Astronomy and Astrophysics – Astrophysics
Scientific paper
2004-07-12
Phys.Rev. D71 (2005) 083517
Astronomy and Astrophysics
Astrophysics
15 pages, 6 colour figures. Significantly more discussion and illustrative examples
Scientific paper
10.1103/PhysRevD.71.083517
Optimising the major next-generation cosmological surveys (such as {\em SNAP, KAOS etc...}) is a key problem given our ignorance of the physics underlying cosmic acceleration and the plethora of surveys planned. We propose a Bayesian design framework which (1) maximises the discrimination power of a survey without assuming any underlying dark energy model, (2) finds the best niche survey geometry given current data and future competing experiments, (3) maximises the cross-section for serendipitous discoveries and (4) can be adapted to answer specific questions (such as `is dark energy dynamical?'). Integrated Parameter Space Optimisation (IPSO) is a design framework that integrates projected parameter errors over an entire dark energy parameter space and then extremises a figure of merit (such as Shannon entropy gain which we show is stable to off-diagonal covariance matrix perturbations) as a function of survey parameters using analytical, grid or MCMC techniques. We discuss examples where the optimisation can be performed analytically. IPSO is thus a general, model-independent and scalable framework that allows us to appropriately use prior information to design the best possible surveys.
No associations
LandOfFree
Eyes Wide Open - Optimising Cosmological Surveys in a Crowded Market does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Eyes Wide Open - Optimising Cosmological Surveys in a Crowded Market, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Eyes Wide Open - Optimising Cosmological Surveys in a Crowded Market will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-57799