Uncovering dark matter density profiles in dwarf galaxies with graph neural networks

• URL: http://arxiv.org/abs/2208.12825v1
• Date: Fri, 26 Aug 2022 18:00:04 GMT
• Title: Uncovering dark matter density profiles in dwarf galaxies with graph neural networks
• Authors: Tri Nguyen, Siddharth Mishra-Sharma, Reuel Williams, Lina Necib
• Abstract summary: We introduce a new method to infer the dark matter density profiles of dwarf galaxies from observable kinematics of stars gravitationally bound to these systems. We show that this novel method can place stronger constraints on dark matter profiles and has the potential to weigh in on some of the ongoing puzzles associated with the small-scale structure of dark matter halos.
• Score: 1.823419686712506
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Dwarf galaxies are small, dark matter-dominated galaxies, some of which are embedded within the Milky Way. Their lack of baryonic matter (e.g., stars and gas) makes them perfect test beds for probing the properties of dark matter -- understanding the spatial dark matter distribution in these systems can be used to constrain microphysical dark matter interactions that influence the formation and evolution of structures in our Universe. We introduce a new method that leverages simulation-based inference and graph-based machine learning in order to infer the dark matter density profiles of dwarf galaxies from observable kinematics of stars gravitationally bound to these systems. Our approach aims to address some of the limitations of established methods based on dynamical Jeans modeling. We show that this novel method can place stronger constraints on dark matter profiles and, consequently, has the potential to weigh in on some of the ongoing puzzles associated with the small-scale structure of dark matter halos, such as the core-cusp discrepancy.

Related papers

• A deep-learning algorithm to disentangle self-interacting dark matter and AGN feedback models [0.0]
  We present a Machine Learning method that ''learns'' how the impact of dark matter self-interactions differs from that of astrophysical feedback. We train a Convolutional Neural Network on images of galaxy clusters from hydro-dynamic simulations.
  arXiv  Detail & Related papers  (2024-05-27T18:00:49Z)
• Probabilistic reconstruction of Dark Matter fields from biased tracers using diffusion models [0.0]
  Galaxies are biased tracers of the underlying cosmic web, dominated by dark matter components that cannot be directly observed. We develop a diffusion generative model to predict the unbiased posterior distribution of the underlying dark matter fields from the given stellar mass fields.
  arXiv  Detail & Related papers  (2023-11-14T21:40:20Z)
• Detecting Gravitationally Interacting Dark Matter with Quantum Interference [47.03992469282679]
  We show that there is a theoretical possibility to directly detect such particles using highly sensitive gravity-mediated quantum phase shifts. In particular, we consider a protocol utilizing Josephson junctions.
  arXiv  Detail & Related papers  (2023-09-15T08:22:46Z)
• Quantum Science and the Search for Axion Dark Matter [91.3755431537592]
  The dark matter puzzle is one of the most important open problems in modern physics. Numerous precision experiments are searching for the three non-gravitational interactions of axion-like dark matter.
  arXiv  Detail & Related papers  (2023-04-24T02:52:56Z)
• 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)
• Snowmass White Paper: Quantum Aspects of Black Holes and the Emergence of Spacetime [0.0]
  Recently quantum information contained in Hawking radiation has been calculated, verifying a key aspect of the consistency of black hole evaporation with quantum mechanical unitarity. We review these developments and describe some of the deep open questions in this subject. These include the nature of the black hole interior, potential applications to quantum cosmology, the gravitational explanation of the fine structure of black holes, and the development of further connections to quantum information and laboratory quantum simulation.
  arXiv  Detail & Related papers  (2022-01-09T22:01:30Z)
• Inferring dark matter substructure with astrometric lensing beyond the power spectrum [0.0]
  We introduce a novel method to search for global dark matter-induced gravitational lensing signatures in astrometric datasets. Our method based on neural likelihood-ratio estimation shows significantly enhanced sensitivity to a cold dark matter population.
  arXiv  Detail & Related papers  (2021-10-04T18:00:00Z)
• To observe, or not to observe, quantum-coherent dark matter in the Milky Way, that is a question [0.0]
  Bose-Einstein-condensed dark matter (BEC-DM) has become a popular alternative to standard, collisionless cold dark matter (CDM) BEC-DM can suppress structure formation and thereby resolve the small-scale crisis of CDM for a range of boson masses. We will discuss some of the existing constraints and potentially new ones with respect to the impact of BEC-DM onto baryonic tracers.
  arXiv  Detail & Related papers  (2021-04-25T20:40:31Z)
• Simulation of Collective Neutrino Oscillations on a Quantum Computer [117.44028458220427]
  We present the first simulation of a small system of interacting neutrinos using current generation quantum devices. We introduce a strategy to overcome limitations in the natural connectivity of the qubits and use it to track the evolution of entanglement in real-time.
  arXiv  Detail & Related papers  (2021-02-24T20:51:25Z)
• Multidimensional dark space and its underlying symmetries: towards dissipation-protected qubits [62.997667081978825]
  We show that a controlled interaction with the environment may help to create a state, dubbed as em dark'', which is immune to decoherence. To encode quantum information in the dark states, they need to span a space with a dimensionality larger than one, so different states act as a computational basis. This approach offers new possibilities for storing, protecting and manipulating quantum information in open systems.
  arXiv  Detail & Related papers  (2020-02-01T15:57:37Z)
• Probing chiral edge dynamics and bulk topology of a synthetic Hall system [52.77024349608834]
  Quantum Hall systems are characterized by the quantization of the Hall conductance -- a bulk property rooted in the topological structure of the underlying quantum states. Here, we realize a quantum Hall system using ultracold dysprosium atoms, in a two-dimensional geometry formed by one spatial dimension. We demonstrate that the large number of magnetic sublevels leads to distinct bulk and edge behaviors.
  arXiv  Detail & Related papers  (2020-01-06T16:59:08Z)

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.