Observational bounds on the cosmic radiation density

We consider the inference of the cosmic radiation density, traditionally parameterised as the effective number of neutrino species N_eff, from precision cosmological data. Paying particular attention to systematic effects, notably scale-dependent biasing in the galaxy power spectrum, we find no evidence for a significant deviation of N_eff from the standard value of N_eff^0=3.046 in any combination of cosmological data sets, in contrast to some recent conclusions of other authors. The combination of all available data in the linear regime prefers, in the context of a ``vanilla+N_eff'' cosmological model, 1.1<N_eff<4.8 (95% C.L.) with a best-fit value of 2.6. Adding data at smaller scales, notably the Lyman-alpha forest, we find 2.2<N_eff<5.8 (95% C.L.) with 3.8 as the best fit. Inclusion of the Lyman-alpha data shifts the preferred N_eff upwards because the sigma_8 value derived from the SDSS Lyman-alpha data is inconsistent with that inferred from CMB. In an extended cosmological model that includes a nonzero mass for N_eff neutrino flavours, a running scalar spectral index and a w parameter for the dark energy, we find 0.8<N_eff<6.1 (95% C.L.) with 3.0 as the best fit.Comment: 23 pages, 3 figures, uses iopart.cls; v2: 1 new figure, references added, matches published versio

Degeneracy of gravitational waveforms in the context of GW150914

We study the degeneracy of theoretical gravitational waveforms for binary black hole mergers using an aligned-spin effective-one-body model. After appropriate truncation, bandpassing, and matching, we identify regions in the mass--spin parameter space containing waveforms similar to the template proposed for GW150914, with masses $m_1 = 36^{+5}_{-4} M_\odot$ and $m_2 = 29^{+4}_{-4} M_\odot$, using the cross-correlation coefficient as a measure of the similarity between waveforms. Remarkably high cross-correlations are found across broad regions of parameter space. The associated uncertanties exceed these from LIGO's Bayesian analysis considerably. We have shown that waveforms with greatly increased masses, such as $m_1 = 70 M_\odot$ and $m_2 = 35 M_\odot$, and strong anti-aligned spins ($\chi_1=0.95$ and $\chi_2=-0.95$) yield almost the same signal-to-noise ratio in the strain data for GW150914.Comment: Accepted for publication in JCA

Approximate Inference for Constructing Astronomical Catalogs from Images

We present a new, fully generative model for constructing astronomical catalogs from optical telescope image sets. Each pixel intensity is treated as a random variable with parameters that depend on the latent properties of stars and galaxies. These latent properties are themselves modeled as random. We compare two procedures for posterior inference. One procedure is based on Markov chain Monte Carlo (MCMC) while the other is based on variational inference (VI). The MCMC procedure excels at quantifying uncertainty, while the VI procedure is 1000 times faster. On a supercomputer, the VI procedure efficiently uses 665,000 CPU cores to construct an astronomical catalog from 50 terabytes of images in 14.6 minutes, demonstrating the scaling characteristics necessary to construct catalogs for upcoming astronomical surveys.Comment: accepted to the Annals of Applied Statistic

Semiparametric regression analysis via Infer.NET

© 2018, American Statistical Association. All rights reserved. We provide several examples of Bayesian semiparametric regression analysis via the Infer.NET package for approximate deterministic inference in Bayesian models. The examples are chosen to encompass a wide range of semiparametric regression situations. Infer.NET is shown to produce accurate inference in comparison with Markov chain Monte Carlo via the BUGS package, but to be considerably faster. Potentially, this contribution represents the start of a new era for semiparametric regression, where large and complex analyses are performed via fast Bayesian inference methodology and software, mainly being developed within Machine Learning

Contamination source inference in water distribution networks

We study the inference of the origin and the pattern of contamination in water distribution networks. We assume a simplified model for the dyanmics of the contamination spread inside a water distribution network, and assume that at some random location a sensor detects the presence of contaminants. We transform the source location problem into an optimization problem by considering discrete times and a binary contaminated/not contaminated state for the nodes of the network. The resulting problem is solved by Mixed Integer Linear Programming. We test our results on random networks as well as in the Modena city network