Approximations to galaxy star formation rate histories: properties and uses of two examples

Galaxies evolve via a complex interaction of numerous different physical processes, scales and components. In spite of this, overall trends often appear. Simplified models for galaxy histories can be used to search for and capture such emergent trends, and thus to interpret and compare results of galaxy formation models to each other and to nature. Here, two approximations are applied to galaxy integrated star formation rate histories, drawn from a semi-analytic model grafted onto a dark matter simulation. Both a lognormal functional form and principal component analysis (PCA) approximate the integrated star formation rate histories fairly well. Machine learning, based upon simplified galaxy halo histories, is somewhat successful at recovering both fits. The fits to the histories give fixed time star formation rates which have notable scatter from their true fixed time rates at final time, especially for quiescent and "green valley" galaxies, and more so for the PCA fit. For classifying galaxies into subfamilies sharing similar integrated histories, both approximations are better than using final stellar mass or specific star formation rate. Several subsamples from the simulation illustrate how these simple parameterizations can provide points of contact for comparisons between different galaxy formation samples, or more generally, models. As a side result, the halo masses of simulated galaxies with early peak star formation rate (according to the lognormal fit) lie on one of two tracks. The small fraction of galaxies with a lower halo mass at peak star formation rate appear to stall in their halo growth, even though they are central in their host halos.Comment: Final version, to appear in the MNRAS. Helpful suggestions and tests from referee now included. Reference to PCA work on star formation rate histories now correctly attributed to Sparre et al (2015

Update on Open Universe Inflationary Models

An overview of some new results in open inflation over the past year, including the calculation of gravity wave contributions to the Cosmic Microwave Background. Proceedings for COSMO-97, held in Ambleside, U.K., hence very short due to space limitations (the version for the web is more than 3 pages because archive numbers have been added for the references).Comment: LaTeX, uses sprocl.tex, 4 pages, corrected reference typ

Open universes from bubbles: an introduction and update

An introduction to models of open universes originating from bubbles, including a summary of recent theoretical results for the power spectrum. To appear in the proceedings of the XXXIth Moriond meeting, "Microwave Background Anisotropies."Comment: LaTeX file, uses epsf.tex, 3 figures, 8 pages, minor wording changes to clarify discussion of coordinate system

The Strength of String Nonperturbative Effects and Strong-Weak Coupling Duality

A strong-weak coupling duality symmetry of the string equations of motion has been suggested in the literature. This symmetry implies that vacua occur in pairs. Since the coupling constant is a dynamical variable in string theory, tunneling solutions between strong and weak coupling vacua may exist. Such solutions would naturally lead to nonperturbative effects with anomalous coupling dependence. A highly simplified example is given.Comment: 7 pages, iassns-hep-93-24, fermilab 93/087-T, nsf-itp-93-45 (A simple example of the mechanism suggested has been added.

Scalable Text and Link Analysis with Mixed-Topic Link Models

Many data sets contain rich information about objects, as well as pairwise relations between them. For instance, in networks of websites, scientific papers, and other documents, each node has content consisting of a collection of words, as well as hyperlinks or citations to other nodes. In order to perform inference on such data sets, and make predictions and recommendations, it is useful to have models that are able to capture the processes which generate the text at each node and the links between them. In this paper, we combine classic ideas in topic modeling with a variant of the mixed-membership block model recently developed in the statistical physics community. The resulting model has the advantage that its parameters, including the mixture of topics of each document and the resulting overlapping communities, can be inferred with a simple and scalable expectation-maximization algorithm. We test our model on three data sets, performing unsupervised topic classification and link prediction. For both tasks, our model outperforms several existing state-of-the-art methods, achieving higher accuracy with significantly less computation, analyzing a data set with 1.3 million words and 44 thousand links in a few minutes.Comment: 11 pages, 4 figure