68ApJ 983, 68·2025·J. Nibauer, …, N. Starkman, et al.J. Nibauer, …, N. Starkman, et al.J. Nibauer, A. Bonaca, D. N. Spergel, A. M. Price-Whelan, J. E. Greene, N. Starkman, K. V. Johnston·The Astrophysical Journal 983, 68ApJ 983, 68·2025

The Astrophysical Journal 983, 68ApJ 983, 68·2025

StreamSculptor uses Hamiltonian perturbation theory to model stellar streams in time-dependent potentials, fast enough to include the LMC, the Galactic bar and the dozens of subhalo impacts expected for streams like GD-1 and Pal 5.

StreamSculptor uses Hamiltonian perturbation theory to model stellar streams in time-dependent potentials, fast enough to include the LMC, the Galactic bar and the dozens of subhalo impacts expected for streams like GD-1 and Pal 5. Because a stream’s velocity dispersion depends on the impacts it has suffered, the model connects whole stream populations directly to dark matter properties. Led by Jacob Nibauer.

nth | 20252025Context ›

@article{Nibauer+:2025:streamsculptor,
  author        = {Nibauer, Jacob and Bonaca, Ana and Spergel, David N. and Price-Whelan, Adrian M. and Greene, Jenny E. and Starkman, Nathaniel and Johnston, Kathryn V.},
  title         = {{StreamSculptor: Hamiltonian Perturbation Theory for Stellar Streams in Flexible Potentials with Differentiable Simulations}},
  journal       = {The Astrophysical Journal},
  volume        = {983},
  pages         = {68},
  year          = {2025},
  month         = {apr},
  doi           = {10.3847/1538-4357/adb8e8},
  eprint        = {2410.21174},
  archivePrefix = {arXiv},
  adsurl        = {https://ui.adsabs.harvard.edu/abs/2025ApJ...983...68N},
  abstract      = {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 $10^5~M_\odot$. 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: A Python package for unit-aware computing with JAXunxt

Journal of Open Source Software 10, 7771JOSS 10, 7771·2025·N. Starkman, A. M. Price-Whelan, et al.N. Starkman, A. M. Price-Whelan, J. NibauerN. Starkman, A. M. Price-Whelan, J. Nibauer·Journal of Open Source Software 10, 7771JOSS 10, 7771·2025

Journal of Open Source Software 10, 7771JOSS 10, 7771·2025

JAX gives scientific Python automatic differentiation, compilation and GPUs, but no physical units.

JAX gives scientific Python automatic differentiation, compilation and GPUs, but no physical units. unxt adds them: quantities that carry their units through jitted, differentiated and vectorized code, with astropy.units under the hood and an interface astropy users will recognize. It underpins the GalacticDynamics stack, including coordinax and galax.

1st | 20252025

Software

@article{Starkman+:2025:unxt,
  author   = {Starkman, Nathaniel and Price-Whelan, Adrian M. and Nibauer, Jake},
  title    = {{unxt: A Python package for unit-aware computing with JAX}},
  journal  = {Journal of Open Source Software},
  volume   = {10},
  number   = {107},
  pages    = {7771},
  year     = {2025},
  month    = {mar},
  doi      = {10.21105/joss.07771},
  adsurl   = {https://ui.adsabs.harvard.edu/abs/2025JOSS...10.7771S},
  abstract = {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

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

Probabilistic graphical model of the stream: each star’s observed astrometry and photometry come from a mixture of Gaussians whose weights, means and covariances are set by neural networks of its position along the stream, with each star’s measurement errors feeding in.

The Astrophysical Journal 980, 253ApJ 980, 253·2025·N. Starkman, J. Nibauer, et al.N. Starkman, J. Nibauer, J. Bovy, et al.N. Starkman, J. Nibauer, J. Bovy, J. Webb, K. Tavangar, A. Price-Whelan, A. Bonaca·The Astrophysical Journal 980, 253ApJ 980, 253·2025

The Astrophysical Journal 980, 253ApJ 980, 253·2025

Mixture density networks model a stellar stream’s track, width, density and kinematics together with each star’s probability of belonging to it.

Mixture density networks model a stellar stream’s track, width, density and kinematics together with each star’s probability of belonging to it. The method uses all the available astrometry and photometry, models the background with normalizing flows, and works even where phase-space data are incomplete. Applied to GD-1 and Palomar 5, it produced public catalogs of stellar membership probabilities.

