Reference priors for high energy physics

Bayesian inferences in high energy physics often use uniform prior distributions for parameters about which little or no information is available before data are collected. The resulting posterior distributions are therefore sensitive to the choice of parametrization for the problem and may even be improper if this choice is not carefully considered. Here we describe an extensively tested methodology, known as reference analysis, which allows one to construct parametrization-invariant priors that embody the notion of minimal informativeness in a mathematically well-defined sense. We apply this methodology to general cross section measurements and show that it yields sensible results. A recent measurement of the single top quark cross section illustrates the relevant techniques in a realistic situation

Bayesian power-spectrum inference for Large Scale Structure data

We describe an exact, flexible, and computationally efficient algorithm for a joint estimation of the large-scale structure and its power-spectrum, building on a Gibbs sampling framework and present its implementation ARES (Algorithm for REconstruction and Sampling). ARES is designed to reconstruct the 3D power-spectrum together with the underlying dark matter density field in a Bayesian framework, under the reasonable assumption that the long wavelength Fourier components are Gaussian distributed. As a result ARES does not only provide a single estimate but samples from the joint posterior of the power-spectrum and density field conditional on a set of observations. This enables us to calculate any desired statistical summary, in particular we are able to provide joint uncertainty estimates. We apply our method to mock catalogs, with highly structured observational masks and selection functions, in order to demonstrate its ability to reconstruct the power-spectrum from real data sets, while fully accounting for any mask induced mode coupling.Comment: 25 pages, 15 figure

A New Bayesian Test to Test for the Intractability-Countering Hypothesis

We present a new test of hypothesis in which we seek the probability of the null conditioned on the data, where the null is a simplification undertaken to counter the intractability of the more complex model, that the simpler null model is nested within. With the more complex model rendered intractable, the null model uses a simplifying assumption that capacitates the learning of an unknown parameter vector given the data. Bayes factors are shown to be known only up to a ratio of unknown data-dependent constants--a problem that cannot be cured using prescriptions similar to those suggested to solve the problem caused to Bayes factor computation, by non-informative priors. Thus, a new test is needed in which we can circumvent Bayes factor computation. In this test, we undertake generation of data from the model in which the null hypothesis is true and can achieve support in the measured data for the null by comparing the marginalised posterior of the model parameter given the measured data, to that given such generated data. However, such a ratio of marginalised posteriors can confound interpretation of comparison of support in one measured data for a null, with that in another data set for a different null. Given an application in which such comparison is undertaken, we alternatively define support in a measured data set for a null by identifying the model parameters that are less consistent with the measured data than is minimally possible given the generated data, and realising that the higher the number of such parameter values, less is the support in the measured data for the null. Then, the probability of the null conditional on the data is given within an MCMC-based scheme, by marginalising the posterior given the measured data, over parameter values that are as, or more consistent with the measured data, than with the generated data.Comment: Accepted for publication in JAS

On the Dialectics of Charisma in Marina Abramović’s The Artist is Present

While ‘charisma’ can be found in dramatic and theatrical parlance, the term enjoys only minimal critical attention in theatre and performance studies, with scholarly work on presence and actor training methods taking the lead in defining charisma’s supposed ‘undefinable’ quality. Within this context, the article examines the appearance of the term ‘charismatic space’ in relation to Marina Abramovic’s retrospective The Artist is Present at New York’s Museum of Modern Art in 2010. Here Abramovic uses this term to describe the shared space in which performer and spectator connect bodily, psychically, and spiritually through a shared sense of presence and energy in the moment of performance. Yet this is a space arguably constituted through a number of dialectical tensions and contradictions which, in dialogue with existing theatre scholarship on charisma, can be further understood by drawing on insights into charismatic leaders and charismatic authority in leadership studies. By examining the performance and its documentary traces in terms of dialectics we consider the political and ethical implications for how we think about power relations between artist/spectator in a neoliberal, market-driven art context. Here an alternative approach to conceiving of and facilitating a charismatic space is proposed which instead foregrounds what Bracha L. Ettinger calls a ‘matrixial encounter-event’: A relation of coexistence and compassion rather than dominance of self over other; performer over spectator; leader over follower. By illustrating the dialectical tensions in The Artist is Present, we consider the potential of the charismatic space not as generated through the seductive power or charm of an individual whose authority is tied to his/her ‘presence’, but as something co-produced within an ethical and relational space of trans-subjectivity

