Scholar iON
Academic Synthesis
The selected articles underscore the importance of advanced technological and mathematical frameworks in addressing complex scientific challenges. Nakazawa et al. (2019) discuss the development of a versatile analog front-end ASIC, crucial for enhancing the performance of negative-ion and dual-phase liquid argon time projection chambers (TPCs) used in dark matter detection and neutrino research, highlighting the need for high dynamic range and low noise operation in extreme conditions. On the other hand, Erban et al. (2007) provide a foundational guide to stochastic simulations of reaction-diffusion processes, emphasizing the transition from deterministic to stochastic models in simulating chemical and molecular diffusion. Together, these studies illustrate the significance of both cutting-edge hardware development and sophisticated mathematical modeling in advancing experimental and theoretical research frontiers in physics and chemistry.
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.