ALICE is the heavy-ion experiment at the CERN Large Hadron Collider. The experiment continuously took data during the first physics campaign of the machine from fall 2009 until early 2013, using proton and lead-ion beams. In this paper we describe the running environment and the data handling procedures, and discuss the performance of the ALICE detectors and analysis methods for various physics observables.

We present a compendium of numerical simulation techniques, based on tensor network methods, aiming to address problems of many-body quantum mechanics on a classical computer. The core setting of this anthology are lattice problems in low spatial dimension at finite size, a physical scenario where tensor network methods, both Density Matrix Renormalization Group and beyond, have long proven to be winning strategies. Here we explore in detail the numerical frameworks and methods employed to deal with low-dimension physical setups, from a computational physics perspective. We focus on symmetries and closed-system simulations in arbitrary boundary conditions, while discussing the numerical data structures and linear algebra manipulation routines involved, which form the core libraries of any tensor network code. At a higher level, we put the spotlight on loop-free network geometries, discussing their advantages, and presenting in detail algorithms to simulate low-energy equilibrium states. Accompanied by discussions of data structures, numerical techniques and performance, this anthology serves as a programmer's companion, as well as a self-contained introduction and review of the basic and selected advanced concepts in tensor networks, including examples of their applications.

We study how to assign the recently observed DsJ (2700) meson to an appropriate level of the cs spectrum by the analysis of its decay modes in final states comprising a light pseudoscalar meson. We use an effective lagrangian approach with heavy quark and chiral symmetries, obtaining that the measurement of the D * K decay width would allow to distinguish between two possible assignments.

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We describe the Density Matrix Renormalization Group algorithms for time dependent and time independent Hamiltonians. This paper is a brief but comprehensive introduction to the subject for anyone willing to enter in the field or write the program source code from scratch. An open source version of the code can be found at: http://qti.sns.it/dmrg/phome.html . The advent of information era has been opening the possibility to perform numerical simulations of quantum many-body systems, thus revealing completely new perspectives in the field of condensed matter theory. Indeed, together with the analytic approaches, numerical techniques provide a lot of information and details otherwise inaccessible. However, the simulation of a quantum mechanical system is generally a very hard task; one of the main reasons is related to the number of parameters required to represent a quantum state. This value usually grows exponentially with the number of constituents of the system, 1 due to the corresponding exponential growth of the Hilbert space. This exponential scaling drastically reduces the possibility of a direct simulation of many-body quantum systems. as well as new couplings; renormalization group approximations consist in physically motivated truncations of the set of couplings newly generated by the elimination of degrees of freedom. In this way one obtains a simplified effective Hamiltonian that should catch the essential physics of the system under study. We point out that the DMRG can also be seen as a variational method under the matrix-product-form ansatz for trial wave functions:the ground state and elementary excited states in the thermodynamic limit can be simply expressed via an ansatz form which can be explored variationally, without referencing to the renormalization construction. 9 Very recently, influence from the quantum information community has led to a DMRG-like algorithm which is able to simulate the temporal evolution of one-dimensional quantum systems. 10,11,12,13,14,15,16,17 2 Quantum information theory has also allowed to clarify the situations in which this method can be applied efficiently. Indeed, it has been shown 10 that the efficiency in simulating a quantum many-body system is strictly connected to its entanglement behavior. More precisely, if the entanglement of a subsystem with respect to the whole is bounded (or grows logarithmically with its size) an efficient simulation with DMRG is possible. Up to now, it is known that ground states of one dimensional lattices (whether critical or not) satisfy this requirement, whereas in higher dimensionality it is not fulfilled as the entanglement is subject to an area law.18 On the other hand, the simulation of the time evolution of critical systems may not be efficient even in one dimensional systems as the block entanglement can grow linearly with time and block size. 19,20 In a different context, it has also been shown that in a quantum computer performing an efficient quantum algorithm (Shor's algorithm and the simulation of a quantum ...