Scholar iON
Academic Synthesis
The scholarly papers from arXiv highlight significant advancements in two distinct areas of mathematical optimization and computational physics. The first paper by Nakazawa et al. explores the development of a prototype analog front-end for Time Projection Chambers (TPCs), crucial for dark matter detection and neutrino research. It addresses the technical challenges of designing ASICs to meet stringent requirements such as dynamic range and noise reduction, which are vital for accurate signal processing in both negative-ion and dual-phase liquid argon TPCs. The second paper by Erban et al. serves as a comprehensive guide to stochastic simulations of reaction-diffusion processes, emphasizing the transition from deterministic to stochastic modeling using methods like the Gillespie algorithm. Both studies underscore the importance of sophisticated computational tools and technologies in advancing experimental and theoretical research, facilitating deeper insights into fundamental physical phenomena.
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.