Scholar iON
Academic Synthesis
The scholarly papers from "math.QA" present advancements in niche areas of physics and applied mathematics, focusing on technological and methodological developments. Nakazawa et al. (2019) detail the creation of a versatile analog front-end ASIC designed for negative-ion and dual-phase liquid argon time projection chambers (TPCs), significant for its potential to enhance dark matter searches and neutrino studies through improved electronics performance under varying temperature conditions. In a different domain, Erban et al. (2007) offer a comprehensive introduction to stochastic simulations for reaction-diffusion processes, highlighting the importance of stochastic models in capturing the random nature of chemical reactions and diffusion, and their connections to deterministic approaches. Together, these works underscore the critical intersection of advanced technology and mathematical modeling in expanding the capabilities of experimental and theoretical physics.
We report on the recent development of a versatile analog front-end compatible with a negative-ion $μ$-TPC for a directional dark matter search as well as a dual-phase, next-generation $\mathcal{O}$(10~kt) liquid argon TPC to study neutrino oscillations, nucleon decay, and astrophysical neutrinos. Although the operating conditions for negative-ion and liquid argon TPCs are quite different (room temperature \textit{vs.} $\sim$88~K operation, respectively), the readout electronics requirements are similar. Both require a wide-dynamic range up to 1600 fC, and less than 2000--5000 e$^-$ noise for a typical signal of 80 fC with a detector capacitance of $C_{\rm det} \approx 300$~pF. In order to fulfill such challenging requirements, a prototype ASIC was newly designed using 180-nm CMOS technology. Here, we report on the performance of this ASIC, including measurements of shaping time, dynamic range, and equivalent noise charge (ENC). We also demonstrate the first operation of this ASIC on a low-pressure negative-ion $μ$-TPC.
A practical introduction to stochastic modelling of reaction-diffusion processes is presented. No prior knowledge of stochastic simulations is assumed. The methods are explained using illustrative examples. The article starts with the classical Gillespie algorithm for the stochastic modelling of chemical reactions. Then stochastic algorithms for modelling molecular diffusion are given. Finally, basic stochastic reaction-diffusion methods are presented. The connections between stochastic simulations and deterministic models are explained and basic mathematical tools (e.g. chemical master equation) are presented. The article concludes with an overview of more advanced methods and problems.