README.md

metadata

license: mit
language:
  - en
tags:
  - tau
  - hep
  - fcc
  - clic
  - ee
  - reconstruction
  - identification
  - decay_mode
  - foundation_model
  - omnijet_alpha

Model Card for Model ID

This modelcard aims to be a base template for new models. It has been generated using this raw template.

Model Details

Model Description

• Developed by: Joschka Birk, Anna Hallin, Gregor Kasieczka
• Model type: Transformer
• Language(s) (NLP): Pytorch
• Finetuned from model: https://doi.org/10.1088/2632-2153/ad66ad

The OmniJet- $\alpha \backslash alpha$ model was published in here was used as the base model for identifying hadronically decaying taus, reconstructing their kinematics and predicting their decay mode. The base model, initially trained on JetClass dataset, was now fine-tuned on Fu $\tau \backslash tau$ure dataset. The models included here are for 3 separate tasks:

• Tau-tagging (binary classification)
• Tau kinematic reconstruction (regression)
• Tau decay mode classification (multiclass-classification)

And for 3 different ways of training:

• From scratch
• Fixed backbone (fine-tune only head)
• Fine-tuning (fine-tune both head and backbone)

This will add up to 9 different models.

Model Sources [optional]

• Repository (base model): https://github.com/uhh-pd-ml/omnijet_alpha
• Repository (fine-tuned model): https://github.com/HEP-KBFI/ml-tau-en-reg
• Paper: https://doi.org/10.1088/2632-2153/ad66ad

Uses

Direct Use

The intended use of the models is to study the feasibility of foundation models for the purposes of reconstructing and identifying hadronically decaying tau leptons.

Out-of-Scope Use

This model is not intended for physics measurements on real data. The trainings have been done on CLIC detector simulations.

Bias, Risks, and Limitations

The model has only been trained on simulation data and has not been validated against real data. Although the base model has been published in a peer-reviewed journal, the fine-tuned model has not been.

How to Get Started with the Model

Use the code below to get started with the model.

# Clone the repository
git clone git@github.com:HEP-KBFI/ml-tau-en-reg.git --recursive
cd ml-tau-en-reg


# Get the models
git clone https://huggingface.co/LauritsT/TauRecoID models

Training Details

Training Data

The data used to fine-tune the base model can be found here: Fu $\tau \backslash tau$ure dataset

Training Hyperparameters

• No hyperparameter tuning has been done.

Speeds, Sizes, Times [optional]

Training on 1M jets on AMD MI250x for 100 epochs takes ~8h.

Evaluation

Testing Data, Factors & Metrics

Testing Data

Testing data can also be found in the same Zenodo entry as the rest of the data.

Software

Software to train and analyze the model

Citation [optional]

OmniJet- $\alpha \backslash alpha$

Model Card Authors [optional]

Laurits Tani (laurits.tani@cern.ch)

Model Card Contact

Laurits Tani (laurits.tani@cern.ch)