A luminosity constraint on the origin of unidentified high energy sources

The identification of point sources poses a great challenge for the high energy community. We present a new approach to evaluate the likelihood of a set of sources being a Galactic population based on the simple assumption that galaxies similar to the Milky Way host comparable populations of gamma-ray emitters. We propose a luminosity constraint on Galactic source populations which complements existing approaches by constraining the abundance and spatial distribution of any objects of Galactic origin, rather than focusing on the properties of a specific candidate emitter. We use M31 as a proxy for the Milky Way, and demonstrate this technique by applying it to the unidentified EGRET sources. We find that it is highly improbable that the majority of the unidentified EGRET sources are members of a Galactic halo population (e.g., dark matter subhalos), but that current observations do not provide any constraints on all of these sources being Galactic objects if they reside entirely in the disk and bulge. Applying this method to upcoming observations by the Fermi Gamma-ray Space Telescope has the potential to exclude association of an even larger number of unidentified sources with any Galactic source class.Comment: 18 pages, 4 figures, to appear in JPhys

A New Approach to Searching for Dark Matter Signals in Fermi-LAT Gamma Rays

Several cosmic ray experiments have measured excesses in electrons and positrons, relative to standard backgrounds, for energies from ~ 10 GeV - 1 TeV. These excesses could be due to new astrophysical sources, but an explanation in which the electrons and positrons are dark matter annihilation or decay products is also consistent. Fortunately, the Fermi-LAT diffuse gamma ray measurements can further test these models, since the electrons and positrons produce gamma rays in their interactions in the interstellar medium. Although the dark matter gamma ray signal consistent with the local electron and positron measurements should be quite large, as we review, there are substantial uncertainties in the modeling of diffuse backgrounds and, additionally, experimental uncertainties that make it difficult to claim a dark matter discovery. In this paper, we introduce an alternative method for understanding the diffuse gamma ray spectrum in which we take the intensity ratio in each energy bin of two different regions of the sky, thereby canceling common systematic uncertainties. For many spectra, this ratio fits well to a power law with a single break in energy. The two measured exponent indices are a robust discriminant between candidate models, and we demonstrate that dark matter annihilation scenarios can predict index values that require "extreme" parameters for background-only explanations.Comment: v1: 11 pages, 7 figures, 1 table, revtex4; v2: 13 pages, 8 figures, 1 table, revtex4, Figure 4 added, minor additions made to text, references added, conclusions unchanged, published versio

Evolution of Robustness to Noise and Mutation in Gene Expression Dynamics

Phenotype of biological systems needs to be robust against mutation in order to sustain themselves between generations. On the other hand, phenotype of an individual also needs to be robust against fluctuations of both internal and external origins that are encountered during growth and development. Is there a relationship between these two types of robustness, one during a single generation and the other during evolution? Could stochasticity in gene expression have any relevance to the evolution of these robustness? Robustness can be defined by the sharpness of the distribution of phenotype; the variance of phenotype distribution due to genetic variation gives a measure of `genetic robustness' while that of isogenic individuals gives a measure of `developmental robustness'. Through simulations of a simple stochastic gene expression network that undergoes mutation and selection, we show that in order for the network to acquire both types of robustness, the phenotypic variance induced by mutations must be smaller than that observed in an isogenic population. As the latter originates from noise in gene expression, this signifies that the genetic robustness evolves only when the noise strength in gene expression is larger than some threshold. In such a case, the two variances decrease throughout the evolutionary time course, indicating increase in robustness. The results reveal how noise that cells encounter during growth and development shapes networks' robustness to stochasticity in gene expression, which in turn shapes networks' robustness to mutation. The condition for evolution of robustness as well as relationship between genetic and developmental robustness is derived through the variance of phenotypic fluctuations, which are measurable experimentally.Comment: 25 page

Constraints on dark matter models from a Fermi LAT search for high-energy cosmic-ray electrons from the Sun

During its first year of data taking, the Large Area Telescope (LAT) onboard the Fermi Gamma-Ray Space Telescope has collected a large sample of high-energy cosmic-ray electrons and positrons (CREs). We present the results of a directional analysis of the CRE events, in which we searched for a flux excess correlated with the direction of the Sun. Two different and complementary analysis approaches were implemented, and neither yielded evidence of a significant CRE flux excess from the Sun. We derive upper limits on the CRE flux from the Sun's direction, and use these bounds to constrain two classes of dark matter models which predict a solar CRE flux: (1) models in which dark matter annihilates to CREs via a light intermediate state, and (2) inelastic dark matter models in which dark matter annihilates to CREs.Comment: 18 pages, 8 figures, accepted for publication in Physical Review D - contact authors: Francesco Loparco ([email protected]), M. Nicola Mazziotta ([email protected]) and Jennifer Siegal-Gaskins ([email protected]

Searches for Cosmic-Ray Electron Anisotropies with the Fermi Large Area Telescope

The Large Area Telescope on board the \textit{Fermi} satellite (\textit{Fermi}-LAT) detected more than 1.6 million cosmic-ray electrons/positrons with energies above 60 GeV during its first year of operation. The arrival directions of these events were searched for anisotropies of angular scale extending from $\sim$ 10 $^\circ$ up to 90$^\circ$, and of minimum energy extending from 60 GeV up to 480 GeV. Two independent techniques were used to search for anisotropies, both resulting in null results. Upper limits on the degree of the anisotropy were set that depended on the analyzed energy range and on the anisotropy's angular scale. The upper limits for a dipole anisotropy ranged from $\sim0.5$ to $\sim10$.Comment: 16 pages, 10 figures, accepted for publication in Physical Review D - contact authors: M.N. Mazziotta and V. Vasileio