1st | 20252025Context ›

@article{Starkman+:2025:stream-members,
  author        = {Starkman, Nathaniel and Nibauer, Jacob and Bovy, Jo and Webb, Jeremy and Tavangar, Kiyan and Price-Whelan, Adrian and Bonaca, Ana},
  title         = {{Stream Members Only: Data-Driven Characterization of Stellar Streams with Mixture Density Networks}},
  journal       = {The Astrophysical Journal},
  volume        = {980},
  pages         = {253},
  year          = {2025},
  month         = {feb},
  doi           = {10.3847/1538-4357/ad94f2},
  eprint        = {2311.16960},
  archivePrefix = {arXiv},
  primaryClass  = {astro-ph.GA},
  adsurl        = {https://ui.adsabs.harvard.edu/abs/2025ApJ...980..253S},
  abstract      = {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, G. Starkman, A. Kosowsky

Monthly Notices of the Royal Astronomical Society 529, 2274–2288·2024

Angular Correlations of Cosmic Microwave Background Spectrum Distortions from Photon DiffusionCMB Spectrum Distortions from Photon DiffusionCMB Spectrum Distortions

Angular power spectra of the squared temperature fluctuations (blue), their cross-correlation with the diffusion y-distortion (purple, scaled by 50), and the y-distortion’s own autocorrelation (red, scaled by 1000). The blue and purple curves carry acoustic oscillations.

Monthly Notices of the Royal Astronomical Society 529, 2274–2288MNRAS 529, 2274–2288·2024·N. Starkman, G. Starkman, et al.N. Starkman, G. Starkman, A. KosowskyN. Starkman, G. Starkman, A. Kosowsky·Monthly Notices of the Royal Astronomical Society 529, 2274–2288MNRAS 529, 2274–2288·2024

Monthly Notices of the Royal Astronomical Society 529, 2274–2288MNRAS 529, 2274–2288·2024

Standard cosmology predicts a small Compton y-distortion in the CMB spectrum that correlates with the squared temperature fluctuations.

Standard cosmology predicts a small Compton y-distortion in the CMB spectrum that correlates with the squared temperature fluctuations. It forms as the universe becomes transparent: the photons reaching us from any one direction last scattered off regions at slightly different temperatures, and their blackbodies blend. ACT and SPT should already detect the correlation, at a forecast signal-to-noise of about 12, and CMB-S4 could make it a new cosmological probe.

1st | 20242024Context ›

Software

cosmology.api

@article{Starkman+:2024:cmb-spectral-distortions,
  author        = {Starkman, Nathaniel and Starkman, Glenn and Kosowsky, Arthur},
  title         = {{Angular Correlations of Cosmic Microwave Background Spectrum Distortions from Photon Diffusion}},
  journal       = {Monthly Notices of the Royal Astronomical Society},
  volume        = {529},
  pages         = {2274--2288},
  year          = {2024},
  month         = {apr},
  doi           = {10.1093/mnras/stae665},
  eprint        = {2310.01578},
  archivePrefix = {arXiv},
  primaryClass  = {astro-ph.CO},
  adsurl        = {https://ui.adsabs.harvard.edu/abs/2024MNRAS.529.2274S},
  abstract      = {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.}
}

