8 TeV Data for Education

The ATLAS Collaboration published two distinct datasets from the 8 TeV proton-proton (pp) collision data, recorded in 2012 at the Large Hadron Collider (LHC). These datasets, released in both XML and ROOT ntuple formats, represent an effort to make LHC data accessible for educational purposes. With 2 fb^-1 provided in XML format and an additional 1 fb^-1 in ROOT ntuple format. These datasets serve as a cornerstone for the ATLAS Open Data initiative.

• Scope: Encompassing nearly 15 million events, this dataset offers a comprehensive view of the ATLAS experiment's 2012 data-taking period.
• Format: Provided in a simplified TTree tuple (or ROOT ntuple) format, it contains 45 branches detailed for ease of analysis.
• Versatility: The 2016 dataset's broad scope and extensive event count allow for diverse, in-depth studies.

Event Selection and Data Quality

Event selection for the ATLAS Open Data 2016 dataset was performed to streamline the data, making it more manageable for analysis while preserving its scientific value. This process involved:

• Corrupted Event Protection: Removal of events affected by short-term detector issues.
• Trigger Satisfaction: Inclusion of events that satisfy single-lepton triggers for electrons or muons, with a pT threshold of 5 GeV.
• Veto on Bad Jets: Exclusion of events containing jets not associated with energy deposits in the calorimeters.
• Primary Vertex Requirement: Selection of events with at least one primary vertex associated with four or more tracks.

The layout is optimised towards simplicity to reduce the complexities encountered in a full-scale analysis, emphasising the educational character of the dataset.

By providing these datasets, the ATLAS Collaboration aims to demystify particle physics, encouraging exploration and discovery among the next generation of scientists. The open data initiative exemplifies the collaboration's commitment to open science, inviting learners and researchers to delve into the intricacies of the universe with real data from the forefront of particle physics.

ATLAS Open Data 2016 contains two data files; one where the presence of an electron triggered the eventrecording (called the ‘egamma dataset’) and the other where a muon triggered the event recording (‘muon dataset’). The data are accompanied by relevant simulated data:

• ATLAS Open Data 2016 egamma dataset containing ∼8 million events.
• ATLAS Open Data 2016 muon dataset containing ∼7 million events.
• ATLAS simulated data consisting of 42 datasets containing ∼45 million events. Overlap removal was applied to ensure that the same event does not exist in both the egamma and muon datasets. The previous means that electron-triggered events may contain a muon with high transverse momentum, for that reason the muon dataset has a veto on such events.

Dataset Details

An important aspect of the data samples is that they were prepared specifically for educational purposes. To this end, precision has been traded for simplicity of use. The introduced simplifications are:

• No facilities to estimate systematic uncertainties have been included as these quickly introduce large complexities.

• The b-tagging scale factor is computed for a specific working point (MV1@70% efficiency). The user, however, is free to specify the b-tagging weight used for tagging jets allowing for a potential mismatch of the definition considered in the scale factor calculation and the one being actually applied.

• No QCD simulated samples were prepared as they would have been insufficient in statistics while introducing large set of additional samples.

• The description of the $W$ boson properties in simulated $W$ + jets events is not ideal. Corrections are only available for samples produced with the Monte Carlo generator Alpgen but not for those produced with Sherpa generator. However, using Alpgen would have introduced a prohibitively large number of samples. Sherpa was therefore used.

• The missing transverse momentum was calculated using the object preselection. A recalculation of the missing transverse momentum is not implemented into the tools provided for simplicity reasons. Therefore, changes in the object selection are not reflected in the missing transverse momentum leading to potential mis-modeling of variables relying on it.

• The simulated data takes into account the pile-up and vertex position profile of the whole 2012 data taking, although the measured data is taken from a small list of runs from period D. This introduces a certain mismatch regarding the number of vertices and the primary vertex position.

Details of the available simulated Monte Carlo datasets

The datasets have been reduced in size to optimise the storage requirements. The available number of events in the samples is given in the column N events, which is after the preselection cuts.

The factor FE denotes the filter efficiency for a given sample and $f_k$ is used for rescaling the leading order estimate to next to leading order in perturbative QCD.

The following samples represent about 6.5 Gb.

