물리학 데이터셋에 대한 참고 자료

1. ~~GitHub - mlr7/Datasets-for-Fundamental-Physics: 기계 학습을 이용한 기본 물리학 탐색을 위한 데이터셋 모음1. ← CERN 거라 못 씀~~
2. ~~GitHub - cogtoolslab/physics-benchmarking-neurips2021: NeurIPS 2021에서 발표된 "Physion: Evaluating Physical Prediction from Vision in Humans and Machines"에 대한 코드와 데이터.~~
3. ~~GitHub - google-deepmind/physical_concepts: Google DeepMind의 물리학 개념 프로젝트입니다3.~~

또한, 논문들도 참고 가능:

1. ~~Machine Learning Datasets | Papers With Code: 물리학과 관련된 다양한 머신러닝 데이터셋 제공.~~
2. ~~BubbleML: A Multi-Physics Dataset and Benchmarks for Machine Learning - arXiv.org: 다양한 끓는 시나리오에 대한 정확한 기본 정보를 제공하는 물리학 기반 시뮬레이션을 활용한 BubbleML 데이터셋을 소개~~
3. ~~Physics-based Deep Learning | Papers With Code: 물리학 기반 딥러닝에 대한 논문을 제공~~

~~이 외에도 Kaggle7에서 search 가능(~~

CERN: Open Data Portal ( https://opendata.cern.ch/ )

CERN: Public Datasets ( https://iml.web.cern.ch/public-datasets )

CERN: Inter-Experimental LHC Machine Learning Working Group ( https://iml.web.cern.ch/homepage )

HEPML-LivingReview: A Living Review of Machine Learning for Particle Physics ( https://iml-wg.github.io/HEPML-LivingReview/ )

Higgs Boson Datasets

• Kaggle: Higgs Boson Machine Learning Challenge - Use the ATLAS experiment to identify the Higgs boson ( https://www.kaggle.com/c/higgs-boson )
• A FAIR and AI-ready Higgs Boson Decay Dataset ( https://arxiv.org/abs/2108.02214 )

SUSY Dataset

• Paper: Baldi, Pierre, Peter Sadowski, and Daniel Whiteson. "Searching for exotic particles in high-energy physics with deep learning." Nature communications 5, no. 1 (2014): 1-9. ( https://www.nature.com/articles/ncomms5308 ) ( https://www.nature.com/articles/ncomms5308.pdf )
• Data access: https://archive.ics.uci.edu/ml/datasets/SUSY
• Tutorial: http://physics.bu.edu/~pankajm/MLnotebooks.html , http://physics.bu.edu/~pankajm/ML-Notebooks/HTML/NB5_CVII-logreg_SUSY.html , http://physics.bu.edu/~pankajm/ML-Notebooks/HTML/NB10_CVIII-XGboost_susy.html , http://physics.bu.edu/~pankajm/ML-Notebooks/HTML/NB13_CIX-DNN_susy_Pytorch.html