Measurement of Muon Neutrino Disappearance with the T2K Experiment

<p>We describe the measurement of muon neutrino disappearance due to</p><p>neutrino oscillation using the Tokai-2-Kamiokande (T2K) experiment's Run 1-4 (6.57&times;10<super>20</super> POT)</p><p>data set. We analyze the data using the conventional</p><p>Pontecorvo-Maki-Nakagawa-Sakata (PMNS) mixing</p><p>matrix for the three Standard Model neutrinos. The output of the</p><p>analysis is a measurement of the parameters sin<super>2</super>&theta;₂₃, &Delta;m<super>2</super>₃₂ for the normal hierarchy and sin<super>2</super>&theta;₂₃, &Delta;m<super>2</super>₁₃ for</p><p>the inverted hierarchy. The best-fit oscillation</p><p>parameters for the normal hierarchy are found to be</p><p>(sin<super>2</super>&theta;₂₃, &Delta;m<super>2</super>₃₂) = ( 0.514, 2.51&times;10<super>-3</super> eV<super>2</super>/c<super>4</super>}). The 90% 1D confidence interval -- determined for both parameters</p><p>using the Feldman-Cousins procedure -- is for the normal hierarchy</p><p>0.428 < sin<super>2</super>&theta;₂₃ < 0.598 and</p><p>2.34&times;10<super>-3</super> eV<super>2</super>/c<super>4</super> < &Delta;m<super>2</super>₃₂ < 2.68\times10^{-3} eV<super>2</super>/c<super>4</super>. </p><p>For the inverted hierarchy, the best-fit oscillation parameters are</p><p>(sin<super>2</super>&theta;₂₃, &Delta;m<super>2</super>₁₃) = (0.511, 2.48&times;10<super>-3</super> eV<super>2</super>/c<super>4</super>. The 90\% 1D Feldman-Cousins confidence intervals for the inverted hierarchy are 2.31&times;10<super>-3</super> eV<super>2</super>/c<super>4</super> < \Delta m^2_{13} < 2.64&times;10<super>-3</super> eV<super>2</super>/c<super>4</super>.</p>Dissertatio

Score-based Diffusion Models for Generating Liquid Argon Time Projection Chamber Images

For the first time, we show high-fidelity generation of LArTPC-like data using a generative neural network. This demonstrates that methods developed for natural images do transfer to LArTPC-produced images, which, in contrast to natural images, are globally sparse but locally dense. We present the score-based diffusion method employed. We evaluate the fidelity of the generated images using several quality metrics, including modified measures used to evaluate natural images, comparisons between high-dimensional distributions, and comparisons relevant to LArTPC experiments

Boosted dark matter at neutrino experiments

Current and future neutrino experiments can be used to discover dark matter, not only in searches for dark matter annihilating to neutrinos, but also in scenarios where dark matter itself scatters off standard model particles in the detector. In this work, we study the sensitivity of different neutrino detectors to a class of models called boosted dark matter, in which a subdominant component of a dark sector acquires a large Lorentz boost today through annihilation of a dominant component in a dark matter-dense region, such as the galactic Center or dwarf spheroidal galaxies. This analysis focuses on the sensitivity of different neutrino detectors, specifically the Cherenkov-based Super-K and the future argon-based DUNE to boosted dark matter that scatters off electrons. We study the dependence of the expected limits on the experimental features, such as energy threshold, volume and exposure in the limit of constant scattering amplitude. We highlight experiment-specific features that enable current and future neutrino experiments to be a powerful tool in finding signatures of boosted dark matter

Convolutional neural networks applied to neutrino events in a liquid argon time projection chamber

We present several studies of convolutional neural networks applied to data coming from the MicroBooNE detector, a liquid argon time projection chamber (LArTPC). The algorithms studied include the classification of single particle images, the localization of single particle and neutrino interactions in an image, and the detection of a simulated neutrino event overlaid with cosmic ray backgrounds taken from real detector data. These studies demonstrate the potential of convolutional neural networks for particle identification or event detection on simulated neutrino interactions. We also address technical issues that arise when applying this technique to data from a large LArTPC at or near ground level

Cost Estimates for the KPipe Experiment

We present estimates for the cost of the KPipe experiment. Excluding the cost of civil engineering, the total cost comes to 4.6 million USD. This report supports statements in arXiv article 1506.05811

Noise Characterization and Filtering in the MicroBooNE Liquid Argon TPC

The low-noise operation of readout electronics in a liquid argon time projection chamber (LArTPC) is critical to properly extract the distribution of ionization charge deposited on the wire planes of the TPC, especially for the induction planes. This paper describes the characteristics and mitigation of the observed noise in the MicroBooNE detector. The MicroBooNE's single-phase LArTPC comprises two induction planes and one collection sense wire plane with a total of 8256 wires. Current induced on each TPC wire is amplified and shaped by custom low-power, low-noise ASICs immersed in the liquid argon. The digitization of the signal waveform occurs outside the cryostat. Using data from the first year of MicroBooNE operations, several excess noise sources in the TPC were identified and mitigated. The residual equivalent noise charge (ENC) after noise filtering varies with wire length and is found to be below 400 electrons for the longest wires (4.7 m). The response is consistent with the cold electronics design expectations and is found to be stable with time and uniform over the functioning channels. This noise level is significantly lower than previous experiments utilizing warm front-end electronics. Keywords: Cold Electronics; Noise; MicroBooNE; Time projection chambers; Noble liquid detectors; Neutrino detector