Bayesian emulator optimisation for cosmology: application to the Lyman-alpha forest

Abstract

The Lyman-alpha forest provides strong constraints on both cosmological parameters and intergalactic medium astrophysics, which are forecast to improve further with the next generation of surveys including eBOSS and DESI. As is generic in cosmological inference, extracting this information requires a likelihood to be computed throughout a high-dimensional parameter space. Evaluating the likelihood requires a robust and accurate mapping between the parameters and observables, in this case the 1D flux power spectrum. Cosmological simulations enable such a mapping, but due to computational time constraints can only be evaluated at a handful of sample points; "emulators" are designed to interpolate between these. The problem then reduces to placing the sample points such that an accurate mapping is obtained while minimising the number of expensive simulations required. To address this, we introduce an emulation procedure that employs Bayesian optimisation of the training set for a Gaussian process interpolation scheme. Starting with a Latin hypercube sampling (other schemes with good space-filling properties can be used), we iteratively augment the training set with extra simulations at new parameter positions which balance the need to reduce interpolation error while focussing on regions of high likelihood. We show that smaller emulator error from the Bayesian optimisation propagates to smaller widths on the posterior distribution. Even with fewer simulations than a Latin hypercube, Bayesian optimisation shrinks the 95% credible volume by 90% and, e.g., the 1 sigma error on the amplitude of small-scale primordial fluctuations by 38%. This is the first demonstration of Bayesian optimisation applied to large-scale structure emulation, and we anticipate the technique will generalise to many other probes such as galaxy clustering, weak lensing and 21cm.

23 pages, 4 figures. Minor changes to match version published in JCAP

References in corpus (16)

• A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning
• The DESI Experiment Part I: Science,Targeting, and Survey Design
• Baryon Acoustic Oscillations in the Lyα forest of BOSS DR11 quasars
• Baryon Acoustic Oscillations in the Ly-α forest of BOSS quasars
• The DESI Experiment Part II: Instrument Design
• New Constraints on IGM Thermal Evolution from the Lyα Forest Power Spectrum
• Possible evidence for an inverted temperature-density relation in the intergalactic medium from the flux distribution of the Lyman-alpha forest
• An improved measurement of the flux distribution of the Ly-alpha forest in QSO absorption spectra: the effect of continuum fitting, metal contamination and noise properties
• Minimally Parametric Power Spectrum Reconstruction from the Lyman-alpha Forest
• Constraint on neutrino masses from SDSS-III/BOSS Ly$\alpha$ forest and other cosmological probes
• Emulating the CFHTLenS Weak Lensing data: Cosmological Constraints from moments and Minkowski functionals
• Novel constraints on non-cold (non-thermal) Dark Matter from Lyman-$\alpha$ forest data
• Bayesian optimisation for likelihood-free cosmological inference
• An Emulator for the Lyman-alpha Forest
• Evaluating machine learning techniques for predicting power spectra from reionization simulations
• Constraints on ionising photon production from the large-scale Lyman-alpha forest

Cited by in corpus (54)

