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In order to achieve the data rates proposed for the future Run 3 upgrade of the LHCb detector, new processing models must be developed to deal with the increased throughput. For this reason, we aim to investigate the feasibility of purely data-driven holistic methods, with the constraint of introducing minimal computational overhead, hence using only raw detector information. These filters should be unbiased - having a neutral effect with respect to the studied physics channels. In particular, the use of machine learning based methods seems particularly suitable, potentially providing a natural formulation for heuristic-free, unbiased filters whose objective would be to optimize between throughput and bandwidth.
The high instantaneous luminosities expected following the upgrade of the Large Hadron Collider (LHC) to the High Luminosity LHC (HL-LHC) pose major experimental challenges for the CMS experiment. A central component to allow efficient operation unde
A study on the use of a machine learning algorithm for the level 1 trigger decision in the JUNO experiment ispresented. JUNO is a medium baseline neutrino experiment in construction in China, with the main goal of determining the neutrino mass hierar
This proceedings describes the XFT stereo upgrade for the CDF Level 2 trigger system. Starting with the stereo finder boards, up to the XFT stereo track algorithim implementation in the Level 2 PC. This proceedings will discuss the effectiveness of t
The LHCb experiment will operate at a luminosity of $2times10^{33}$ cm$^{-2}$s$^{-1}$ during LHC Run 3. At this rate the present readout and hardware Level-0 trigger become a limitation, especially for fully hadronic final states. In order to maintai
The main b-physics trigger algorithm used by the LHCb experiment is the so-called topological trigger. The topological trigger selects vertices which are a) detached from the primary proton-proton collision and b) compatible with coming from the deca