On-chip probabilistic inference for charged-particle tracking at the sensor edge
Tracking charged-particles at the sensor edge, using on-chip AI inference, under strict latency, precision, and area constraints.
Tracking charged-particles at the sensor edge, using on-chip AI inference, under strict latency, precision, and area constraints.
A deep dive into the regression models being developed for the HL-LHC era as part of the SmartPixels collaboration.
Smart Pixels is a project focused on implementing machine learning models directly on silicon pixels or future detectors to enhance the inference of charged particle track …
PQuant/PQuantML: config-driven pruning + quantization pipeline (PyTorch/TensorFlow) with hardware-aware MDMM optimization; aimed at FPGA/ASIC deployment workflows (hls4ml …
In-pixel signal processing with integrated AI-based filtering for real-time data reduction in tracking detectors.
Sensor/algorithm co-design studies for smart pixel filtering: geometry, magnetic field, radiation damage, and noise.
Performance characterization of the DarkQuest electromagnetic calorimeter and its readout electronics.
Di-Higgs (bbττ) is a search for two boosted (high transverse momentum) Higgs bosons decaying into two beauty quarks ($b$) and two tau leptons ($\tau$). This analysis focuses on Run …
Nonlinear hydrodynamical resonance model with Euler Fluid for modified QPOs, enabling spin determination of NS & BH.
Vacuum rotating black-hole solution in an asymptotically flat modified gravity theory.