Scholar iON
Academic Synthesis
The papers from Nakazawa et al. (2019) and Erban et al. (2007) explore advanced technological and methodological developments in their respective fields, highlighting the significance of interdisciplinary approaches in scientific research. Nakazawa et al. focus on the development of a versatile analog front-end ASIC for TPCs, crucial for advancing dark matter and neutrino studies, showcasing the challenges and innovations in electronic design for extreme conditions. Meanwhile, Erban et al. provide a foundational guide to stochastic simulations in reaction-diffusion processes, offering insights into the transition from deterministic to stochastic models in chemical and biological systems. Both works underscore the importance of precision and adaptability, whether in hardware for particle physics experiments or in computational models for complex biochemical interactions.
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.