L. Winslow

A. Li, Z. Fu, L. Winslow et al.

Rare event searches allow us to search for new physics at energy scales inaccessible with other means by leveraging specialized large-mass detectors. Machine learning provides a new tool to maximize the information provided by these detectors. The information is sparse, which forces these algorithms to start from the lowest level data and exploit all symmetries in the detector to produce results. In this work we present KamNet which harnesses breakthroughs in geometric deep learning and spatiotemporal data analysis to maximize the physics reach of KamLAND-Zen, a kiloton scale spherical liquid scintillator detector searching for neutrinoless double beta decay ($0νββ$). Using a simplified background model for KamLAND we show that KamNet outperforms a conventional CNN on benchmarking MC simulations with an increasing level of robustness. Using simulated data, we then demonstrate KamNet's ability to increase KamLAND-Zen's sensitivity to $0νββ$ and $0νββ$ to excited states. A key component of this work is the addition of an attention mechanism to elucidate the underlying physics KamNet is using for the background rejection.

A. L. Baxter, S. Y. BenZvi, W. Bonivento et al.

Developing sustainable software for the scientific community requires expertise in software engineering and domain science. This can be challenging due to the unique needs of scientific software, the insufficient resources for software engineering practices in the scientific community, and the complexity of developing for evolving scientific contexts. While open-source software can partially address these concerns, it can introduce complicating dependencies and delay development. These issues can be reduced if scientists and software developers collaborate. We present a case study wherein scientists from the SuperNova Early Warning System collaborated with software developers from the Scalable Cyberinfrastructure for Multi-Messenger Astrophysics project. The collaboration addressed the difficulties of open-source software development, but presented additional risks to each team. For the scientists, there was a concern of relying on external systems and lacking control in the development process. For the developers, there was a risk in supporting a user-group while maintaining core development. These issues were mitigated by creating a second Agile Scrum framework in parallel with the developers' ongoing Agile Scrum process. This Agile collaboration promoted communication, ensured that the scientists had an active role in development, and allowed the developers to evaluate and implement the scientists' software requirements. The collaboration provided benefits for each group: the scientists actuated their development by using an existing platform, and the developers utilized the scientists' use-case to improve their systems. This case study suggests that scientists and software developers can avoid scientific computing issues by collaborating and that Agile Scrum methods can address emergent concerns.

Z. Fu, C. Grant, D. M. Krawiec et al.

The next generation of searches for neutrinoless double beta decay (0ν\b{eta}\b{eta}) are poised to answer deep questions on the nature of neutrinos and the source of the Universe's matter-antimatter asymmetry. They will be looking for event rates of less than one event per ton of instrumented isotope per year. To claim discovery, accurate and efficient simulations of detector events that mimic 0ν\b{eta}\b{eta} is critical. Traditional Monte Carlo (MC) simulations can be supplemented by machine-learning-based generative models. In this work, we describe the performance of generative models designed for monolithic liquid scintillator detectors like KamLAND to produce highly accurate simulation data without a predefined physics model. We demonstrate its ability to recover low-level features and perform interpolation. In the future, the results of these generative models can be used to improve event classification and background rejection by providing high-quality abundant generated data.