分类： 天文学 >> 天文学 提交时间： 2023-02-19
摘要： Supernova remnant (SNR) G106.3+2.7 was recently found to be one of the few potential Galactic hadronic PeVatrons. Aiming to test how solid the SNR is associated with the molecular clouds (MCs) that are thought to be responsible for hadronic interaction, we performed a new CO observation with the IRAM 30m telescope toward its "belly" region, which is coincident with the centroid of the $\gamma$-ray emission. There is a filament structure in the local-standard-of-rest velocity interval $-8$ to $-5$ km/s that nicely follows the northern radio boundary of the SNR. We have seen asymmetric broad profiles of $^{12}$CO lines, with widths of a few km/s along the northern boundary and in the "belly" region of G106.3+2.7, but similar $^{12}$CO line profiles are also found outside the SNR boundary. Further, the low $^{12}$CO J=2-1/J=1-0 line ratios suggest the MCs are cool. Therefore, it is still uncertain whether the MCs are directly disturbed by the SNR shocks, but we do find some clues that the MCs are nearby and thus can still be illuminated by the escaped protons from the SNR. Notably, we find an expanding molecular structure with a velocity of $\sim$3.5 km/s and a velocity gradient of the MCs across the SNR from $\sim -3$ to $-7$ km/s, which could be explained as the effect of the wind blown by the SNR's progenitor star.
分类： 物理学 >> 核物理学 提交时间： 2023-10-13
摘要： The Electron Cyclotron Resonance (ECR) ion source is a critical device for producing highly charged ion beams in various applications. Analyzing the charge-state distribution of the ion beams is essential, but the manual analysis is labor-intensive and prone to inaccuracies due to impurity ions. An automatic spectrum recognition system based on intelligent algorithms was proposed for rapid and accurate chargestate analysis of ECR ion sources. The system employs an adaptive window-length Savitzky-Golay (SG) filtering algorithm, an improved automatic multiscale peak detection (AMPD) algorithm, and a greedy matching algorithm based on the relative distance to accurately match different peaks in the spectra with the corresponding charge-state ion species. Additionally, a user-friendly operator interface was developed for ease of use. Extensive testing on the online ECR ion source platform demonstrates that the system achieves high accuracy, with an average root mean square error of less than 0.1 A for identifying charge-state spectra of ECR ion sources. Moreover, the system minimizes the standard deviation of the first-order derivative of the smoothed signal to 81.1846 A. These results indicate the capability of the designed system to identify ion beam spectra with mass numbers less than Xe, including Xe itself. The proposed automatic spectrum recognition system represents a significant advancement in ECR ion source analysis, offering a rapid and accurate approach for charge-state analysis while enhancing supply efficiency. The exceptional performance and successful implementation of the proposed system on multiple ECR ion source platforms at IMPCAS highlight its potential for widespread adoption in ECR ion source research and applications.
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