| 1. |
- Anderson, Lauren, et al.
(författare)
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Cosmological Hydrodynamic Simulations with Suppressed Variance in the Ly alpha Forest Power Spectrum
- 2019
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Ingår i: Astrophysical Journal. - : American Astronomical Society. - 0004-637X .- 1538-4357. ; 871:2
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Tidskriftsartikel (refereegranskat)abstract
- We test a method to reduce unwanted sample variance when predicting Ly alpha forest power spectra from cosmological hydrodynamical simulations. Sample variance arises due to sparse sampling of modes on large scales and propagates to small scales through nonlinear gravitational evolution. To tackle this, we generate initial conditions in which the density perturbation amplitudes are fixed to the ensemble average power spectrum-and are generated in pairs with exactly opposite phases. We run 50 such simulations (25 pairs) and compare their performance against 50 standard simulations by measuring the Ly alpha 1D and 3D power spectra at redshifts z = 2, 3, and 4. Both ensembles use periodic boxes of 40 h(-1)Mpc containing 512(3) particles each of dark matter and gas. As a typical example of improvement, for wavenumbers k = 0.25 hMpc(-1) at z = 3, we find estimates of the 1D and 3D power spectra converge 34 and 12 times faster in a paired-fixed ensemble compared with a standard ensemble. We conclude that, by reducing the computational time required to achieve fixed accuracy on predicted power spectra, the method frees up resources for exploration of varying thermal and cosmological parameters-ultimately allowing the improved precision and accuracy of statistical inference.
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| 2. |
- Bird, Simeon, et al.
(författare)
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An emulator for the Lyman-alpha forest
- 2019
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Ingår i: Journal of Cosmology and Astroparticle Physics. - : IOP Publishing. - 1475-7516. ; :2
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Tidskriftsartikel (refereegranskat)abstract
- We present methods for interpolating between the 1-D flux power spectrum of the Lyman-alpha forest, as output by cosmological hydrodynamic simulations. Interpolation is necessary for cosmological parameter estimation due to the limited number of simulations possible. We construct an emulator for the Lyman-alpha forest flux power spectrum from 21 small simulations using Latin hypercube sampling and Gaussian process interpolation. We show that this emulator has a typical accuracy of 1 : 5% and a worst-case accuracy of 4%, which compares well to the current statistical error of 3-5% at z < 3 from BOSS DR9. We compare to the previous state of the art, quadratic polynomial interpolation. The Latin hypercube samples the entire volume of parameter space, while quadratic polynomial emulation samples only lower-dimensional subspaces. The Gaussian process provides an estimate of the emulation error and we show using test simulations that this estimate is reasonable. We construct a likelihood function and use it to show that the posterior constraints generated using the emulator are unbiased. We show that our Gaussian process emulator has lower emulation error than quadratic polynomial interpolation and thus produces tighter posterior confidence intervals, which will be essential for future Lyman-alpha surveys such as DESI.
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| 3. |
- Rogers, Keir K., et al.
(författare)
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Bayesian emulator optimisation for cosmology : application to the Lyman-alpha forest
- 2019
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Ingår i: Journal of Cosmology and Astroparticle Physics. - : IOP Publishing. - 1475-7516. ; :2
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Tidskriftsartikel (refereegranskat)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.
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