Simplified fast detector simulation in MadAnalysis 5

We introduce a new simplified fast detector simulator in the MadAnalysis 5 platform. The Python-like interpreter of the programme has been augmented by new commands allowing for a detector parametrisation through smearing and efficiency functions. On run time, an associated C++ code is automatically generated and executed to produce reconstructed-level events. In addition, we have extended the MadAnalysis 5 recasting infrastructure to support our detector emulator, and we provide predefined LHC detector configurations. We have compared predictions obtained with our approach to those resulting from the usage of the Delphes 3 software, both for Standard Model processes and a few new physics signals. Results generally agree to a level of about 10% or better, the largest differences in the predictions stemming from the different strategies that are followed to model specific detector effects. Equipped with these new functionalities, MadAnalysis 5 now offers a new user-friendly way to include detector effects when analysing collider events, the simulation of the detector and the analysis being both handled either through a set of intuitive Python commands or directly within the C++ core of the platform.

The response of an autonomous underwater telescope to high energy neutrinos for the observation of Gamma-ray bursts (GRBs)

The response of an underwater neutrino detector is discussed for investigating its performance to the detection of muons and high energy neutrinos. The afformentioned telescope consists of an autonomous battery operated detector string to a central 4-floor tower. In this aim, we utilised a fast detector simulation program, SIRENE, to simulate the hits from Cherenkov photons at ultra high energies (as high as 1020 eV). In order to optimize the detector, analytical studies for different configurations and characteristics of the photo-multiplier tubes inside the optical modules of the telescope was also examined.

Anomalous Behavior Detection Using the Geometrical Complex Moments in Crowd Scenes of Smart Surveillance Systems

In this research work a data stream clustering method done by extracting regions of interest from the frames of video clips (UCSD pedestrian dataset (ped1 and ped2 datasets) video clips, and VIRAT VIDEO dataset video clips). In extraction process the HARRIS or FAST detector applied on the frames of video clips to extract list of pairs of interest points. From these pairs a list of features such as: distance, direction, x-coordinate, y-coordinate obtained to use as an input to the clustering method based on seed based region growing technique. From these clusters a regions of interest extracted according the pairs coordinates of each cluster. Finally, from these regions a set of geometrical complex moments obtained and then used in anomaly detection system. The results indicated that using HARRIS detector achieved detection rates are 7.88%, 51.30%, and 56.67% with false alarms are 19.39%, 32.61%, and 60.00% by using Ped1, Ped2, and VIRAT datasets respectively. For the case of using FAST detector, the best detection rates are 6.67%, 44.78%, 53.33% with false alarm rates are 33.33%, 41.74%, 70% by using the datasets respectively.