In the realm of astrophysics and energy research, a team of scientists from the University of Cambridge, led by David Chemaly and Elisabeth Sola, has developed a novel approach to understanding the structure of dark matter halos around galaxies. Their work, published in the journal Monthly Notices of the Royal Astronomical Society, leverages the power of stellar streams—remnants of tidally disrupted satellites—to probe the gravitational potential and, consequently, the dark matter distribution within galaxies.
Dark matter, an invisible and mysterious substance, constitutes a significant portion of the universe’s mass and plays a crucial role in the formation and evolution of galaxies. Understanding its distribution and structure is vital for comprehending the cosmos and, by extension, the energy dynamics that govern it. The researchers’ innovative method involves using hierarchical Bayesian inference, a statistical technique that combines individual data points to make broader inferences about a population.
The team’s approach focuses on the shapes of dark matter halos, specifically their flattening, which can be oblate (flattened like a disk), spherical, or prolate (elongated like a cigar). By analyzing the projected tracks of stellar streams around external galaxies, they can infer the population distribution of halo shapes. This is particularly valuable because, until now, such inferences have typically required detailed kinematic data, which is often unavailable for galaxies beyond our own.
To achieve this, the researchers developed a new tool called StreaMAX, which is accelerated by JAX, a numerical computing library. StreaMAX allows for the rapid generation of stream models, making it possible to fit axisymmetric dark matter halo models to each stream and obtain a posterior distribution on the flattening. These individual posteriors are then combined through a process called hierarchical reweighting to constrain the population distribution of halo shapes.
Using mock data, the team demonstrated that their method can accurately and confidently distinguish between different population distributions of dark matter halo morphologies. Importantly, the computational cost of this approach scales linearly with the sample size, making it feasible to apply to large datasets. With the upcoming data from the Euclid space telescope and the Rubin Observatory’s Legacy Survey of Space and Time (LSST), this method offers a practical way to study dark matter halo shapes at the population level without the need for kinematic measurements.
For the energy sector, understanding the distribution and structure of dark matter can have implications for large-scale energy projects, such as the siting of renewable energy infrastructure or the planning of interstellar missions. By providing a clearer picture of the gravitational landscape of the universe, this research contributes to our broader understanding of the cosmos and its energy dynamics.
This article is based on research available at arXiv.