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.

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 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.

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.

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.

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 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.

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 ${\sigma }_x⪆6\times {10}^{-9}{\mathrm{cm}}^2$, 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.

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.

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 ${\rho }_{\mathrm{s}}$ of DM haloes is nearly constant over 5 decades in galaxy luminosity. The scale radius $r_{\mathrm{s}}$ and the characteristic surface density ${\rho }_{\mathrm{s}}\cdot r_{\mathrm{s}}$, instead, correlate with galaxy luminosity. These scaling relations provide an empirical benchmark to cosmological simulations of galaxy formation.

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 $\sim 0.7{\mathrm{M}}_{\odot }/{\mathrm{L}}_{\odot }$ 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 ${\mathrm{M}}_{\odot }/{\mathrm{L}}_{\odot }$), 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 $V_{\mathrm{bar}}/V_{\mathrm{p}}\approx 0.88$, 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.