Energy Deposition due to Secondary Particles in The Helical Undulator Wall at ILC-250GeV

Alharbi, Khaled; Riemann, S.; Alrashdi, Ayash; Moortgat‐Pick, Gudrid; Ushakov, Andriy; ., .

doi:10.48550/arxiv.2001.08024

Cited by 2 publications

(2 citation statements)

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“…The depthwise separable convolution consists of datasetspecific 3 Â 3 channel convolutions and shared 1 Â 1 point convolutions across all datasets. The global network consists of five dilated convolution layers with 3 Â 3 kernels and dilation factors of [1,2,5,2,1]. Dilated convolutions in the global network expand the receptive field, which is beneficial for capturing crucial global information.…”

Section: Backbone Networkmentioning

confidence: 99%

A universal medically assisted model for anatomical landmark detection in radioactive images

Pang,

Liao,

Liu

et al. 2024

Int J Imaging Syst Tech

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Anatomical landmark detection is a critical task in medical image analysis with significant research and practical value. The analysis of landmarks in radiological images is beneficial for its diagnosis and treatment. Currently, most methods are applied to datasets from specific anatomical regions, with only a few deep neural network models designed for mixed datasets. Meanwhile, the precise and fast localization of landmarks remains a significant challenge due to interdependencies among landmarks and the demanding accuracy requirements in medical clinical applications. In this work, we leverage the advantages of attention mechanisms for focusing on specific information, and propose a universal landmark detection model trained on mixed‐domain radioactive image datasets. The model consists of three main components: the local network, the global network, and the attention feature extraction module. The local network employs a U‐Net architecture with depthwise separable convolutions to extract local image features. Notably, we introduce partial depthwise separable convolutions as an option for further lightweight models. The global network comprises a sequence of dilated convolutions with varying dilation rates, expanding the receptive field to extract global features. The attention feature extraction module efficiently utilizes intermediate outputs to refine the precise location information of landmarks. Additionally, we introduce the regional attention loss function to provide the model with landmark offset information. We conduct experiments on three publicly available x‐ray image datasets covering head, hand, and chest regions. The experimental results demonstrate that the proposed universal model achieves fast convergence and excellent performance across multiple datasets.

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Section: Backbone Networkmentioning

confidence: 99%

A universal medically assisted model for anatomical landmark detection in radioactive images

Pang,

Liao,

Liu

et al. 2024

Int J Imaging Syst Tech

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show abstract

“…To achieve the required vacuum pressure, the power deposited on the undulator walls due to the synchrotron radiation must be below 1 W/m [5]. This was studied for ILC-250GeV on [6]. This study shows that 22 photon masks with 0.22 cm radius must be placed along the undulator line to keep the power deposited at the undulator walls below the limit.…”

Section: The Ilc Helical Undulator Photon Masksmentioning

confidence: 99%

Photon Masks for the ILC Positron Source with 175 and 250 GeV Electron Drive Beam

Alharbi,

Riemann,

Alrashdi

et al. 2021

Preprint

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In the future the International Linear Collider (ILC), a helical undulator-based polarized positron source, is expected to be chosen. A high energy electron beam passes through a superconducting helical undulator in order to create circularly polarized photons which will be directed to a conversion target, resulting in electron-positron pairs. The resulting positron beam is longitudinally polarized. Since the photons are produced with an opening angle and pass through a long superconducting helical undulator, some of these photons will strike the undulator walls. Therefore photon masks must be placed along the undulator line in order to keep the power deposited in the undulator walls below the acceptable limit of 1W/m. The baseline design of the ILC is focused on 250 GeV center-of-mass energy and upgrade to center-of-mass energies of 350 and 500 GeV is foreseen. This paper shows a detailed study of the ideal power deposited along the masks for both 350 and 500 GeV center-of-mass energies.

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Energy Deposition due to Secondary Particles in The Helical Undulator Wall at ILC-250GeV

Cited by 2 publications

References 1 publication

A universal medically assisted model for anatomical landmark detection in radioactive images

A universal medically assisted model for anatomical landmark detection in radioactive images

Photon Masks for the ILC Positron Source with 175 and 250 GeV Electron Drive Beam

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