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The Computing Alliance of Hispanic Serving Institutions (CAHSI), a national INCLUDES alliance, is committed to supporting students in attaining credentials in computing. Its latest effort focuses on advancing undergraduate computing majors into graduate school to address the low numbers of Hispanics, or Latinx, attaining graduate degrees in computing. CAHSI expands adoption of evidence-based, multi-institutional graduate support structures that lead to Latinx students’ success. This paper describes strategic efforts to address well-documented barriers among graduate students (across all areas of study), e.g., feeling of isolation, lack of support structures, deficit thinking, and negative departmental climate. 

This paper presents an innovative approach, applicable to all research-based fields, that identifies and broadly engages future computer science researchers. The Computing Alliance of Hispanic Serving Institutions (CAHSI) piloted a national virtual Research Experience for Undergraduates (vREU) during the summer of 2020. Funded by an NSF grant, the goal of the program was to ensure that students, in particular those with financial need, had opportunities to engage in research and gain critical skills while advancing their knowledge and financial resources to complete their undergraduate degrees and possibly move to advanced studies. The vREU pilot provided undergraduate research experiences for 51 students and 21 faculty drawn from 14 colleges and universities. The Affinity Research Group (ARG) model, based on a cooperative learning model, was used to guide faculty mentors throughout the eight-week vREU. ARG is a CAHSI signature practice with a focus on deliberate, structured faculty and student research, technical, communication, and professional skills development. At weekly meetings, faculty were provided resources and discussed a specific skill to support students’ research experience and development, which faculty put into immediate practice with their students. Evaluation findings include no statistical difference in student development between the face-to-face and virtual models with faculty and the benefit of training as an opportunity for faculty professional growth and impact. This faculty development model allows for rapid dissemination of the ARG model through practice and application with weekly faculty cohort meetings, coaching, and reflection. 

This paper presents an innovative approach, applicable to all research-based fields, that identifies and broadly engages future computer science researchers. The Computing Alliance of Hispanic Serving Institutions (CAHSI) piloted a national virtual Research Experience for Undergraduates (vREU) during the summer of 2020. Funded by an NSF grant, the goal of the program was to ensure that students, in particular those with financial need, had opportunities to engage in research and gain critical skills while advancing their knowledge and financial resources to complete their undergraduate degrees and possibly move to advanced studies. The vREU pilot provided undergraduate research experiences for 51 students and 21 faculty drawn from 14 colleges and universities. The Affinity Research Group (ARG) model, based on a cooperative learning model, was used to guide faculty mentors throughout the eight-week vREU. ARG is a CAHSI signature practice with a focus on deliberate, structured faculty and student research, technical, communication, and professional skills development. At weekly meetings, faculty were provided resources and discussed a specific skill to support students’ research experience and development, which faculty put into immediate practice with their students. Evaluation findings include no statistical difference in student development between the face-to-face and virtual models with faculty and the benefit of training as an opportunity for faculty professional growth and impact. This faculty development model allows for rapid dissemination of the ARG model through practice and application with weekly faculty cohort meetings, coaching, and reflection. 

To address the low number of baccalaureate degrees in computing to meet the demand for computing professionals, the Computing Alliance of Hispanic-Serving Institutions (CAHSI) was selected by the National Science Foundation (NSF) in 2018 to serve as the lead partner of a national INCLUDES alliance. The Inclusion Across the Nation of Communities of Learners (INCLUDES) initiative is one of NSF’s Ten Big Ideas with the goal of broadening participation in STEM fields by creating networked relationships among organizations and across sectors, using a collaborative approach with stakeholders who share a common agenda. The CAHSI Alliance is using the collective impact framework to accelerate change in broadening participation, particularly of Latinx, in computing fields. One aspect of collective impact is using a common set of data for decision-making within and across institutions. This paper will provide a short description of our data collection and analysis process, which helps populate a dashboard that compares student outcomes for each 2- and 4-year CAHSI institution with other institutions of higher education nationally.  

This Innovative Practice Work-In-Progress paper elucidates [redacted name of the alliance] approach for creating change by highlighting an effort across six institutions to support the delivery of one- and two-credit hour courses for three levels of problem solving: problem solving, computational thinking in problem solving, and algorithmic thinking in problem solving. The courses were developed to address feedback from industry partners regarding the need for improved problem-solving skills. The first of its kind for [name of Alliance], the problem-solving courses are fewer credit hours than typical courses designed to fit within traditional curriculum. The intent is to instill the complementary computational thinking skills and logical reasoning needed to succeed in computer science, and make this content available across different student populations at various stages in their academic pathways. The paper describes the process for designing the courses; the efforts to refine and improve course delivery, and the assessment and evaluation of the courses.  

