29 November 2022 to 2 December 2022
Wang Center, Stony Brook University
US/Eastern timezone

Smart pixels with data reduction at source

30 Nov 2022, 16:20
20m
Theater (Wang Center)

Theater

Wang Center

Contribution Talk WG6: TDAQ and AI/ML Early Career Plenary

Speaker

Jennet Dickinson (Fermilab)

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. Signal processing within one bunch crossing clock cycle and smart data reduction within the pixelated region of the detector will improve the accuracy and efficiency of event selection for triggering and analysis. 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 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

Benjamin Parpillon (Fermi Lab) Douglas Berry (Fermilab) Farah Fahim (Fermilab) Gauri Pradhan (Fermilab) Giuseppe Di Guglielmo (Fermilab) Jieun Yoo (University of Illinois Chicago) Jim Hirschauer (Fermilab) Lindsey Gray (Fermilab) Morris Swartz (Johns Hopkins University) Nhan Tran (Fermilab) Petar Maksimovic (Johns Hopkins University) Ronald Lipton (Fermilab) Shruti Kulkarni (Oak Ridge National Laboratory) corrinne mills (University of Illinois Chicago)

Presentation materials