Reconstruction of the gravitational wave signal h(t) during the Virgo science runs and independent validation with a photon calibrator

The Virgo detector is a kilometer-scale interferometer for gravitational wave detection located near Pisa (Italy). About 13 months of data were accumulated during four science runs (VSR1, VSR2, VSR3 and VSR4) between May 2007 and September 2011, with increasing sensitivity. In this paper, the method used to reconstruct, in the range 10 Hz-10 kHz, the gravitational wave strain time series h(t) from the detector signals is described. The standard consistency checks of the reconstruction are discussed and used to estimate the systematic uncertainties of the h(t) signal as a function of frequency. Finally, an independent setup, the photon calibrator, is described and used to validate the reconstructed h(t) signal and the associated uncertainties. The systematic uncertainties of the h(t) time series are estimated to be 8% in amplitude. The uncertainty of the phase of h(t) is 50 mrad at 10 Hz with a frequency dependence following a delay of 8 mu s at high frequency. A bias lower than 4 mu s and depending on the sky direction of the GW is also present.

Extended depth from focus reconstruction using NIH ImageJ plugins: Quality and resolution of elevation maps

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Time reconstruction and performance of the CMS electromagnetic calorimeter

The resolution and the linearity of time measurements made with the CMS electromagnetic calorimeter are studied with samples of data from test beam electrons, cosmic rays, and beam-produced muons. The resulting time resolution measured by lead tungstate crystals is better than 100 ps for energy deposits larger than 10 GeV. Crystal-to-crystal synchronization with a precision of 500 ps is performed using muons produced with the first LHC beams in 2008. © 2010 IOP Publishing Ltd and SISSA.

Performance of CMS muon reconstruction in cosmic-ray events

The performance of muon reconstruction in CMS is evaluated using a large data sample of cosmic-ray muons recorded in 2008. Efficiencies of various high-level trigger, identification, and reconstruction algorithms have been measured for a broad range of muon momenta, and were found to be in good agreement with expectations from Monte Carlo simulation. The relative momentum resolution for muons crossing the barrel part of the detector is better than 1% at 10 GeV/c and is about 8% at 500 GeV/c, the latter being only a factor of two worse than expected with ideal alignment conditions. Muon charge misassignment ranges from less than 0.01% at 10GeV/c to about 1% at 500 GeV/c. © 2010 IOP Publishing Ltd and SISSA.

Signal classification of submerged aquatic vegetation based on the hemispherical-conical reflectance factor spectrum shape in the yellow and red regions

The water column overlying the submerged aquatic vegetation (SAV) canopy presents difficulties when using remote sensing images for mapping such vegetation. Inherent and apparent water optical properties and its optically active components, which are commonly present in natural waters, in addition to the water column height over the canopy, and plant characteristics are some of the factors that affect the signal from SAV mainly due to its strong energy absorption in the near-infrared. By considering these interferences, a hypothesis was developed that the vegetation signal is better conserved and less absorbed by the water column in certain intervals of the visible region of the spectrum; as a consequence, it is possible to distinguish the SAV signal. To distinguish the signal from SAV, two types of classification approaches were selected. Both of these methods consider the hemispherical-conical reflectance factor (HCRF) spectrum shape, although one type was supervised and the other one was not. The first method adopts cluster analysis and uses the parameters of the band (absorption, asymmetry, height and width) obtained by continuum removal as the input of the classification. The spectral angle mapper (SAM) was adopted as the supervised classification approach. Both approaches tested different wavelength intervals in the visible and near-infrared spectra. It was demonstrated that the 585 to 685-nm interval, corresponding to the green, yellow and red wavelength bands, offered the best results in both classification approaches. However, SAM classification showed better results relative to cluster analysis and correctly separated all spectral curves with or without SAV. Based on this research, it can be concluded that it is possible to discriminate areas with and without SAV using remote sensing. © 2013 by the authors; licensee MDPI, Basel, Switzerland.