cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17182/hepdata.88291.v1/t9
title: "Table 9" of "Nuclear modification of $\Upsilon$ states in pPb collisions at $\sqrt{s_\mathrm{NN}}$ = 5.02 TeV"

description: Differential cross section times dimuon branching fraction of Y(3S) as a function of pT in pp collisions. The global uncertainty arises from the integrated luminosity uncertainty in pp collisions.
description: Production cross sections of $\Upsilon(1S)$, $\Upsilon(2S)$, and $\Upsilon(3S)$ states decaying into $\mu^{+}\mu^{-}$ in proton-lead (pPb) collisions are reported using data collected by the CMS experiment at $\sqrt{s_{_{\mathrm{NN}}}} = $ 5.02 TeV. A comparison is made with corresponding cross sections obtained with pp data measured at the same collision energy and scaled by the Pb nucleus mass number. The nuclear modification factor for $\Upsilon(1S)$ is found to be $R_{\mathrm{pPb}}(\Upsilon(1S)) = $ 0.806 $ \pm\ $ 0.024 (stat) $ \pm\ $ 0.059 (syst). Similar results for the excited states indicate a sequential suppression pattern, such that $R_{\mathrm{pPb}}(\Upsilon(1S)) &gt; R_{\mathrm{pPb}}(\Upsilon(2S)) &gt; R_{\mathrm{pPb}}(\Upsilon(3S))$. The suppression is much less pronounced in pPb than in PbPb collisions, and independent of transverse momentum $p^{\Upsilon}_{T}$ and center-of-mass rapidity $y^{\Upsilon}_{CM}$ of the individual $\Upsilon$ state in the studied range $p^{\Upsilon}_{T} &lt; $ 30 GeV/c and $|y^{\Upsilon}_{CM}| &lt; $ 1.93. Models that incorporate sequential suppression of bottomonia in pPb collisions are in better agreement with the data than those which only assume initial-state modifications.
DOI: 10.3535/qcn-5hf-m5p
title: Jodis putata (Linnaeus, 1758)

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.17182/hepdata.116281.v1/t3
title: "Figure 4 - top left, low pt DeepTau vs jet WPs" of "Identification of hadronic tau lepton decays using a deep neural network"

description: Efficiency for quark and gluon jets to pass different tau identification discriminators versus the efficiency for genuine tau_h. The upper two plots are obtained with jets from the W+jets simulated sample and the lower two plots with jets from the tt sample. The left two plots include jets and genuine tau_h with pt &lt; 100 GeV, whereas the right two plots include those with pt &gt; 100 GeV. The working points are indicated as full circles. The efficiency for jets from the W+jets event sample, enriched in quark jets, to pass the discriminators is higher compared to jets from the tt event sample, which has a larger fraction of gluon and b-quark jets. The jet efficiency for a given tau_h efficiency is larger for jets and tau_h with pt &lt; 100 GeV than for those with pt &gt; 100 GeV. Compared with the previously used MVA discriminator, the DEEPTAU discriminator reduces the jet efficiency for a given tau_h efficiency by consistently more than a factor of 1.8, and by more at high tau_h efficiency. The additional gain at high pt comes from the inclusion of updated decay modes in the tau_h reconstruction, as illustrated by the curves for the previously used MVA discriminator but including reconstructed tau_h candidates with additional decay modes.
description: A new algorithm is presented to discriminate reconstructed hadronic decays of tau leptons (tau_h) that originate from genuine tau leptons in the CMS detector against tau_h candidates that originate from quark or gluon jets, electrons, or muons. The algorithm inputs information from all reconstructed particles in the vicinity of a tau_h candidate and employs a deep neural network with convolutional layers to efficiently process the inputs. This algorithm leads to a significantly improved performance compared with the previously used one. For example, the efficiency for a genuine tau_h to pass the discriminator against jets increases by 10-30% for a given efficiency for quark and gluon jets. Furthermore, a more efficient tau_h reconstruction is introduced that incorporates additional hadronic decay modes. The superior performance of the new algorithm to discriminate against jets, electrons, and muons and the improved tau_h reconstruction method are validated with LHC proton-proton collision data at sqrt(s) = 13 TeV.
DOI: 10.17182/hepdata.116281.v1/t4
title: "Figure 4 - top left, low pt MVA WPs" of "Identification of hadronic tau lepton decays using a deep neural network"

