Constraints on a mixed inflaton and curvaton scenario for the generation of the curvature perturbation

We consider a supersymmetric grand unified model which naturally solves the strong CP and mu problems via a Peccei-Quinn symmetry and leads to the standard realization of hybrid inflation. We show that the Peccei-Quinn field of this model can act as curvaton. In contrast to the standard curvaton hypothesis, both the inflaton and the curvaton contribute to the total curvature perturbation. The model predicts an isocurvature perturbation too which has mixed correlation with the adiabatic one. The cold dark matter of the universe is mostly constituted by axions plus a small amount of lightest sparticles. The predictions of the model are confronted with the Wilkinson microwave anisotropy probe and other cosmic microwave background radiation data. We analyze two representative choices of parameters and derive bounds on the curvaton contribution to the adiabatic perturbation. We find that, for the choice which provides the best fitting of the data, the curvaton contribution to the adiabatic amplitude must be smaller than about 67% (at 95% confidence level). The best-fit power spectra are dominated by the adiabatic part of the inflaton contribution. We use Bayesian model comparison to show that this choice of parameters is disfavored with respect to the pure inflaton scale-invariant case with odds of 50 to 1. For the second choice of parameters, the adiabatic mode is dominated by the curvaton, but this choice is strongly disfavored relative to the pure inflaton scale-invariant case (with odds of 10^7 to 1). We conclude that in the present framework the perturbations must be dominated by the adiabatic component from the inflaton.Comment: 27 pages including 16 figures, uses Revte

Naturalness of the relaxion mechanism

The relaxion mechanism is a novel solution to the hierarchy problem. In this first statistical analysis of the relaxion mechanism, we quantify the relative plausibility of a QCD and a non-QCD relaxion model versus the Standard Model with Bayesian statistics, which includes an automatic penalty for fine-tuning. We find that in light of the hierarchy between the weak and Planck scales, relaxion models are favoured by colossal Bayes-factors. Constraints upon \eg the vacuum energy during relaxation, however, shrink the Bayes-factors such that relaxion models are only slightly favoured. Including the bounds on $\left|\theta_\text{QCD}\right|$ shatters the plausibility of the QCD relaxion model as it typically yields $\left|\theta_\text{QCD}\right| \gg 0$. Finally, we augment our models with scalar-field inflation and consider measurements of inflationary observables from BICEP/Planck. We find that, all told, the Standard Model is favoured by huge Bayes-factors as the relaxion models require fine-tuning such that the Hubble parameter is less than the height of the periodic barriers. Thus, whilst we confirm that relaxion models could solve the hierarchy problem, we find that their unconventional cosmology demolishes their plausibility

Single-field inflation constraints from CMB and SDSS data

We present constraints on canonical single-field inflation derived from WMAP five year, ACBAR, QUAD, BICEP data combined with the halo power spectrum from SDSS LRG7. Models with a non-scale-invariant spectrum and a red tilt n_s < 1 are now preferred over the Harrison-Zel'dovich model (n_s = 1, tensor-to-scalar ratio r = 0) at high significance. Assuming no running of the spectral indices, we derive constraints on the parameters (n_s, r) and compare our results with the predictions of simple inflationary models. The marginalised credible intervals read n_s = 0.962^{+0.028}_{-0.026} and r < 0.17 (at 95% confidence level). Interestingly, the 68% c.l. contours favour mainly models with a convex potential in the observable region, but the quadratic potential model remains inside the 95% c.l. contours. We demonstrate that these results are robust to changes in the datasets considered and in the theoretical assumptions made. We then consider a non-vanishing running of the spectral indices by employing different methods, non-parametric but approximate, or parametric but exact. With our combination of CMB and LSS data, running models are preferred over power-law models only by a Delta chi^2 ~ 5.8, allowing inflationary stages producing a sizable negative running -0.063^{+0.061}_{-0.049} and larger tensor-scalar ratio r < 0.33 at the 95% c.l. This requires large values of the third derivative of the inflaton potential within the observable range. We derive bounds on this derivative under the assumption that the inflaton potential can be approximated as a third order polynomial within the observable range.Comment: 32 pages, 7 figures. v2: additional references, some typos corrected, passed to JCAP style. v3: minor changes, matches published versio

Computational statistics using the Bayesian Inference Engine

This paper introduces the Bayesian Inference Engine (BIE), a general parallel, optimised software package for parameter inference and model selection. This package is motivated by the analysis needs of modern astronomical surveys and the need to organise and reuse expensive derived data. The BIE is the first platform for computational statistics designed explicitly to enable Bayesian update and model comparison for astronomical problems. Bayesian update is based on the representation of high-dimensional posterior distributions using metric-ball-tree based kernel density estimation. Among its algorithmic offerings, the BIE emphasises hybrid tempered MCMC schemes that robustly sample multimodal posterior distributions in high-dimensional parameter spaces. Moreover, the BIE is implements a full persistence or serialisation system that stores the full byte-level image of the running inference and previously characterised posterior distributions for later use. Two new algorithms to compute the marginal likelihood from the posterior distribution, developed for and implemented in the BIE, enable model comparison for complex models and data sets. Finally, the BIE was designed to be a collaborative platform for applying Bayesian methodology to astronomy. It includes an extensible object-oriented and easily extended framework that implements every aspect of the Bayesian inference. By providing a variety of statistical algorithms for all phases of the inference problem, a scientist may explore a variety of approaches with a single model and data implementation. Additional technical details and download details are available from http://www.astro.umass.edu/bie. The BIE is distributed under the GNU GPL.Comment: Resubmitted version. Additional technical details and download details are available from http://www.astro.umass.edu/bie. The BIE is distributed under the GNU GP

Extracting information from informal communication

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.Includes bibliographical references (leaves 89-93).This thesis focuses on the problem of extracting information from informal communication. Textual informal communication, such as e-mail, bulletin boards and blogs, has become a vast information resource. However, such information is poorly organized and difficult for a computer to understand due to lack of editing and structure. Thus, techniques which work well for formal text, such as newspaper articles, may be considered insufficient on informal text. One focus of ours is to attempt to advance the state-of-the-art for sub-problems of the information extraction task. We make contributions to the problems of named entity extraction, co-reference resolution and context tracking. We channel our efforts toward methods which are particularly applicable to informal communication. We also consider a type of information which is somewhat unique to informal communication: preferences and opinions. Individuals often expression their opinions on products and services in such communication. Others' may read these "reviews" to try to predict their own experiences. However, humans do a poor job of aggregating and generalizing large sets of data. We develop techniques that can perform the job of predicting unobserved opinions.(cont.) We address both the single-user case where information about the items is known, and the multi-user case where we can generalize opinions without external information. Experiments on large-scale rating data sets validate our approach.by Jason D.M. Rennie.Ph.D