The Future of Astronomical Data Infrastructure: Meeting Report

M. R. Blanton, …, N. Starkman, et al.

arXiv:2311.04272·2023

@misc{Blanton+:2023:data-infrastructure-report,
  author        = {Blanton, Michael R. and Evans, Janet D. and Norman, Dara and O'Mullane, William and Price-Whelan, Adrian and Rizzi, Luca and Accomazzi, Alberto and Ansdell, Megan and Bailey, Stephen and Barrett, Paul and Berukoff, Steven and Bolton, Adam and Borrill, Julian and Cruz, Kelle and Dalcanton, Julianne and Desai, Vandana and Dubois-Felsmann, Gregory P. and Economou, Frossie and Ferguson, Henry and Field, Bryan and Foreman-Mackey, Dan and Forero-Romero, Jaime and Gaffney, Niall and Gillies, Kim and Graham, Matthew J. and Gwyn, Steven and Hennawi, Joseph and Hughes, Anna L. H. and Jaffe, Tess and Jagannathan, Preshanth and Jenness, Tim and Jurić, Mario and Kavelaars, JJ and Kee, Kerk and Kern, Jeff and Kremin, Anthony and Labrie, Kathleen and Lacy, Mark and Law, Casey and Martínez-Galarza, Rafael and McCully, Curtis and McEnery, Julie and Miller, Bryan and Moriarty, Christopher and Muench, August and Muna, Demitri and Murillo, Angela and Narayan, Gautham and Neill, James D. and Nikutta, Robert and Ojha, Roopesh and Olsen, Knut and O'Meara, John and Rusholme, Ben and Seaman, Robert and Starkman, Nathaniel and Still, Martin and Stoehr, Felix and Swinbank, John D. and Teuben, Peter and Toledo, Ignacio and Tollerud, Erik and Turk, Matthew D. and Turner, James and Vacca, William and Vieira, Joaquin and Weaver, Benjamin and Weiner, Benjamin and Weiss, Jason and Westfall, Kyle and Willman, Beth and Zhao, Lily},
  title         = {{The Future of Astronomical Data Infrastructure: Meeting Report}},
  year          = {2023},
  month         = {nov},
  eprint        = {2311.04272},
  archivePrefix = {arXiv},
  primaryClass  = {astro-ph.IM},
  adsurl        = {https://ui.adsabs.harvard.edu/abs/2023arXiv231104272B},
  abstract      = {The astronomical community is grappling with the increasing volume and complexity of data produced by modern telescopes, due to difficulties in reducing, accessing, analyzing, and combining archives of data. To address this challenge, we propose the establishment of a coordinating body, an "entity," with the specific mission of enhancing the interoperability, archiving, distribution, and production of both astronomical data and software. This report is the culmination of a workshop held in February 2023 on the Future of Astronomical Data Infrastructure. Attended by 70 scientists and software professionals from ground-based and space-based missions and archives spanning the entire spectrum of astronomical research, the group deliberated on the prevailing state of software and data infrastructure in astronomy, identified pressing issues, and explored potential solutions. In this report, we describe the ecosystem of astronomical data, its existing flaws, and the many gaps, duplication, inconsistencies, barriers to access, drags on productivity, missed opportunities, and risks to the long-term integrity of essential data sets. We also highlight the successes and failures in a set of deep dives into several different illustrative components of the ecosystem, included as an appendix.}
}

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, 5022–5036·2023

On the fast track: Rapid construction of stellar stream pathsOn the Fast Track

A simulated stellar stream from a 47 Tucanae-like progenitor, looping around the Galactic centre in Galactocentric x–y coordinates. Blue and orange lines trace the fitted path of each tidal arm, wrapped in grey uncertainty ellipses that grow largest at the arms’ sparse far ends.

Monthly Notices of the Royal Astronomical Society 522, 5022–5036MNRAS 522, 5022–5036·2023·N. Starkman, J. Bovy, et al.N. Starkman, J. Bovy, J. Webb, et al.N. Starkman, J. Bovy, J. Webb, D. Calvetti, E. Somersalo·Monthly Notices of the Royal Astronomical Society 522, 5022–5036MNRAS 522, 5022–5036·2023

Monthly Notices of the Royal Astronomical Society 522, 5022–5036MNRAS 522, 5022–5036·2023

This method traces a stellar stream’s path and its uncertainty without assuming any model of the Galaxy.

This method traces a stellar stream’s path and its uncertainty without assuming any model of the Galaxy. It first puts the stars in order along the stream, then fits the path through them. It works on simulated streams and on real ones like Palomar 5 and GD-1, and is available as the Python package TrackStream.

1st | 20232023Context ›

Software

trackstream

@article{Starkman+:2023:stream-tracks,
  author        = {Starkman, Nathaniel and Bovy, Jo and Webb, Jeremy and Calvetti, Daniela and Somersalo, Erkki},
  title         = {{On the fast track: Rapid construction of stellar stream paths}},
  journal       = {Monthly Notices of the Royal Astronomical Society},
  volume        = {522},
  pages         = {5022--5036},
  year          = {2023},
  month         = {jul},
  doi           = {10.1093/mnras/stad1166},
  eprint        = {2212.00949},
  archivePrefix = {arXiv},
  primaryClass  = {astro-ph.GA},
  adsurl        = {https://ui.adsabs.harvard.edu/abs/2023MNRAS.522.5022S},
  abstract      = {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: Sustaining and Growing a Community-oriented Open-source Project and the Latest Major Release (v5.0) of the Core PackageThe Astropy Project: v5.0 and BeyondAstropy v5.0

The Astrophysical Journal 935, 167ApJ 935, 167·2022·The Astropy Collaboration, A. M. Price-Whelan, …, N. Starkman, et al.The Astropy Collaboration, A. M. Price-Whelan, …, N. Starkman, et al.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, 167ApJ 935, 167·2022

The Astrophysical Journal 935, 167ApJ 935, 167·2022

Astropy is the shared foundation of Python astronomy: a core package for units, coordinates, tables, file I/O and more, and an ecosystem of interoperable packages built on it.

