CMS Machine Learning Hackathons¶
Welcome to the CMS ML Hackathons! Here we encourage the exploration of cutting edge ML methods to particle physics problems through multi-day focused work. Form hackathon teams and work together with the ML Innovation group to get support with organization and announcements, hardware/software infrastructure, follow-up meetings and ML-related technical advise.
If you are interested in proposing a hackathon, please send an e-mail to the CMS ML Innovation conveners with a potential topic and we will get in touch!
Below follows a list of previous successful hackathons.
HGCAL TICL reconstruction¶
20 Jun 2022 - 24 Jun 2022
https://indico.cern.ch/e/ticlhack
Abstract: The HGCAL reconstruction relies on “The Iterative CLustering” (TICL) framework. It follows an iterative approach, first clusters energy deposits in the same layer (layer clusters) and then connect these layer clusters to reconstruct the particle shower by forming 3-D objects, the “tracksters”. There are multiple areas that could benefit from advanced ML techniques to further improve the reconstruction performance.
In this project we plan to tackle the following topics using ML:
- trackster identification (ie, identification of the type of particle initiating the shower) and energy regression linking of tracksters stemming from the same particle to reconstruct the full shower and/or use a high-purity trackster as a seed and collect 2D (ie. layer clusters) and/or 3D (ie, tracksters) energy deposits in the vicinity of the seed trackster to fully reconstruct the particle shower
- tuning of the existing pattern recognition algorithms
- reconstruction under HL-LHC pile-up scenarios (eg., PU=150-200)
- trackster characterization, ie. predict if a trackster is a sound object in itself or determine if it is more likely to be a composite one.
Material:¶
A CodiMD document has been created with an overview of the topics and to keep track of the activities during the hackathon:
https://codimd.web.cern.ch/s/hMd74Yi7J
Jet tagging¶
8 Nov 2021 - 11 Nov 2021
https://indico.cern.ch/e/jethack
Abstract: The identification of the initial particle (quark, gluon, W/Z boson, etc..) responsible for the formation of the jet, also known as jet tagging, provides a powerful handle in both standard model (SM) measurements and searches for physics beyond the SM (BSM). In this project we propose the development of jet tagging algorithms both for small-radius (i.e. AK4) and large-radius (i.e., AK8) jets using as inputs the PF candidates.
Two main projects are covered:
- Jet tagging for scouting
- Jet tagging for Level-1
Jet tagging for scouting¶
Using as inputs the PF candidates and local pixel tracks reconstructed in the scouting streams, the main goals of this project are the following:
Develop a jet-tagging baseline for scouting and compare the performance with the offline reconstruction Understand the importance of the different input variables and the impact of -various configurations (e.g., on pixel track reconstruction) in the performance Compare different jet tagging approaches with mind performance as well as inference time. Proof of concept: ggF H->bb, ggF HH->4b, VBF HH->4b
Jet tagging for Level-1¶
Using as input the newly developed particle flow candidates of Seeded Cone jets in the Level1 Correlator trigger, the following tasks will be worked on:
- Developing a quark, gluon, b, pileup jet classifier for Seeded Cone R=0.4 jets using a combination of tt,VBF(H) and Drell-Yan Level1 samples
- Develop tools to demonstrate the gain of such a jet tagging algorithm on a signal sample (like q vs g on VBF jets)
- Study tagging performance as a function of the number of jet constituents
- Study tagging performance for a "real" input vector (zero-paddes, perhaps unsorted)
- Optimise jet constituent list of SeededCone Jets (N constituents, zero-removal, sorting etc)
- Develop q/g/W/Z/t/H classifier for Seeded Cone R=0.8 jets
GNN-4-tracking¶
27 Sept 2021 - 1 Oct 2021
https://indico.cern.ch/e/gnn4tracks
Abstract: The aim of this hackathon is to integrate graph neural nets (GNNs) for particle tracking into CMSSW.
The hackathon will make use of a GNN model reported by the paper Charged particle tracking via edge-classifying interaction networks by Gage DeZoort, Savannah Thais, et.al. They used a GNN to predict connections between detector pixel hits, and achieved accurate track building. They did this with the TrackML dataset, which uses a generic detector designed to be similar to CMS or ATLAS. Work is ongoing to apply this GNN approach to CMS data.
Tasks: The hackathon aims to create a workflow that allows graph building and GNN inference within the framework of CMSSW. This would enable accurate testing of future GNN models and comparison to existing CMSSW track building methods. The hackathon will be divided into the following subtasks:
- Task 1: Create a package for extracting graph features and building graphs in CMSSW.
- Task 2. GNN inference on Sonic servers
- Task 3: Track fitting after GNN track building
- Task 4. Performance evaluation for the new track collection
Material:¶
Code is provided at this GitHub organisation. Project are listed here.
Anomaly detection¶
In this four day Machine Learning Hackathon, we will develop new anomaly detection algorithms for New Physics detection, intended for deployment in the two main stages of the CMS data aquisition system: The Level-1 trigger and the High Level Trigger.
There are two main projects:
Event-based anomaly detection algorithms for the Level-1 Trigger¶
Jet-based anomaly detection algorithms for the High Level Trigger, specifically targeting Run 3 scouting¶
Material:¶
A list of projects can be found in this document. Instructions for fetching the data and example code for the two projects can be found at Level-1 Anomaly Detection.