Periodic Reporting for period 1 - ARNU (The Impact of Neutrino-Nucleus Interactions on DUNE's CP-Violation Measurement)
Período documentado: 2020-10-01 hasta 2022-09-30
The Deep Underground Neutrino Experiment (DUNE) will aim to discover if neutrinos and their antimatter counterparts, antineutrinos, oscillate in the same way. If this is found not to be the case, it may help to explain why we live in a matter dominated Universe, and indeed how we can exist at all. In order to make this measurement, an intense, accelerator produced beam of neutrinos will be made at Fermilab (near Chicago, USA) and then sent 1300 km through the Earth to massive underground neutrino detectors in Lead, South Dakota. Along the way the neutrino beam is also characterised by a complex of "near" neutrino detectors close to the beam production point. The DUNE detectors use a novel liquid argon time projector chamber technology, allowing high resolution imaging of the neutrino interactions.
The overall objectives of this project are to understand how the differences between neutrino and antineutrino interactions with the matter nuclei in our detectors may impact our ability to measure differences in neutrino oscillations, and to improve our understanding of the detector technologies involved, using data from the test beam of the DUNE prototype, ProtoDUNE.
The overall objectives of this project are to understand how the differences between neutrino and antineutrino interactions with the matter nuclei in our detectors may impact our ability to measure differences in neutrino oscillations, and to improve our understanding of the detector technologies involved, using data from the test beam of the DUNE prototype, ProtoDUNE.
I developed a convolutional neural network (CNN) to identify different particle types in ProtoDUNE, including protons. I used test beam data and Monte Carlo (MC) simulation thereof to evaluate and train the network respectively. There were found to be differences between the data and Monte Carlo simulations. This is due to systematic differences in how we simulate the liquid argon detector and the actual microphysics of the detector. I extended this work to mitigate for these uncertainties by using a Domain Adversarial Neural Network (DANN). The concept of the DANN is to simultaneously learn the problem (classification of particles) and unlearn any domain differences (between data and MC). The classification was trained on the MC as before, and the domain differences were unlearned using images labelled as data or MC, but without class labels. This means the data class labels (from the beamline instrumentation) were still unseen during training and could be used for evaluation of the performance of the network. Now the performance not only matched between data and MC, but was significantly improved for proton identification. Protons from the ProtoDUNE test beam data were then used to perform a measurement of electron-ion recombination in liquid argon. This is a vital measurement for the DUNE experiment as it will be used to convert observed charge in the detector to an energy deposit, needed to deduce the neutrino’s energy and be able to measure a matter-antimatter asymmetry in neutrino oscillations.
I helped to install the new readout chamber for the High Pressure gaseous Time Project Chamber (HPgTPC) for DUNE and made preparations for its beam test which will eventually take place after the end of this fellowship. In addition, I participated in the preparation for the liquid argon near detector prototype neutrino beam test run. I participated in the reinstallation of the MINERvA planes which will for the muon range detector for the test run. I was responsible for getting the nearline data monitoring system working, which was successfully completed. The nearline monitoring first needed to unpack the raw data files into a standardised data format that could be used for analysis. The monitoring then checks that the voltages read out are close to those set, and that there are no significant time variations or light leaks. I also helped to install the photo-multiplier tubes and cable up the detector.
I used the neutrino interaction generator, GENIE 3.2 to simulate neutrino interactions with argon and other elements for reference. GENIE 3.2 has a correlated Fermi gas model that tries to account for short-range correlations (SRCs) between pairs of nucleons. These nucleons in SRC pairs then occupy high momentum states. For heavier nuclei, fractionally more protons should be in high momentum states compared to neutrons, so a bigger modification is observed for lead than argon. To see if GENIE 3.2 was reproducing the correct behaviour, I compared the struck nucleon momenta distributions to electron scattering data from CLAS. From this work it has become clear that the correct behaviour is not being produced for heavier nuclei, in fact GENIE is simulating a higher fraction of neutrons in high momentum vs low momentum states than for protons. This means the predictions for the modification to neutrino/antineutrino interaction cross-sections are likely to be incorrect in this simulation.
I helped to install the new readout chamber for the High Pressure gaseous Time Project Chamber (HPgTPC) for DUNE and made preparations for its beam test which will eventually take place after the end of this fellowship. In addition, I participated in the preparation for the liquid argon near detector prototype neutrino beam test run. I participated in the reinstallation of the MINERvA planes which will for the muon range detector for the test run. I was responsible for getting the nearline data monitoring system working, which was successfully completed. The nearline monitoring first needed to unpack the raw data files into a standardised data format that could be used for analysis. The monitoring then checks that the voltages read out are close to those set, and that there are no significant time variations or light leaks. I also helped to install the photo-multiplier tubes and cable up the detector.
I used the neutrino interaction generator, GENIE 3.2 to simulate neutrino interactions with argon and other elements for reference. GENIE 3.2 has a correlated Fermi gas model that tries to account for short-range correlations (SRCs) between pairs of nucleons. These nucleons in SRC pairs then occupy high momentum states. For heavier nuclei, fractionally more protons should be in high momentum states compared to neutrons, so a bigger modification is observed for lead than argon. To see if GENIE 3.2 was reproducing the correct behaviour, I compared the struck nucleon momenta distributions to electron scattering data from CLAS. From this work it has become clear that the correct behaviour is not being produced for heavier nuclei, in fact GENIE is simulating a higher fraction of neutrons in high momentum vs low momentum states than for protons. This means the predictions for the modification to neutrino/antineutrino interaction cross-sections are likely to be incorrect in this simulation.
During this project a new way of doing particle identification in liquid argon time projection chambers using deep learning and test beam data was developed. A domain adversarial neural network was trained with labelled simulated data and unlabelled test beam data, and then evaluated using labelled test beam data. This allows us to understand for the first time how data/Monte Carlo simluation differences will effect our ability to identify different types of neutrino interactions in the DUNE far detectors. Stopping protons from the ProtoDUNE test beam were also used to make a new measurement of electron-ion recombination in liquid argon and publication of this result is currently in progress. Comparison of state of the art neutrino interaction generators to electron scattering data was also performed. This showed that improvements to these simulations will need to be made before they can correctly predict neutrino/antineutrino cross-section differences for the DUNE neutrino oscillation measurement.