IGUANA Architecture, Framework and Toolkit for Interactive Graphics

IGUANA is a generic interactive visualisation framework based on a C++ component model. It provides powerful user interface and visualisation primitives in a way that is not tied to any particular physics experiment or detector design. The article describes interactive visualisation tools built using IGUANA for the CMS and D0 experiments, as well as generic GEANT4 and GEANT3 applications. It covers features of the graphical user interfaces, 3D and 2D graphics, high-quality vector graphics output for print media, various textual, tabular and hierarchical data views, and integration with the application through control panels, a command line and different multi-threading models.Comment: Presented at the 2003 Computing in High Energy and Nuclear Physics (CHEP03), La Jolla, Ca, USA, March 2003, 6 pages LaTeX, 4 eps figures. PSN MOLT008 More and higher res figs at http://iguana.web.cern.ch/iguana/snapshot/main/gallery.htm

Simulation of a detection of Hidden Valley Z' decay into jets in the CMS experiment

Generated by Matt Strassler with help from Peter Skands. Processed through CMS by Albert De Roeck, Christophe Saout and Joanna Weng. Visualized by Ianna Osborne

CMS geometry through 2020

CMS faces real challenges with upgrade of the CMS detector through 2020. One of the challenges, from the software point of view, is managing upgrade simulations with the same software release as the 2013 scenario. We present the CMS geometry description software model, its integration with the CMS event setup and core software. The CMS geometry configuration and selection is implemented in Python. The tools collect the Python configuration fragments into a script used in CMS workflow. This flexible and automated geometry configuration allows choosing either transient or persistent version of the same scenario and specific version of the same scenario. We describe how the geometries are integrated and validated, how we define and handle different geometry scenarios in simulation and reconstruction. We discuss how to transparently manage multiple incompatible geometries in the same software release. Several examples are shown based on current implementation assuring consistent choice of scenario conditions. The consequences and implications for multiple/different code algorithms are discussed

Python and Fast Imperative Code: Lowering the Barriers

In a typical HEP data analysis process, data is explored by a physicist loading large amounts of data into an interactive Python environment. The physicist performs various analyses of this data. The results of the first analysis tell the physicist what the next steps should be. Python as a dynamically typed language is ideal for this task. The downside is that Python is not very fast. C++ as a statically typed language is fast. It is perfect for writing the performance critical components that speed things up. Python is used to arrange and connect these components. Thus at runtime the physicist can rearrange these components interactively, without reloading the data. We will look at a few examples how to write your own analysis components and connect them via: * Conversions of Awkward Arrays to and from RDataFrame (C++) * Standalone cppyy (C++) * Passing Awkward Arrays to and from Python functions compiled by Numba * Passing Awkward Arrays to Python functions compiled for GPUs by Numba Header-only libraries for populating Awkward Arrays from C++ without any Python dependencies We will introduce Awkward Arrays in Julia via a recent development of Awkward Arrays PyJulia/PyCall.jl-based bridges

A cosmic ray muon going through CMS with the magnet at full field. The line shows the path of the muon reconstructed from information recorded in the various detectors.

The event display of the event 3981 from the MTCC run 2605. The data has been taken with a magnetic field of 3.8 T. A detailed model of the magnetic field corresponding to 4T is shown as a color gradient from 4T in the center (red) to 0 T outside of the detector (blue). The cosmic muon has been detected by all four detectors participating in the run: the drift tubes, the HCAL, the tracker and the ECAL subdetectors and it has been reconstructed online. The event display shows the reconstructed 4D segments in the drift tubes (magenta), the reconstructed hits in HCAL (blue), the locally reconstructed track in the tracker (green), the uncalibrated rec hits in ECAL (light green). A muon track was reconstructed in the drift tubes and extrapolated back into the detector taking the magnetic field into account (green)