• Strong bound on canonical ultra-light axion dark matter from the Lyman-alpha forest
• Fast likelihood-free cosmology with neural density estimators and active learning
• The BACCO Simulation Project: Exploiting the full power of large-scale structure for cosmology
• COSMOPOWER: emulating cosmological power spectra for accelerated Bayesian inference from next-generation surveys
• Large-scale dark matter simulations
• Euclid preparation: IX. EuclidEmulator2 -- Power spectrum emulation with massive neutrinos and self-consistent dark energy perturbations
• Ultra-light axions and the $S_8$ tension: joint constraints from the cosmic microwave background and galaxy clustering
• SPECULATOR: Emulating stellar population synthesis for fast and accurate galaxy spectra and photometry
• New limits on light dark matter - proton cross section from the cosmic large-scale structure
• Cosmic Shear Covariance Matrix in $w$CDM: Cosmology Matters
• Fuzzy Dark Matter and the Dark Energy Survey Year 1 Data
• The Aemulus Project V: Cosmological constraint from small-scale clustering of BOSS galaxies
• Fast and Credible Likelihood-Free Cosmology with Truncated Marginal Neural Ratio Estimation
• On the road to percent accuracy II: calibration of the non-linear matter power spectrum for arbitrary cosmologies
• Cosmological direct detection of dark energy: non-linear structure formation signatures of dark energy scattering with visible matter
• The Mira-Titan Universe IV. High Precision Power Spectrum Emulation
• CONNECT: A neural network based framework for emulating cosmological observables and cosmological parameter inference
• An emulator for the Lyman-$\alpha$ forest in beyond-$\Lambda$CDM cosmologies
• On the road to percent accuracy V: the non-linear power spectrum beyond $\Lambda$CDM with massive neutrinos and baryonic feedback
• General framework for cosmological dark matter bounds using $N$-body simulations
• Cosmological parameter estimation via iterative emulation of likelihoods
• Simulating intergalactic gas for DESI-like small scale Lymanα forest observations
• Beyond two-point statistics: using the minimum spanning tree as a tool for cosmology
• Multi-Fidelity Emulation for the Matter Power Spectrum using Gaussian Processes
• PRIYA: A New Suite of Lyman-alpha Forest Simulations for Cosmology
• Parameter Inference for Weak Lensing using Gaussian Processes and MOPED
• Cosmological Constraints from the eBOSS Lyman-$\alpha$ Forest using the PRIYA Simulations
• Improved Mixed Dark Matter Halo Model for Ultralight Axions
• Compressing the cosmological information in one-dimensional correlations of the Lyman-$\alpha$ forest
• Fast and robust Bayesian Inference using Gaussian Processes with GPry
• An Analytic Hybrid Halo + Perturbation Theory Model for Small-scale Correlators: Baryons, Halos, and Galaxies
• Massive neutrinos and degeneracies in Lyman-alpha forest simulations
• A neural network emulator for the Lyman-$\alpha$ 1D flux power spectrum
• Accelerating cosmological inference with Gaussian processes and neural networks -- an application to LSST Y1 weak lensing and galaxy clustering
• The Sejong Suite: Cosmological Hydrodynamical Simulations with Massive Neutrinos, Dark Radiation, and Warm Dark Matter
• How do we optimally sample model grids of exoplanet spectra?
• A Multi-Fidelity Emulator for the Lyman-$\alpha$ Forest Flux Power Spectrum
• The BAHAMAS project: Evaluating the accuracy of the halo model in predicting the non-linear matter power spectrum
• Fast likelihood-free inference in the LSS Stage IV era
• A field-level emulator for modeling baryonic effects across hydrodynamic simulations
• Constraining Mixed Dark Matter models with high redshift Lyman-alpha forest data
• Improving IGM temperature constraints using wavelet analysis on high-redshift quasars
• Batch simulations and uncertainty quantification in Gaussian process surrogate approximate Bayesian computation
• Small scale clustering of BOSS galaxies: dependence on luminosity, color, age, stellar mass, specific star formation rate and other properties
• MF-Box: Multi-fidelity and multi-scale emulation for the matter power spectrum
• Prospects for disentangling dark matter with weak lensing
• Comparing sampling techniques to chart parameter space of 21 cm Global signal with Artificial Neural Networks
• Interference in Fuzzy Dark Matter Filaments: Idealised Models and Statistics
• Stellar halos tracing the assembly of ultra-faint dwarf galaxies
• Harvesting the Lyα forest with convolutional neural networks
• ForestFlow: predicting the Lyman-$\alpha$ forest clustering from linear to nonlinear scales
• Parameter inference with non-linear galaxy clustering: accounting for theoretical uncertainties
• Neural network emulator to constrain the high-$z$ IGM thermal state from Lyman-$\alpha$ forest flux auto-correlation function
• Using Statistical Emulation and Knowledge of Grain-Surface Diffusion for Bayesian Inference of Reaction Rate Parameters: An Application to a Glycine Network