https://github.com/attpc/attpc-event-classification
Science Score: 10.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
○CITATION.cff file
-
○codemeta.json file
-
○.zenodo.json file
-
○DOI references
-
✓Academic publication links
Links to: arxiv.org -
○Academic email domains
-
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (10.1%) to scientific vocabulary
Repository
Basic Info
- Host: GitHub
- Owner: ATTPC
- Language: Jupyter Notebook
- Default Branch: master
- Size: 15.6 MB
Statistics
- Stars: 0
- Watchers: 7
- Forks: 0
- Open Issues: 13
- Releases: 0
Metadata Files
README.md
attpc-event-classification
Evaluating Machine Learning Methods for Event Classification in the Active-Target Time Projection Chamber
This work is a survey of methods to use for track classification in the AT-TPC. The work was done with the goal of classifying proton tracks from the 46Ar(p,p) experiment that ran in August of 2015.
This repository contains code produced for Jack Taylor's 2017-18 academic year independent research project. All results found in this work are presented in my physics honors thesis, and are also available on the arXiv: https://arxiv.org/abs/1810.10350.
Algorithms tested include those available in the scikit-learn package and neural networks written using Keras with a Tensorflow backend.
Dependencies / Packages used
- pytpc
- numpy
- matplotlib
- scipy
- pandas
- scikit-learn
- keras
- tensorflow
See requirements.txt for more exhaustive list with release information.
Models/Algorithms Explored
- Logistic Regression
- Single-Layer Densely-Connected Neural Network
- Two Layer Densely-Connected Neural Network
- Pre-Trained Convolutional Neural Network (VGG16 Architecture - Image Recognition Problem)
- Support Vector Machines (One Class Classification)
Owner
- Name: AT-TPC Group
- Login: ATTPC
- Kind: organization
- Repositories: 9
- Profile: https://github.com/ATTPC
GitHub Events
Total
Last Year
Dependencies
- Babel ==2.4.0
- Cython ==0.25.2
- Jinja2 ==2.11.3
- Keras ==2.0.8
- Markdown ==2.6.11
- MarkupSafe ==1.0
- Pillow ==8.1.1
- PyQt5 ==5.9
- PyYAML ==5.4
- Pygments ==2.7.4
- SQLAlchemy ==1.1.10
- Sphinx ==1.6.2
- Werkzeug ==0.15.3
- absl-py ==0.1.10
- alabaster ==0.7.10
- args ==0.1.0
- bleach ==3.3.0
- certifi ==2017.4.17
- chardet ==3.0.4
- clint ==0.5.1
- cycler ==0.10.0
- decorator ==4.0.11
- docutils ==0.13.1
- entrypoints ==0.2.3
- enum34 ==1.1.6
- graphviz ==0.8.2
- h5py ==2.7.0
- html5lib ==0.9999999
- idna ==2.5
- imagesize ==0.7.1
- ipykernel ==4.6.1
- ipython ==6.1.0
- ipython-genutils ==0.2.0
- ipywidgets ==6.0.0
- jedi ==0.10.2
- jsonschema ==2.6.0
- jupyter ==1.0.0
- jupyter-client ==5.1.0
- jupyter-console ==5.1.0
- jupyter-core ==4.3.0
- matplotlib ==2.0.2
- mistune ==0.8.1
- nbconvert ==5.2.1
- nbformat ==4.3.0
- notebook ==6.1.5
- numexpr ==2.6.2
- numpy ==1.14.0
- olefile ==0.44
- pandas ==0.20.2
- pandocfilters ==1.4.1
- patsy ==0.4.1
- pbr ==3.1.0
- pexpect ==4.2.1
- pickleshare ==0.7.4
- prompt-toolkit ==1.0.14
- protobuf ==3.5.1
- ptyprocess ==0.5.2
- pydot ==1.2.4
- pyparsing ==2.2.0
- python-dateutil ==2.6.0
- pytpc ==1.1.0
- pytz ==2017.2
- pyzmq ==16.0.2
- qtconsole ==4.3.0
- requests ==2.20.0
- scikit-learn ==0.19.0
- scipy ==0.19.0
- seaborn ==0.7.1
- simplegeneric ==0.8.1
- sip ==4.19.3
- six ==1.11.0
- sklearn ==0.0
- snowballstemmer ==1.2.1
- sphinx-rtd-theme ==0.2.4
- sphinxcontrib-websupport ==1.0.1
- statsmodels ==0.8.0
- stevedore ==1.23.0
- tables ==3.4.2
- tensorflow ==1.15.4
- tensorflow-tensorboard ==0.4.0
- terminado ==0.6
- testpath ==0.3.1
- tornado ==4.5.1
- traitlets ==4.3.2
- urllib3 ==1.24.2
- virtualenv ==15.1.0
- virtualenv-clone ==0.2.6
- virtualenvwrapper ==4.7.2
- wcwidth ==0.1.7
- webencodings ==0.5.1
- widgetsnbextension ==2.0.0
- xarray ==0.9.6