Inferring the intensity of Poisson processes at the limit of the detector sensitivity (with a case study on gravitational wave burst search)

We consider the issue of reporting the result of search experiment in the most unbiased and efficient way, i.e. in a way which allows an easy interpretation and combination of results and which do not depend on whether the experimenters believe or not to having found the searched-for effect. Since this work uses the language of Bayesian theory, to which most physicists are not used, we find that it could be useful to practitioners to have in a single paper a simple presentation of Bayesian inference, together with an example of application of it in search of rare processes.Comment: 36 pages, 11 figures, Latex files using cernart.cls (included). This paper and related work are also available at http://www-zeus.roma1.infn.it/~agostini/prob+stat.htm

Bayesian model comparison applied to the Explorer-Nautilus 2001 coincidence data

Bayesian reasoning is applied to the data by the ROG Collaboration, in which gravitational wave (g.w.) signals are searched for in a coincidence experiment between Explorer and Nautilus. The use of Bayesian reasoning allows, under well defined hypotheses, even tiny pieces of evidence in favor of each model to be extracted from the data. The combination of the data of several experiments can therefore be performed in an optimal and efficient way. Some models for Galactic sources are considered and, within each model, the experimental result is summarized with the likelihood rescaled to the insensitivity limit value (``${\cal R}$ function''). The model comparison result is given in in terms of Bayes factors, which quantify how the ratio of beliefs about two alternative models are modified by the experimental observationComment: 16 pages, 4 figures. Presented at the GWDAW2002 conference, held in Kyoto on Dec.,2002. This version includes comments by the referees of CQG, which has accepted the paper for pubblication in the special issue of the conference. In particular, note that in Eq. 12 there was a typeset error. As suggested by one of the referees, a uniform prior in Log(alpha) has also been considere

2000 CKM-Triangle Analysis A Critical Review with Updated Experimental Inputs and Theoretical Parameters

Within the Standard Model, a review of the current determination of the sides and angles of the CKM unitarity triangle is presented, using experimental constraints from the measurements of |\epsilon_K|, |V_{ub}/V_{cb}|, \Delta m_d and from the limit on \Delta m_s, available in September 2000. Results from the experimental search for {B}^0_s-\bar{B}^0_s oscillations are introduced in the present analysis using the likelihood. Special attention is devoted to the determination of the theoretical uncertainties. The purpose of the analysis is to infer regions where the parameters of interest lie with given probabilities. The BaBar "95 %, C.L. scanning" method is also commented.Comment: 44 pages (revised version

Bayesian Inference in Processing Experimental Data: Principles and Basic Applications

This report introduces general ideas and some basic methods of the Bayesian probability theory applied to physics measurements. Our aim is to make the reader familiar, through examples rather than rigorous formalism, with concepts such as: model comparison (including the automatic Ockham's Razor filter provided by the Bayesian approach); parametric inference; quantification of the uncertainty about the value of physical quantities, also taking into account systematic effects; role of marginalization; posterior characterization; predictive distributions; hierarchical modelling and hyperparameters; Gaussian approximation of the posterior and recovery of conventional methods, especially maximum likelihood and chi-square fits under well defined conditions; conjugate priors, transformation invariance and maximum entropy motivated priors; Monte Carlo estimates of expectation, including a short introduction to Markov Chain Monte Carlo methods.Comment: 40 pages, 2 figures, invited paper for Reports on Progress in Physic