description: Efficiency for quark and gluon jets to pass different tau identification discriminators versus the efficiency for genuine tau_h. The upper two plots are obtained with jets from the W+jets simulated sample and the lower two plots with jets from the tt sample. The left two plots include jets and genuine tau_h with pt &lt; 100 GeV, whereas the right two plots include those with pt &gt; 100 GeV. The working points are indicated as full circles. The efficiency for jets from the W+jets event sample, enriched in quark jets, to pass the discriminators is higher compared to jets from the tt event sample, which has a larger fraction of gluon and b-quark jets. The jet efficiency for a given tau_h efficiency is larger for jets and tau_h with pt &lt; 100 GeV than for those with pt &gt; 100 GeV. Compared with the previously used MVA discriminator, the DEEPTAU discriminator reduces the jet efficiency for a given tau_h efficiency by consistently more than a factor of 1.8, and by more at high tau_h efficiency. The additional gain at high pt comes from the inclusion of updated decay modes in the tau_h reconstruction, as illustrated by the curves for the previously used MVA discriminator but including reconstructed tau_h candidates with additional decay modes.
description: A new algorithm is presented to discriminate reconstructed hadronic decays of tau leptons (tau_h) that originate from genuine tau leptons in the CMS detector against tau_h candidates that originate from quark or gluon jets, electrons, or muons. The algorithm inputs information from all reconstructed particles in the vicinity of a tau_h candidate and employs a deep neural network with convolutional layers to efficiently process the inputs. This algorithm leads to a significantly improved performance compared with the previously used one. For example, the efficiency for a genuine tau_h to pass the discriminator against jets increases by 10-30% for a given efficiency for quark and gluon jets. Furthermore, a more efficient tau_h reconstruction is introduced that incorporates additional hadronic decay modes. The superior performance of the new algorithm to discriminate against jets, electrons, and muons and the improved tau_h reconstruction method are validated with LHC proton-proton collision data at sqrt(s) = 13 TeV.
DOI: 10.17182/hepdata.116281.v1/t5
title: "Figure 4 - top left, low pt MVA updated Decay Mode WPs" of "Identification of hadronic tau lepton decays using a deep neural network"

description: Efficiency for quark and gluon jets to pass different tau identification discriminators versus the efficiency for genuine tau_h. The upper two plots are obtained with jets from the W+jets simulated sample and the lower two plots with jets from the tt sample. The left two plots include jets and genuine tau_h with pt &lt; 100 GeV, whereas the right two plots include those with pt &gt; 100 GeV. The working points are indicated as full circles. The efficiency for jets from the W+jets event sample, enriched in quark jets, to pass the discriminators is higher compared to jets from the tt event sample, which has a larger fraction of gluon and b-quark jets. The jet efficiency for a given tau_h efficiency is larger for jets and tau_h with pt &lt; 100 GeV than for those with pt &gt; 100 GeV. Compared with the previously used MVA discriminator, the DEEPTAU discriminator reduces the jet efficiency for a given tau_h efficiency by consistently more than a factor of 1.8, and by more at high tau_h efficiency. The additional gain at high pt comes from the inclusion of updated decay modes in the tau_h reconstruction, as illustrated by the curves for the previously used MVA discriminator but including reconstructed tau_h candidates with additional decay modes.
description: A new algorithm is presented to discriminate reconstructed hadronic decays of tau leptons (tau_h) that originate from genuine tau leptons in the CMS detector against tau_h candidates that originate from quark or gluon jets, electrons, or muons. The algorithm inputs information from all reconstructed particles in the vicinity of a tau_h candidate and employs a deep neural network with convolutional layers to efficiently process the inputs. This algorithm leads to a significantly improved performance compared with the previously used one. For example, the efficiency for a genuine tau_h to pass the discriminator against jets increases by 10-30% for a given efficiency for quark and gluon jets. Furthermore, a more efficient tau_h reconstruction is introduced that incorporates additional hadronic decay modes. The superior performance of the new algorithm to discriminate against jets, electrons, and muons and the improved tau_h reconstruction method are validated with LHC proton-proton collision data at sqrt(s) = 13 TeV.
DOI: 10.17182/hepdata.116281.v1/t6
title: "Figure 4 - top right, high pt DeepTau vs jet" of "Identification of hadronic tau lepton decays using a deep neural network"

