The Software Architecture for Performing Scientific Computation with the JLAPACK Libraries in ScalaLab

Although LAPACK is a powerful library its utilization is difficult. JLAPACK, a Java translation obtained automatically from the Fortran LAPACK sources, retains exactly the same difficult to use interface of LAPACK routines. The MTJ library implements an object oriented Java interface to JLAPACK that hides many complicated details. ScalaLab exploits the flexibility of the Scala language to present an even more friendly and convenient interface to the powerful but complicated JLAPACK library. The article describes the interfacing of the low-level JLAPACK routines within the ScalaLab environment. This is performed rather easily by exploiting well suited features of the Scala language. Also, the paper demonstrates the convenience of using JLAPACK routines for linear algebra operations from within ScalaLab

ScalaLab and GroovyLab: Comparing Scala and Groovy for Scientific Computing

ScalaLab and GroovyLab are both MATLAB-like environments for the Java Virtual Machine. ScalaLab is based on the Scala programming language and GroovyLab is based on the Groovy programming language. They present similar user interfaces and functionality to the user. They also share the same set of Java scientific libraries and of native code libraries. From the programmer's point of view though, they have significant differences. This paper compares some aspects of the two environments and highlights some of the strengths and weaknesses of Scala versus Groovy for scientific computing. The discussion also examines some aspects of the dilemma of using dynamic typing versus static typing for scientific programming. The performance of the Java platform is continuously improved at a fast pace. Today Java can effectively support demanding high-performance computing and scales well on multicore platforms. Thus, both systems can challenge the performance of the traditional C/C++/Fortran scientific code with an easier to use and more productive programming environment

The CMS Barrel Calorimeter Response to Particle Beams from 2 to 350 GeV/c

The response of the CMS barrel calorimeter (electromagnetic plus hadronic) to hadrons, electrons and muons over a wide momentum range from 2 to 350 GeV/c has been measured. To our knowledge, this is the widest range of momenta in which any calorimeter system has been studied. These tests, carried out at the H2 beam-line at CERN, provide a wealth of information, especially at low energies. The analysis of the differences in calorimeter response to charged pions, kaons, protons and antiprotons and a detailed discussion of the underlying phenomena are presented. We also show techniques that apply corrections to the signals from the considerably different electromagnetic (EB) and hadronic (HB) barrel calorimeters in reconstructing the energies of hadrons. Above 5 GeV/c, these corrections improve the energy resolution of the combined system where the stochastic term equals 84.7$\pm$1.6$\%$ and the constant term is 7.4$\pm$0.8$\%$. The corrected mean response remains constant within 1.3$\%$ rms

Precision measurement of the structure of the CMS inner tracking system using nuclear interactions

The structure of the CMS inner tracking system has been studied using nuclear interactions of hadrons striking its material. Data from proton-proton collisions at a center-of-mass energy of 13 TeV recorded in 2015 at the LHC are used to reconstruct millions of secondary vertices from these nuclear interactions. Precise positions of the beam pipe and the inner tracking system elements, such as the pixel detector support tube, and barrel pixel detector inner shield and support rails, are determined using these vertices. These measurements are important for detector simulations, detector upgrades, and to identify any changes in the positions of inactive elements

Precision measurement of the structure of the CMS inner tracking system using nuclear interactions

The structure of the CMS inner tracking system has been studied using nuclear interactions of hadrons striking its material. Data from proton-proton collisions at a center-of-mass energy of 13 TeV recorded in 2015 at the LHC are used to reconstruct millions of secondary vertices from these nuclear interactions. Precise positions of the beam pipe and the inner tracking system elements, such as the pixel detector support tube, and barrel pixel detector inner shield and support rails, are determined using these vertices. These measurements are important for detector simulations, detector upgrades, and to identify any changes in the positions of inactive elements

Strategies and performance of the CMS silicon tracker alignment during LHC Run 2

The strategies for and the performance of the CMS silicon tracking system alignment during the 2015–2018 data-taking period of the LHC are described. The alignment procedures during and after data taking are explained. Alignment scenarios are also derived for use in the simulation of the detector response. Systematic effects, related to intrinsic symmetries of the alignment task or to external constraints, are discussed and illustrated for different scenarios