DOE OSTI · 1979924
Modifying PyUltraLight to model scalar dark matter with self-interactions
Abstract
Here we introduce a modification of the pysiultralight code that models the dynamical evolution of ultralight axionlike scalar dark matter fields. Our modified code, pysiultralight, adds a quartic, self-interaction term to reflect the one which arises naturally in axionlike particle models. Using a particle mass of 10−22 eV/c 2 , we show that pysiultralight produces spatially oscillating solitons, exploding solitons, and collapsing solitons which prior analytic work shows will occur with attractive self-interactions. Using our code we calculate the oscillation frequency as a function of soliton mass and equilibrium radius in the presence of attractive self-interactions. We show that when the soliton mass is below the critical mass ($M_c$ = $\frac{\sqrt{3}}{2} M_{max}$) described by Chavanis and the initial radius is within a specific range, solitons are unstable and explode. We test the maximum mass criteria described by Chavanis and Chavanis and Delfini for a soliton to collapse when attractive self-interactions are included. We also analyze both binary soliton collisions and a soliton rotating around a central mass with attractive and repulsive self-interactions. We find that when attractive self-interactions are included, the density profiles get distorted after a binary collision. We also find that a soliton is less susceptible to tidal stripping when attractive self-interactions are included. We find that the opposite is true for repulsive self-interactions in that solitons would be more easily tidally stripped. Including self-interactions might therefore influence the survival timescales of infalling solitons.
Explore related subjects
Keep this discovery
Explore connections, maps & timelines
Glennon, Noah, Prescod-Weinstein, Chanda. 2021-10-20. Modifying PyUltraLight to model scalar dark matter with self-interactions. https://doi.org/10.1103/physrevd.104.083532
Cite the original work for its findings. Save a collection to share your selection of sources.