Characterizing the Epoch of Reionization with the small-scale CMB: constraints on the optical depth and physical parameters

Patchy reionization leaves a number of imprints on the small-scale cosmic microwave background (CMB) temperature fluctuations, the largest of which is the kinematic Sunyaev-Zel'dovich (kSZ), the Doppler shift of CMB photons scattering off moving electrons in ionized bubbles. It has long been known that in the CMB power spectrum, this imprint of reionization is largely degenerate with the kSZ signal produced by late-time galaxies and clusters, thus limiting our ability to constrain reionization. Following Smith & Ferraro (2017), it is possible to isolate the reionization contribution in a model independent way, by looking at the large scale modulation of the small scale CMB power spectrum. In this paper we extend the formalism to use the full shape information of the small scale power spectrum (rather than just its broadband average), and argue that this is necessary to break the degeneracy between the optical depth $\tau$ and parameters setting the duration of reionization. In particular, we show that the next generation of CMB experiments could achieve up to a factor of 3 improvement on the optical depth $\tau$ and at the same time, constrain the duration of reionization to $\sim$ 25 %. This can help tighten the constrains on neutrino masses, which will be limited by our knowledge of $\tau$, and shed light on the physical processes responsible for reionization.Comment: 8 pages, 3 figures. Comments welcom

Detecting patchy reionization in the CMB

Upcoming cosmic microwave background (CMB) experiments will measure temperature fluctuations on small angular scales with unprecedented precision. Small-scale CMB fluctuations are a mixture of late-time effects: gravitational lensing, Doppler shifting of CMB photons by moving electrons (the kSZ effect), and residual foregrounds. We propose a new statistic which separates the kSZ signal from the others, and also allows the kSZ signal to be decomposed in redshift bins. The decomposition extends to high redshift, and does not require external datasets such as galaxy surveys. In particular, the high-redshift signal from patchy reionization can be cleanly isolated, enabling future CMB experiments to make high-significance and qualitatively new measurements of the reionization era

Supersonic baryon-CDM velocities and CMB B-mode polarization

It has recently been shown that supersonic relative velocities between dark matter and baryonic matter can have a significant effect on formation of the first structures in the universe. If this effect is still non-negligible during the epoch of hydrogen reionization, it generates large-scale anisotropy in the free electron density, which gives rise to a CMB B-mode. We compute the B-mode power spectrum and find a characteristic shape with acoustic peaks at l ~ 200, 400, ... The amplitude of this signal is a free parameter which is related to the dependence of the ionization fraction on the relative baryon-CDM velocity during the epoch of reionization. However, we find that the B-mode signal is undetectably small for currently favored reionization models in which hydrogen is reionized promptly at z ~ 10, although constraints on this signal by future experiments may help constrain models in which partial reionization occurs at higher redshift, e.g. by accretion onto primordial black holes.Comment: 5 pages, 3 figure

L1-Regularized Distributed Optimization: A Communication-Efficient Primal-Dual Framework

Despite the importance of sparsity in many large-scale applications, there are few methods for distributed optimization of sparsity-inducing objectives. In this paper, we present a communication-efficient framework for L1-regularized optimization in the distributed environment. By viewing classical objectives in a more general primal-dual setting, we develop a new class of methods that can be efficiently distributed and applied to common sparsity-inducing models, such as Lasso, sparse logistic regression, and elastic net-regularized problems. We provide theoretical convergence guarantees for our framework, and demonstrate its efficiency and flexibility with a thorough experimental comparison on Amazon EC2. Our proposed framework yields speedups of up to 50x as compared to current state-of-the-art methods for distributed L1-regularized optimization

Exploring the Impact of Bibliotherapy and Family Literacy Bags on Elementary Students Experiencing Divorce

The purpose of this research project was to use picture books to create family literacy bags surrounding the topic of divorce, in order to explore their impact on children and families. Divorce is on the rise and so many children are facing, or may face divorce, in their preadolescent lives. This thesis involved creating a tool that will help enrich family support and guidance with issues or stressors that arise from the divorce. As educators, caregivers and school personnel consider the use of family literacy bags, my research has the potential to impact these students and their families. Through my research, I was able to create six family literacy bags that included a book, with a lesson plan and activity. While creating these six family literacy bags, I considered how the child may feel before, after or during a divorce. I included a variety of different picture books about divorce to ensure that the story would be relevant to each family. I used activities in my lesson plans that would give the parent or guardian and child the opportunity to share their feelings with one another, while spending time together. Two families participated in my research study. I allowed the students to choose two family literacy bags with their family and allowed them a week\u27s time to complete the family literacy bag. In addition, survey results of the participating parent or guardian after completing both family literacy bags with their child will be included

Simone Smith to Mr. Meredithe (21 September 1962)

https://egrove.olemiss.edu/mercorr_pro/1279/thumbnail.jp

Old Ideas in New Skins: Examining Discourses of Diversity on the Websites of 10 Urban-Serving Universities

Deficit discourse, the idea that minorities lack intellectually, runs through current ideas about diversity in higher education. Diversity is viewed as a policy that helps the deficient. Recent litigation about diversity, Fisher v. University of Texas (2013), embodied the alignment of deficit and diversity. This study examined portrayals, visual and textual, of diversity on the websites of ten urban-serving universities, using a method of critical discourse analysis and a lens of critical race theory, to uncover the ways they defined diversity and if notions of deficit were attached. This study also addressed the ways these universities, a part of the Coalition of Urban-Serving Universities, discussed their communities and if deficit was attached. Diversity was defined as deficient racial minorities and communities as well as diversity as tokens and a form of compliance. The findings of this study show that these college websites, through their portrayals of racial minorities as deficient, duplicate inequality and encourage the maintenance of White hegemony

CoCoA: A General Framework for Communication-Efficient Distributed Optimization

The scale of modern datasets necessitates the development of efficient distributed optimization methods for machine learning. We present a general-purpose framework for distributed computing environments, CoCoA, that has an efficient communication scheme and is applicable to a wide variety of problems in machine learning and signal processing. We extend the framework to cover general non-strongly-convex regularizers, including L1-regularized problems like lasso, sparse logistic regression, and elastic net regularization, and show how earlier work can be derived as a special case. We provide convergence guarantees for the class of convex regularized loss minimization objectives, leveraging a novel approach in handling non-strongly-convex regularizers and non-smooth loss functions. The resulting framework has markedly improved performance over state-of-the-art methods, as we illustrate with an extensive set of experiments on real distributed datasets