In this research the seasonal variation of the atmospheric muon rate at the KM3NeT/ARCA detector was studied in order to determine the temperature correlation coefficient αT. KM3NeT is a cubic...Show moreIn this research the seasonal variation of the atmospheric muon rate at the KM3NeT/ARCA detector was studied in order to determine the temperature correlation coefficient αT. KM3NeT is a cubic kilometer neutrino telescope consisting of two large volume water-Cherenkov detectors, ARCA and ORCA, located in the Mediterranean sea. The Cherenkov radiation emitted from high energy muons traveling through the seawater gets detected by an assembly of 31 Photo-multiplier tubes situated inside a sphere shaped Digital Optical Module (DOM). 18 of such DOMs are connected to form a Detection Unit (DU). For the ARCA detector, these DUs are located at the seafloor at around 3.5 km depth, extending vertically to about 2.7 km below sea level. Taking advantage of correlations between hits registered at different PMTs within a DOM, a measurement of the atmospheric muon rate at each DOM can be determined. Furthermore, the difference in height of the DOMs in each DU enables the utilization of the depth dependence of atmospheric muons to precisely determine the muon rate at the ARCA detector. This makes it possible to detect rate variations of a few percent. Additionally, the effective temperature is determined through a weighted integral of the available temperature data above the geographic location of the ARCA detector. Comparing the atmospheric muon rate and the effective temperature during the data taking period of 26.09.2021 until 1.06.2022 a temperature correlation of αT = 1.166 ± 0.128 was established. This is slightly above the theoretically predicted value of 0.86. To verify the robustness of the proposed method of determining the rate and temperature correlation, cross checks were done with Monte Carlo files, background signals and the examination of the depth relationship with time. All returned the expected results. However, when the same method was employed on a smaller data set covering the data taking period between 12.05.2021 and 2.09.2021, no significant correlation between the atmospheric muon rate and the effective temperature could be established. Furthermore, the performed cross checks on this data set did not confirm expectations. This is most likely due to the small data set which is not able to accurately capture the long term seasonal effect. However these results should not be neglected. Therefore, while the employed method did return promising results for a larger set of data, more investigation into the efficiency determination and the errors on the fitted slope are needed to confidently verify the reliability of the final result. It is further suggested to revisit this study once a consistent data set of minimally one year is available.Show less
Statistical physicists are recently focusing on the network structure of financial systems in order to model cascading defaults in those systems. Active research is on the characteristics of...Show moreStatistical physicists are recently focusing on the network structure of financial systems in order to model cascading defaults in those systems. Active research is on the characteristics of financial networks. These networks are inferred from statistical ensembles constructed from partial information, due to the fact that researchers usually don’t have full access to the entire network underlying the financial system, because of privacy reasons. Other research simulates the propagation of shocks such as defaults through financial systems and focuses on the characteristics of the network governing the dynamics. This research combines the two areas of research using a unique data set containing all transactions of commercial Dutch ING accounts from the year 2019. A state-of-the-art random network ensemble is tested in the research, whereafter networks are sampled from this ensemble to run the cascading defaults simulation on. Furthermore, the simulation of cascading defaults is improved by implementing non-trivial payment strategies, motivated by experiences from bankers dealing with defaulted companies. The simulation using the empirical network yields similar results to the networks sampled from the ensemble, indicating that the random network ensemble captures information governing the dynamics.Show less
