JFlow: Model-Independent Spherical Jeans Analysis using Equivariant Continuous Normalizing Flows

• URL: http://arxiv.org/abs/2505.00763v2
• Date: Mon, 02 Jun 2025 11:11:02 GMT
• Title: JFlow: Model-Independent Spherical Jeans Analysis using Equivariant Continuous Normalizing Flows
• Authors: Sung Hak Lim, Kohei Hayashi, Shun'ichi Horigome, Shigeki Matsumoto, Mihoko M. Nojiri,
• Abstract summary: We introduce an unsupervised machine learning method for solving the spherical Jeans equation.<n>We demonstrate that spherically symmetric stellar phase space densities and velocity dispersions can be estimated without model assumptions.<n>Our method can identify halo structures accurately, even with a small number of tracer stars.
• Score: 0.9204149287692593
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The kinematics of stars in dwarf spheroidal galaxies have been studied to understand the structure of dark matter halos. However, the kinematic information of these stars is often limited to celestial positions and line-of-sight velocities, making full phase space analysis challenging. Conventional methods rely on projected analytic phase space density models with several parameters and infer dark matter halo structures by solving the spherical Jeans equation. In this paper, we introduce an unsupervised machine learning method for solving the spherical Jeans equation in a model-independent way as a first step toward model-independent analysis of dwarf spheroidal galaxies. Using equivariant continuous normalizing flows, we demonstrate that spherically symmetric stellar phase space densities and velocity dispersions can be estimated without model assumptions. As a proof of concept, we apply our method to Gaia challenge datasets for spherical models and measure dark matter mass densities for given velocity anisotropy profiles. Our method can identify halo structures accurately, even with a small number of tracer stars.

Related papers

• Scalar and spinor fields in gravitating cosmic string spacetimes [0.0]
  We study the scattering behavior of scalar and spinor fields in the background of a cosmic string spacetime.<n>We make an exhaustive analysis and compare all the results in the non-abelian model with the corresponding abelian one for both massless and massive fields.
  arXiv  Detail & Related papers  (2025-08-06T21:04:01Z)
• Shared Stochastic Gaussian Process Latent Variable Models: A Multi-modal Generative Model for Quasar Spectra [2.3099448395832956]
  We focus on an application in astrophysics where data sets typically contain both observed spectral features and scientific properties of astrophysical objects such as galaxies or exoplanets.<n>We study the spectra of very luminous galaxies known as quasars, along with their properties, in multiple observation spaces.<n>A single data point is then characterized by different classes of observations, each with different likelihoods.
  arXiv  Detail & Related papers  (2025-02-27T06:57:23Z)
• Hybrid Classical-Quantum Newtonian Gravity with stable vacuum [0.0]
  We investigate a hybrid classical-quantum model in which classical Newtonian gravity emerges from collapses of the mass density operator.<n>GPSL ensures vacuum stability; this, together with its applicability to identical particles and fields, makes it a promising candidate for a relativistic generalization.<n>We provide explicit examples, including the dynamics of a single particle and a rigid sphere, to illustrate the distinctive phenomenology of the model.
  arXiv  Detail & Related papers  (2025-02-07T15:19:13Z)
• Lindbladian reverse engineering for general non-equilibrium steady states: A scalable null-space approach [49.1574468325115]
  We introduce a method for reconstructing the corresponding Lindbaldian master equation given any target NESS. The kernel (null-space) of the correlation matrix corresponds to Lindbladian solutions. We illustrate the method in different systems, ranging from bosonic Gaussian to dissipative-driven collective spins.
  arXiv  Detail & Related papers  (2024-08-09T19:00:18Z)
• Geometric Neural Diffusion Processes [55.891428654434634]
  We extend the framework of diffusion models to incorporate a series of geometric priors in infinite-dimension modelling. We show that with these conditions, the generative functional model admits the same symmetry.
  arXiv  Detail & Related papers  (2023-07-11T16:51:38Z)
• Emergent Spacetime in Quantum Lattice Models [0.0]
  Many quantum lattice models have an emergent relativistic description in their continuum limit. In this thesis, we investigate novel features of this relativistic description for a range of quantum lattice models. We show how to generate emergent curved spacetimes and identify observables at the lattice level which reveal this emergent behaviour.
  arXiv  Detail & Related papers  (2022-12-23T19:00:04Z)
• Towards solving model bias in cosmic shear forward modeling [2.967246997200238]
  Weak gravitational lensing generates a slight shearing of galaxy morphologies called cosmic shear. Modern techniques of shear estimation based on statistics of ellipticity measurements suffer from the fact that the ellipticity is not a well-defined quantity for arbitrary galaxy light profiles. We show that a hybrid physical and deep learning Hierarchical Bayesian Model, where a generative model captures the galaxy morphology, enables us to recover an unbiased estimate of the shear on realistic galaxies.
  arXiv  Detail & Related papers  (2022-10-28T16:23:49Z)
• Inferring Structural Parameters of Low-Surface-Brightness-Galaxies with Uncertainty Quantification using Bayesian Neural Networks [70.80563014913676]
  We show that a Bayesian Neural Network (BNN) can be used for the inference, with uncertainty, of such parameters from simulated low-surface-brightness galaxy images. Compared to traditional profile-fitting methods, we show that the uncertainties obtained using BNNs are comparable in magnitude, well-calibrated, and the point estimates of the parameters are closer to the true values.
  arXiv  Detail & Related papers  (2022-07-07T17:55:26Z)
• Geometric phase in a dissipative Jaynes-Cummings model: theoretical explanation for resonance robustness [68.8204255655161]
  We compute the geometric phases acquired in both unitary and dissipative Jaynes-Cummings models. In the dissipative model, the non-unitary effects arise from the outflow of photons through the cavity walls. We show the geometric phase is robust, exhibiting a vanishing correction under a non-unitary evolution.
  arXiv  Detail & Related papers  (2021-10-27T15:27:54Z)
• Visualizing spinon Fermi surfaces with time-dependent spectroscopy [62.997667081978825]
  We propose applying time-dependent photo-emission spectroscopy, an established tool in solid state systems, in cold atom quantum simulators. We show in exact diagonalization simulations of the one-dimensional $t-J$ model that the spinons start to populate previously unoccupied states in an effective band structure. The dependence of the spectral function on the time after the pump pulse reveals collective interactions among spinons.
  arXiv  Detail & Related papers  (2021-05-27T18:00:02Z)
• Determination of the critical exponents in dissipative phase transitions: Coherent anomaly approach [51.819912248960804]
  We propose a generalization of the coherent anomaly method to extract the critical exponents of a phase transition occurring in the steady-state of an open quantum many-body system.
  arXiv  Detail & Related papers  (2021-03-12T13:16:18Z)
• Fast Gravitational Approach for Rigid Point Set Registration with Ordinary Differential Equations [79.71184760864507]
  This article introduces a new physics-based method for rigid point set alignment called Fast Gravitational Approach (FGA) In FGA, the source and target point sets are interpreted as rigid particle swarms with masses interacting in a globally multiply-linked manner while moving in a simulated gravitational force field. We show that the new method class has characteristics not found in previous alignment methods.
  arXiv  Detail & Related papers  (2020-09-28T15:05:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.