Astropy is the shared foundation of Python astronomy: a core package for units, coordinates, tables, file I/O and more, and an ecosystem of interoperable packages built on it. This paper describes the v5.0 release and the state of the Project, including its ties to observatories and missions and Learn Astropy, and confronts the challenge of sustaining a community-run codebase that much of astronomy now depends on.

4th | 20222022

Software

Astropy

@article{Astropy+Price-Whelan+:2022:astropy-v5,
author = {{The Astropy Collaboration} and Price-Whelan, Adrian M. and Lim, Pey Lian and Earl, Nicholas and Starkman, Nathaniel and Bradley, Larry and Shupe, David L. and Patil, Aarya A. and Corrales, Lia and Brasseur, C. E. and Nöthe, Maximilian and Donath, Axel and Tollerud, Erik and Morris, Brett M. and Ginsburg, Adam and Vaher, Eero and Weaver, Benjamin A. and Tocknell, James and Jamieson, William and van Kerkwijk, Marten H. and Robitaille, Thomas P. and Merry, Bruce and Bachetti, Matteo and Günther, H. Moritz and Aldcroft, Thomas L. and Alvarado-Montes, Jaime A. and Archibald, Anne M. and Bódi, Attila and Bapat, Shreyas and Barentsen, Geert and Bazán, Juanjo and Biswas, Manish and Boquien, Médéric and Burke, D. J. and Cara, Daria and Cara, Mihai and Conroy, Kyle E. and Conseil, Simon and Craig, Matthew W. and Cross, Robert M. and Cruz, Kelle L. and D'Eugenio, Francesco and Dencheva, Nadia and Devillepoix, Hadrien A. R. and Dietrich, Jörg P. and Eigenbrot, Arthur Davis and Erben, Thomas and Ferreira, Leonardo and Foreman-Mackey, Daniel and Fox, Ryan and Freij, Nabil and Garg, Suyog and Geda, Robel and Glattly, Lauren and Gondhalekar, Yash and Gordon, Karl D. and Grant, David and Greenfield, Perry and Groener, Austen M. and Guest, Steve and Gurovich, Sebastian and Handberg, Rasmus and Hart, Akeem and Hatfield-Dodds, Zac and Homeier, Derek and Hosseinzadeh, Griffin and Jenness, Tim and Jones, Craig K. and Joseph, Prajwel and Kalmbach, J. Bryce and Karamehmetoglu, Emir and Kałuszyński, Mikołaj and Kelley, Michael S. P. and Kern, Nicholas and Kerzendorf, Wolfgang E. and Koch, Eric W. and Kulumani, Shankar and Lee, Antony and Ly, Chun and Ma, Zhiyuan and MacBride, Conor and Maljaars, Jakob M. and Muna, Demitri and Murphy, N. A. and Norman, Henrik and O'Steen, Richard and Oman, Kyle A. and Pacifici, Camilla and Pascual, Sergio and Pascual-Granado, J. and Patil, Rohit R. and Perren, Gabriel I. and Pickering, Timothy E. and Rastogi, Tanuj and Roulston, Benjamin R. and Ryan, Daniel F. and Rykoff, Eli S. and Sabater, Jose and Sakurikar, Parikshit and Salgado, Jesús and Sanghi, Aniket and Saunders, Nicholas and Savchenko, Volodymyr and Schwardt, Ludwig and Seifert-Eckert, Michael and Shih, Albert Y. and Jain, Anany Shrey and Shukla, Gyanendra and Sick, Jonathan and Simpson, Chris and Singanamalla, Sudheesh and Singer, Leo P. and Singhal, Jaladh and Sinha, Manodeep and Sipőcz, Brigitta M. and Spitler, Lee R. and Stansby, David and Streicher, Ole and Šumak, Jani and Swinbank, John D. and Taranu, Dan S. and Tewary, Nikita and Tremblay, Grant R. and de Val-Borro, Miguel and Van Kooten, Samuel J. and Vasović, Zlatan and Verma, Shresth and de Miranda Cardoso, José Vinícius and Williams, Peter K. G. and Wilson, Tom J. and Winkel, Benjamin and Wood-Vasey, W. M. and Xue, Rui and Yoachim, Peter and Zhang, Chen and Zonca, Andrea},
title = {{The Astropy Project: Sustaining and Growing a Community-oriented Open-source Project and the Latest Major Release (v5.0) of the Core Package}},
journal = {The Astrophysical Journal},
volume = {935},
number = {2},
pages = {167},
year = {2022},
month = {aug},
doi = {10.3847/1538-4357/ac7c74},
eprint = {2206.14220},
archivePrefix = {arXiv},
primaryClass = {astro-ph.IM},
adsurl = {https://ui.adsabs.harvard.edu/abs/2022ApJ...935..167A},
abstract = {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, H. Winch, J. S. Sidhu, G. Starkman

