Scholar iON
Academic Synthesis
The papers presented explore advancements in two distinct fields: the development of analog front-end electronics for gas and liquid argon time projection chambers (TPCs) and the practical applications of stochastic simulations in reaction-diffusion processes. Nakazawa et al. focus on creating a versatile ASIC using CMOS technology that can operate under varying conditions (room temperature and cryogenic), addressing the need for high dynamic range and low noise in TPCs used for dark matter detection and neutrino studies. Meanwhile, Erban et al. provide an accessible introduction to stochastic simulations, highlighting the significance of the Gillespie algorithm and stochastic models in understanding reaction-diffusion processes, and bridging the gap between stochastic and deterministic approaches. Both works underscore the importance of technological and methodological innovations in advancing research capabilities 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.