Receiver Design and On-Sun Testing for G3P3-USA (presentation).
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An anionic sorption agent, method for forming the anionic sorption agent and a barrier system are disclosed. The anionic sorption agent including a modified pseudo or glycol-boehmite base comprising a structure having cationic metal ion sites. The method for forming the anionic sorption agent includes providing a pseudo or glycol-boehmite base and contacting the pseudo or glycol-boehmite base a modifying composition comprising a metallic ion to form the modified pseudo or glycol-boehmite base comprising a structure having cationic metal ion sites. The barrier system includes the anionic sorption agent comprising a first barrier component comprising a modified pseudo or glycol-boehmite base comprising a structure having cationic metal ion sites and a second barrier component comprising a cationic sorption agent.
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This work explores whether ion beam modification can be used to modulate the austenite to martensite phase transformation in Nickel-Titanium (NiTi), thereby achieving novel or localized transformation properties in near-surface regions. We report this could provide alternatives to laser shot peening or other surface treatment methods and possibly expand applications in biomedical, aerospace, and other fields. Irradiation induces defects and internal stress that can serve as nucleation and/or pinning sites for the phase transformation. Thus, it can augment more convention- al approaches, including alloying, severe mechanical work, grain size reduction, and precipitation of coherent precipitates. A range of outcomes is possible in principle, including a shift of the critical stress or temperature for onset of the transformation, linearization, reduction of hysteresis, stabilization, and extent of transformation strain.
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Recoil imaging entails the detection of spatially resolved ionization tracks generated by particle interactions. This is a highly sought-after capability in many classes of detector, with broad applications across particle and astroparticle physics. However, at low energies, where ionization signatures are small in size, recoil imaging only seems to be a practical goal for micro-pattern gas detectors. This white paper outlines the physics case for recoil imaging, and puts forward a decadal plan to advance towards the directional detection of low-energy recoils with sensitivity and resolution close to fundamental performance limits. The science case covered includes: the discovery of dark matter into the neutrino fog, directional detection of sub-MeV solar neutrinos, the precision study of coherent-elastic neutrino-nucleus scattering, the detection of solar axions, the measurement of the Migdal effect, X-ray polarimetry, and several other applied physics goals. We also outline the R&D programs necessary to test concepts that are crucial to advance detector performance towards their fundamental limit: single primary electron sensitivity with full 3D spatial resolution at the $\sim$100 micron-scale. These advancements include: the use of negative ion drift, electron counting with high-definition electronic readout, time projection chambers with optical readout, and the possibility for nuclear recoil tracking in high-density gases such as argon. We also discuss the readout and electronics systems needed to scale-up such detectors to the ton-scale and beyond.
We describe a search for νµ disappearance using the MicroBooNE Deep Learning analysis 1µ1p selection. Presently, the unexplained MiniBooNE and LSND anomalies could be explained by a sterile neutrino impacting neutrino oscillations. Our analysis searches for the allowed parameter space that could describe such a sterile neutrino. We determine the allowed and excluded region of a 3+1 sterile-based muon neutrino disappearance model in MicroBooNE at 90% confidence. Our allowed region includes both the null model, and current global best fit model. Context for the underlying Deep Learning analysis is provided and several validation studies surrounding both the disappearance search, and originating 1µ1p selection are performed to strengthen confidence in the result. In addition, a next-generation deep learning tool for cosmic-ray-muon event discrimination is proposed and evaluated, demonstrating a removal of 70% of the remaining event background when added to current methods, under the cut criteria used.
We describe a search for νµ disappearance using the MicroBooNE Deep Learning analysis 1µ1p selection. Presently, the unexplained MiniBooNE and LSND anomalies could be explained by a sterile neutrino impacting neutrino oscillations. Our analysis searches for the allowed parameter space that could describe such a sterile neutrino. We determine the allowed and excluded region of a 3+1 sterile-based muon neutrino disappearance model in MicroBooNE at 90% confidence. Our allowed region includes both the null model, and current global best fit model. Context for the underlying Deep Learning analysis is provided and several validation studies surrounding both the disappearance search, and originating 1µ1p selection are performed to strengthen confidence in the result. In addition, a next-generation deep learning tool for cosmic-ray-muon event discrimination is proposed and evaluated, demonstrating a removal of 70% of the remaining event background when added to current methods, under the cut criteria used.
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