A muon scattering tomography system which uses extruded plastic scintillator bars for muon tracking and a dedicated muon spectrometer that measures scattering through steel slabs has been constructed and successfully tested. The atmospheric muon detection efficiency is measured to be 97% per plane on average and the average intrinsic hit resolution is 2.5mm. In addition to creating a variety of three-dimensional images of objects of interest, a quantitative study has been carried out to investigate the impact of including muon momentum measurements when attempting to detect high-density, high-Z material. As expected, the addition of momentum information improves the performance of the system. For a fixed data-taking time of 60s and a fixed false positive fraction, the probability to detect a target increases when momentum information is used. This is the first demonstration of the use of muon momentum information from dedicated spectrometer measurements in muon scattering tomography.

The CRIPT Cosmic Ray Imaging and Passive Tomography system began data taking in September 2012. CRIPT is a “proof of principle” muon tomography system originally proposed to inspect cargo in shipping containers and to determine the presence of special nuclear materials. CRIPT uses 4 layers of 2 m x 2 m scintillation counter trackers, each layer measuring two coordinates. Two layers are used to track the incoming muon and two for the outgoing muon allowing the trajectories of the muon to be determined. The target volume is divided into voxels, and a Point of Closest Approach algorithm is used to determine the number of scattering events in each voxel, producing a 3D image. The system has been tested with various targets of depleted uranium, lead bricks, and tungsten rods. Data on the positional resolution has been taken and the intrinsic resolution is unfolded with the help of a simulation using GEANT4. The next steps include incorporation of data from the spectrometer section, which will assist in determining the muon's momentum and improve the determination of the density of the target.

The Cosmic Ray Inspection and Passive Tomography (CRIPT) project is investigating muon scattering tomography (MST) for applications in border security, nuclear non-proliferation, and nuclear waste characterization. The construction of the full-scale prototype MST system began in the Summer of 2011 and was completed in September 2012. The CRIPT detector employs 12 layers of scintillator to track atmospheric muons before and after passage through a volume of interest, and to estimate each muon's momentum. The total height of the system is 5.5 m and its weight is 20 tonnes. Details of its construction are presented. After the integration of the custom data acquisition electronics, the commissioning of the CRIPT detector began. The first tomographic images were obtained in October 2012 and are presented here.

Muons, which are produced naturally in the upper atmosphere, can be used to scan cargo for special nuclear materials (SNM). Preliminary simulated results show that detecting the presence of these materials can be accomplished by measuring the scattering of cosmic ray muons. Machine learning tools have been used on these data to classify it as SNM or not.The muon exists long enough, and is penetrating enough, that it can be used to passively scan cargo to detect SNM. By measuring the deflection angles of muons after they exit a container, one can determine whether or not SNM are present. Different detector approaches have been evaluated by considering the performance, cost, and robustness of several technologies. Simulations have been performed to help design the detectors and to determine the effectiveness of the proposed techniques. Realistic cargo containers have been simulated.Two types of techniques can be used to determine whether the cargo containers contain SNM. More traditional methods use an expert system which uses knowledge of physics to compute physical information about the cargo. The other approach is to use Machine Learning classifiers, which can be used to determine if the cargo contains SNM. These techniques include the following algorithms: decision trees, neural networks, special vector machines, and k nearest neighbours. Preliminary results from the two approaches to classification have been obtained and will be discussed in the paper.

With the commissioning of the LHC in 2010 and upgrades expected in 2015, ATLAS and CMS are planning to upgrade their innermost tracking layers with radiation hard technologies. Chemical Vapor Deposition diamond has been used extensively in beam conditions monitors as the innermost detectors in the highest radiation areas of BaBar, Belle, CDF and all LHC experiments. This material is now being considered as a sensor material for use very close to the interaction region where the most extreme radiation conditions exist. Recently the RD42 collaboration constructed, irradiated and tested polycrystalline and single-crystal chemical vapor deposition diamond sensors to the highest fluences expected at the super-LHC. We present beam test results of chemical vapor deposition diamond up to fluences of 1.8×1016 protons/cm2 illustrating that both polycrystalline and single-crystal chemical vapor deposition diamonds follow a single damage curve. We also present beam test results of irradiated complete diamond pixel modules.