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Numerical and Perturbative Computations of the Fuzzy Dark Matter Model
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abstract
We investigate nonlinear structure formation in the fuzzy dark matter (FDM) model using both numerical and perturbative techniques. On the numerical side, we examine the virtues and limitations of a Schrodinger-Poisson solver (wave formulation) versus a fluid dynamics solver (Madelung formulation). We also carry out a perturbative computation of the one-loop mass power spectrum. We find that (1) in many cases, the fluid dynamics solver is capable of producing the expected interference patterns, but it fails where destructive interference causes the density to vanish which generally occurs in the nonlinear regime. (2) The Schrodinger-Poisson solver works well in all test cases, but it is demanding in resolution: one must resolve the small de Broglie scale to obtain the correct dynamics on large scales. (3) We compare the mass power spectrum from perturbation theory against that from the Schrodinger-Poisson solver, and find good agreement in the mildly nonlinear regime. Compared with fluid perturbation theory, wave perturbation theory has a more limited range of validity. (4) As an application, we compare the Lyman-alpha forest flux power spectrum obtained from the Schrodinger-Poisson solver versus one from an N-body simulation (which is often used as an approximate method to make predictions for FDM). At redshift 5, the two, starting from the same initial condition, agree to better than 10 % on observationally relevant scales as long as the FDM mass exceeds $2 \times 10^{-23}$ eV.
Forward citations
Cited by 3 Pith papers
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Generating Moving Field Initial Conditions with Spatially Varying Boost
A 'spatially varying boost' algorithm assigns arbitrary, position-dependent bulk velocities to field initial data by composing local Lorentz boosts, demonstrated on solitons, Proca fields, and spin-1 wave dark matter.
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Cosmo-SPINN: Fuzzy Dark Matter Simulations with Physics-Informed Generative Networks
Physics-informed generative U-Nets evolve and super-resolve fuzzy dark matter fields under Schrödinger–Poisson constraints with far less supervised data than pure data-driven baselines.
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Lyman-$\alpha$ forest constraints on pure and mixed fuzzy dark matter
Lyman-alpha forest data yield m_FDM > 1.9e-21 eV (95% CL) for pure FDM and f_FDM upper limits of 0.07-0.65 for mixed FDM at log10(m_FDM/eV) = -23 to -21.
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