Speaker
Description
MicroBooNE utilizes an 85-tonne active volume Liquid Argon Time Projection Chamber (LArTPC) to pursue an ambitious physics programme including the search for oscillations between active and sterile neutrinos, and a broad range of cross section measurements and searches for new physics. LArTPCs are high-precision imaging detectors that capture fine details of particle interactions, driving the need for advanced reconstruction techniques. The principal reconstruction frameworks at MicroBooNE employ multi-algorithm, deep learning and tomographic techniques to address a range of pattern-recognition problems in reconstructing 3D images of neutrino interactions in order to fully exploit the LArTPC imaging capabilities. Enhanced signal processing and energy reconstruction, a crucial observable for numerous physics goals, has been achieved via deep learning and neutron tagging. This talk presents an overview of these techniques, which enable the MicroBooNE physics programme.
| Alternate track | 14. Computing, AI and Data Handling |
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| I read the instructions above | Yes |
