1. Online Monitoring and Analysis for Muon Tomography Readout System M. Phipps, M. Staib, C. Zelenka, M. Hohlmann Florida Institute of Technology Department of Physics and Space Sciences 150 West University Blvd Melbourne, FL 32901

2. Outline Motivation Muon Tomography Station Online Monitoring GUI Explanation of Individual Plots Future Plans

3. Perform all monitoring and analysis in real time Cut analysis time in half Integrate all previous work on MTS software Standardize the DAQ system and make it easily accessible Motivation

4. Muon Tomography Station

5. Scalable Readout System (SRS) GEM1GEM2GEM3GEM4GEM5GEM6GEM7GEM8 Scintillator1Scintillator2Scintillator3Scintillator4 DAQ Card FEC1FEC6FEC5FEC4FEC3 FEC2 DATE (Data Acquisition and Test Environment) AMORE (Automatic MOnitoRing Environment)

6. AMORE: Publisher and Subscribers AMORE agent as an FSM with a single publisher and many subscribers Each agent has its own table in MySQL database to store published data Subscriber retrieves data by subscribing to the agent Allows for dual processing Secure monitoring Book Monitor Objects Start of Run Start of Cycle Monitor Cycle End of Cycle End of Run

7. Online Visualization Shell

8. Raw Data

9. Pedestals X-Strip Number Y-Strip Number X-Strip Cluster Y-Strip Cluster X-Strip Number Y-Strip Number X-Strip Cluster Y-Strip Cluster

10. Pedestals: Intuitive Explanation X-Strip Number Y-Strip Number X-Strip Cluster Y-Strip Cluster Mean noise level in each channel during pedestal run is subtracted from event data A sample event on one detector in the X and Y strips following pedestal subtraction

11. Charge Clusters

12. Charge Clusters: Intuitive Explanation Individual Strips Track Reconstruction with Multiple Clusters x xx x x xx x GEM 1 GEM 2 GEM 3 GEM 4

13. 2d Charge and Position Maps

14. Challenges Speed up event processing: Taking data at 30 Hz; Processing data at 10-15 Hz on 4 core processor with 10 GB RAM Software solutions Code optimization Hardware solutions Perform analysis on cluster Parallel processing with GPUs

15. What's Next: Add Functionality for... Event Display Analysis Plots 3D OpenGL Recreation of Data

16. Conclusions Successfully implemented a customized GUI that makes the performance of the readout system easily accessible in real time Revived and integrated past work into our display Converted data analysis to online platform that is significantly faster and better suited for real-life applications