Matthew Connell | Searcharxiv

arXiv2026

Characterisation of the Thermoflow due to the Dry Nitrogen Flushing Scheme in the ATLAS Inner Tracker using Computational Fluid Dynamics

The planned High Luminosity upgrade to the Large Hadron Collider at CERN aims to increase the instantaneous luminosity peak to about 7.5 x 10^{34} cm^{-2}s^{-1}. The ATLAS detector will be extensively re-designed to meet the challenges of this upgrade. This paper focuses on the use of computational fluid dynamics to characterise the thermoflow in order to model the dry nitrogen flushing scheme in the Common Environmental Monitoring and Interlock System for the ATLAS Inner Tracker as part of the upgrade process. The Technical Design Report considers the possibility for the bi-phase CO2 coolant temperature to drop to as low as -55 degrees C in the case of a fault. The specification for the highest Relative Humidity within the ITk volume is therefore equivalent to a dew point temperature at or below -60 degrees C in order to prevent condensation which could damage the detector electronics. The design accommodates for humidity monitoring to detect the onset of such events and dry nitrogen flushing to remove moisture. Therefore, it is important to thoroughly understand all consequences of atmospheric air ingress due to air-leaks and/or air-ingress from the outlets due to the over-pressure. The computational fluid dynamics model presented in this study was used to provide quantitative and qualitative insight into the various operational and failure conditions, informing engineering design changes to optimise the flushing scheme and ensure that the ITk remains dry and within the design specification of the acceptable dew point range.

physics.ins-det↗

arXiv2021

Nonparametric empirical Bayes estimation based on generalized Laguerre series

In this work, we delve into the nonparametric empirical Bayes theory and approximate the classical Bayes estimator by a truncation of the generalized Laguerre series and then estimate its coefficients by minimizing the prior risk of the estimator. The minimization process yields a system of linear equations the size of which is equal to the truncation level. We focus on the empirical Bayes estimation problem when the mixing distribution, and therefore the prior distribution, has a support on the positive real half-line or a subinterval of it. By investigating several common mixing distributions, we develop a strategy on how to select the parameter of the generalized Laguerre function basis so that our estimator {possesses a finite} variance. We show that our generalized Laguerre empirical Bayes approach is asymptotically optimal in the minimax sense. Finally, our convergence rate is compared and contrasted with {several} results from the literature.

math.ST↗