Sesame: A power spectrum emulator pipeline for beyond-\(\Lambda\)CDM models

• Published in: arXiv.org
• Main Authors: Mauland, Renate, Winther, Hans A, Cheng-Zong, Ruan
• Format: Paper
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 10.06.2024
• Subjects: Astronomical models; Clustering; Computation; Cosmology; Emulators; Initial conditions; Machine learning; Neutrinos; Simulation
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.2309.13295

The mysterious nature of the dark sector of the \(\Lambda\)CDM model is one of the main motivators behind the study of alternative cosmological models. A central quantity of interest for these models is the matter power spectrum, which quantifies structure formation on various scales and can be cross-validated through theory, simulations, and observations. We present a tool that can be used to create emulators for the non-linear matter power spectrum, and similar global clustering statistics, for models beyond \(\Lambda\)CDM with very little computation effort and without the need for supercomputers. We use fast approximate N-body simulations to emulate the boost, \(B(k,z)=P_{\text{beyond}-\Lambda \rm CDM}(k,z) / P_{\Lambda \rm CDM}(k, z)\), and then rely on existing high-quality emulators made for \(\Lambda\)CDM to isolate \(P_{\text{beyond}-\Lambda \rm CDM}(k,z)\). Since both the \(\Lambda\)CDM and beyond-\(\Lambda\)CDM models are simulated in our approach, some of the lack of power on small scales is factored out, allowing us to extend the emulator to \(k \sim 3-5\,h\,\mathrm{Mpc}^{-1}\) and still maintain good accuracy. In addition, errors from the simulation and emulation process can be estimated and factored into the covariance when using the emulator on data. As an example of using the pipeline, we create an emulator for the well-studied \(f(R)\) model with massive neutrinos, using approximately 3000 CPU hours of computation time. Provided with the paper is a fully functioning pipeline that generates parameter samples, runs a Boltzmann solver to produce initial conditions, runs the simulations, and then gathers all the data and runs it through a machine learning module to develop the emulator. This tool, named Sesame, can be used by anyone to generate a power spectrum emulator for the cosmological model of their choice.