5 papers
On-chip probabilistic inference for charged-particle tracking at the sensor edge
Arghya Ranjan Das, David Jiang, Rachel Kovach-Fuentes +33
Modern scientific instruments operate under increasingly extreme constraints on bandwidth, latency, and power. Inference at the sensor edge determines experimental data collection…
Machine Learning on Heterogeneous, Edge, and Quantum Hardware for Particle Physics (ML-HEQUPP)
Julia Gonski, Jenni Ott, Shiva Abbaszadeh +118
The next generation of particle physics experiments will face a new era of challenges in data acquisition, due to unprecedented data rates and volumes along with extreme environmen…
Characterization of a 28 nm ASIC With On-Chip ML for Particle Tracking Detectors
Benjamin Parpillon, Anthony Badea, Danush Shekar +39
We present a 28 nm CMOS pixel readout integrated circuit implementing in-pixel analog signal processing and on-chip machine learning data filtering for particle tracking detectors.…
Sensor Co-design for
Danush Shekar, Ben Weiss, Morris Swartz +40
Pixel tracking detectors at upcoming collider experiments will see unprecedented charged-particle densities. Real-time data reduction on the detector will enable higher granularity…
LHC EFT WG Note: SMEFT predictions, event reweighting, and simulation
Alberto Belvedere, Saptaparna Bhattacharya, Giacomo Boldrini +16
This note gives an overview of the tools for predicting expectations in the Standard Model effective field theory (SMEFT) at the tree level and one loop available through event gen…