The reconstruction of the trajectories of charged particles, or track reconstruction, is a key computational challenge for particle and nuclear physics experiments. While the tuning of track reconstruction algorithms can depend strongly on details of the detector geometry, the algorithms currently in use by experiments share many common features. At the same time, the intense environment of the High-Luminosity LHC accelerator and other future experiments is expected to put even greater computational stress on track reconstruction software, motivating the development of more performant algorithms. We present here A Common Tracking Software (ACTS) toolkit, which draws on the experience with track reconstruction algorithms in the ATLAS experiment and presents them in an experiment-independent and framework-independent toolkit. It provides a set of high-level track reconstruction tools which are agnostic to the details of the detection technologies and magnetic field configuration and tested for strict thread-safety to support multi-threaded event processing. We discuss the conceptual design and technical implementation of ACTS, selected applications and performance of ACTS, and the lessons learned.
Direct imaging of exoplanets and circumstellar disks at optical and infrared wavelengths requires reaching high contrasts at short angular separations. This can only be achieved through the synergy of advanced instrumentation, such as adaptive optics and coronagraphy, with a relevant combination of observing strategy and post-processing algorithms to model and subtract residual starlight. In this context, VIP is a Python package providing the tools to reduce, post-process and analyze high-contrast imaging datasets, enabling the detection and characterization of directly imaged exoplanets, circumstellar disks, and stellar environments.
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