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Dhani, P. K., Rodrigo, G., & Sborlini, G. F. R. (2023). Triple-collinear splittings with massive particles. J. High Energy Phys., 12(12), 188–20pp.
Abstract: We analyze in detail the most singular behaviour of processes involving triple-collinear splittings with massive particles in the quasi-collinear limit, and present compact expressions for the splitting amplitudes and the corresponding splitting kernels at the squared-amplitude level. Our expressions fully agree with well-known triple-collinear splittings in the massless limit, which are used as a guide to achieve the final expressions. These results are important to quantify dominant mass effects in many observables, and constitute an essential ingredient of current high-precision computational frameworks for collider phenomenology.
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Giachino, A., van Hameren, A., & Ziarko, G. (2024). A new subtraction scheme at NLO exploiting the privilege of kT-factorization. J. High Energy Phys., 06(6), 167–39pp.
Abstract: We present a subtraction method for the calculation of real-radiation integrals at NLO in hybrid k(T)-factorization. The main difference with existing methods for collinear factorization is that we subtract the momentum recoil, occurring due to the mapping from an (n + 1)-particle phase space to an n-particle phase space, from the initial-state momenta, instead of distributing it over the final-state momenta.
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Rodriguez-Alvarez, M. J., Sanchez, F., Soriano, A., & Iborra, A. (2010). Sparse Givens resolution of large system of linear equations: Applications to image reconstruction. Math. Comput. Model., 52(7-8), 1258–1264.
Abstract: In medicine, computed tomographic images are reconstructed from a large number of measurements of X-ray transmission through the patient (projection data). The mathematical model used to describe a computed tomography device is a large system of linear equations of the form AX = B. In this paper we propose the QR decomposition as a direct method to solve the linear system. QR decomposition can be a large computational procedure. However, once it has been calculated for a specific system, matrices Q and R are stored and used for any acquired projection on that system. Implementation of the QR decomposition in order to take more advantage of the sparsity of the system matrix is discussed.
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