Readout Technologies for Future Detectors
It is essential to develop novel cost-effective readout technologies to maximize the discovery potential of future HEP experiments.
arXiv subjects
Publications and source records attributed to M. Trovato.
It is essential to develop novel cost-effective readout technologies to maximize the discovery potential of future HEP experiments.
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 under these conditions is the reconstruction of charged particle trajectories and their inclusion in the hardware-based trigger system. There are many challenges involved in achieving this: a large input data rate of about 20--40 Tb/s; processing a new batch of input data every 25 ns, each consisting of about 15,000 precise position measurements and rough transverse momentum measurements of particles ("stubs''); performing the pattern recognition on these stubs to find the trajectories; and producing the list of trajectory parameters within 4 $μ\,$s. This paper describes a proposed solution to this problem, specifically, it presents a novel approach to pattern recognition and charged particle trajectory reconstruction using an all-FPGA solution. The results of an end-to-end demonstrator system, based on Xilinx Virtex-7 FPGAs, that meets timing and performance requirements are presented along with a further improved, optimized version of the algorithm together with its corresponding expected performance.
This article describes a new charged-particle track fitting algorithm designed for use in high-speed electronics applications such as hardware-based triggers in high-energy physics experiments. Following a novel technique designed for fast electronics, the positions of the hits on the detector are transformed before being passed to a linearized track parameter fit. This transformation results in fitted track parameters with a very linear dependence on the hit positions. The approach is demonstrated in a representative detector geometry based on the CMS detector at the Large Hadron Collider. The fit is implemented in FPGA chips and optimized for track fitting throughput and obtains excellent track parameter performance. Such an algorithm is potentially useful in any high-speed track-fitting application.
We present the development and validation of the Higgs Optimized b Identification Tagger (HOBIT), a multivariate b-jet identification algorithm optimized for Higgs boson searches at the CDF experiment at the Fermilab Tevatron. At collider experiments, b taggers allow one to distinguish particle jets containing B hadrons from other jets; these algorithms have been used for many years with great success at CDF. HOBIT has been designed specifically for use in searches for light Higgs bosons decaying via H ! b\bar{b}. This fact combined with the extent to which HOBIT synthesizes and extends the best ideas of previous taggers makes HOBIT unique among CDF b-tagging algorithms. Employing feed-forward neural network architectures, HOBIT provides an output value ranging from approximately -1 ("light-jet like") to 1 ("b-jet like"); this continuous output value has been tuned to provide maximum sensitivity in light Higgs boson search analyses. When tuned to the equivalent light jet rejection rate, HOBIT tags 54% of b jets in simulated 120 GeV/c2 Higgs boson events compared to 39% for SecVtx, the most commonly used b tagger at CDF. We present features of the tagger as well as its characterization in the form of b-jet finding efficiencies and false (light-jet) tag rates.