An high resolution FDIRC for the measurement of cosmic-ray isotopic abundances

Measurements of the relative abundance of cosmic isotopes and of the energy dependence of their fluxes may clarify our present understanding on the confinement time of charged cosmic rays in the Galaxy. Experimental studies of these propagation clocks have been carried out by balloon and space missions at energies of a few 100 MeV/amu by means of detection techniques based on multiple dE/dx sampling, coupled with a measurement of the energy released in a thick absorber. At larger energies, the isotopic separation of light nuclei (as, for instance,⁹Be/¹⁰Be) can be achieved by combining a precise measurement of the particle’s rigidity with an high resolution determination of its velocity, via the observation of the Cherenkov effect in a radiator.In this paper, we propose the introduction - for the first time in a space experiment - of the DIRC technique (Detection of Internal Reflected Cherenkov light) for the identification of cosmic-ray isotopes. This type of detector has been successfully used in electron-positron colliders for particle identification and in particular for π-K separation. While for particles with unit charge the light yield is a limiting factor, in the case of a nucleus of charge Z the larger photostatistics (due to the Z² dependence of Cherenkov light emission) is the key to reach an adequate angular resolution to provide a mass discrimination for isotopes of astrophysical interest. We report on the early development phase of a DIRC prototype with a focussing scheme (FDIRC) to collect the Cherenkov light onto a detector plane instrumented with a Silicon PhotoMultiplier (SiPM) array.     

Forest cover and landuse mapping in Karbi Anglong and North Cachar Hills districts of assam using landsat MSS data

Karbi Anglong and North Cachar Hills districts of Assam are endowed with rich and diverse vegetation resources. Increased human pressure due to shifting cultivation and raw material extraction for industrial purposes are heavily altering the forested landscape. The present study deals with mapping of forest types in the two districts using LANDSAT-MSS digital data. The maps thus generated provide spatial distribution of bioclimatic vegetation types. Supervised maximum likelihood classification has been performed using training sets collected during field work. The spectral behaviour of vegetation types have been studied for optimising classification scheme. The classification accuracy of classes mapped has been calculated.