2,610 research outputs found
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 modified gravity revisited
In this work, we revisit the perturbations that are generated in the bounce
inflation scenario constructed within the framework of theory. It has
been well known that pure 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 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
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