The Future of Nuclear Weapons

Assessing the future of nuclear weapons means addressing two questions: What changes are likely in the design, deployment, and distribution of nuclear armaments? And how will those changes, if any, affect international politics

Intrinsic alignments of group and cluster galaxies in photometric surveys

Intrinsic alignments of galaxies have been shown to contaminate weak gravitational lensing observables on linear scales, $r>$ 10 $h^{-1}$Mpc, but studies of alignments in the non-linear regime have thus far been inconclusive. We present an estimator for extracting the intrinsic alignment signal of galaxies around stacked clusters of galaxies from multiband imaging data. Our estimator removes the contamination caused by galaxies that are gravitationally lensed by the clusters and scattered in redshift space due to photometric redshift uncertainties. It uses posterior probability distributions for the redshifts of the galaxies in the sample and it is easily extended to obtain the weak gravitational lensing signal while removing the intrinsic alignment contamination. We apply this algorithm to groups and clusters of galaxies identified in the Sloan Digital Sky Survey `Stripe 82' coadded imaging data over $\sim 150$ deg$^2$. We find that the intrinsic alignment signal around stacked clusters in the redshift range $0.1<z<0.4$ is consistent with zero. In terms of the tidal alignment model of Catelan et al. (2001), we set joint constraints on the strength of the alignment and the bias of the lensing groups and clusters on scales between 0.1 and $10\,h^{-1}$ Mpc, $b_LC_1\rho_{\rm crit} = -2_{-14}^{+14} \times 10^{-4}$. This constrains the contamination fraction of alignment to lensing signal to the range between $[-18,23]$ per cent below scales of 1 $h^{-1}$ Mpc at 95 per cent confidence level, and this result depends on our photometric redshift quality and selection criteria used to identify background galaxies. Our results are robust to the choice of photometric band in which the shapes are measured ($i$ and $r$) and to centring on the Brightest Cluster Galaxy or on the geometrical centre of the clusters.Comment: 30 pages, 16 figures, published in MNRA

The Impact of Intrinsic Alignments: Cosmological Constraints from a Joint Analysis of Cosmic Shear and Galaxy Survey Data

Constraints on cosmology from recent cosmic shear observations are becoming increasingly sophisticated in their treatment of potential systematic effects. Here we present cosmological constraints which include modelling of intrinsic alignments. We demonstrate how the results are changed for three different intrinsic alignment models, and for two different models of the cosmic shear galaxy population. We find that intrinsic alignments can either reduce or increase measurements of the fluctuation amplitude parameter sigma_8 depending on these decisions, and depending on the cosmic shear survey properties. This is due to the interplay between the two types of intrinsic alignment, II and GI. It has been shown that future surveys must make a careful treatment of intrinsic alignments to avoid significant biases, and that simultaneous constraints from shear-shear and shear-position correlation functions can mitigate the effects. For the first time we here combine constraints from cosmic shear surveys (shear-shear correlations) with those from "GI" intrinsic alignment data sets (shear-position correlations). We produce updated constraints on cosmology marginalised over two free parameters in the halo model for intrinsic alignments. We find that the additional freedom is well compensated by the additional information, in that the constraints are very similar indeed to those obtained when intrinsic alignments are ignored, both in terms of best fit values and uncertainties.Comment: 16 pages, 11 figure

The Masses and Shapes of Dark Matter Halos from Galaxy-Galaxy Lensing in the CFHTLS

We present the first galaxy-galaxy weak lensing results using early data from the Canada-France-Hawaii Telescope Legacy Survey (CFHTLS). These results are based on ~22 sq. deg. of i' data. From this data, we estimate the average velocity dispersion for an L* galaxy at a redshift of 0.3 to be 137 +- 11 km/s, with a virial mass, M_{200}, of 1.1 +- 0.2 \times 10^{12} h^{-1} Msun and a rest frame R-band mass-to-light ratio of 173 +- 34 h Msun/Lsun. We also investigate various possible sources of systematic error in detail. Additionally, we separate our lens sample into two sub-samples, divided by apparent magnitude, thus average redshift. From this early data we do not detect significant evolution in galaxy dark matter halo mass-to-light ratios from a redshift of 0.45 to 0.27. Finally, we test for non-spherical galaxy dark matter halos. Our results favor a dark matter halo with an ellipticity of ~0.3 at the 2-sigma level when averaged over all galaxies. If the sample of foreground lens galaxies is selected to favor ellipticals, the mean halo ellipticity and significance of this result increase.Comment: 12 pages, 11 figures, accepted to ApJ, uses emulateap

