12–14 Apr 2023
Brookhaven National Laboratory
America/New_York timezone

On-chip intelligence and real-time data-processing

Not scheduled
20m
Berkner Hall (Brookhaven National Laboratory)

Berkner Hall

Brookhaven National Laboratory

Early Career Scientist

Description

Highly granular pixel detectors allow for increasingly precise measurements of charged particle tracks, both in space and time. A reduction in pixel size by a factor of four in next-generation detectors will lead to unprecedented data rates, exceeding those foreseen at the High Luminosity Large Hadron Collider. Despite this increase in data volume, smart data reduction within the pixelated region of the detector will enable physics information to be extracted from the pixel detector with high efficiency and low latency, and has the potential to enable the use of precise vertex information at the LHC bunch crossing frequency of 40 MHz (Level-1 trigger) for the first time. Using the shape of charge clusters deposited in arrays of small pixels, the physical properties of the traversing particle can be extracted by locally customized neural networks. Data from the sensor will be processed with a custom readout integrated circuit designed on 28 nm CMOS technology capable of operating at 40MHz and in extreme radiation environments. This talk will present several promising methods of on-chip data reduction including the application of a momentum selection, as well as reconstruction of particle hit position and incident angle.

Primary author

Co-authors

Alice Bean Farah Fahim (Fermilab) Aaron Young (Oak Ridge National Lab) Douglas Berry (Fermilab) Manuel Blanco Valentin (Fermilab - Northwestern University) Giuseppe Di Guglielmo (Fermilab) Lindsey Gray (Fermilab) Jim Hirschauer (Fermilab) Shruti Kulkarni (Oak Ridge National Lab) Ron Lipton (Fermilab) Petar Maksimovic (Johns Hopkins) Benjamin Parpillon (Fermi Lab) Morris Swartz (Johns Hopkins) Nhan Tran (Fermilab) Jieun Yoo (University of Houston) corrinne mills

Presentation materials

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