44 research outputs found
The Physics of Heavy Flavours at SuperB
This is a review of the SuperB project, covering the accelerator, detector,
and highlights of the broad physics programme. SuperB is a flavour factory
capable of performing precision measurements and searches for rare and
forbidden decays of , , and
particles. These results can be used to test fundamental symmetries and
expectations of the Standard Model, and to constrain many different
hypothesised types of new physics. In some cases these measurements can be used
to place constraints on the existence of light dark matter and light Higgs
particles with masses below . The potential impact of the
measurements that will be made by SuperB on the field of high energy physics is
also discussed in the context of data taken at both high energy in the region
around the \Upsilon({\mathrm{4S}})$, and near charm threshold.Comment: 49 pages, topical review submitted to J. Phys
Indo-Ganges River Basin Land Use/Land Cover (LULC) and Irrigated Area Mapping
This study was conducted to map detailed land use/land cover (LULC) and irrigated area categories in the Ganges and Indus River basins using near-continuous time-series 250 m resolution moderate-resolution imaging spectroradiometer (MODIS) data. The study used a unique data set—a stack of 46 images, 23 MODIS images each of 2-bands, compiled from MODIS terra images for the years 2013 and 2014. Field-plot data were gathered from 553 precise geographic locations covering about 8000 km in the basins. Spatial information on cropland and irrigated area distribution was restricted by the district-level crop statistics published by the state or national governments in India and Pakistan. Statistics were collected by irrigation and agriculture departments, but there was discrepancy in the irrigated area between departments. Water availability in major command areas varied frequently due to rainfall fluctuations, which leads to an inadequate water supply during critical crop growth stages. The study analyzed MODIS 16-day normalized difference vegetation index (NDVI) time-series data acquired for 2013 and 2014 using spectral matching techniques (SMTs). The map output accuracies were evaluated based on independent ground data and compared with subnational level statistics. The producer's and user's accuracies of the cropland classes were between 70% and 85%. The overall accuracy and the kappa coefficient estimated for irrigated areas were both 84%