Physical Review D 103, 063024·2020

Straight Lightning as a Signature of Macroscopic Dark MatterStraight Lightning from Macroscopic Dark MatterMacro Lightning

Macro dark matter parameter space, cross-section against mass, both on log axes. Colored regions are existing constraints; black and grey hatching marks what a search for straight lightning on Earth could probe, and cyan hatching what Jupiter could, reaching to heavier macros than the Earth search.

Physical Review D 103, 063024PRD 103, 063024·2020·N. Starkman, H. Winch, et al.N. Starkman, H. Winch, J. S. Sidhu, et al.N. Starkman, H. Winch, J. S. Sidhu, G. Starkman·Physical Review D 103, 063024PRD 103, 063024·2020

Physical Review D 103, 063024PRD 103, 063024·2020

If dark matter comes as large objects — macros — rather than tiny particles, a macro crossing a thunderstorm could trigger a perfectly straight lightning bolt.

If dark matter comes as large objects — macros — rather than tiny particles, a macro crossing a thunderstorm could trigger a perfectly straight lightning bolt. Ordinary lightning is jagged, so searching for straight bolts, on Earth or on Jupiter, can constrain macro dark matter.

1st | 20202020Context ›

Software

macro_lightning

@article{Starkman+:2020:macro-lightning,
  author        = {Starkman, Nathaniel and Winch, Harrison and Sidhu, Jagjit Singh and Starkman, Glenn},
  title         = {{Straight Lightning as a Signature of Macroscopic Dark Matter}},
  journal       = {Physical Review D},
  volume        = {103},
  pages         = {063024},
  year          = {2021},
  month         = {mar},
  doi           = {10.1103/PhysRevD.103.063024},
  eprint        = {2006.16272},
  archivePrefix = {arXiv},
  primaryClass  = {astro-ph.CO},
  adsurl        = {https://ui.adsabs.harvard.edu/abs/2021PhRvD.103f3024S},
  abstract      = {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 $\sigma_x \gtrapprox 6 \times 10^{-9}~{\rm cm}^2$, 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

An extended Pal 5 stream in Gaia DR2An Extended Pal 5 Stream in Gaia DR2Extended Pal 5

Smoothed density map of Gaia DR2 stars around the Palomar 5 globular cluster, in coordinates aligned with its tidal tails. A dark band marks the stream detected in earlier CFHT data, mostly the trailing arm; black dots trace the tails found here, matching that band and continuing about 7 degrees further along the leading arm.

Monthly Notices of the Royal Astronomical SocietyMNRAS·2020·N. Starkman, J. Bovy, et al.N. Starkman, J. Bovy, J. WebbN. Starkman, J. Bovy, J. Webb·Monthly Notices of the Royal Astronomical SocietyMNRAS·2020

Monthly Notices of the Royal Astronomical SocietyMNRAS·2020

Gaia data show that Palomar 5’s leading tail runs about 7° further than was known, making the stream about 30° long.

Gaia data show that Palomar 5’s leading tail runs about 7° further than was known, making the stream about 30° long. Palomar 5 is a star cluster being pulled apart by the Milky Way, and its two tails trace the Galaxy’s gravity. The tails come out roughly equal in length, which limits how much the Galactic bar can have shortened one of them.

1st | 20202020Context ›

@article{Starkman+:2020:pal5-gaia-dr2,
  author        = {Starkman, Nathaniel and Bovy, Jo and Webb, Jeremy},
  title         = {{An extended Pal 5 stream in Gaia DR2}},
  journal       = {Monthly Notices of the Royal Astronomical Society},
  year          = {2020},
  month         = {apr},
  doi           = {10.1093/mnras/staa534},
  eprint        = {1909.03048},
  archivePrefix = {arXiv},
  adsurl        = {https://ui.adsabs.harvard.edu/abs/2020MNRAS.493.4978S},
  abstract      = {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

A constant characteristic volume density of dark matter haloes from SPARC rotation curve fitsA Constant Dark-Matter Halo DensitySPARC Halo Density

Characteristic dark-matter density of each SPARC galaxy’s Einasto halo against its 3.6-micron luminosity, coloured by Hubble type from S0 to blue compact dwarf. The points scatter around a flat line across five decades in luminosity.

