A neural network-based tagger for the identification of bottom quarks in the CMS Level-1 trigger

The Phase II upgrade of the CMS detector for the High Luminosity upgrade of the LHC (HL-LHC) includes the introduction of many new capabilities into the Level-1 trigger, including tracking and the new high-granularity calorimeter. The inclusion of tracking in particular offers the possibility of developing an algorithm to identify jets originating from bottom quarks for use in the Level-1 trigger for the first time at CMS. We present the logic of this algorithm, along with the inputs and a possible implementation using prototype hardware. We show that this implementation is capable of operating within the budgeted latency requirements of the Level-1 trigger environment. The expected performance and physics implication of such an algorithm is shown using Monte Carlo samples with 200 pileup, simulating the harsh conditions of the HL-LHC.