Tensor perturbations from FOPT and domain-wall sources are claimed to induce second-order scalar perturbations large enough to form primordial black holes, potentially all of the dark matter.
Minimally Parametric Power Spectrum Reconstruction from the Lyman-alpha Forest
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abstract
Current results from the Lyman alpha forest assume that the primordial power spectrum of density perturbations follows a simple power law form, with running. We present the first analysis of Lyman alpha data to study the effect of relaxing this strong assumption on primordial and astrophysical constraints. We perform a large suite of numerical simulations, using them to calibrate a minimally parametric framework for describing the power spectrum. Combined with cross-validation, a statistical technique which prevents over-fitting of the data, this framework allows us to reconstruct the power spectrum shape without strong prior assumptions. We find no evidence for deviation from scale-invariance; our analysis also shows that current Lyman alpha data do not have sufficient statistical power to robustly probe the shape of the power spectrum at these scales. In contrast, the ongoing Baryon Oscillation Sky Survey (BOSS) will be able to do so with high precision. Furthermore, this near-future data will be able to break degeneracies between the power spectrum shape and astrophysical parameters.
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Phase transitions in dark sectors can generate CMB B-modes with amplitudes competitive with inflation but peaking at smaller angular scales.
Review of machine learning applications for analyzing Lyman-alpha forest observations to probe cosmology, reionization, and dark matter.
citing papers explorer
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Primordial Black Hole from Tensor-induced Density Fluctuation: First-order Phase Transitions and Domain Walls
Tensor perturbations from FOPT and domain-wall sources are claimed to induce second-order scalar perturbations large enough to form primordial black holes, potentially all of the dark matter.
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Observable CMB B-modes from Cosmological Phase Transitions
Phase transitions in dark sectors can generate CMB B-modes with amplitudes competitive with inflation but peaking at smaller angular scales.
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Machine Learning Techniques for Astrophysics and Cosmology: Lyman-$\alpha$ forest
Review of machine learning applications for analyzing Lyman-alpha forest observations to probe cosmology, reionization, and dark matter.