Welcome to FAIR Universe HEP Challenge’s documentation!

Click Here For Tutorial Slides

The Fair Universe project is organising the HiggsML Uncertainty Challenge, following on from the HiggsML challenge on Kaggle in 2014, the context is the measurement of the Higgs Boson signal but in the presence of systematics unicertainities. Participants should design an advanced analysis technique that can not only measure the signal strength but also provide a confidence interval, from which correct coverage will be evaluated automatically from pseudo-experiments.

The confidence interval should include statistical and systematic uncertainties (concerning detector calibration, background levels, etc…). It is expected that analysis techniques that can control the impact of systematics will perform best, thereby pushing the field of uncertainty-aware AI techniques for HEP and beyond.

There are several information sources regarding the FAIR Universe - HiggsML Uncertainty Challenge:

• Codabench : This serves as the platform to submit entries to the competition. It hosts the public training data Click here to download the dataset (6.5GB).

• Tutorial Slides : These slides will help you register and submit a sample dummy submission.

• Documentation : This contains detailed information about the science behind the challenge, the specifics of the data, and documents the code used to facilitate the evaluation of the competition. It also describes the evaluation metric.

• Github Repo : This hosts the code for testing submissions, as well as the starting kit notebook. The starting kit is also available on Google Colab.

• White Paper : This serves as a full breakdown of the competition in detail.

Pages:

• Getting Started
• Overview
• Data
• Evaluation

Core Modules:

• Ingestion Program
  • Datasets module
  • Derived_quantities module
  • Ingestion module
  • Ingestion_parallel module
  • Systematics module
  • Visualization module
• Scoring Program
  • Score module
• Simple One Syst Model
  • Example: Statistical-Only and 1-Systematic Case Analysis
  • Boosted_Decision_Tree module
  • Model One Syst
  • Statistical_Analysis module
• Simple Stat Only Model
  • Model
  • calculate_saved_info()
  • compute_mu()
  • train_test_split()

Indices and tables

• Index

• Module Index

• Search Page