269622
doi
10.17632/4r4v785rgx.1
oai:zenodo.org:269622
de Oliveira, Luke
Lawrence Berkeley National Laboratory
Paganini, Michela
Yale University
Pythia Generated Jet Images for Location Aware Generative Adversarial Network Training
Nachman, Benjamin
Lawrence Berkeley National Laboratory
arxiv:arXiv:1701.05927
doi:10.5281/zenodo.268592
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
machine learning
deep learning
pythia
jet images
hep
<p>Dataset containing 872666 jet images to train Location Aware Generative Adversarial Networks (LAGAN) for High Energy Physics. Results are published in [arXiv:1701.05927].</p>
<p><strong>Format</strong>:<br>
HDF5 file with the following fields:</p>
<ul>
<li>'image' : array of dim (872666, 25, 25), contains the pixel intensities of each 25x25 image</li>
<li>'signal' : binary array to identify signal (1, i.e. W boson) vs background (0, i.e. QCD)</li>
<li>'jet_eta': eta coordinate per jet</li>
<li>'jet_phi': phi coordinate per jet</li>
<li>'jet_mass': mass per jet</li>
<li>'jet_pt': transverse momentum per jet</li>
<li>'jet_delta_R': distance between leading and subleading subjets if 2 subjets present, else 0</li>
<li>'tau_1', 'tau_2', 'tau_3': substructure variables per jet (a.k.a. n-subjettiness, where n=1, 2, 3)</li>
<li>'tau_21': tau<sub>2</sub>/tau<sub>1</sub> per jet</li>
<li>'tau_32': tau<sub>3</sub>/tau<sub>2</sub> per jet</li>
</ul>
<p><strong>Details</strong>:</p>
<ul>
<li>Simulated using Pythia 8.219 at √ s = 14 TeV</li>
<li>Image pre-processing using method from in L. de Oliveira et al., Jet-Images -- Deep Learning Edition [arXiv:1511.05190]</li>
<li>scikit-image==0.12.0 implementation of cubic spline rotation</li>
<li>Finite calorimeter granularity simulated with 0.1×0.1 grid in η and φ, with η × φ ∈ [−1.25, 1.25] × [−1.25, 1.25]</li>
<li>Jet clustering with anti-k<sub>t</sub> algorithm with a radius R = 1.0 using FastJet 3.2.1; constituent re-clustering into R = 0.3 k<sub>t</sub> subjets</li>
<li>Intensity of pixel = p<sub>T</sub> of cell</li>
<li>60 GeV < m<sup>jet</sup> < 100 GeV</li>
<li>250 GeV < p<sub>T</sub><sup>jet</sup> < 300 GeV</li>
<li>Sparse images (~10% NNZ)</li>
</ul>
<p>Full dataset description in [arXiv:1701.05927].</p>
Generation and analysis code available at https://github.com/lukedeo/adversarial-jets
To reproduce this dataset:
- follow instructions available at https://github.com/lukedeo/adversarial-jets/tree/master/generation
- docker image available (from Docker Hub) under lukedeo/ji:latest
- tested on MacOS Sierra, Ubuntu16.04
- depends on Pythia, ROOT, FastJet and modern Python installation (only tested on 2.7, but should work on 3.4+)
Zenodo
2017-02-07
info:eu-repo/semantics/other
759354
1579893960.085615
2220068544
md5:f9b11c46b6a0ff928bec2eccf865ecf0
https://zenodo.org/records/269622/files/jet-images_Mass60-100_pT250-300_R1.25_Pix25.hdf5
public
arXiv:1701.05927
Is supplement to
arxiv
10.5281/zenodo.268592
Is new version of
doi