Dark Matter & Heavy Sterile Neutrino Searches in MicroBooNE/SBND
Lead Research Organisation:
University of Manchester
Department Name: Physics and Astronomy
Abstract
Heavy sterile neutrinos could be produced in the BNB and NUMI neutrino beams at Fermilab. The MicroBooNE and SBND detectors could detect such heavy-sterile neutrino decays in the liquid argon. The student is developing algorithms based on Convolutional Neural Networks to select these events. SBND could also be used to detect dark matter production either through signatures that are delayed with respect to the beam arrival time or by operating SBND as a beam dump experiment. The student is performing studies that predict sensitivities for such dark matter signatures.
Publications
Abi B
(2020)
First results on ProtoDUNE-SP liquid argon time projection chamber performance from a beam test at the CERN Neutrino Platform
in Journal of Instrumentation
Abratenko P
(2020)
Search for heavy neutral leptons decaying into muon-pion pairs in the MicroBooNE detector
in Physical Review D
Abratenko P
(2019)
First Measurement of Inclusive Muon Neutrino Charged Current Differential Cross Sections on Argon at E_{?}~0.8 GeV with the MicroBooNE Detector.
in Physical review letters
Adams C
(2019)
First measurement of ? ยต charged-current p 0 production on argon with the MicroBooNE detector
in Physical Review D
Adams C
(2019)
Rejecting cosmic background for exclusive charged current quasi elastic neutrino interaction studies with Liquid Argon TPCs; a case study with the MicroBooNE detector
in The European Physical Journal C
Adams C
(2019)
Comparison of $${\varvec{\nu }}_{\varvec{\mu }}-$$Ar multiplicity distributions observed by MicroBooNE to GENIE model predictions MicroBooNE Collaboration
in The European Physical Journal C
Adams C
(2020)
Reconstruction and measurement of (100) MeV energy electromagnetic activity from p 0 arrow ?? decays in the MicroBooNE LArTPC
in Journal of Instrumentation
Adams C
(2019)
Design and construction of the MicroBooNE Cosmic Ray Tagger system
in Journal of Instrumentation
Adams C
(2019)
Deep neural network for pixel-level electromagnetic particle identification in the MicroBooNE liquid argon time projection chamber
in Physical Review D
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| ST/R504956/1 | 30/09/2017 | 29/09/2021 | |||
| 2023228 | Studentship | ST/R504956/1 | 30/09/2017 | 29/06/2021 | Owen Goodwin |
| Description | Member of DUNE collab |
| Organisation | Fermilab - Fermi National Accelerator Laboratory |
| Department | DUNE |
| Country | United States |
| Sector | Public |
| PI Contribution | Detector construction help. Detector running shifts |
| Collaborator Contribution | Access to ProtoDUNE beam data. |
| Impact | 10.1088/1748-0221/15/12/P12004 |
| Start Year | 2017 |
| Description | Member of MicroBooNE collab |
| Organisation | Fermilab - Fermi National Accelerator Laboratory |
| Department | MicroBooNE Experiment |
| Country | United States |
| Sector | Public |
| PI Contribution | Processing and data quality work Analysis of Physics data |
| Collaborator Contribution | Access to physics data |
| Impact | https://doi.org/10.1103/PhysRevD.101.052001 10.1088/1748-0221/14/04/P04004 10.1088/1748-0221/15/02/P02007 10.1103/PhysRevLett.123.131801 10.1103/PhysRevD.99.092001 10.1103/PhysRevD.99.091102 10.1140/epjc/s10052-019-6742-3 10.1140/epjc/s10052-019-7184-7 |
| Start Year | 2017 |