Semi-automated non-target processing in GC × GC–MS metabolomics analysis: applicability for biomedical studies

Due to the complexity of typical metabolomics samples and the many steps required to obtain quantitative data in GC × GC–MS consisting of deconvolution, peak picking, peak merging, and integration, the unbiased non-target quantification of GC × GC–MS data still poses a major challenge in metabolomics analysis. The feasibility of using commercially available software for non-target processing of GC × GC–MS data was assessed. For this purpose a set of mouse liver samples (24 study samples and five quality control (QC) samples prepared from the study samples) were measured with GC × GC–MS and GC–MS to study the development and progression of insulin resistance, a primary characteristic of diabetes type 2. A total of 170 and 691 peaks were quantified in, respectively, the GC–MS and GC × GC–MS data for all study and QC samples. The quantitative results for the QC samples were compared to assess the quality of semi-automated GC × GC–MS processing compared to targeted GC–MS processing which involved time-consuming manual correction of all wrongly integrated metabolites and was considered as golden standard. The relative standard deviations (RSDs) obtained with GC × GC–MS were somewhat higher than with GC–MS, due to less accurate processing. Still, the biological information in the study samples was preserved and the added value of GC × GC–MS was demonstrated; many additional candidate biomarkers were found with GC × GC–MS compared to GC–MS

Interpreting LHC SUSY searches in the phenomenological MSSM

We interpret within the phenomenological MSSM (pMSSM) the results of SUSY searches published by the CMS collaboration based on the first ~1 fb^-1 of data taken during the 2011 LHC run at 7 TeV. The pMSSM is a 19-dimensional parametrization of the MSSM that captures most of its phenomenological features. It encompasses, and goes beyond, a broad range of more constrained SUSY models. Performing a global Bayesian analysis, we obtain posterior probability densities of parameters, masses and derived observables. In contrast to constraints derived for particular SUSY breaking schemes, such as the CMSSM, our results provide more generic conclusions on how the current data constrain the MSSM.Comment: 15 pages, 7 figures; minor revision, some references and a comment on prior dependence added; version accepted by JHE

Superior antigen-specific CD4+ T-cell response with AS03-adjuvantation of a trivalent influenza vaccine in a randomised trial of adults aged 65 and older

BACKGROUND: The effectiveness of trivalent influenza vaccines may be reduced in older versus younger adults because of age-related immunosenescence. The use of an adjuvant in such a vaccine is one strategy that may combat immunosenescence, potentially by bolstering T-cell mediated responses. METHODS: This observer-blind study, conducted in the United States (US) and Spain during the 2008-2009 influenza season, evaluated the effect of Adjuvant System AS03 on specific T-cell responses to a seasonal trivalent influenza vaccine (TIV) in >/=65 year-old adults.Medically-stable adults aged >/=65 years were randomly allocated to receive a single dose of AS03-adjuvanted TIV (TIV/AS03) or TIV. Healthy adults aged 18-40 years received only TIV. Blood samples were collected on Day 0, Day 21, Day 42 and Day 180. Influenza-specific CD4+ T cells, defined by the induction of the immune markers CD40L, IL-2, IFN-gamma, or TNF-alpha, were measured in ex vivo cultures of antigen-stimulated peripheral blood mononuclear cells. RESULTS: A total of 192 adults were vaccinated: sixty nine and seventy three >/=65 year olds received TIV/AS03 and TIV, respectively; and fifty 18 - 40 year olds received TIV. In the >/=65 year-old group on Day 21, the frequency of CD4+ T cells specific to the three vaccine strains was superior in the TIV/AS03 recipients to the frequency in TIV (p /=65 year-old recipients of TIV/AS03 than in the 18 - 40 year old recipients of TIV on Days 21 (p = 0.006) and 42 (p = 0.011). CONCLUSION: This positive effect of AS03 Adjuvant System on the CD4+ T-cell response to influenza vaccine strains in older adults could confer benefit in protection against clinical influenza disease in this population. TRIAL REGISTRATION: (Clinicaltrials.gov.). NCT00765076