Application and performance of an ML-EM algorithm in NEXT
The goal of the NEXT experiment is the observation of neutrinoless double beta decay in (136)Xe using a gaseous xenon TPC with electroluminescent amplification and specialized photodetector arrays for calorimetry and tracking. The NEXT Collaboration is exploring a number of reconstruction algorithms to exploit the full potential of the detector. This paper describes one of them: the Maximum Likelihood Expectation Maximization (ML-EM) method, a generic iterative algorithm to find maximum-likelihood estimates of parameters that has been applied to solve many different types of complex inverse problems. In particular, we discuss a bi-dimensional version of the method in which the photosensor signals integrated over time are used to reconstruct a transverse projection of the event. First results show that, when applied to detector simulation data, the algorithm achieves nearly optimal energy resolution (better than 0.5% FWHM at the Q value of (136)Xe) for events distributed over the full active volume of the TPC.
- Research Organization:
- Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), High Energy Physics (HEP)
- Contributing Organization:
- NEXT
- DOE Contract Number:
- AC02-07CH11359
- OSTI ID:
- 1469018
- Report Number(s):
- FERMILAB-PUB-17-219-CD; arXiv:1705.10270; 1601510
- Journal Information:
- JINST, Vol. 12, Issue 08
- Country of Publication:
- United States
- Language:
- English
Similar Records
The NEXT White (NEW) detector
Sensitivity of NEXT-100 to neutrinoless double beta decay