Calibrating Long Period Variables as Standard Candles with Machine Learning

Variable stars with well-calibrated period-luminosity relationships provide accurate distance measurements to nearby galaxies and are therefore a vital tool for cosmology and astrophysics. While these measurements typically rely on samples of Cepheid and RR-Lyrae stars, abundant populations of luminous variable stars with longer periods of $10 - 1000$ days remain largely unused. We apply machine learning to derive a mapping between lightcurve features of these variable stars and their magnitude to extend the traditional period-luminosity (PL) relation commonly used for Cepheid samples. Using photometric data for long period variable stars in the Large Magellanic cloud (LMC), we demonstrate that our predictions produce residual errors comparable to those obtained on the corresponding Cepheid population. We show that our model generalizes well to other samples by performing a blind test on photometric data from the Small Magellanic Cloud (SMC). Our predictions on the SMC again show small residual errors and biases, comparable to results that employ PL relations fitted on Cepheid samples. The residual biases are complementary between the long period variable and Cepheid fits, which provides exciting prospects to better control sources of systematic error in cosmological distance measurements. We finally show that the proposed methodology can be used to optimize samples of variable stars as standard candles independent of any prior variable star classification.Comment: 14 pages, 10 figures, 1 table, updated to match the version accepted by the MNRA

The outlier paradox: The role of iterative ensemble coding in discounting outliers

Ensemble perception—the encoding of objects by their group properties—is known to be resistant to outlier noise. However, this resistance is somewhat paradoxical: how can the visual system determine which stimuli are outliers without already having derived statistical properties of the ensemble? A simple solution would be that ensemble perception is not a simple, one-step process; instead, outliers are detected through iterative computations that identify items with high deviance from the mean and reduce their weight in the representation over time. Here we tested this hypothesis. In Experiment 1, we found evidence that outliers are discounted from mean orientation judgments, extending previous results from ensemble face perception. In Experiment 2, we tested the timing of outlier rejection by having participants perform speeded judgments of sets with or without outliers. We observed significant increases in reaction time (RT) when outliers were present, but a decrease compared to no-outlier sets of matched range suggesting that range alone did not drive RTs. In Experiment 3 we tested the timing by which outlier noise reduces over time. We presented sets for variable exposure durations and found that noise decreases linearly over time. Altogether these results suggest that ensemble representations are optimized through iterative computations aimed at reducing noise. The finding that ensemble perception is an iterative process provides a useful framework for understanding contextual effects on ensemble perception

Photometric Redshift Uncertainties in Weak Gravitational Lensing Shear Analysis: Models and Marginalization

Recovering credible cosmological parameter constraints in a weak lensing shear analysis requires an accurate model that can be used to marginalize over nuisance parameters describing potential sources of systematic uncertainty, such as the uncertainties on the sample redshift distribution $n(z)$. Due to the challenge of running Markov Chain Monte-Carlo (MCMC) in the high dimensional parameter spaces in which the $n(z)$ uncertainties may be parameterized, it is common practice to simplify the $n(z)$ parameterization or combine MCMC chains that each have a fixed $n(z)$ resampled from the $n(z)$ uncertainties. In this work, we propose a statistically-principled Bayesian resampling approach for marginalizing over the $n(z)$ uncertainty using multiple MCMC chains. We self-consistently compare the new method to existing ones from the literature in the context of a forecasted cosmic shear analysis for the HSC three-year shape catalog, and find that these methods recover similar cosmological parameter constraints, implying that using the most computationally efficient of the approaches is appropriate. However, we find that for datasets with the constraining power of the full HSC survey dataset (and, by implication, those upcoming surveys with even tighter constraints), the choice of method for marginalizing over $n(z)$ uncertainty among the several methods from the literature may significantly impact the statistical uncertainties on cosmological parameters, and a careful model selection is needed to ensure credible parameter intervals.Comment: 15 pages, 8 figures, submitted to mnra

Contending cultures of counterterrorism: transatlantic divergence or convergence?

Terrorist attacks on the United States, Spain and the United Kingdom have underlined the differing responses of Europe and the United States to the 'new terrorism'. This article analyses these responses through the prism of historically determined strategic cultures. For the last four years the United States has directed the full resources of a 'national security' approach towards this threat and has emphasized unilateralism. Europe, based on its own past experience of terrorism, has adopted a regulatory approach pursued through multilateralism. These divergences in transatlantic approaches, with potentially major implications for the future of the relationship, have appeared to be mitigated by a revised American strategy of counterterrorism that has emerged during 2005. However, this article contends that while strategic doctrines may change, the more immutable nature of strategic culture will make convergence difficult. This problem will be compounded by the fact that neither Europe nor America have yet addressed the deeper connections between terrorism and the process of globalization