Training Dips
For the main part of the study, the training of the Dips algorithm with different training datasets, there is a gitlab project which hosts the analysis code.
You can use the Terminal in the jupyter notebook web interface to download the code with
Then, the notebooks and the auxiliary material supplied with them becomes available and you can execute them with the web interface.
There are two notebooks. While prepare_samples.ipynb is meant to provide some context and explanation how the hybrid training dataset has been prepared, the main resource is the notebook train_Dips.ipynb.
In this notebook, the Dips neural network is defined using the Keras high-level interface for the TensorFlow machine learning library.
The network is trained for a certain number of epochs using the hybrid training dataset.
Task 1: Read through the notebooks and try to understand how the code blocks are functioning and what the steps in the preprocessing are meant to achieve.
Task 2: Experiment with different numbers of epochs for the training and observe how the validation loss and the training loss are evolving over time.
Task 3: Compare the performance of the trained Dips algorithm with that of the evaluated Dips versions which were trained on particle-flow jets.
Two hybrid datasets are provided, one composed from ttbar + Zprime, and another one composed from ttbar + graviton events.
Both can be used for training the algorithms and are available at DESY NAF:
ttbar + Zprime:
/nfs/dust/atlas/user/pgadow/summie2022/data/vr_dips_samples/hybrid_ttbar_zprime/ttbar + graviton:
/nfs/dust/atlas/user/pgadow/summie2022/data/vr_dips_samples/hybrid_ttbar_graviton/
The structure in both directories is similar, the final dataset to be used for training the algorithm is VR-hybrid-resampled_scaled_shuffled.h5.
Task 4: Run a training with the "ttbar + Zprime" dataset and the "ttbar + graviton" dataset each, using the same number of jets, epochs and batch size to have a fair comparison. Evaluate the performance on the ttbar, Zprime and graviton samples in prepared_samples, (inclusive_testing_ttbar_TrackJets.h5, inclusive_testing_zprime_TrackJets.h5, inclusive_testing_graviton_TrackJets.h) and compare which training dataset results in the best possible performance.
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