SwePub
Sök i LIBRIS databas

  Utökad sökning

WFRF:(Font Ribera Andreu)
 

Sökning: WFRF:(Font Ribera Andreu) > (2019) > Bayesian emulator o...

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

Rogers, Keir K. (författare)
Stockholms universitet,Fysikum,Oskar Klein-centrum för kosmopartikelfysik (OKC)
Peiris, Hiranya V. (författare)
Stockholms universitet,Fysikum,Oskar Klein-centrum för kosmopartikelfysik (OKC),University College London, U.K
Pontzen, Andrew (författare)
visa fler...
Bird, Simeon (författare)
Verde, Licia (författare)
Font-Ribera, Andreu (författare)
visa färre...
 (creator_code:org_t)
2019-02-14
2019
Engelska.
Ingår i: Journal of Cosmology and Astroparticle Physics. - : IOP Publishing. - 1475-7516. ; :2
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
Stäng  
  • 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.

Ämnesord

NATURVETENSKAP  -- Fysik (hsv//swe)
NATURAL SCIENCES  -- Physical Sciences (hsv//eng)

Nyckelord

cosmological parameters from LSS
cosmological simulations
Lyman alpha forest

Publikations- och innehållstyp

ref (ämneskategori)
art (ämneskategori)

Hitta via bibliotek

Till lärosätets databas

Sök utanför SwePub

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.

 
pil uppåt Stäng

Kopiera och spara länken för att återkomma till aktuell vy