A3D3 HEP activities
Mia
HEP Activities in A3D3
HEP PIs: Shih-Chieh, Phil, Mark, Javier, Mia
Projects (A complete list can be found in later slides):
2022 Q1 Publications, Conference submissions planned
Semi-supervised Graph PUPPI
People: Yongbin Feng (Fermilab), Tianchun Liu (Purdue), Shikun Li(Purdue), Lisa Paspalaki (Purdue), Pan Li (Purdue), Mia Liu(Purdue), Nhan Tran (Fermilab)
Goal: advanced pileup mitigation with graph neural networks
Status:
Short paper presented in NeurIPS 2021 Workshop AI4Science: Including particle label classification performance
Long paper (PRD) in preparation, including improvements in physics observables.
Training with CMS full simulation.
Particle flow reconstruction using a GNN
Goal: perform particle-flow (PF) reconstruction using a graph neural network (GNN) and apply explainable AI techniques to interpret the GNN model
Motivation: PF may be casted as a ML task to take advantage of highly parallelizable chips to perform accurate and fast reconstruction. GNNs are able to take advantage of the irregular geometry of the LHC detector to achieve better reconstruction. Furthermore, interpreting the GNN may help in increasing our confidence of the model and ensure robustness under changing conditions.
People: Javier Duarte, Raghav Kansal, Joosep Pata
Meeting: Thursdays 9am PST (biweekly)
Document/Code: https://github.com/faroukmokhtar/particleflow
Calorimetry Clustering with SPVCNN
Goal: Develop low-latency, scalable and accelerable ML algorithms for calorimetry clustering in HLT.
Motivation: Pushing the energy, intensity frontiers at colliders w/ conventional algorithms is limited by computational budget. High pileup is a rich environment for ML, opportunity for improvement over conventional approach.
People: Alex Schuy (UW), Shih-Chieh Hsu (UW), Scott Hauck (UW), Zhijian Liu (MIT), Phil Harris (MIT), Song Han (MIT), Jeff Krupa (MIT)
Meeting: 3:30 pm (PST) Wed https://indico.cern.ch/event/1111394/
Document/Code: mit-han-lab/spvnas-dev at dev/calo (github.com)
Using Autoencoder Latent Spaces to Derive L1 Trigger Physics Quantities
Goal: Derive more effective L1 Trigger information for the upcoming HGCAL installation at the HL-LHC
Motivation: Data transmitted off the Compact Muon Solenoid (CMS) experiment at LHC will arrive at such a fast rate that there is little time to derive meaningful physics quantities used to run the trigger systems. This can be done from the latent spaces which are produced in the hardwares that run HGCAL wafers.
People: Phil Harris (MIT), Duc Hoang (MIT), Christin Herwig (Fermilab), Cristina Mantilla Suarez (Fermilab), Nhan Tran (Fermilab)
Meeting: Biweekly Thursday 3PM EST
Document/Code: https://github.com/eric-moreno/ECON-Regression
Machine learning for HEP simulations
Goal: Use machine learning (ML) and graph neural networks (GNNs) for simulating LHC collisions
Motivation: Detector simulation takes up ~40% of LHC computing resources, and simulation complexity is only going to increase over the next decade with the upcoming HL-LHC upgrades. ML has the potential to both provide a significant (up to five-orders-of-magnitude) speed-up and increase simulation performance.
People: Javier Duarte (UCSD), Raghav Kansal (UCSD), Maurizio Pierini (CERN)
Meeting: Thursdays 8am PST (Weekly)
Document/Code: https://github.com/rkansal47/MPGAN/
Paper: Kansal et. al., Particle Cloud Generation with Message Passing Generative Adversarial Networks, NeurIPS 2021, arXiv:2106.11535
Energy corrections with graph neural networks
Goal: Develop new approaches to energy corrections in the CMS calorimeters to provide improved energy resolutions and reduced sensitivity to pileup, noise, nonfunctioning detector elements, etc.
