Abigail Kopec, Amanda L. Baxter, Michael Clark, Rafael F. Lang, Shengchao Li, Juehang Qin, Riya Singh
We characterize single- and few-electron backgrounds that are observed in dual-phase liquid xenon time projection chambers at timescales greatly exceeding a maximum drift time after an interaction. These instrumental backgrounds limit a detector's sensitivity to dark matter and cosmogenic neutrinos. Using the ~150g liquid xenon detector at Purdue University, we investigate how these backgrounds, produced after 122keV $^{57}$Co Compton interactions, behave under different detector conditions. We find that the rates of single- and few-electron signals follow power-laws with time after the interaction. We observe linearly increasing rates with increased extraction field, and increased rates in the single-electron background with increased drift field. Normalizing the rates to the primary interaction's measured ionization signal, the rates increase linearly with the depth of the interaction. We test the hypothesis that infrared photons (1550nm) would stimulate and accelerate electron emission via photodetachment from impurities, but find that even 1 Watt of infrared light fails to reduce these backgrounds. We thus provide a characterization that can inform background models for low-energy rare event searches.
Amanda L. Baxter, Rafael F. Lang, Craig Zywicki, Stephanie M. Gardner, Abigail Kopec, Andreas Jung
Course-based undergraduate research experiences (CUREs) increase students' access to research. This lesson plan describes an interdisciplinary CURE developed to be able to involve over 60 students per semester in original research using data from large particle physics experiments and telescopes, although the methods described can easily be adopted by other areas of data science. Students are divided into research teams of four, which greatly leverages the instruction time needed for mentoring, while increasing research productivity by creating accountability amongst the students. This CURE provides a strong framework, which minimizes barriers that students may perceive. This helps increase the number of students that benefit from a research opportunity while providing guidance and certainty. Through this CURE, students can engage in original research with the potential for publication-quality results, develop communication skills in various modes, and gain confidence in their performance as a scientist.
Amanda L. Baxter, Segev BenZvi, Joahan Castaneda Jaimes, Alexis Coleiro, Marta Colomer Molla, Damien Dornic, Tomer Goldhagen, Anne M. Graf, Spencer Griswold, Alec Habig, Remington Hill, Shunsaku Horiuchi James P. Kneller Rafael F. Lang, Massimiliano Lincetto, Jost Migenda, Ko Nakamura, Evan O'Connor, Andrew Renshaw, Kate Scholberg, Navya Uberoi, Arkin Worlikar
Sep 16, 2021·astro-ph.IM·PDF Current neutrino detectors will observe hundreds to thousands of neutrinos from a Galactic supernovae, and future detectors will increase this yield by an order of magnitude or more. With such a data set comes the potential for a huge increase in our understanding of the explosions of massive stars, nuclear physics under extreme conditions, and the properties of the neutrino. However, there is currently a large gap between supernova simulations and the corresponding signals in neutrino detectors, which will make any comparison between theory and observation very difficult. SNEWPY is an open-source software package which bridges this gap. The SNEWPY code can interface with supernova simulation data to generate from the model either a time series of neutrino spectral fluences at Earth, or the total time-integrated spectral fluence. Data from several hundred simulations of core-collapse, thermonuclear, and pair-instability supernovae is included in the package. This output may then be used by an event generator such as sntools or an event rate calculator such as SNOwGLoBES. Additional routines in the SNEWPY package automate the processing of the generated data through the SNOwGLoBES software and collate its output into the observable channels of each detector. In this paper we describe the contents of the package, the physics behind SNEWPY, the organization of the code, and provide examples of how to make use of its capabilities.