Abstract The Pandora Software Development Kit and algorithm libraries perform reconstruction of neutrino interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at the Deep Underground Neutrino Experiment, which will operate four large-scale liquid argon time projection chambers at the far detector site in South Dakota, producing high-resolution images of charged particles emerging from neutrino interactions. While these high-resolution images provide excellent opportunities for physics, the complex topologies require sophisticated pattern recognition capabilities to interpret signals from the detectors as physically meaningful objects that form the inputs to physics analyses. A critical component is the identification of the neutrino interaction vertex. Subsequent reconstruction algorithms use this location to identify the individual primary particles and ensure they each result in a separate reconstructed particle. A new vertex-finding procedure described in this article integrates a U-ResNet neural network performing hit-level classification into the multi-algorithm approach used by Pandora to identify the neutrino interaction vertex. The machine learning solution is seamlessly integrated into a chain of pattern-recognition algorithms. The technique substantially outperforms the previous BDT-based solution, with a more than 20% increase in the efficiency of sub-1 cm vertex reconstruction across all neutrino flavours. 

The determination of the direction of a stellar core collapse via its neutrino emission is crucial for the identification of the progenitor for a multimessenger follow-up. A highly effective method of reconstructing supernova directions within the Deep Underground Neutrino Experiment (DUNE) is introduced. The supernova neutrino pointing resolution is studied by simulating and reconstructing electron-neutrino charged-current absorption on ${}^{40}\mathrm{Ar}$and elastic scattering of neutrinos on electrons. Procedures to reconstruct individual interactions, including a newly developed technique called “brems flipping,” as well as the burst direction from an ensemble of interactions are described. Performance of the burst direction reconstruction is evaluated for supernovae happening at a distance of 10 kpc for a specific supernova burst flux model. The pointing resolution is found to be 3.4 degrees at 68% coverage for a perfect interaction-channel classification and a fiducial mass of 40 kton, and 6.6 degrees for a 10 kton fiducial mass respectively. Assuming a 4% rate of charged-current interactions being misidentified as elastic scattering, DUNE’s burst pointing resolution is found to be 4.3 degrees (8.7 degrees) at 68% coverage. Published by the American Physical Society2025 

The Super-Kamiokande and T2K Collaborations present a joint measurement of neutrino oscillation parameters from their atmospheric and beam neutrino data. It uses a common interaction model for events overlapping in neutrino energy and correlated detector systematic uncertainties between the two datasets, which are found to be compatible. Using 3244.4 days of atmospheric data and a beam exposure of $19.7\left(16.3\right)\times {10}^{20}$protons on target in (anti)neutrino mode, the analysis finds a $1.9\sigma$exclusion of $CP$conservation (defined as $J_{CP}=0$) and a $1.2\sigma$exclusion of the inverted mass ordering. Published by the American Physical Society2025 

Abstract This paper introduces a novel track-length extension fitting algorithm for measuring the kinetic energies of inelastically interacting particles in liquid argon time projection chambers (LArTPCs). The algorithm finds the most probable offset in track length for a track-like object by comparing the measured ionization density as a function of position with a theoretical prediction of the energy loss as a function of the energy, including models of electron recombination and detector response. The algorithm can be used to measure the energies of particles that interact before they stop, such as charged pions that are absorbed by argon nuclei. The algorithm's energy measurement resolutions and fractional biases are presented as functions of particle kinetic energy and number of track hits using samples of stopping secondary charged pions in data collected by the ProtoDUNE-SP detector, and also in a detailed simulation. Additional studies describe the impact of thedE/dxmodel on energy measurement performance. The method described in this paper to characterize the energy measurement performance can be repeated in any LArTPC experiment using stopping secondary charged pions. 

The international collaboration designing and constructing the Deep Underground Neutrino Experiment (DUNE) at the Long-Baseline Neutrino Facility (LBNF) has developed a two-phase strategy toward the implementation of this leading-edge, large-scale science project. The 2023 report of the US Particle Physics Project Prioritization Panel (P5) reaffirmed this vision and strongly endorsed DUNE Phase I and Phase II, as did the European Strategy for Particle Physics. While the construction of the DUNE Phase I is well underway, this White Paper focuses on DUNE Phase II planning. DUNE Phase-II consists of a third and fourth far detector (FD) module, an upgraded near detector complex, and an enhanced 2.1 MW beam. The fourth FD module is conceived as a "Module of Opportunity", aimed at expanding the physics opportunities, in addition to supporting the core DUNE science program, with more advanced technologies. This document highlights the increased science opportunities offered by the DUNE Phase II near and far detectors, including long-baseline neutrino oscillation physics, neutrino astrophysics, and physics beyond the standard model. It describes the DUNE Phase II near and far detector technologies and detector design concepts that are currently under consideration. A summary of key R&D goals and prototyping phases needed to realize the Phase II detector technical designs is also provided. DUNE's Phase II detectors, along with the increased beam power, will complete the full scope of DUNE, enabling a multi-decadal program of groundbreaking science with neutrinos.