Motivation: New approaches will be needed to handle increase HL-LHC pileup, and to provide improved sensitivity in many flagship analyses, eg Higgs->diphoton
People (in no particular order): Simon Rothman, Phil Harris (MIT), Rajdeep Chatterjee, Bhargav Joshi, Roger Rusack (UMN), Alpana, Nitish Kumar, Seema Sharma, Shubham Pandey (IISER-Pune), Lindsey Gray (FNAL)
Meeting: TBA
Document/Code: TBA
Tau3Mu reconstruction using GNNs
Goal: Develop neural network models for real-time global event reconstruction in CMS L1 trigger.
Motivation: Hardware upgrades in the HL-LHC will allow for more sophisticated algorithms in the online filtering systems. Neural networks can be used to achieve high efficiency in identifying exotic events that indicate new physics.
People: Miaoyuan Liu (PU), Pan Li (PU), Jacobo Konigsberg (UF), Daniel Guerrero (UF), Lisa Paspalaki (PU), Siqi Miao (PU), Hyeon-Seo Yun (PU), Benjamin Simon (PU), Eric Reinhardt (PU)
Meeting: Wednesday, 3:30pm (EST)
Document/Code: TBA
CMS L1BTag
Goal: Develop a NN based tagger for identifying b jets in the CMS L1 Trigger
Motivation: Numerous applications for b quark identification; di-Higgs Golden Channel (HH4b), b Meson Decay, etc.
People: Aidan Chambers, Dylan Rankin, Phil Harris, Duc Hoang
Meeting: Tuesday 12PM EST
Document/Code: Some basic code here: https://github.com/aidan-dc/L1BTag
SONIC in CMS
People: Phil, Patrick, Javier, Mia, Yongbin, Nhan, Kevin + many others
Goal: CMS data processing workflow based on SONIC running in CMS central production (tier-2 sites)
Status: Two CMS data processing workflows developed. Tests on-going with servers manually set up. Infrastructure development for production systems planned for 2022.
Interpretable Models for Point Cloud Data
Goal: Study models that are inherently interpretable when trained with point cloud data
Motivation: ML models trained on point cloud data (especially in HEP) lack interpretability due to the complex nature of physical processes. Yet point cloud data interpretation has not been investigated in depth. With interpretability, ML models are more trustworthy in science, and they can also provide us with more insights regarding the datasets used.
People: Siqi Miao, Miaoyuan Liu, Pan Li
Meeting: Friday 4pm EST (biweekly)
Document/Code: https://github.com/Graph-COM/GSAT
ONNX Support of LSTM/GRU Networks for HLS4ML Implementations
Goal: Establish a LSTM/GRU->ONNX->HLS4ML workflow.
Motivation: LSTM is a type of recurrent neural network for deep learning. The work we are doing for QONNX is collaboration between the hls4ml team and Xilinx/AMD. LSTM can be applied to our HLS4ML workflow.
People: Xiaohan Liu(UW), Jeffery Xu(UW), Aidan Yokuda(UW), Andrew(Yihui) Chen(UW), Scott Hauck(UW), Shih-Chieh Hsu(UW)
Meeting: Tuesday 10:30am PST (Weekly)
Document/Code:
LSTM: https://people.ece.uw.edu/hauck/publications/Thesis-RichaRao.pdf
QONNX: https://xilinx.github.io/finn/2021/11/03/qonnx-and-finn.html
GNNs in hls4ml
Goal: GNNs on FPGAs with hls4ml
People (currently active): Javier Duarte (UCSD), Suki Krishna (UCSD), Simon Poon (UCSD), Tony Aportela (UCSD), Abdel Elabd (Penn), Shih-Chieh Hsu (UW), Mia Liu (Purdue), Jan Schulte (Purdue).
Short Summary:
Recursive NNs (LSTM & GRU) in hls4ml
Goal: Fast LSTM/GRU on FPGAs with hls4ml
People (currently active): Elham E Khoda (UW), Rafael Teixeira de Lima (SLAC), Dylan Rankin (MIT), Phil Harris (MIT), Michael Kegan (SLAC), Shih-Chieh Hsu (UW), Scott Hauck (UW)
Short Summary: