On the influence of statistics on the determination of the mean value of the depth of shower maximum for ultra high energy cosmic ray showers

The chemical composition of ultra high energy cosmic rays is still uncertain. The latest results obtained by the Pierre Auger Observatory and the HiRes Collaboration, concerning the measurement of the mean value and the fluctuations of the atmospheric depth at which the showers reach the maximum development, Xmax, are inconsistent. From comparison with air shower simulations it can be seen that, while the Auger data may be interpreted as a gradual transition to heavy nuclei for energies larger than ~ 2-3x10^18 eV, the HiRes data are consistent with a composition dominated by protons. In Ref. [1] it is suggested that a possible explanation of the observed deviation of the mean value of Xmax from the proton expectation, observed by Auger, could originate in a statistical bias arising from the approximated exponential shape of the Xmax distribution, combined with the decrease of the number of events as a function of primary energy. In this paper we consider a better description of the Xmax distribution and show that the possible bias in the Auger data is at least one order of magnitude smaller than the one obtained when assuming an exponential distribution. Therefore, we conclude that the deviation of the Auger data from the proton expectation is unlikely explained by such statistical effect.Comment: To be published in Journal of Physics G: Nuclear and Particle Physic

Robust design of a reentry unmanned space vehicle by multifidelity evolution control

This paper addresses the preliminary robust design of a small-medium scale re-entry unmanned space vehicle. A hybrid optimization technique is proposed that couples an evolutionary multi-objective algorithm with a direct transcription method for optimal control problems. Uncertainties on the aerodynamic forces and vehicle mass are integrated in the design process and the hybrid algorithm searches for geometries that a) minimize the mean value of the maximum heat flux, b) maximize the mean value of the maximum achievable distance, and c) minimize the variance of the maximum heat flux. The evolutionary part handles the system design parameters of the vehicle and the uncertain functions, while the direct transcription method generates optimal control profiles for the re-entry trajectory of each individual of the population. During the optimization process, artificial neural networks are used to approximate the aerodynamic forces required by the direct transcription method. The artificial neural networks are trained and updated by means of a multi-fidelity, evolution control approach

Broad Band Optical Polarimetric Study of IC 1805

We present the BVR broad band polarimetric observations of 51 stars belonging to the young open cluster IC 1805. Along with the photometric data from the literature we have modeled and subtracted the foreground dust contribution from the maximum polarization (P_{max}) and colour excess (E_{B-V}). The mean value of the P_max for intracluster medium and the foreground are found to be 5.008 +/-0.005 % and 4.865 +/-0.022 % respectively. Moreover, the mean value of the wavelength of maximum polarization (lambda_{max}) for intracluster medium is 0.541 +/- 0.003 micro m, which is quite similar as the general interstellar medium (ISM). The resulting intracluster dust component is found to have negligible polarization efficiency as compared to interstellar dust. Some of the observed stars in IC 1805 have shown the indication of intrinsic polarization in their measurements.Comment: 8 pages, 7 figures, Accepted for publication in MNRA

Minority Challenge of Majority Actions in a Close Corporation in Italy and the United States

This paper addresses the problem of segmenting a time-series with respect to changes in the mean value or in the variance. The first case is when the time data is modeled as a sequence of independent and normal distributed random variables with unknown, possibly changing, mean value but fixed variance. The main assumption is that the mean value is piecewise constant in time, and the task is to estimate the change times and the mean values within the segments. The second case is when the mean value is constant, but the variance can change. The assumption is that the variance is piecewise constant in time, and we want to estimate change times and the variance values within the segments. To find solutions to these problems, we will study an l_1 regularized maximum likelihood method, related to the fused lasso method and l_1 trend filtering, where the parameters to be estimated are free to vary at each sample. To penalize variations in the estimated parameters, the $l_1$-norm of the time difference of the parameters is used as a regularization term. This idea is closely related to total variation denoising. The main contribution is that a convex formulation of this variance estimation problem, where the parametrization is based on the inverse of the variance, can be formulated as a certain $l_1$ mean estimation problem. This implies that results and methods for mean estimation can be applied to the challenging problem of variance segmentation/estimationQC 20140908</p