Annihilation vs. Decay: Constraining dark matter properties from a gamma-ray detection

Most proposed dark matter candidates are stable and are produced thermally in the early Universe. However, there is also the possibility of unstable (but long-lived) dark matter, produced thermally or otherwise. We propose a strategy to distinguish between dark matter annihilation and/or decay in the case that a clear signal is detected in gamma-ray observations of Milky Way dwarf spheroidal galaxies with gamma-ray experiments. The sole measurement of the energy spectrum of an indirect signal would render the discrimination between these cases impossible. We show that by examining the dependence of the intensity and energy spectrum on the angular distribution of the emission, the origin could be identified as decay, annihilation, or both. In addition, once the type of signal is established, we show how these measurements could help to extract information about the dark matter properties, including mass, annihilation cross section, lifetime, dominant annihilation and decay channels, and the presence of substructure. Although an application of the approach presented here would likely be feasible with current experiments only for very optimistic dark matter scenarios, the improved sensitivity of upcoming experiments could enable this technique to be used to study a wider range of dark matter models.Comment: 29 pp, 8 figs; replaced to match published version (minor changes and some new references

Is It Rational to Assume that Infants Imitate Rationally? A Theoretical Analysis and Critique

It has been suggested that preverbal infants evaluate the efficiency of others' actions (by applying a principle of rational action) and that they imitate others' actions rationally. The present contribution presents a conceptual analysis of the claim that preverbal infants imitate rationally. It shows that this ability rests on at least three assumptions: that infants are able to perceive others' action capabilities, that infants reason about and conceptually represent their own bodies, and that infants are able to think counterfactually. It is argued that none of these three abilities is in place during infancy. Furthermore, it is shown that the idea of a principle of rational action suffers from two fallacies. As a consequence, is it suggested that it is not rational to assume that infants imitate rationally. Copyright (C) 2012 S. Karger AG, Base

An Analytically Solvable Model for Rapid Evolution of Modular Structure

Biological systems often display modularity, in the sense that they can be decomposed into nearly independent subsystems. Recent studies have suggested that modular structure can spontaneously emerge if goals (environments) change over time, such that each new goal shares the same set of sub-problems with previous goals. Such modularly varying goals can also dramatically speed up evolution, relative to evolution under a constant goal. These studies were based on simulations of model systems, such as logic circuits and RNA structure, which are generally not easy to treat analytically. We present, here, a simple model for evolution under modularly varying goals that can be solved analytically. This model helps to understand some of the fundamental mechanisms that lead to rapid emergence of modular structure under modularly varying goals. In particular, the model suggests a mechanism for the dramatic speedup in evolution observed under such temporally varying goals

Proportionality between variances in gene expression induced by noise and mutation: consequence of evolutionary robustness

<p>Abstract</p> <p>Background</p> <p>Characterization of robustness and plasticity of phenotypes is a basic issue in evolutionary and developmental biology. The robustness and plasticity are concerned with changeability of a biological system against external perturbations. The perturbations are either genetic, i.e., due to mutations in genes in the population, or epigenetic, i.e., due to noise during development or environmental variations. Thus, the variances of phenotypes due to genetic and epigenetic perturbations provide quantitative measures for such changeability during evolution and development, respectively.</p> <p>Results</p> <p>Using numerical models simulating the evolutionary changes in the gene regulation network required to achieve a particular expression pattern, we first confirmed that gene expression dynamics robust to mutation evolved in the presence of a sufficient level of transcriptional noise. Under such conditions, the two types of variances in the gene expression levels, i.e. those due to mutations to the gene regulation network and those due to noise in gene expression dynamics were found to be proportional over a number of genes. The fraction of such genes with a common proportionality coefficient increased with an increase in the robustness of the evolved network. This proportionality was generally confirmed, also under the presence of environmental fluctuations and sexual recombination in diploids, and was explained from an evolutionary robustness hypothesis, in which an evolved robust system suppresses the so-called error catastrophe - the destabilization of the single-peaked distribution in gene expression levels. Experimental evidences for the proportionality of the variances over genes are also discussed.</p> <p>Conclusions</p> <p>The proportionality between the genetic and epigenetic variances of phenotypes implies the correlation between the robustness (or plasticity) against genetic changes and against noise in development, and also suggests that phenotypic traits that are more variable epigenetically have a higher evolutionary potential.</p

Model-Based Deconvolution of Cell Cycle Time-Series Data Reveals Gene Expression Details at High Resolution

In both prokaryotic and eukaryotic cells, gene expression is regulated across the cell cycle to ensure “just-in-time” assembly of select cellular structures and molecular machines. However, present in all time-series gene expression measurements is variability that arises from both systematic error in the cell synchrony process and variance in the timing of cell division at the level of the single cell. Thus, gene or protein expression data collected from a population of synchronized cells is an inaccurate measure of what occurs in the average single-cell across a cell cycle. Here, we present a general computational method to extract “single-cell”-like information from population-level time-series expression data. This method removes the effects of 1) variance in growth rate and 2) variance in the physiological and developmental state of the cell. Moreover, this method represents an advance in the deconvolution of molecular expression data in its flexibility, minimal assumptions, and the use of a cross-validation analysis to determine the appropriate level of regularization. Applying our deconvolution algorithm to cell cycle gene expression data from the dimorphic bacterium Caulobacter crescentus, we recovered critical features of cell cycle regulation in essential genes, including ctrA and ftsZ, that were obscured in population-based measurements. In doing so, we highlight the problem with using population data alone to decipher cellular regulatory mechanisms and demonstrate how our deconvolution algorithm can be applied to produce a more realistic picture of temporal regulation in a cell