Nonlinear Behavior of Baryon Acoustic Oscillations from the Zel'dovich Approximation Using a Non-Fourier Perturbation Approach

Baryon acoustic oscillations are an excellent technique to constrain the properties of dark energy in the Universe. In order to accurately characterize the dark energy equation of state, we must understand the effects of both the nonlinearities and redshift space distortions on the location and shape of the acoustic peak. In this paper, we consider these effects using the Zel'dovich approximation and a novel approach to 2nd order perturbation theory. The second order term of the Zel'dovich power spectrum is built from convolutions of the linear power spectrum with polynomial kernels in Fourier space, suggesting that the corresponding term of the the Zel'dovich correlation function can be written as a sum of quadratic products of a broader class of correlation functions, expressed through simple spherical Bessel transforms of the linear power spectrum. We show how to systematically perform such a computation. We explicitly prove that our result is the Fourier transform of the Zel'dovich power spectrum, and compare our expressions to numerical simulations. Finally, we highlight the advantages of writing the nonlinear expansion in configuration space, as this calculation is easily extended to redshift space, and the higher order terms are mathematically simpler than their Fourier counterparts.Comment: Accepted to ApJ. 7 pages, 2 figure

Removing BAO-peak Shifts with Local Density Transforms

Large-scale bulk flows in the Universe distort the initial density field, broadening the baryon-acoustic-oscillation (BAO) feature that was imprinted when baryons were strongly coupled to photons. Additionally, there is a small shift inward in the peak of the conventional overdensity correlation function, a mass-weighted statistic. This shift occurs when high density peaks move toward each other. We explore whether this shift can be removed by applying to the density field a transform (such as a logarithm) that gives fairer statistical weight to fluctuations in underdense regions. Using configuration-space perturbation theory in the Zel'dovich approximation, we find that the log-density correlation function shows a much smaller inward shift in the position of the BAO peak at low redshift than is seen in the overdensity correlation function. We also show that if the initial, Lagrangian density of matter parcels could be estimated at their Eulerian positions, giving a displaced-initial-density field, its peak shift would be even smaller. In fact, a transformed field that accentuates underdensities, such as the reciprocal of the density, pushes the peak the other way, outward. In our model, these shifts in the peak position can be attributed to shift terms, involving the derivative of the linear correlation function, that entirely vanish in this displaced-initial-density field.Comment: 5 pages, 3 figures, Accepted by Apj

Probabilistic Clustering of Time-Evolving Distance Data

We present a novel probabilistic clustering model for objects that are represented via pairwise distances and observed at different time points. The proposed method utilizes the information given by adjacent time points to find the underlying cluster structure and obtain a smooth cluster evolution. This approach allows the number of objects and clusters to differ at every time point, and no identification on the identities of the objects is needed. Further, the model does not require the number of clusters being specified in advance -- they are instead determined automatically using a Dirichlet process prior. We validate our model on synthetic data showing that the proposed method is more accurate than state-of-the-art clustering methods. Finally, we use our dynamic clustering model to analyze and illustrate the evolution of brain cancer patients over time

Distributed utterances

I propose an apparatus for handling intrasentential change in context. The standard approach has problems with sentences with multiple occurrences of the same demonstrative or indexical. My proposal involves the idea that contexts can be complex. Complex contexts are built out of (“simple”) Kaplanian contexts by ordered n-tupling. With these we can revise the clauses of Kaplan’s Logic of Demonstratives so that each part of a sentence is taken in a different component of a complex context. I consider other applications of the framework: to agentially distributed utterances (ones made partly by one speaker and partly by another); to an account of scare-quoting; and to an account of a binding-like phenomenon that avoids what Kit Fine calls “the antinomy of the variable.

Design and analysis of fractional factorial experiments from the viewpoint of computational algebraic statistics

We give an expository review of applications of computational algebraic statistics to design and analysis of fractional factorial experiments based on our recent works. For the purpose of design, the techniques of Gr\"obner bases and indicator functions allow us to treat fractional factorial designs without distinction between regular designs and non-regular designs. For the purpose of analysis of data from fractional factorial designs, the techniques of Markov bases allow us to handle discrete observations. Thus the approach of computational algebraic statistics greatly enlarges the scope of fractional factorial designs.Comment: 16 page

Binary Models for Marginal Independence

Log-linear models are a classical tool for the analysis of contingency tables. In particular, the subclass of graphical log-linear models provides a general framework for modelling conditional independences. However, with the exception of special structures, marginal independence hypotheses cannot be accommodated by these traditional models. Focusing on binary variables, we present a model class that provides a framework for modelling marginal independences in contingency tables. The approach taken is graphical and draws on analogies to multivariate Gaussian models for marginal independence. For the graphical model representation we use bi-directed graphs, which are in the tradition of path diagrams. We show how the models can be parameterized in a simple fashion, and how maximum likelihood estimation can be performed using a version of the Iterated Conditional Fitting algorithm. Finally we consider combining these models with symmetry restrictions

Application of asymptotic expansions of maximum likelihood estimators errors to gravitational waves from binary mergers: the single interferometer case

In this paper we describe a new methodology to calculate analytically the error for a maximum likelihood estimate (MLE) for physical parameters from Gravitational wave signals. All the existing litterature focuses on the usage of the Cramer Rao Lower bounds (CRLB) as a mean to approximate the errors for large signal to noise ratios. We show here how the variance and the bias of a MLE estimate can be expressed instead in inverse powers of the signal to noise ratios where the first order in the variance expansion is the CRLB. As an application we compute the second order of the variance and bias for MLE of physical parameters from the inspiral phase of binary mergers and for noises of gravitational wave interferometers . We also compare the improved error estimate with existing numerical estimates. The value of the second order of the variance expansions allows to get error predictions closer to what is observed in numerical simulations. It also predicts correctly the necessary SNR to approximate the error with the CRLB and provides new insight on the relationship between waveform properties SNR and estimation errors. For example the timing match filtering becomes optimal only if the SNR is larger than the kurtosis of the gravitational wave spectrum

Adjusting for Network Size and Composition Effects in Exponential-Family Random Graph Models

Exponential-family random graph models (ERGMs) provide a principled way to model and simulate features common in human social networks, such as propensities for homophily and friend-of-a-friend triad closure. We show that, without adjustment, ERGMs preserve density as network size increases. Density invariance is often not appropriate for social networks. We suggest a simple modification based on an offset which instead preserves the mean degree and accommodates changes in network composition asymptotically. We demonstrate that this approach allows ERGMs to be applied to the important situation of egocentrically sampled data. We analyze data from the National Health and Social Life Survey (NHSLS).Comment: 37 pages, 2 figures, 5 tables; notation revised and clarified, some sections (particularly 4.3 and 5) made more rigorous, some derivations moved into the appendix, typos fixed, some wording change

Rural men and mental health: their experiences and how they managed

There is a growing awareness that a primary source of information about mental health lies with the consumers. This article reports on a study that interviewed rural men with the aim of exploring their mental health experiences within a rural environment. The results of the interviews are a number of stories of resilience and survival that highlight not only the importance of exploring the individuals' perspective of their issues, but also of acknowledging and drawing on their inner strengths. Rural men face a number of challenges that not only increase the risk of mental illness but also decrease the likelihood of them seeking and/or finding professional support. These men's stories, while different from each other, have a common thread of coping. Despite some support from family and friends participants also acknowledged that seeking out professional support could have made the recovery phase easier. Mental health nurses need to be aware, not only of the barrier to professional support but also of the significant resilience that individuals have and how it can be utilised