Monthly Notices of the Royal Astronomical SocietyMNRAS·2018·P. Li, …, N. Starkman, et al.P. Li, …, N. Starkman, et al.P. Li, F. Lelli, S. S. McGaugh, N. Starkman, J. M. Schombert·Monthly Notices of the Royal Astronomical SocietyMNRAS·2018

Monthly Notices of the Royal Astronomical SocietyMNRAS·2018

Fits to the rotation curves of 175 SPARC galaxies show that bigger galaxies have bigger dark-matter haloes, but the haloes’ typical density stays about the same.

Fits to the rotation curves of 175 SPARC galaxies show that bigger galaxies have bigger dark-matter haloes, but the haloes’ typical density stays about the same. That holds across galaxies that differ a hundred-thousand-fold in brightness, and it constrains how haloes grow along with the galaxies inside them.

nth | 20182018Context ›

@article{Li+:2018:sparc-halo-density,
  author        = {Li, P. and Lelli, F. and McGaugh, S. S. and Starkman, Nathaniel and Schombert, J. M.},
  title         = {{A constant characteristic volume density of dark matter haloes from SPARC rotation curve fits}},
  journal       = {Monthly Notices of the Royal Astronomical Society},
  year          = {2019},
  month         = {feb},
  doi           = {10.1093/mnras/sty2968},
  eprint        = {1811.00553},
  archivePrefix = {arXiv},
  adsurl        = {https://ui.adsabs.harvard.edu/abs/2019MNRAS.482.5106L},
  abstract      = {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 $\rho_{\rm s}$ of DM haloes is nearly constant over 5 decades in galaxy luminosity. The scale radius $r_{\rm s}$ and the characteristic surface density $\rho_{\rm s} \cdot r_{\rm s}$, 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, 2292–2301·2018

A new algorithm to quantify maximum discs in galaxiesQuantifying Maximum DiscsMaximum Discs

Rotation curve of the low-surface-brightness galaxy UGC 128: the observed speeds as points with error bars, the gas contribution, and the stellar disc and total baryonic contributions at both the stellar-population mass-to-light ratio and the maximum-disc value. Making its disc maximal needs a far heavier disc than its stars imply.

Monthly Notices of the Royal Astronomical Society 480, 2292–2301MNRAS 480, 2292–2301·2018·N. Starkman, F. Lelli, et al.N. Starkman, F. Lelli, S. S. McGaugh, et al.N. Starkman, F. Lelli, S. S. McGaugh, J. Schombert·Monthly Notices of the Royal Astronomical Society 480, 2292–2301MNRAS 480, 2292–2301·2018

Monthly Notices of the Royal Astronomical Society 480, 2292–2301MNRAS 480, 2292–2301·2018

A new algorithm measures how close 153 SPARC galaxies come to a “maximum disc”, in which the stars supply as much of the rotation as they can.

A new algorithm measures how close 153 SPARC galaxies come to a “maximum disc”, in which the stars supply as much of the rotation as they can. The term had been used for decades without a precise definition. Bright galaxies come close to a maximum disc; faint ones cannot, because their stars would have to be unrealistically heavy for their light.

1st | 20182018Context ›

@article{Starkman+:2018:maximum-discs,
  author        = {Starkman, Nathaniel and Lelli, F. and McGaugh, S. S. and Schombert, J.},
  title         = {{A new algorithm to quantify maximum discs in galaxies}},
  journal       = {Monthly Notices of the Royal Astronomical Society},
  volume        = {480},
  pages         = {2292--2301},
  year          = {2018},
  month         = {oct},
  doi           = {10.1093/mnras/sty2011},
  eprint        = {1802.09967},
  archivePrefix = {arXiv},
  primaryClass  = {astro-ph.GA},
  adsurl        = {https://ui.adsabs.harvard.edu/abs/2018MNRAS.480.2292S},
  abstract      = {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 $\sim 0.7\;{\rm M}_\odot/{\rm L}_\odot$ 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 ${\rm M}_\odot/{\rm L}_\odot$), 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 $V_{\rm bar}/V_{\rm p} \approx 0.88$, 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.}
}

Assists