Mon·Jun 26, 2023Monday·June 26, 2023

1. 01*

   An Analysis of Muon Flux from Angle Variation of the QuarkNet Cosmic Ray Detector

   Ricco Venterea · Urbas Ekka

   We present one of the first cosmic ray muon flux-angle variation experiments on the QuarkNet Cosmic Ray Detector (QNCRD). We first describe QNCRD and its calibration. The main focus is then quantifying muon flux decrease as a function of angle from the zenith. The angle of counters of QNCRD were incremented 15 degrees on average every $3.1$ days over the range of 0 degrees to 90 degrees for a period of approximately one month. Results showed that as the angle of the detector increased from the zenith, muon flux decreased, which agrees with previous studies. An estimate for the flux based on the model $I(\theta )=I_{0}cos(\theta {)}^n$ had an exponent value of $n=1.39\pm 0.01$ for $\theta <75$ degrees, an underestimate of values in other literature. These findings provided a reasonable, although not entirely accurate, estimate for the value of $n$ considering the duration of the study and sensitivity of the instrument. Our results constrain the accuracy of QNCRD and provide a source for future long-term experiments. This study also demonstrates the feasibility of conducting science experiments in high school classrooms, increasing science accessibility.

   ↳ astro-ph.HEastro-ph.IMnucl-exphysics.ins-detPhys.Teacher(2023)·1 citation
2. 02*

   Neutron Yield Predictions with Artificial Neural Networks: A Predictive Modeling Approach

   Benedikt Schmitz · Stefan Scheuren

   The development of compact neutron sources for applications is extensive and features many approaches. Let alone ion-based approaches, several projects with different parameters exist. This article focuses on ion-based neutron production below the spallation barrier for arbitrary light ion beams. With this model, it is possible to compare different ion-based neutron source concepts against each other quickly. This contribution derives a predictive model using Monte Carlo simulations (50k simulations) and deep neural networks. This model can skip the necessary Monte Carlo simulations, which individually take a long time to complete, increasing the effort for optimization and predictions. The models' shortcomings are addressed, and mitigation strategies are proposed.

   ↳ physics.ins-detnucl-ex0 citations

* Reconstructed cohort: no mailing for this day survives in the archive. Papers are grouped by their submission times and arXiv's announcement cut-off, assuming announcement without delay; positions follow identifier order. Validated at ~91% exact-day agreement against the archived era.