Creating an Efficient and Useful Ntuple for Analyzing Dilepton Data for Contact Interactions and Large Extra Dimensions
Advisor(s)
Dr. Peter Dong, Illinois Mathematics and Science Academy
Dr. Leonard Spiegel, Fermi National Accelerator Lab
Location
Room A147
Start Date
26-4-2019 10:05 AM
End Date
26-4-2019 10:20 AM
Abstract
In the search for contact interactions and large extra dimensions, one of the first steps is organizing large amounts of raw data from the CMS (Compact Muon Solenoid) experiment and Monte Carlo events. A data management structure called an ntuple was created to select only variables of interest. We made substantial changes to the existing program to make it easier to use and to include additional functionality, including invariant mass calculation, Collins-Soper angle calculation, and left-right/right-left helicity reweighting. Furthermore, we updated this program for a new analysis framework so that it would be compatible with new data and Monte Carlo samples. We will present the functionality of the new program and the automated generation of ntuples using the CRAB parallel-processing framework.
Creating an Efficient and Useful Ntuple for Analyzing Dilepton Data for Contact Interactions and Large Extra Dimensions
Room A147
In the search for contact interactions and large extra dimensions, one of the first steps is organizing large amounts of raw data from the CMS (Compact Muon Solenoid) experiment and Monte Carlo events. A data management structure called an ntuple was created to select only variables of interest. We made substantial changes to the existing program to make it easier to use and to include additional functionality, including invariant mass calculation, Collins-Soper angle calculation, and left-right/right-left helicity reweighting. Furthermore, we updated this program for a new analysis framework so that it would be compatible with new data and Monte Carlo samples. We will present the functionality of the new program and the automated generation of ntuples using the CRAB parallel-processing framework.