41 research outputs found

    Modeling Local Crabbing Dynamics in the JLEIC Ion Collider Ring

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    The Jefferson Lab Electron-Ion Collider (JLEIC) design considers a 50 mrad crossing angle at the Interaction Point. Without appropriate compensation, this could geometrically reduce the luminosity by an order of magnitude. A local crabbing scheme is implemented to avoid the luminosity loss: crab cavities are placed at both sides of the interaction region to restore a head-on collision scenario. In this contribution, we report on the implementation of a local crabbing scheme in the JLEIC ion ring. The effects of this correction scheme on the stability of proton bunches are analyzed using the particle tracking software elegant

    The Concept and Applications of a Dual Energy Storage Ring

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    A dual energy electron storage ring configuration is initially proposed as an electron cooler to cool the ion beam in a collider. It consists of two energy loops, the electron beam in the high energy loop undergoes the synchrotron radiation damping to obtain the desired beam property and the beam in the low energy loop is for cooling of the ion beam. The two different energy loops are connected by an energy recovery linac. A lattice design of such a dual energy storage ring has been completed and beam stability conditions are established. We performed numerical simulations to demonstrate the beam qualities and evaluated the cooling performance. In this paper, we present the study results and discuss possible applications of such a concept in many physics research and medical fields

    Innovative Applications of Genetic Algorithms to Problems in Accelerator Physics

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    The genetic algorithm (GA) is a powerful technique that implements the principles nature uses in biological evolution to optimize a multidimensional nonlinear problem. The GA works especially well for problems with a large number of local extrema, where traditional methods (such as conjugate gradient, steepest descent, and others) fail or, at best, underperform. The field of accelerator physics, among others, abounds with problems which lend themselves to optimization via GAs. In this paper, we report on the successful application of GAs in several problems related to the existing Continuous Electron Beam Accelerator Facility nuclear physics machine, the proposed Medium-energy Electron-Ion Collider at Jefferson Lab, and a radio frequency gun-based injector. These encouraging results are a step forward in optimizing accelerator design and provide an impetus for application of GAs to other problems in the field. To that end, we discuss the details of the GAs used, include a newly devised enhancement which leads to improved convergence to the optimum, and make recommendations for future GA developments and accelerator applications

    Beam-Beam Effect: Crab Dynamics Calculation in JLEIC

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    The electron and ion beams of a future Electron Ion Collider (EIC) must collide at an angle for detection, machine and engineering design reasons. To avoid associated luminosity reduction, a local crabbing scheme is used where each beam is crabbed before collision and de-crabbed after collision. The crab crossing scheme then provides a head-on collision for beams with a non-zero crossing angle. We develop a framework for accurate simulation of crabbing dynamics with beam-beam effects by combining symplectic particle tracking codes with a beam-beam model based on the Bassetti-Erskine analytic solution. We present simulation results using our implementation of such a framework where the beam dynamics around the ring is tracked using Elegant and the beam-beam kick is modeled in Python

    US Cosmic Visions: New Ideas in Dark Matter 2017: Community Report

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    This white paper summarizes the workshop "U.S. Cosmic Visions: New Ideas in Dark Matter" held at University of Maryland on March 23-25, 2017.Comment: 102 pages + reference

    A systematic review and meta-analysis of artificial intelligence diagnostic accuracy in prostate cancer histology identification and grading

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    [Background] Artificial intelligence (AI) is a promising tool in pathology, including cancer diagnosis, subtyping, grading, and prognostic prediction.[Methods] The aim of the study is to assess AI application in prostate cancer (PCa) histology. We carried out a systematic literature search in 3 databases. Primary outcome was AI accuracy in differentiating between PCa and benign hyperplasia. Secondary outcomes were AI accuracy in determining Gleason grade and agreement among AI and pathologists.[Results] Our final sample consists of 24 studies conducted from 2007 to 2021. They aggregate data from roughly 8000 cases of prostate biopsy and 458 cases of radical prostatectomy (RP). Sensitivity for PCa diagnostic exceeded 90% and ranged from 87% to 100%, and specificity varied from 68% to 99%. Overall accuracy ranged from 83.7% to 98.3% with AUC reaching 0.99. The meta-analysis using the Mantel-Haenszel method showed pooled sensitivity of 0.96 with I2 = 80.7% and pooled specificity of 0.95 with I2 = 86.1%. Pooled positive likehood ratio was 15.3 with I2 = 87.3% and negative – was 0.04 with I2 = 78.6%. SROC (symmetric receiver operating characteristics) curve represents AUC = 0.99. For grading the accuracy of AI was lower: sensitivity for Gleason grading ranged from 77% to 87%, and specificity from 82% to 90%.[Conclusions] The accuracy of AI for PCa identification and grading is comparable to expert pathologists. This is a promising approach which has several possible clinical applications resulting in expedite and optimize pathology reports. AI introduction into common practice may be limited by difficult and time-consuming convolutional neural network training and tuning.Peer reviewe

    Biological Earth observation with animal sensors

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    Space-based tracking technology using low-cost miniature tags is now delivering data on fine-scale animal movement at near-global scale. Linked with remotely sensed environmental data, this offers a biological lens on habitat integrity and connectivity for conservation and human health; a global network of animal sentinels of environmen-tal change
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