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We consider QCD corrections to Higgs boson production through gluon-gluon fusion in hadron collisions. Using the recently evaluated [14] two-loop amplitude for this process and the corresponding factorization formulae [15]-[18] describing soft-gluon bremsstrahlung at O(α 2 S ), we compute the soft and virtual contributions to the next-to-next-to-leading order cross section. We also discuss soft-gluon resummation at next-to-next-to-leading logarithmic accuracy. Numerical results for Higgs boson production at the LHC are presented. ‡ Partially supported by Fundación Antorchas JHEP05(2001)025exceeds all the other production channels by a factor decreasing from 8 to 5 when M H increases from 100 to 200 GeV. When M H approaches 1 TeV, gg fusion still provides about 50% of the total production cross section.QCD radiative corrections at next-to-leading order (NLO) to gg-fusion were computed and found to be large [10]- [12]. Since approximate evaluations [13] of higher-order terms suggest that their effect can still be sizeable, the evaluation of the next-to-next-to-leading order (NNLO) corrections is highly desirable.In this paper, we perform a first step towards the complete NNLO calculation. We use the recently evaluated [14] two-loop amplitude for the process gg → H and the soft-gluon factorization formulae [15]-[18] for the bremsstrahlung subprocesses gg → Hg and gg → Hgg, Hqq, and we compute the soft and virtual contributions to the NNLO partonic cross section. We also discuss all-order resummation of soft-gluon contributions to next-to-next-to-leading logarithmic (NNLL) accuracy.We use the approximation M t M H , where M t is the mass of the top quark. The results of the NLO calculation in ref. [12] show that this is a good numerical approximation [13] of the full NLO correction, provided the exact dependence on M H /M t is included in the leading-order (LO) term. We can thus assume that the limit M t M H continues to be a good numerical approximation at NNLO. The hadronic cross section for Higgs boson production is obtained by convoluting the perturbative partonic cross sections with the parton distributions of the colliding hadrons. Besides the partonic cross sections, the other key ingredients of the NNLO calculation are the NNLO parton distributions. Even though their NNLO evolution kernels are not fully available, some of their Mellin moments have been computed [19] and, from these, approximated kernels have been constructed [20]. Recently, the new MRST [21] sets of distributions became available 1 , including the (approximated) NNLO densities, which allows an evaluation of the hadronic cross section to (almost full) NNLO accuracy.We use our NNLO result for the partonic cross sections and the MRST parton distributions at NNLO to compute the Higgs boson production cross section at the LHC. In this paper, we do not present numerical results for Run II at the Tevatron. Inclusive production of Higgs boson through gluon-gluon fusion is phenomenologically less relevant at the Tevatron: it is not regarded as a main ...

Sputtering of a single-element material surface by monatomic ion impact is one of the simplest and most fundamental phenomena of plasma–surface interaction. Despite its seemingly simple and well-defined nature, its collision cascade dynamics is so complex that no widely applicable formula of the sputtering yield has ever been derived analytically from the first principles. When the first-principles approach to a complex problem fails to unveil its nature, a data-driven approach, or machine learning, may be used to transform the problem into a tractable model. In this study, regression models of sputtering yields of such systems were constructed based on publicly available data derived from a large number of past experiments. The analysis has also identified the descriptors (i.e., physical variables characterizing the surface and incident ion species) on which the sputtering phenomena depend most strongly and presented quantitative evaluation on how sensitively the regression models depend on each descriptor or group of descriptors. Information obtained in this study can facilitate an understanding of the fundamental workings of the sputtering phenomena in the absence of rigorous analytical theory.

Low-temperature plasma processing technologies is essential for material synthesis, device fabrication, and surface treatment. The development of plasma-related products and services requires an understanding of the multiscale complex behaviors of plasma and the hierarchical integration of plasma generation, energy and mass transports through sheath region, surface reactions, and other processes. The importance of science-based and data-driven approaches to controlling systems is argued. The state-of-the-art of deep learning, machine learning, and artificial intelligence in low-temperature plasma science and technology is reviewed. In this review, the requirements and challenges for plasma parameter prediction and processing recipe discovery are asserted by researchers in the fields of material science and plasma processing. It also outlined a science-based, data-driven approach for development of virtual metrology in plasma processes.

The self-sputtering yield of the (100) face-centered cubic crystal surface consisting of particles interacting with the Lennard–Jones (LJ) potential is presented as a function of the normalized incident particle kinetic energy for normal incidence. Because the self-sputtering yield depends only on the normalized incident energy, the yield curve presented here is the universal curve, independent of the Lennard–Jones parameters, and therefore serves as the fundamental reference data for the LJ system. The self-sputtering yield data are also compared with experimentally obtained self-sputtering yields of some metals, which shows reasonable agreement at relatively low ion incident energy where mostly deposition occurs. At higher ion energy, the self-sputtering of such an LJ material does not represent those of real solids. This is because the repulsive interactions of the LJ potential do not represent those of actual atoms at short distances. The angle dependence of the self-sputtering yield is also presented for some selected normalized energies.