Material Budget Calculation of the new Inner Tracking System, ALICE

The ALICE Collaboration aims at studying the physics of strongly interacting matter by building up a dedicated heavy-ion detector. The Inner Tracking System (ITS) is located in the heart of the ALICE Detector surrounding the interaction point. Now, ALICE has a plan to upgrade the inner tracking system for rare probes at low transverse momentum. The new ITS composes of seven layers of silicon pixel sensor on the supporting structure. One goal of the new design is to reduce the material budget ($X/X_0$) per layer to 0.3$\%$ for inner layers and 0.8$\%$ for middle and outer layers. In this work, we perform the calculations based on detailed geometry descriptions of different supporting structures for inner and outer barrel using ALIROOT. Our results show that it is possible to reduce the material budget of the inner and outer barrel to the value that we have expected. The manufacturing of such prototypes are also possible.Comment: 13 pages, 9 figures, regular pape

รายงานการวิจัยการศึกษาอะตอมเอ็กโซติกโดยวิธีทางฟังก์ชันสเตอร์เมียน

SUT1-105-48-36-10 การศึกษาอะตอมของปฏิยานภาคโปรตรอนโดยวิธีทางฟังก์ชันสเตอร์เมียน SUT1-105-48-36-11 การศึกษาอะตอมพายออนิกโดยวิธีทางฟังก์ชันสเตอร์เมียน SUT1-105-48-36-12 การศึกษาพายออเนียมโดยวิธีทางฟังก์ชันสเตอร์เมียนThis work was supported by Suranaree University of Technology in fiscal year 2005-200

Towards a statistical mechanics of nonabelian vortices

A study is presented of classical field configurations describing nonabelian vortices in two spatial dimensions, when a global $SO(3)$ symmetry is spontaneously broken to a discrete group \IK isomorphic to the group of integers mod 4. The vortices in this model are characterized by the nonabelian fundamental group \pi_1 (SO(3)/{\IK}) , which is isomorphic to the group of quaternions. We present an ansatz describing isolated vortices and prove that it is stable to perturbations. Kinematic constraints are derived which imply that at a finite temperature, only two species of vortices are stable to decay, due to `dissociation'. The latter process is the nonabelian analogue of the instability of charge $|q| >1$ abelian vortices to dissociation into those with charge $|q| = 1$. The energy of configurations containing at maximum two vortex-antivortex pairs, is then computed. When the pairs are all of the same type, we find the usual Coulombic interaction energy as in the abelian case. When they are different, one finds novel interactions which are a departure from Coulomb like behavior. Therefore one can compute the grand canonical partition function (GCPF) for thermal pair creation of nonabelian vortices, in the approximation where the fugacities for vortices of each type are small. It is found that the vortex fugacities depend on a real continuous parameter $a$ which characterize the degeneracy of the vacuum. Depending on the relative sizes of these fugacities, the vortex gas will be dominated by one of either of the two types mentioned above. In these regimes, we expect the standard Kosterlitz-Thouless phase transitions to occur, as in systems of abelian vortices in 2-dimensions. Between these two regimes, the gas contains pairs of both types, so nonabelian effects will be important.Comment: 40 pages in a4 LaTeX including 2 tables and 5 uuencoded Postscript figures, QMW-93/15.( The 6th figure, due to its size, is available by directly request from [email protected]. Some typos are corrected and the choice of choosing \r_c has been argued.

Uncertainty-aware spot rejection rate as quality metric for proton therapy using a digital tracking calorimeter

Objective. Proton therapy is highly sensitive to range uncertainties due to the nature of the dose deposition of charged particles. To ensure treatment quality, range verification methods can be used to verify that the individual spots in a pencil beam scanning treatment fraction match the treatment plan. This study introduces a novel metric for proton therapy quality control based on uncertainties in range verification of individual spots. Approach. We employ uncertainty-aware deep neural networks to predict the Bragg peak depth in an anthropomorphic phantom based on secondary charged particle detection in a silicon pixel telescope designed for proton computed tomography. The subsequently predicted Bragg peak positions, along with their uncertainties, are compared to the treatment plan, rejecting spots which are predicted to be outside the 95% confidence interval. The such-produced spot rejection rate presents a metric for the quality of the treatment fraction. Main results. The introduced spot rejection rate metric is shown to be well-defined for range predictors with well-calibrated uncertainties. Using this method, treatment errors in the form of lateral shifts can be detected down to 1 mm after around 1400 treated spots with spot intensities of 1 × 107 protons. The range verification model used in this metric predicts the Bragg peak depth to a mean absolute error of 1.107 ± 0.015 mm. Significance. Uncertainty-aware machine learning has potential applications in proton therapy quality control. This work presents the foundation for future developments in this area.publishedVersio

Characterisation of analogue Monolithic Active Pixel Sensor test structures implemented in a 65 nm CMOS imaging process

Analogue test structures were fabricated using the Tower Partners Semiconductor Co. CMOS 65 nm ISC process. The purpose was to characterise and qualify this process and to optimise the sensor for the next generation of Monolithic Active Pixels Sensors for high-energy physics. The technology was explored in several variants which differed by: doping levels, pixel geometries and pixel pitches (10-25 $\mu$m). These variants have been tested following exposure to varying levels of irradiation up to 3 MGy and $10^{16}$ 1 MeV n$_\text{eq}$ cm$^{-2}$. Here the results from prototypes that feature direct analogue output of a 4$\times$4 pixel matrix are reported, allowing the systematic and detailed study of charge collection properties. Measurements were taken both using $^{55}$Fe X-ray sources and in beam tests using minimum ionizing particles. The results not only demonstrate the feasibility of using this technology for particle detection but also serve as a reference for future applications and optimisations

Uncertainty-aware spot rejection rate as quality metric for proton therapy using a digital tracking calorimeter

Objective. Proton therapy is highly sensitive to range uncertainties due to the nature of the dose deposition of charged particles. To ensure treatment quality, range verification methods can be used to verify that the individual spots in a pencil beam scanning treatment fraction match the treatment plan. This study introduces a novel metric for proton therapy quality control based on uncertainties in range verification of individual spots. Approach. We employ uncertainty-aware deep neural networks to predict the Bragg peak depth in an anthropomorphic phantom based on secondary charged particle detection in a silicon pixel telescope designed for proton computed tomography. The subsequently predicted Bragg peak positions, along with their uncertainties, are compared to the treatment plan, rejecting spots which are predicted to be outside the 95% confidence interval. The such-produced spot rejection rate presents a metric for the quality of the treatment fraction. Main results. The introduced spot rejection rate metric is shown to be well-defined for range predictors with well-calibrated uncertainties. Using this method, treatment errors in the form of lateral shifts can be detected down to 1 mm after around 1400 treated spots with spot intensities of 1 × 107 protons. The range verification model used in this metric predicts the Bragg peak depth to a mean absolute error of 1.107 ± 0.015 mm. Significance. Uncertainty-aware machine learning has potential applications in proton therapy quality control. This work presents the foundation for future developments in this area