Speaker
Description
Trigger systems enable to quickly inspect the reconstructed physical quantities obtained from collisions at hadron colliders, in order to decide whether to save the corresponding detector data for offline analysis. The processing of the data coming from pixel detectors is a crucial challenge for the experiments running at the Large Hadron Collider (LHC) at CERN, because of the large number of secondary collisions per bunch crossing, so-called pile-up vertices, which give rise to extremely high hit occupancies. Track reconstruction is a combinatorial problem for which the processing time strongly depends on the average pile-up per event; considering the future accelerator-complex upgrade to the High-Luminosity LHC, the computational cost of the current trigger strategies is expected to exceed the available computing resources. To address this issue, a new approach to assist the track reconstruction by filtering out unnecessary pile-up hits is presented and characterized. The algorithm is based on a convolutional neural network (CNN) architecture, which can be easily deployed on accelerator cards, with the goal of receiving as input a 2D representation of signal and pile-up hits overlaid and return as output an image with only signal hits. Training and testing the algorithm on a independently generated synthetic dataset with signal tracks in the range of 20 to 50 GeV, we show a background rejection factor of order 1500 for the 99\% efficiency working point, hence proving the potential of this approach in terms of both the physics performance and the computational gain.