clustering

Gamma rays are the most energetic photons in the electromagnetic spectrum, detected with ground-based and space-based detectors in different energy ranges from sources in our galaxy and beyond. Gamma-ray point sources can be identified by special clustering of these photons. The minimum spanning tree (MST) algorithm is a graph-based method in order to find clusters. In this paper, we use the MST algorithm for finding point sources in Fermi gamma-ray space telescope data which is sensitive to photons with energies of 20 MeV up to more than 300 GeV. To this end, we selected eight completely random (10°×10°) fields of Fermi gamma-ray sky and tested the algorithm on the 12-year Fermi-LAT sky (Pass 8) at energy ranges above 3 GeV and above 6 GeV and with different cluster selection criteria. The calculation of Precision and Recall for both fields shows that MST is a useful algorithm in order to identify the point.

The orbit determination in one sentence is the application of a variety of techniques for estimating the orbits of objects such as the moon, planets and spacecraft. In dynamic astronomy, the orbit determination is the process of determining orbital parameters with observations. Considering the visibility of the satellite motion trace and the fundamental need to determine and modify satellites’ orbital parameters as well as identify special satellites, determining the positional parameters of the satellite is also one of the modern and important applications of vision-based astronomical systems. In the modern vision-based astronomical systems, data collection is done using a charge-coupled device (CCD) array. In this paper, a new method is presented for satellite streak detection through an optical imaging system. This automatic and efficient method, which has the ability of real-time data analysis, is based on the sidereal image using CCDs. The images captured by this method have a large amount of information about stars, galaxy, and satellites’ streaks. In this paper, an automatic method is presented for streak detection. The purpose of this research is to find an optimal method for satellite streak detection and different methods in clustering such as k_means, particle swarm optimization (PSO), genetic algorithm (GA), and Gaussian mixture model (GMM). Finally, some assessment criteria were compared and concluded that GA is an optimal algorithm in satellite streak detection.

Groundwater vulnerability assessment is an effective informative method to provide basis for determining source of pollution. Vulnerability maps are employed as an important solution in order to handle entrance of pollution into the aquifers. A common way to develop groundwater vulnerability map is DRASTIC index. Meanwhile, application of the method is not easy for any aquifer due to choosing appropriate constant values of weights and ranks. Clustering technique would be an influential method for regionalization of groundwater flow zone to facilitate vulnerability assessment of groundwater aquifers. In this study, a new approach using k-means clustering is applied to make vulnerability maps. Four features of depth to groundwater, hydraulic conductivity, recharge value and vadose zone are considered at the same time as features of clustering. Five regions are recognized out of the Hashtgerd plain. Each zone corresponds to a different level of vulnerability. The results show that clustering provides a more realistic vulnerability map so that, Pearson’s correlation coefficients between nitrate concentrations and clustering vulnerability is 72%.