Mechanisms Of Inhibition Of Cigarette Smoke Genotoxicity And Carcinogenicity

Epidemiological studies have demonstrated that it is possible to prevent lung cancer and other smoke-related diseases by avoiding exposures to tobacco smoke. A complementary strategy is chemoprevention, which is based on the administration of dietary and pharmacological agents, which is addressed to (a) addicted active smokers, who are unable to quit smoking, (b) ex-smokers, who are still at risk for several years, and (c) involuntary smokers, including passively exposed individuals as well as transplacentally exposed individuals. The biological effects of cigarette smoke (CS) as a complex mixture, either mainstream (MCS) or sidestream (SCS) or environmental (ECS), have been poorly explored. We showed that MCS and ECS induce a broad variety of alterations of intermediate biomarkers in animal models, including adducts to nuclear DNA and mtDNA, oxidatively generated DNA damage, proliferation, apoptosis, alterations of oncogenes and tumor suppressor genes, multigene expression, microRNA and proteome profiles as well as cytogenetic damage in the respiratory tract, bone marrow and peripheral blood. CS-altered end-points were variously modulated by chemopreventive agents of natural or pharmacological origin, such as N-acetyl-L-cysteine (NAC), 1,2-dithiole-3-thione, oltipraz, 5,6-benzoflavone, phenethyl isothiocyanate (PEITC), indole-3-carbinol, sulindac, and budesonide. Combinations of agents were also assayed. Since it is difficult to assess the efficacy of chemopreventives in clinical trials, it is essential to understand the mechanisms by which certain agents are expected to prevent smoke-related cancer. Preclinical studies are also useful to demonstrate the potential efficacy of chemopreventive agents. Unfortunately, until recently a suitable animal model for evaluating CS carcinogenicity and its chemoprevention was not available. We demonstrated that ECS and especially MCS become potently carcinogenic when exposure of mice starts at birth, as shown by very short latency times, high incidence and multiplicity of benign lung tumors, early occurrence of malignant lung tumors, and lesions in other organs. This mouse model was successfully used to demonstrate the ability of NAC, PEITC, and budesonide to prevent smoke-induced lung cancer, according to protocols mimicking the situation either in current smokers or in ex-smokers. Other dietary or pharmacological agents, including curcumin, anthocyanins, myo-inositol, SAHA, bexarotene and pioglitazone, are now under study. NAC was even successful to prevent lung cancer induced by MCS after birth when it was administered during the prenatal life. Therefore, it is now possible to investigate in vivo not only alterations of intermediate biomarkers but also the modulation of CS carcinogenesis by chemopreventive agents working with different mechanisms

A population-based approach to background discrimination in particle physics

Background properties in experimental particle physics are typically estimated using control samples corresponding to large numbers of events. This can provide precise knowledge of average background distributions, but typically does not consider the effect of fluctuations in a data set of interest. A novel approach based on mixture model decomposition is presented as a way to estimate the effect of fluctuations on the shapes of probability distributions in a given data set, with a view to improving on the knowledge of background distributions obtained from control samples. Events are treated as heterogeneous populations comprising particles originating from different processes, and individual particles are mapped to a process of interest on a probabilistic basis. The proposed approach makes it possible to extract from the data information about the effect of fluctuations that would otherwise be lost using traditional methods based on high-statistics control samples. A feasibility study on Monte Carlo is presented, together with a comparison with existing techniques. Finally, the prospects for the development of tools for intensive offline analysis of individual events at the Large Hadron Collider are discussed.Comment: Updated according to the version published in J. Phys.: Conf. Ser. Minor changes have been made to the text with respect to the published article with a view to improving readabilit

Effects of age and gender on neural correlates of emotion imagery

Mental imagery is part of people's own internal processing and plays an important role in everyday life, cognition and pathology. The neural network supporting mental imagery is bottom-up modulated by the imagery content. Here, we examined the complex associations of gender and age with the neural mechanisms underlying emotion imagery. We assessed the brain circuits involved in emotion mental imagery (vs. action imagery), controlled by a letter detection task on the same stimuli, chosen to ensure attention to the stimuli and to discourage imagery, in 91 men and women aged 14–65 years using fMRI. In women, compared with men, emotion imagery significantly increased activation within the right putamen, which is involved in emotional processing. Increasing age, significantly decreased mental imagery-related activation in the left insula and cingulate cortex, areas involved in awareness of ones' internal states, and it significantly decreased emotion verbs-related activation in the left putamen, which is part of the limbic system. This finding suggests a top-down mechanism by which gender and age, in interaction with bottom-up effect of type of stimulus, or directly, can modulate the brain mechanisms underlying mental imagery