2005.14199
Data analysis recipes: products of multivariate Gaussians in Bayesian inferences
Hogg, et al
A product of two Gaussians (or normal distributions) is another Gaussian. That's a valuable and useful fact! Here we use it to derive a refactoring of a common product of multivariate Gaussians: The product of a Gaussian likelihood times a Gaussian prior, where some or all of those parameters enter the likelihood only in the mean and only linearly. That is, a linear, Gaussian, Bayesian model. This product of a likelihood times a prior pdf can be refactored into a product of a marginalized likelihood (or a Bayesian evidence) times a posterior pdf, where (in this case) both of these are also Gaussian. The means and variance tensors of the refactored Gaussians are straightforward to obtain as closed-form expressions; here we deliver these expressions, with discussion. The closed-form expressions can be used to speed up and improve the precision of inferences that contain linear parameters with Gaussian priors. We connect these methods to inferences that arise frequently in physics and astronomy. If all you want is the answer, the question is posed and answered at the beginning of Section 3. We show two toy examples, in the form of worked exercises, in Section 4. The solutions, discussion, and exercises in this Note are aimed at someone who is already familiar with the basic ideas of Bayesian inference and probability.
Data analysis recipes: products of multivariate Gaussians in Bayesian inferences
Hogg, et al
A product of two Gaussians (or normal distributions) is another Gaussian. That's a valuable and useful fact! Here we use it to derive a refactoring of a common product of multivariate Gaussians: The product of a Gaussian likelihood times a Gaussian prior, where some or all of those parameters enter the likelihood only in the mean and only linearly. That is, a linear, Gaussian, Bayesian model. This product of a likelihood times a prior pdf can be refactored into a product of a marginalized likelihood (or a Bayesian evidence) times a posterior pdf, where (in this case) both of these are also Gaussian. The means and variance tensors of the refactored Gaussians are straightforward to obtain as closed-form expressions; here we deliver these expressions, with discussion. The closed-form expressions can be used to speed up and improve the precision of inferences that contain linear parameters with Gaussian priors. We connect these methods to inferences that arise frequently in physics and astronomy. If all you want is the answer, the question is posed and answered at the beginning of Section 3. We show two toy examples, in the form of worked exercises, in Section 4. The solutions, discussion, and exercises in this Note are aimed at someone who is already familiar with the basic ideas of Bayesian inference and probability.
2005.14351
Testing gravity using galaxy-galaxy lensing and clustering amplitudes in KiDS-1000, BOSS and 2dFLenS
Blake, et al
The physics of gravity on cosmological scales affects both the rate of assembly of large-scale structure, and the gravitational lensing of background light through this cosmic web. By comparing the amplitude of these different observational signatures, we can construct tests that can distinguish General Relativity from its potential modifications. We use the latest weak gravitational lensing dataset from the Kilo-Degree Survey, KiDS-1000, in conjunction with overlapping galaxy spectroscopic redshift surveys BOSS and 2dFLenS, to perform the most precise existing amplitude-ratio test. We measure the associated E_G statistic with 15-20% errors, in five dz = 0.1 tomographic redshift bins in the range 0.2 < z < 0.7, on projected scales up to 100 Mpc/h. The scale-independence and redshift-dependence of these measurements are consistent with the theoretical expectation of General Relativity in a Universe with matter density Omega_m = 0.27 +/- 0.04. We demonstrate that our results are robust against different analysis choices, including schemes for correcting the effects of source photometric redshift errors, and compare the performance of angular and projected galaxy-galaxy lensing statistics.
2005.14387
Aquatic biospheres on temperate planets around Sun-like stars and M-dwarfs
Lingam, Loeb
Aquatic biospheres reliant on oxygenic photosynthesis are expected to play an important role on Earth-like planets with large-scale oceans insofar as carbon fixation (i.e., biosynthesis of organic compounds) is concerned. We investigate the properties of aquatic biospheres comprising Earth-like biota for habitable rocky planets orbiting Sun-like stars and late-type M-dwarfs such as TRAPPIST-1. In particular, we estimate how these characteristics evolve with the ambient ocean temperature ($T_W$), which is a key environmental variable. We show that many salient properties, such as the depth of the photosynthesis zone and the net primary productivity (i.e., the effective rate of carbon fixation), are sensitive to $T_W$, and eventually decline substantially as the ocean temperature is increased. We conclude by discussing the implications of our analysis for the past and future Earth, and exoplanets orbiting M-dwarfs.
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