Click to view full list

process	DataSet ID	Generator	$\sigma$*FE [pb]	$f_k$	L [$fb^{-1}$]	N events	size/Mb
ttbar -> l + X	117050	PowHeg+Pythia	114.51	1.2	26.236	1500000	291
ttbar -> Jets	117049	PowHeg+Pythia	96.35	1.2	85.027	25170	5.7
single top t-chan top	110090	PowHeg+Pythia	17.52	1.05	24.21	150000	21
single top t-chan antitop	110091	PowHeg+Pythia	9.4	1.06	43.23	150000	15
single top s-chan	110119	PowHeg+Pythia	1.64	1.107	167.73	100000	15
single top Wt-chan	110140	PowHeg+Pythia	20.46	1.09	28.50	150000	26
Z+Jets ee	147770	Sherpa	1207.4	1.028	10.08	7500000	938
Z+Jets mumu	147771	Sherpa	1207.4	1.028	9.63	7500000	918
Z+Jets tautau	147772	Sherpa	1207.1	1.028	11.08	750000	93
Drell-Yan ee M08to15	173041	Sherpa	92.15	1.0	45.95	400000	57
Drell-Yan ee M15to40	173042	Sherpa	279.19	1.0	47.22	750000	100
Drell-Yan mumu M08to15	173043	Sherpa	92.08	1.0	51.93	500000	74
Drell-Yan mumu M15to40	173044	Sherpa	279.2	1.0	41.01	750000	103
Drell-Yan tautau M08to15	173045	Sherpa	92.12	1.0	27.13	9993	1.5
Drell-Yan tautau M15to40	173046	Sherpa	279.11	1.0	49.54	32393	4.5
W+Jets enu with b	167740	Sherpa	140.34	1.1	12.333	750000	86
W+Jets enu with jets, bveto	167741	Sherpa	537.84	1.1	9.563	2600000	296
W+Jets enu no jets, bveto	167742	Sherpa	10295	1.1	1.971	8000000	722
W+Jets munu with b	167743	Sherpa	140.39	1.1	11.935	750000	84
W+Jets munu with jets, bveto	167744	Sherpa	466.47	1.1	10.582	2500000	287
W+Jets munu no jets, bveto	167745	Sherpa	10368	1.1	1.719	7500000	666
W+Jets taunu with b	167746	Sherpa	140.34	1.1	18.245	100000	13
W+Jets taunu with jets, bveto	167747	Sherpa	506.45	1.1	9.821	250000	31
W+Jets taunu no jets, bveto	167748	Sherpa	10327	1.1	1.945	550000	55
WW	105985	Herwig	12.42	1.683	46.32	500000	63
ZZ	105986	Herwig	0.992	1.55	151.19	125000	20
WZ	105987	Herwig	3.667	1.9	138.44	500000	68

The $Z'$ and Higgs samples represent a further 150 Mb.

Click to view full list

Process	DataSet ID	Generator	σ*FE [pb]	fk	L [fb−1]	N events	size/Mb
Z' -> ttbar [ 400] GeV	110899	Pythia	4.259	1.0	23.48	18307	4.3
Z' -> ttbar [ 500] GeV	110901	Pythia	3.925	1.0	25.48	19737	4.7
Z' -> ttbar [ 750] GeV	110902	Pythia	1.243	1.0	80.45	21051	5.3
Z' -> ttbar [1000] GeV	110903	Pythia	0.394	1.0	253.81	20649	5.5
Z' -> ttbar [1250] GeV	110904	Pythia	0.139	1.0	719.43	19274	5.5
Z' -> ttbar [1500] GeV	110905	Pythia	0.0524	1.0	1908	17695	5.4
Z' -> ttbar [1750] GeV	110906	Pythia	0.0211	1.0	4739	15949	5.1
Z' -> ttbar [2000] GeV	110907	Pythia	0.00894	1.0	11186	14455	4.9
Z' -> ttbar [2250] GeV	110908	Pythia	0.00394	1.0	25381	13389	4.7
Z' -> ttbar [2500] GeV	110909	Pythia	0.00180	1.0	55556	12723	4.5
Z' -> ttbar [3000] GeV	110910	Pythia	0.000434	1.0	230415	12387	4.3
gg-> H-> WW-> llnunu ; M(H) = 125 GeV	161005	PowHeg+Pythia	6.463	1.0	32.13	100000	14
VBF H-> WW-> llnunu ; M(H) = 125 GeV	161055	PowHeg+Pythia	0.819	1.0	229.93	100000	18
gg-> H-> ZZ -> 4l ; M(H) = 125 GeV	160155	PowHeg+Pythia	13.17	1.0	14.31	100000	15
VBF H-> ZZ -> 4l ; M(H) = 125 GeV	160205	PowHeg+Pythia	1.617	1.0	104.96	100000	19

More information

For detailed information about this release, you can read "Review of ATLAS Open Data 8 TeV datasets, tools and activities"