Fiducial cross-section measurements of the production of a prompt photon in association with a top-quark pair at $\sqrt{s}=13$ TeV with the ATLAS detector at the LHC
The cross sections for top-quark pair production in association with a photon are measured in a fiducial volume with the ATLAS detector at a centre-of-mass energy of 13 TeV. Results are presented using proton-proton collision data collected by the LHC during 2015 and 2016, amounting to a total of 36...
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Zusammenfassung: | The cross sections for top-quark pair production in association with a photon
are measured in a fiducial volume with the ATLAS detector at a centre-of-mass
energy of 13 TeV. Results are presented using proton-proton collision data
collected by the LHC during 2015 and 2016, amounting to a total of 36.1
fb$^{-1}$. This also presents the first $t\bar{t}\gamma$ cross-section
measurements performed in the dilepton channels. Exactly one photon is required
to have $p_{\text{T}} > 20$ GeV and be isolated based on track and calorimeter
information. At least two (four) jets are required in the dilepton
(single-lepton) channels, with at least one jet originating from a $b$-quark.
Two separate neural network algorithms are used to help reduce the impact
backgrounds play in the final measurements. The Prompt Photon Tagger is trained
on information from energy deposits in the calorimeters to distinguish prompt
photons from hadronic fake photons. The output of this neural network is fed
into the Event-level Discriminator that uses event information to classify
signal from the sum of all backgrounds. A maximum likelihood fit is performed
on the output of the Event-level Discriminator to determine the fiducial cross
section of the signal process. The fiducial cross section for the single-lepton
and dilepton channel are measured to be $521 \pm 9 \text{(stat.)} \pm 41
\text{(sys.)}$ fb and $69 \pm 3 \text{(stat.)} \pm 4\text{(sys.)}$ fb,
respectively. In total, eight cross-section measurements are performed and all
agree with theoretical next-to-leading-order predictions. |
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DOI: | 10.48550/arxiv.1811.08780 |