e+ e- -> t anti-t H including decays: on the size of background contributions

We present results for the lowest order cross sections, calculated with the complete set of the standard model Feynman diagrams, of all possible detection channels of the associated production of the top quark pair and the light Higgs boson, which may be used for determination of the top-Higgs Yukawa coupling at the future e+e- linear collider. We show that, for typical particle identification cuts, the background contributions are large. In particular, the QCD background contributions are much bigger than could be expected when taking into account a possibly low virtuality of exchanged gluons. Moreover, we include the initial state radiation effects and discuss the dependence of the cross sections on the Higgs boson and top quark masses.Comment: 13 pages, 1 figure; substantially revised version, accepted for publication in Eur.Phys.J.

Differentiable Programming Tensor Networks

Differentiable programming is a fresh programming paradigm which composes parameterized algorithmic components and trains them using automatic differentiation (AD). The concept emerges from deep learning but is not only limited to training neural networks. We present theory and practice of programming tensor network algorithms in a fully differentiable way. By formulating the tensor network algorithm as a computation graph, one can compute higher order derivatives of the program accurately and efficiently using AD. We present essential techniques to differentiate through the tensor networks contractions, including stable AD for tensor decomposition and efficient backpropagation through fixed point iterations. As a demonstration, we compute the specific heat of the Ising model directly by taking the second order derivative of the free energy obtained in the tensor renormalization group calculation. Next, we perform gradient based variational optimization of infinite projected entangled pair states for quantum antiferromagnetic Heisenberg model and obtain start-of-the-art variational energy and magnetization with moderate efforts. Differentiable programming removes laborious human efforts in deriving and implementing analytical gradients for tensor network programs, which opens the door to more innovations in tensor network algorithms and applications.Comment: Typos corrected, discussion and refs added; revised version accepted for publication in PRX. Source code available at https://github.com/wangleiphy/tensorgra