Large Radius Tracking Events in pp collisions with a Generic detector

This dataset is generated with the ACTS framework, using a generic detector description (similar to the one in the TrackML challenge) and a constant magnetic field of 2 Tesla in the beam direction (i.e. z-axis). The event simulation was done via the Fastras and the digitization was done via smearing functions, which is configured with the default configuration file in ACTS. We did not use an exact release when producing the dataset, but we believe release v15.0.0 should reproduce the dataset. We processed the original outputs from ACTS so that the dataset is machine-learning friendly. Four different physics processes are generated with the Pythia8 generator at 13 TeV of proton-proton collisions without the presence of addition collision (i.e. no pileup events). Heavy Neural Lepton with the neutral lepton mass of 15 GeV and a lifetime of 100 mm Heavy Neural Lepton with the neutral lepton mass of 10 GeV and a lifetime of 100 mm. Heavy Neural Lepton with the neutral lepton mass of 15 GeV and a lifetime of 200 mm. ttbar events The heavy neural lepton generated along with a neutrino through a virtual W boson is forced to decay to two muons and a muon neutrino. The dataset is saved as the torch dataset. One can access it via the following code snippet. <pre><code class="language-python">&gt;&gt;&gt; import numpy as np &gt;&gt;&gt; arrays = np.load("ttbar_PileUp000_10K/100") &gt;&gt;&gt; [print(key,) for key in arrays.keys()] hid layerless_true_edges layers pid x</code></pre> <em>hid</em> is the spacepoint id <em>layerless_true_edges</em> is a table of the true-level edges where edges connect spacepoints coming from the same track. <em>layers</em> is the layer id where the spacepoint is recorded <em>pid</em> is a table of particle IDs each spacepoint associated with. hid is a table of spacepoint IDs, <em>x</em> is a table of spacepoints with dimension of [number of spacepoints, number of spacepoint features], the spacepoint features are their 3D global positions. The first Version 1.0 saved the dataset in torch DataSet format which requires torch 1.7.1 and PyG 1.7.1 in order to read those files. The version 1.1 saved the dataset in numpy file format.