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Register a new userEnergy deposition studies for the High-Luminosity Large Hadron Collider inner triplet magnets
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A detailed model of the High Luminosity LHC inner triplet region with new large-aperture Nb3Sn magnets, field maps, corrector packages, and segmented tungsten inner absorbers was built and implemented into the FLUKA and MARS15 codes. In the optimized configuration, the peak power density averaged over the magnet inner cable width is safely below the quench limit. For the integrated luminosity of 3000 fb-1, the peak dose in the innermost magnet insulator ranges from 20 to 35 MGy. Dynamic heat loads to the triplet magnet cold mass are calculated to evaluate the cryogenic capability. In general, FLUKA and MARS results are in a very good agreement.
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A detailed model of the High Luminosity LHC inner triplet region with new large-aperture Nb3Sn magnets, field maps, corrector packages, and segmented tungsten inner absorbers was built and implemented into the FLUKA and MARS15 codes. In the optimized configuration, the peak power density averaged over the magnet inner cable width is safely below the quench limit. For the integrated luminosity of 3000 fb -1, the peak dose in the innermost magnet insulator ranges from 20 to 35 MGy. Dynamic heat loads to the triplet magnet cold mass are calculated to evaluate the cryogenic capability. In general, FLUKA and MARS results are in a very good agreement.
HL-LHC federates the efforts and R&D of a large international community towards the ambitious HL- LHC objectives and contributes to establishing the European Research Area (ERA) as a focal point of global research cooperation and a leader in frontier knowledge and technologies. HL-LHC relies on strong participation from various partners, in particular from leading US and Japanese laboratories. This participation will be required for the execution of the construction phase as a global project. In particular, the US LHC Accelerator R&D Program (LARP) has developed some of the key technologies for the HL-LHC, such as the large-aperture niobium-tin ($Nb_{3}Sn) quadrupoles and the crab cavities. The proposed governance model is tailored accordingly and should pave the way for the organization of the construction phase.
This paper presents one of the case studies of the Gamma Factory initiative -- a proposal of a new operation scheme of ion beams in the CERN accelerator complex. Its goal is to extend the scope and precision of the LHC-based research by complementing the proton-proton collision programme with the high-luminosity nucleus-nucleus one. Its numerous physics highlights include studies of the exclusive Higgs-boson production in photon-photon collisions and precision measurements of the electroweak (EW) parameters. There are two principal ways to increase the LHC luminosity which do not require an upgrade of the CERN injectors: (1) modification of the beam-collision optics and (2) reduction of the transverse emittance of the colliding beams. The former scheme is employed by the ongoing high-luminosity (HL-LHC) project. The latter one, applicable only to ion beams, is proposed in this paper. It is based on laser cooling of bunches of partially stripped ions at the SPS flat-top energy. For isoscalar calcium beams, which fulfil the present beam-operation constrains and which are particularly attractive for the EW physics, the transverse beam emittance can be reduced by a factor of $5$ within the $8$ seconds long cooling phase. The predicted nucleon-nucleon luminosity of $L_{NN}= 4.2 times 10^{34},$s$^{-1}$cm$^{-2}$ for collisions of the cooled calcium beams at the LHC top energy is comparable to the levelled luminosity for the HL-LHC proton-proton collisions, but with reduced pile-up background. The scheme proposed in this paper, if confirmed by the future Gamma Factory proof-of-principle experiment, could be implemented at CERN with minor infrastructure investments.
Machine learning entails a broad range of techniques that have been widely used in Science and Engineering since decades. High-energy physics has also profited from the power of these tools for advanced analysis of colliders data. It is only up until recently that Machine Learning has started to be applied successfully in the domain of Accelerator Physics, which is testified by intense efforts deployed in this domain by several laboratories worldwide. This is also the case of CERN, where recently focused efforts have been devoted to the application of Machine Learning techniques to beam dynamics studies at the Large Hadron Collider (LHC). This implies a wide spectrum of applications from beam measurements and machine performance optimisation to analysis of numerical data from tracking simulations of non-linear beam dynamics. In this paper, the LHC-related applications that are currently pursued are presented and discussed in detail, paying also attention to future developments.
A high-energy muon collider scenario require a final cooling system that reduces transverse emittance by a factor of ~10 while allowing longitudinal emittance increase. The baseline approach has low-energy transverse cooling within high-field solenoids, with strong longitudinal heating. This approach and its recent simulation are discussed. Alternative approaches which more explicitly include emittance exchange are also presented. Round-to-flat beam transform, transverse slicing, and longitudinal bunch coalescence are possible components of an alternative approach. Wedge-based emittance exchange could provide much of the required transverse cooling with longitudinal heating. Li-lens and quadrupole focusing systems could also provide much of the required final cooling.
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