There is considerable interest world-wide in developing large area atmospheric Cherenkov detectors for ground-based gamma-ray astronomy. This interest stems, in large part, from the fact that the gamma-ray energy region between 20 and 250 GeV is unexplored by any experiment. Atmospheric Cherenkov detectors offer a possible way to explore this region, but large photon collection areas are needed to achieve low energy thresholds. We are developing an experiment using the heliostat mirrors of a solar power plant as the primary collecting element. As part of this development, we built a detector using four heliostat mirrors, a secondary Fresnel lens, and a fast photon detection system. In November 1994, we used this detector to record atmospheric Cherenkov radiation produced by cosmic ray particles showering in the atmosphere. The detected rate of cosmic ray events was consistent with an energy threshold near 1 TeV. The data presented here represent the first detection of atmospheric Cherenkov radiation using solar heliostats viewed from a central tower. 相似文献
The results of a systematic radial velocity survey of two Hii regions in the Cygnus-X complex are presented. The [Nii] emission line components obtained at many positions over each object have been compared and correlated with other phenomena.
It is suggested that they are probably connected objects and may be part of a giant shell or cylinder. 相似文献
Pleistocene proboscidean fossils recovered from the Bering Land Bridge strengthen the hypothesis that man may have originally colonized the Americas via this route during times of lowered sea level. 相似文献
The Gilt Edge Superfund Site is a former heap-leach gold mine that currently is being remediated in the Black Hills of South Dakota. Mine runoff water is treated before release from the site. The field pH, before treatment, is about 3; the water contains arsenic at low levels and some trace metals at elevated levels, in addition to total dissolved solids concentrations of more than 1,900 mg/L. In the Keystone area of the Black Hills, naturally occurring arsenic has been detected at elevated concentrations in groundwater samples from wells. The City of Keystone’s Roy Street Well, which is not used currently, showed arsenic concentrations of 36 parts per billion and total dissolved solids of 320 mg/L. With field samples of water from the Gilt Edge site, a limestone-based method was successful in reducing trace metals concentrations to about 0.001 mg/L or less; at the Keystone site, the limestone method reduced arsenic levels to about 0.006 mg/L. The results are significant because previous research with the limestone-based method mainly had involved samples prepared with distilled water in the laboratory, in which interference of other ions such as sulfate did not occur. The research indicates the potential for broader applications of the limestone-based removal method, including scale-up work at field sites for water treatment. 相似文献
The main objective of this study is to integrate adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM) and artificial neural network (ANN) to design an integrated supervised committee machine artificial intelligence (SCMAI) model to spatially predict the groundwater vulnerability to seawater intrusion in Gharesoo-Gorgan Rood coastal aquifer placed in the northern part of Iran. Six hydrological GALDIT parameters (i.e., G groundwater occurrence, A aquifer hydraulic conductivity, L level of groundwater above sea level, D distance from the shore, I impact of the existing status of seawater intrusion in the region, and T thickness of the aquifer) were considered as inputs for each model. In the training step, the values of GALDIT’s vulnerability index were conditioned by using the values of TDS concentration in order to obtain the conditioned vulnerability index (CVI). The CVI was considered as the target for each model. After training the models, each model was tested using a separate TDS dataset. The results indicated that the ANN and ANFIS algorithms performed better than the SVM algorithm. The values of correlation were obtained as 88, 87, and 80% for ANN, ANFIS, and SVM models, respectively. In the testing step of the SCMAI model, the values of RMSE, R2, and r were obtained as 6.4, 0.95, and 97%, respectively. Overall, SCMAI model outperformed other models to spatially predicting vulnerable zones. The result of the SCMAI model confirmed that the western zones along the shoreline had the highest vulnerability to seawater intrusion; therefore, it seems critical to consider emergency protection plans for study area.
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