sparticles 0.0.4.4

A package to manipulate particle collisions

Sparticles

Manipulate collision events via graphs and graph neural networks.

TL;DR

The EventsDataset, a Pytorch Geometric Dataset, allows you to download a dataset of graphs representing collisions.

from dataset import EventsDataset

graphs = EventsDataset(
            root='/Users/alessiodevoto/Desktop/test_dataset3',
            delete_raw_archive=False,
            event_subsets={'signal': 100, 'singletop': 100, 'ttbar': 100},
            url='<secret_url>')

graphs

EventsDataset(300)

Each event is a graph with 6/7 nodes. Each node is built from the raw file as follows:

Particle	Feature 1	Feature 2	Feature 3	Feature 4	Feature 5	Feature 6
jet1	'pTj1'	'etaj1'	'phij1'	'j1_quantile'	nan	nan
jet2	'pTj2'	'etaj2'	'phij2'	'j2_quantile'	nan	nan
jet3 (optional)	'pTj3'	'etaj3'	'phij3'	'j3_quantile'	nan	nan
b1	'pTb1'	'etab1'	'phib1'	'b1_quantile'	'b1m'	nan
b2	'pTb2'	'etab2'	'phib2'	'b2_quantile'	'b2m'	nan
lepton	'pTl1'	'etal1'	'phil1'	nan	nan	nan
energy	'ETMiss'	nan	'ETMissPhi'	nan	nan	'metsig_New'

g = graphs[0]
print(g)

Data(x=[6, 6], edge_index=[2, 30], y=[1], event_id='signal_6350')

from visualize import plot_event_2d
plot_event_2d(graphs[100])

a_list_of_graphs = [graphs[i] for i in range(0, 300, 30)]
plot_event_2d(a_list_of_graphs, height=1500)

Changelog

• version 0.0.4.3. Now the plotting function allows you to pass a show_edges and edges_weights parameters to display edges. Useful for visualizing attention maps.