NA1-FAIRnet: QCD physics at GSI/FAIR

Two large international collaborations will study the nature of QCD at FAIR: The PANDA experiment (Antiproton Annihilation at Darmstadt) will investigate the nature of the strong force at the quark level using antiproton annihilations in the charmonium region, whereas CBM (Compressed Baryonic Matter) will explore the properties of strongly interacting matter under extreme conditions and highest densities with heavy ion beams at energies of several 10 GeV per nucleon. Both experiments will operate at very high reaction rates (averages up to 107/s) without a hardware trigger. One of the key challenges is to transfer the physics fingerprint of the signal events into the hardware to filter out the rare events of interest which will then be analyzed. The analysis requires physics input from theory concerning partial waves and resonances. The novel concepts of triggerless, time based data acquisition and an online first level event selection will allow to collect the highest statistics for a wide range of physics topics.

While the data and information processing at a rate of 10 GByte/s on each FPGA for the feature extraction at the front end level is already challenging, data at a rate of 1 TByte/s has to be combined for the first level event selection at the online processing farm (FLES). To obtain the best and precise physics results it is essential to extract the important information from the vast amount of data while preserving highest background rejection rates of about a factor 100-1000.

This requires input from theory for event generators and quantum corrections.

The FAIRnet networking activity will foster the interchange of ideas and methods on dead time free front end electronics, FPGA based feature extraction, hard- and software for online event selection on heterogeneous multi-core systems (CPU +GPU), and data acquisition networks as well as physics analysis methods. In the framework of FAIRnet experts from the detectors, front-end electronics, data acquisition, software and physics analysis will be brought together to develop tools to meet the data and precision challenge of the experiments. The intensified collaboration will be supported by the exchange of people between institutions in different countries, workshops and schools for young researchers. The interchange of ideas and methods with other hadron physics experiments (LHC experiments, BESIII, GlueX, Belle II) will help to get best results. While the first FAIRnet network concentrated on exploring the physics reach of FAIR, this second network concentrates on bringing the physics into the readout hard- and software in terms of trigger algorithms. It will also help establishing a new IT division for the FAIR host laboratory capable to support and address the needs of the present and future FAIR experiments. The aim is to fix the standards and the general framework as was done earlier for the analysis and simulation software with a similar networking activity. That networking was initiating a new group of software experts that proposed the standards for the offline data analysis that nowadays is the official framework for FAIR experiments.

Industrial applications are within reach in the field of medicine (e.g. trigger-less DAQ for TOF PET), in high performance computing (cooperation of FZJ with NVIDIA and FIAS with Intel), in machine learning (deep learning) and energy efficiency (Green-IT cube).

To transport the fascinating field of hadron physics and its high tech and information processing challenges to the outside world a significant part of the activities will be spent on outreach activities. Examples are public talks and activities for high school children at university labs.

Work Package: 12
Lead beneficiary: RUB - Germany
Spokespersons: This email address is being protected from spambots. You need JavaScript enabled to view it.
Partners: FAIR - This email address is being protected from spambots. You need JavaScript enabled to view it., UHEI - This email address is being protected from spambots. You need JavaScript enabled to view it.

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 824093

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