No Arabic abstract
A common problem in ultra-high energy cosmic ray physics is the comparison of energy spectra. The question is whether the spectra from two experiments or two regions of the sky agree within their statistical and systematic uncertainties. We develop a method to directly compare energy spectra for ultra-high energy cosmic rays from two different regions of the sky in the same experiment without reliance on agreement with a theoretical model of the energy spectra. The consistency between the two spectra is expressed in terms of a Bayes factor, defined here as the ratio of the likelihood of the two-parent source hypothesis to the likelihood of the one-parent source hypothesis. Unlike other methods, for example chi^2 tests, the Bayes factor allows for the calculation of the posterior odds ratio and correctly accounts for non-Gaussian uncertainties. The latter is particularly important at the highest energies, where the number of events is very small.
A new hybrid experiment has been started by AS{gamma} experiment at Tibet, China, since August 2011, which consists of a low threshold burst-detector-grid (YAC-II, Yangbajing Air shower Core array), the Tibet air-shower array (Tibet-III) and a large underground water Cherenkov muon detector (MD). In this paper, the capability of the measurement of the chemical components (proton, helium and iron) with use of the (Tibet-III+YAC-II) is investigated by means of an extensive Monte Carlo simulation in which the secondary particles are propagated through the (Tibet-III+YAC-II) array and an artificial neural network (ANN) method is applied for the primary mass separation. Our simulation shows that the new installation is powerful to study the chemical compositions, in particular, to obtain the primary energy spectrum of the major component at the knee.
The Laser Interferometer Space Antenna (LISA) defines new demands on data analysis efforts in its all-sky gravitational wave survey, recording simultaneously thousands of galactic compact object binary foreground sources and tens to hundreds of background sources like binary black hole mergers and extreme mass ratio inspirals. We approach this problem with an adaptive and fully automatic Reversible Jump Markov Chain Monte Carlo sampler, able to sample from the joint posterior density function (as established by Bayes theorem) for a given mixture of signals out of the box, handling the total number of signals as an additional unknown parameter beside the unknown parameters of each individual source and the noise floor. We show in examples from the LISA Mock Data Challenge implementing the full response of LISA in its TDI description that this sampler is able to extract monochromatic Double White Dwarf signals out of colored instrumental noise and additional foreground and background noise successfully in a global fitting approach. We introduce 2 examples with fixed number of signals (MCMC sampling), and 1 example with unknown number of signals (RJ-MCMC), the latter further promoting the idea behind an experimental adaptation of the model indicator proposal densities in the main sampling stage. We note that the experienced runtimes and degeneracies in parameter extraction limit the shown examples to the extraction of a low but realistic number of signals.
Model fitting is possibly the most extended problem in science. Classical approaches include the use of least-squares fitting procedures and maximum likelihood methods to estimate the value of the parameters in the model. However, in recent years, Bayesian inference tools have gained traction. Usually, Markov chain Monte Carlo methods are applied to inference problems, but they present some disadvantages, particularly when comparing different models fitted to the same dataset. Other Bayesian methods can deal with this issue in a natural and effective way. We have implemented an importance sampling algorithm adapted to Bayesian inference problems in which the power of the noise in the observations is not known a priori. The main advantage of importance sampling is that the model evidence can be derived directly from the so-called importance weights -- while MCMC methods demand considerable postprocessing. The use of our adaptive target, adaptive importance sampling (ATAIS) method is shown by inferring, on the one hand, the parameters of a simulated flaring event which includes a damped oscillation {and, on the other hand, real data from the Kepler mission. ATAIS includes a novel automatic adaptation of the target distribution. It automatically estimates the variance of the noise in the model. ATAIS admits parallelisation, which decreases the computational run-times notably. We compare our method against a nested sampling method within a model selection problem.
Development of the Silicon photomultiplier Elementary Cell Add-on camera (SiECA) has provided extensive information regarding the use of SiPMs for future cosmic ray detection systems. We present the technical aspects of sensor readout development utilizing Citiroc ASIC chips from Weeroc controlled by a Xilinx FPGA to process and package events from four 64 channel Hamamatsu MPPC S13361 arrays generating 128 frame events with an integration time of 2.5ms (parameters are based on JEM-EUSO geometry but can be easily adjusted). With single photon counting capability, SiECA proves SiPM are viable sensors to replace Multi-Anode PhotoMultiplier Tubes in future devices, especially when high luminosity exposure is possible potentially damaging MAPMT based systems. Complementary to the technical aspects, computational and analysis methods for sensor array characterization and in depth device flat-fielding are presented. Provided channel by channel biasing, in comparison to uniform biasing with MAPMTs, fine tuning of operating parameters with MPPC arrays allows for substantial improvements in detector and signal uniformity.
We present a concept for large-area, low-cost detection of ultra-high energy cosmic rays (UHECRs) with a Fluorescence detector Array of Single-pixel Telescopes (FAST), addressing the requirements for the next generation of UHECR experiments. In the FAST design, a large field of view is covered by a few pixels at the focal plane of a mirror or Fresnel lens. We report first results of a FAST prototype installed at the Telescope Array site, consisting of a single 200 mm photomultiplier tube at the focal plane of a 1 m$^2$ Fresnel lens system taken from the prototype of the JEM-EUSO experiment. The FAST prototype took data for 19 nights, demonstrating remarkable operational stability. We detected laser shots at distances of several kilometres as well as 16 highly significant UHECR shower candidates.