Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userApplication of machine learning techniques to lepton energy reconstruction in water Cherenkov detectors
107
0
0.0
(
0
)
Added by
Evangelia Drakopoulou
Publication date
2017
fields
Physics
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
The application of machine learning techniques to the reconstruction of lepton energies in water Cherenkov detectors is discussed and illustrated for TITUS, a proposed intermediate detector for the Hyper-Kamiokande experiment. It is found that applying these techniques leads to an improvement of more than 50% in the energy resolution for all lepton energies compared to an approach based upon lookup tables. Machine learning techniques can be easily applied to different detector configurations and the results are comparable to likelihood-function based techniques that are currently used.
rate research
Read More
BESIII is a currently running tau-charm factory with the largest samples of on threshold charm meson pairs, directly produced charmonia and some other unique datasets at BEPCII collider. Machine learning techniques have been employed to improve the performance of BESIII software. The studies for reweighing MC, particle identification and cluster reconstruction for the CGEM (Cylindrical Gas Electron Multiplier) inner tracker are presented.
The Jiangmen Underground Neutrino Observatory (JUNO) is an experiment designed to study neutrino oscillations. Determination of neutrino mass ordering and precise measurement of neutrino oscillation parameters $sin^2 2theta_{12}$, $Delta m^2_{21}$ and $Delta m^2_{32}$ are the main goals of the experiment. A rich physical program beyond the oscillation analysis is also foreseen. The ability to accurately reconstruct particle interaction events in JUNO is of great importance for the success of the experiment. In this work we present a few machine learning approaches applied to the vertex and the energy reconstruction. Multiple models and architectures were compared and studied, including Boosted Decision Trees (BDT), Deep Neural Networks (DNN), a few kinds of Convolution Neural Networks (CNN), based on ResNet and VGG, and a Graph Neural Network based on DeepSphere. Based on a study, carried out using the dataset, generated by the official JUNO software, we demonstrate that machine learning approaches achieve the necessary level of accuracy for reaching the physical goals of JUNO: $sigma_E=3%$ at $E_text{vis}=1~text{MeV}$ for the energy and $sigma_{x,y,z}=10~text{cm}$ at $E_text{vis}=1~text{MeV}$ for the position.
Silicon-based photosensors (SiPMs) working in the Geiger-mode represent an elegant solution for the readout of particle detectors working at low-light levels like Cherenkov detectors. Especially the insensitivity to magnetic fields makes this kind of sensors suitable for modern detector systems in subatomic physics which are usually employing magnets for momentum resolution. In our institute we are characterizing SiPMs of different manufacturers for selecting sensors and finding optimum operating conditions for given applications. Recently we designed and built a light concentrator prototype with 8x8 cells to increase the active photon detection area of an 8x8 SiPM (Hamamatsu MPPC S10931-100P) array. Monte Carlo studies, measurements of the collection efficiency, and tests with the MPPC were carried out. The status of these developments are presented.
This Letter of Intent outlines a proposal to build a large, yet cost-effective, 100 kton fiducial mass water Cherenkov detector that will initially run in the NuMI beam line. The CHIPS detector (CHerenkov detector In Mine PitS) will be deployed in a flooded mine pit, removing the necessity and expense of a substantial external structure capable of supporting a large detector mass. There are a number of mine pits in northern Minnesota along the NuMI beam that could be used to deploy such a detector. In particular, the Wentworth Pit 2W is at the ideal off-axis angle to contribute to the measurement of the CP violating phase. The detector is designed so that it can be moved to a mine pit in the LBNE beam line once that becomes operational.
Cherenkov detectors employ various methods to maximize light collection at the photomultiplier tubes (PMTs). These generally involve the use of highly reflective materials lining the interior of the detector, reflective materials around the PMTs, or wavelength-shifting sheets around the PMTs. Recently, the use of water-soluble wavelength-shifters has been explored to increase the measurable light yield of Cherenkov radiation in water. These wave-shifting chemicals are capable of absorbing light in the ultravoilet and re-emitting the light in a range detectable by PMTs. Using a 250 L water Cherenkov detector, we have characterized the increase in light yield from three compounds in water: 4-Methylumbelliferone, Carbostyril-124, and Amino-G Salt. We report the gain in PMT response at a concentration of 1 ppm as: 1.88 $pm$ 0.02 for 4-Methylumbelliferone, stable to within 0.5% over 50 days, 1.37 $pm$ 0.03 for Carbostyril-124, and 1.20 $pm$ 0.02 for Amino-G Salt. The response of 4-Methylumbelliferone was modeled, resulting in a simulated gain within 9% of the experimental gain at 1 ppm concentration. Finally, we report an increase in neutron detection performance of a large-scale (3.5 kL) gadolinium-doped water Cherenkov detector at a 4-Methylumbelliferone concentration of 1 ppm.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
