Calibration Studies and the Investigation of Track Segments within Showers with an Imaging Hadronic Calorimeter

The CALICE collaboration has constructed a highly granular hadronic sampling calorimeter prototype with small scintillator tiles individually read out by silicon photomultipliers (SiPM) to evaluate technologies for the ILC. The imaging capability of the detector allows detailed studies of the substructure of hadronic events, such as the identification of minimum ionizing track segments within the hadronic shower. These track segments are of high quality, so that they can be used for calibration, as an additional tool to Muons and to the built-in LED system used to monitor the SiPMs. These track segments also help to constrain hadronic shower models used in Geant4. Detailed MC studies with a realistic model of an ILC detector were performed to study the calibration requirements of a complete calorimeter system. The calibration strategy was tested on real data by transporting calibration constants from a Fermilab beam test to data recorded at CERN under different conditions.Comment: 5 pages, 3 figures, 11th ICATPP Conference Proceedin

Comparison of hadron shower data with simulations

An analog hadron calorimeter (AHCAL) prototype of 5.3 nuclear interaction lengths thickness has been designed and constructed by members of the CALICE Collaboration. The AHCAL prototype consists of a 38-layer sandwich structure of steel plates and 7608 scintillator tiles that are read out by wavelength-shifting fibres coupled to SiPMs. The signal is amplified and shaped with a custom-designed ASIC. A calibration/monitoring system based on LED light was developed to monitor the SiPM gain and to measure the full SiPM response curve in order to correct for non-linearity. Ultimately, the physics goals are the study of hadronic shower shapes and testing the concept of particle flow. The technical goal consists of measuring the performance and reliability of 7608 SiPMs. The AHCAL prototype was commissioned in test beams at DESY, CERN and FNAL, and recorded hadronic showers, electron showers and muons at different energies and incident angles.Comment: 6 pages, 19 figures, LCWS2010 Proceedin

Perturbations of bounce inflation scenario from f(T)f(T) modified gravity revisited

In this work, we revisit the perturbations that are generated in the bounce inflation scenario constructed within the framework of $f(T)$ theory. It has been well known that pure $f(T)$ theory cannot give rise to bounce inflation behavior, so aside from the gravity part, we also employ a canonical scalar field for minimal extension. We calculate the perturbations in $f(T)$ theory using the well-established ADM formalism, and find various conditions to avoid their pathologies. We find that it is indeed very difficult to obtain a healthy model without those pathologies, however, one may find a way out if a potential requirement, say, to keep every function continuous, is abandoned.Comment: 5 pages, 1 figures. Comments are welcom

Variance Estimation Using Refitted Cross-validation in Ultrahigh Dimensional Regression

Variance estimation is a fundamental problem in statistical modeling. In ultrahigh dimensional linear regressions where the dimensionality is much larger than sample size, traditional variance estimation techniques are not applicable. Recent advances on variable selection in ultrahigh dimensional linear regressions make this problem accessible. One of the major problems in ultrahigh dimensional regression is the high spurious correlation between the unobserved realized noise and some of the predictors. As a result, the realized noises are actually predicted when extra irrelevant variables are selected, leading to serious underestimate of the noise level. In this paper, we propose a two-stage refitted procedure via a data splitting technique, called refitted cross-validation (RCV), to attenuate the influence of irrelevant variables with high spurious correlations. Our asymptotic results show that the resulting procedure performs as well as the oracle estimator, which knows in advance the mean regression function. The simulation studies lend further support to our theoretical claims. The naive two-stage estimator which fits the selected variables in the first stage and the plug-in one stage estimators using LASSO and SCAD are also studied and compared. Their performances can be improved by the proposed RCV method