Low pT Hadronic Physics with CMS

The pixel detector of CMS can be used to reconstruct very low pT charged particles down to about 0.1 GeV/c. This can be achieved with high efficiency, good resolution and a negligible fake rate for elementary collisions. In the case of central PbPb collisions the fake rate can be kept low for pT > 0.4 GeV/c. In addition, the detector can be employed for identification of neutral hadrons (V0s) and converted photons.Comment: 6 pages. Presented at the Poster Session of Quark Matter 2006 Conference, Shanghai, 14-20 November 2006. Submitted to IJMP

Quality assurance for the ALICE Monte Carlo procedure

We implement the already existing macro,$ALICE_ROOT/STEER /CheckESD.C that is ran after reconstruction to compute the physics efficiency, as a task that will run on proof framework like CAF. The task was implemented in a C++ class called AliAnalysisTaskCheckESD and it inherits from AliAnalysisTaskSE base class. The function of AliAnalysisTaskCheckESD is to compute the ratio of the number of reconstructed particles to the number of particle generated by the Monte Carlo generator.The class AliAnalysisTaskCheckESD was successfully implemented. It was used during the production for first physics and permitted to discover several problems (missing track in the MUON arm reconstruction, low efficiency in the PHOS detector etc.). The code is committed to the SVN repository and will become standard tool for quality assurance.Comment: 7 pages, 7 figure

About the ''accurate mode'' of the IEEE 1788-2015 standard for interval arithmetic

The IEEE 1788-2015 standard for interval arithmetic defines three accuracy modes for the so-called set-based flavor: tightest, accurate and valid. This work in progress focuses on the accurate mode.First, an introduction to interval arithmetic and to the IEEE 1788-2015 standard is given, then the accurate mode is defined. How can this accurate mode be tested, when a library implementing interval arithmetic claims to provide this mode? The chosen approach is unit testing, and the elaboration of testing pairs for this approach is developed.A discussion closes this paper: how can the tester be tested? And if we go to the roots of the subject, is the accurate mode really relevant or should it be dropped off in the next version of the standard

Numerical reproducibility in HPC: issues in interval arithmetic

International audienceThe problem of numerical reproducibility is the problem of getting the same result when a numerical computation is run several times, whether on the same machine or on different machines. The accuracy of the result is a different issue. As far as interval arithmetic is concerned, the relevant issue is the inclusion property, that is, the guarantee that the exact result belongs to the computed resulting interval

Motivations for an arbitrary precision interval arithmetic and the MPFI library

This paper justifies why an arbitrary precision interval arithmetic is needed: to provide accurate results, interval computations require small input intervals; this explains why bisection is so often employed in interval algorithms. The MPFI library has been built in order to fulfill this need: indeed, no existing library met the required specifications. The main features of this library are briefly given and a comparison with a fixed-preci- sion interval arithmetic, on a specific problem, is presented: it shows that the overhead due to the multiple precision is completely admissible. Eventually, some applications based on MPFI are given: robotics, isolation of polynomial real roots (by an algorithm combining symbolic and numerical computations) and approximation of real roots with arbitrary accuracy.Cet article justifie le besoin d’une arithmétique par intervalles en précision arbitraire : pour fournir des résultats précis, un calcul par intervalles requiert des intervalles en entrée qui soient fins ; c’est pour cette raison que la bissection est un procédé si souvent employé dans les algorithmes par intervalles. La bibliothèque MPFI a été développée pour répondre à ce besoin : en effet, aucune bibliothèque existante n’offrait de spécifications satisfaisantes. Les caractéristiques de cette bibliothèque sont rapidement données puis une comparaison avec une bibliothèque d’arithmétique par intervalles en précision fixée est menée sur un problème spécifique : elle met en évidence le fait que le surcoût lié à la gestion de la précision multiple est tout à fait acceptable. Pour terminer, quelques applications basées sur MPFI sont présentées : robotique, isolation des racines réelles de polynômes (par un algorithme combinant calcul symbolique et calcul numérique) et approximation avec une précision arbitraire de zéros réel

Particle physics contribution to the elimination of nuclear waste

Progress in particle accelerator technology makes it possible to use a proton accelerator to eliminate nuclear waste efficiently. The Energy Amplifier (EA) proposed by C. Rubbia and his group is a subcritical system driven by a proton accelerator. It is particularly attractive for destroying, through fission, transuranic elements produced by present nuclear reactors. The EA could also transform efficiently and at minimal cost long-lived fission fragments using the concept of Adiabatic Resonance Crossing (ARC) recently tested at CERN with the TARC experiment. The ARC concept can be extended to several other application domains (radioactive isotopes production for medicine and industry, neutron research applications, etc.)

Parallel Implementation of Interval Matrix Multiplication

International audienceTwo main and not necessarily compatible objectives when implementing the product of two dense matrices with interval coefficients are accuracy and efficiency. In this work, we focus on an implementation on multicore architectures. One direction successfully explored to gain performance in execution time is the representation of intervals by their midpoints and radii rather than the classical representation by endpoints. Computing with the midpoint-radius representation enables the use of optimized floating-point BLAS and consequently the performances benefit from the performances of the BLAS routines. Several variants of interval matrix multiplication have been proposed, that correspond to various trade-offs between accuracy and efficiency, including some efficient ones proposed by Rump in 2012. However, in order to guarantee that the computed result encloses the exact one, these efficient algorithms rely on an assumption on the order of execution of floating-point operations which is not verified by most implementations of BLAS. In this paper, an algorithm for interval matrix product is proposed that verifies this assumption. Furthermore, several optimizations are proposed and the implementation on a multicore architecture compares reasonably well with a non-guaranteed implementation based on MKL, the optimized BLAS of Intel: the overhead is most of the time less than 2 and never exceeds 3. This implementation also exhibits a good scalability

Solving and Certifying the Solution of a Linear System

The Reliable Computing journal has no more paper publication, only free, electronic publication.International audienceUsing floating-point arithmetic to solve a linear system yields a computed result, which is an approximation of the exact solution because of roundoff errors. In this paper, we present an approach to certify the computed solution. Here, "certify" means computing a guaranteed enclosure of the error. Our method is an iterative refinement method and thus it also improves the computed result. The method we present is inspired from the verifylss function of the IntLab library, with a first step, using floating-point arithmetic, to solve the linear system, followed by interval computations to get and refine an enclosure of the error. The specificity of our method is to relax the requirement of tightness of the error, in order to gain in performance. Indeed, only the order of magnitude of the error is needed. Experiments show a gain in accuracy and in performance, for various condition number of the matrix of the linear system

Certification of a Numerical Result: Use of Interval Arithmetic and Multiple Precision

International audienceUsing floating-point arithmetic to solve a numerical problem yields a computed result, which is an approximation of the exact solution because of roundoff errors. In this paper, we present an approach to certify the computed solution. Here, "certify" means computing a guaranteed enclosure of the error between the computed, approximate, result and the exact, unknown result. We discuss an iterative refinement method: classically, such methods aim at computing an approximation of the error and they add it to the previous result to improve its accuracy. We add two ingredients: interval arithmetic is used to get an enclosure of the error instead of an approximation, and multiple precision is used to reach higher accuracy. We exemplify this approach on the certification of the solution of a linear system