Neutrino Oscillations, MicroBooNE, and Hand-Scanning Neutrino Oscillations Alex Yeagle, Cornell College Cristian Gaidau, University of Minnesota Recent experiments have shown that neutrinos change flavors. Data from the KamLAND, MINOS, Super-Kamiokande, KEK to Kamioka Kansas State University REU: Summer 2010 (K2K), and Sudbury Neutrino Observatory (SNO) experiments have all determined that these flavor changes occur in neutrinos produced in Hand-Scanning the atmosphere, accelerators, reactors, and the sun. The MicroBooNE Detector

The model put forth describes these flavor changes as the mixing of Our goal for the summer was to create an MicroBooNE is a liquid argon based detector to be different mass states. In a simplified version with 2 (instead of 3) algorithmic procedure for identifying νe events built at Fermilab (Figure 5). Its goal is to detect flavors, the matrix describing this mixing is: and rejecting νμ events in Monte Carlo events arising from neutrinos, determine their simulations of the MicroBooNE data (Figure 1). flavors, and compare the frequencies to the This was done by examining simulations of calculated probabilities to measure the mixing angle. various particle types and determining their characteristics. From this information and a Charged particles traveling through the detector where θ is the mixing angle. The probability of a flavor change is then knowledge of neutrino interactions, a set of ionize the argon atoms as they pass by. The ions given by: criteria could be developed. then drift to wire detection planes under the influence of a strong electric field (500 V/cm). The The procedure developed is as follows: Figure 1: The LArSoft event display information collected at the wires can then be used Step 1: to reconstruct the event with extreme resolution. - Discard all events not containing a primary where Δm is the mass difference, L is the distance traveled, and E is ν interaction vertex. the energy of the neutrino. This model can be expanded to a situation - These are intractable and can not be used. with three flavors and three distinct mixing angles. Step 2: - Discard all events not containing an electromagnetic shower (Figure 2).

- Charged-current νe events will always produce an electron, which creates a tell-tale Results shower. Figure 2: An electromagnetic shower Step 3: - Discard all events containing distinctive μ An efficiency trial was conducted with 100 random events of all types. tracks (Figure 3). Our algorithmic procedure was able to reject 98% of the background - μ’s have characteristically long, straight, noise and keep all of the ν signal. However, several π0 ‘s were e minimum ionizing tracks. misidentified as electrons. Further studies still need to be conducted on - s are produced in charged-current events 0 + + 0 μ’ νμ π identification as well as on heavier particles, such as K , Σ , Λ . and as such can be ruled out. Step 4: - Determine the shower origin and discard non- Figure 3: A typical μ track electron induced events. Figure 5: The MicroBooNE detector - The candidates are π0, π+, γ, and electrons Acknowledgements - γ‘s leave gaps between the shower and the vertex and can be identified by this (Figure 4). A special thanks to Tim Bolton, Glenn Horton-Smith, David McKee, - π+’s go through a decay before showering and Larry Weaver, and Kristan Corwin of Kansas State University for can be found through bends in their tracks their help in this project and throughout the summer program. (Figure 6).

Figure 4: A showering π0 (decay to γ‘s) Figure 6: A π+ induced shower