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All major CERN experiments along with the accelerator will enter soon in the phase of a big upgrade cycle, called the High-Luminosity LHC, in order to further broaden the physics reach. ATLAS has a plethora of upgrades concerning the HL-LHC era which will equip the detector with many exciting new opportunities. One of the core upgrades in ATLAS concerns the way of reading out the calorimeter sub-detector. In contrast to previous runs ATLAS will be able to read the full granularity of the calorimeter at the level of the hardware trigger system. Having this information available in such a challenging environment provides a unique opportunity to explore Machine Learning ideas on the edge within the context of the Next Generation Trigger project. With the current project we would like to explore the concept of enhancing the vertexing capabilities of ATLAS by using only the calorimeter information for inference. The bulk of the work will be focused on designing and implementing a Symbolic Regression based model which during training will include both the tracking detector information and the calorimeter cells and eventually aim to run inference only with the calorimeter cells. 

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