Should we doubt the cosmological constant?

While Bayesian model selection is a useful tool to discriminate between competing cosmological models, it only gives a relative rather than an absolute measure of how good a model is. Bayesian doubt introduces an unknown benchmark model against which the known models are compared, thereby obtaining an absolute measure of model performance in a Bayesian framework. We apply this new methodology to the problem of the dark energy equation of state, comparing an absolute upper bound on the Bayesian evidence for a presently unknown dark energy model against a collection of known models including a flat LambdaCDM scenario. We find a strong absolute upper bound to the Bayes factor B between the unknown model and LambdaCDM, giving B < 3. The posterior probability for doubt is found to be less than 6% (with a 1% prior doubt) while the probability for LambdaCDM rises from an initial 25% to just over 50% in light of the data. We conclude that LambdaCDM remains a sufficient phenomenological description of currently available observations and that there is little statistical room for model improvement.Comment: 10 pages, 2 figure

Pippi - painless parsing, post-processing and plotting of posterior and likelihood samples

Interpreting samples from likelihood or posterior probability density functions is rarely as straightforward as it seems it should be. Producing publication-quality graphics of these distributions is often similarly painful. In this short note I describe pippi, a simple, publicly-available package for parsing and post-processing such samples, as well as generating high-quality PDF graphics of the results. Pippi is easily and extensively configurable and customisable, both in its options for parsing and post-processing samples, and in the visual aspects of the figures it produces. I illustrate some of these using an existing supersymmetric global fit, performed in the context of a gamma-ray search for dark matter. Pippi can be downloaded and followed at http://github.com/patscott/pippi .Comment: 4 pages, 1 figure. v3: Updated for pippi 2.0. New features include hdf5 support, out-of-core processing, inline post-processing with arbitrary Python code in the input file, and observable-specific data cuts. Pippi can be downloaded from http://github.com/patscott/pipp

The impact of priors and observables on parameter inferences in the Constrained MSSM

We use a newly released version of the SuperBayeS code to analyze the impact of the choice of priors and the influence of various constraints on the statistical conclusions for the preferred values of the parameters of the Constrained MSSM. We assess the effect in a Bayesian framework and compare it with an alternative likelihood-based measure of a profile likelihood. We employ a new scanning algorithm (MultiNest) which increases the computational efficiency by a factor ~200 with respect to previously used techniques. We demonstrate that the currently available data are not yet sufficiently constraining to allow one to determine the preferred values of CMSSM parameters in a way that is completely independent of the choice of priors and statistical measures. While b->s gamma generally favors large m_0, this is in some contrast with the preference for low values of m_0 and m_1/2 that is almost entirely a consequence of a combination of prior effects and a single constraint coming from the anomalous magnetic moment of the muon, which remains somewhat controversial. Using an information-theoretical measure, we find that the cosmological dark matter abundance determination provides at least 80% of the total constraining power of all available observables. Despite the remaining uncertainties, prospects for direct detection in the CMSSM remain excellent, with the spin-independent neutralino-proton cross section almost guaranteed above sigma_SI ~ 10^{-10} pb, independently of the choice of priors or statistics. Likewise, gluino and lightest Higgs discovery at the LHC remain highly encouraging. While in this work we have used the CMSSM as particle physics model, our formalism and scanning technique can be readily applied to a wider class of models with several free parameters.Comment: Minor changes, extended discussion of profile likelihood. Matches JHEP accepted version. SuperBayeS code with MultiNest algorithm available at http://www.superbayes.or

Effects of exposures to repeated heat stress on the survival of the pea aphid Acyrthosiphon pisum and its endoparasitoid Aphidius ervi

Organisms could be exposed to several heat waves during their life, and their ability to survive a heat wave strongly depends on the effects of the previous one. Exposure to extreme temperatures can have important effects on the outcome of host-parasitoid interactions, as the ability of the parasitoid to survive depends on the ability of its host to cope successfully with these stresses. In the present study we address the impact of repeated exposure to heat stress on the survival of the pea aphid Acyrthosiphon pisum (Harris) (Hemiptera Aphididae) and its endoparasitoid Aphidius ervi Haliday Hymenoptera Braconidae). The first treatment consisted of a heat stress of 35 °C for 30 minutes performed on 4 days old aphids, the second and third heat stresses of 39 °C were performed on 5 days old and on adult aphids, espectively. The three treatments were applied alone or in all their ombinations. We found that aphid thermal tolerance is positively influenced by heat hardening if a severe stress occurs a few days after the first event. Adult parasitized aphids show significantly higher survival than unparasitized ones; however, the effects of parasitization and hardening on host survival after heat shock are not additive. We also found that A. ervi has a lower thermotolerance capacity than its host and does not show apparent hardening effects. In addition, parasitoid survival after mummification is not affected by the previously experienced heat shock. The possible explanations of the observed phenomena are discussed

Fast Acceleration of Transrelativistic Electrons in Astrophysical Turbulence

Highly energetic, relativistic electrons are commonly present in many astrophysical systems, from solar flares to the intra-cluster medium, as indicated by observed electromagnetic radiation. However, open questions remain about the mechanisms responsible for their acceleration, and possible re-acceleration. Ubiquitous plasma turbulence is one of the possible universal mechanisms. We study the energization of transrelativistic electrons in turbulence using hybrid particle-in-cell, which provide a realistic model of Alfv\'{e}nic turbulence from MHD to sub-ion scales, and test particle simulations for electrons. We find that, depending on the electron initial energy and turbulence strength, electrons may undergo a fast and efficient phase of energization due to the magnetic curvature drift during the time they are trapped in dynamic magnetic structures. In addition, electrons are accelerated stochastically which is a slower process that yields lower maximum energies. The combined effect of these two processes determines the overall electron acceleration. With appropriate turbulence parameters, we find that superthermal electrons can be accelerated up to relativistic energies. For example, with heliospheric parameters and a relatively high turbulence level, rapid energization to MeV energies is possible.Comment: Accepted for publication in The Astrophysical Journa