description: Efficiency for quark and gluon jets to pass different tau identification discriminators versus the efficiency for genuine tau_h. The upper two plots are obtained with jets from the W+jets simulated sample and the lower two plots with jets from the tt sample. The left two plots include jets and genuine tau_h with pt &lt; 100 GeV, whereas the right two plots include those with pt &gt; 100 GeV. The working points are indicated as full circles. The efficiency for jets from the W+jets event sample, enriched in quark jets, to pass the discriminators is higher compared to jets from the tt event sample, which has a larger fraction of gluon and b-quark jets. The jet efficiency for a given tau_h efficiency is larger for jets and tau_h with pt &lt; 100 GeV than for those with pt &gt; 100 GeV. Compared with the previously used MVA discriminator, the DEEPTAU discriminator reduces the jet efficiency for a given tau_h efficiency by consistently more than a factor of 1.8, and by more at high tau_h efficiency. The additional gain at high pt comes from the inclusion of updated decay modes in the tau_h reconstruction, as illustrated by the curves for the previously used MVA discriminator but including reconstructed tau_h candidates with additional decay modes.
description: A new algorithm is presented to discriminate reconstructed hadronic decays of tau leptons (tau_h) that originate from genuine tau leptons in the CMS detector against tau_h candidates that originate from quark or gluon jets, electrons, or muons. The algorithm inputs information from all reconstructed particles in the vicinity of a tau_h candidate and employs a deep neural network with convolutional layers to efficiently process the inputs. This algorithm leads to a significantly improved performance compared with the previously used one. For example, the efficiency for a genuine tau_h to pass the discriminator against jets increases by 10-30% for a given efficiency for quark and gluon jets. Furthermore, a more efficient tau_h reconstruction is introduced that incorporates additional hadronic decay modes. The superior performance of the new algorithm to discriminate against jets, electrons, and muons and the improved tau_h reconstruction method are validated with LHC proton-proton collision data at sqrt(s) = 13 TeV.
DOI: 10.17182/hepdata.88291.v1/t14
title: "Table 14" of "Nuclear modification of $\Upsilon$ states in pPb collisions at $\sqrt{s_\mathrm{NN}}$ = 5.02 TeV"

description: Nuclear modification factor of Y(2S) as a function of pT. The global uncertainty arises from the integrated luminosity uncertainties in pPb and pp collisions.
description: Production cross sections of $\Upsilon(1S)$, $\Upsilon(2S)$, and $\Upsilon(3S)$ states decaying into $\mu^{+}\mu^{-}$ in proton-lead (pPb) collisions are reported using data collected by the CMS experiment at $\sqrt{s_{_{\mathrm{NN}}}} = $ 5.02 TeV. A comparison is made with corresponding cross sections obtained with pp data measured at the same collision energy and scaled by the Pb nucleus mass number. The nuclear modification factor for $\Upsilon(1S)$ is found to be $R_{\mathrm{pPb}}(\Upsilon(1S)) = $ 0.806 $ \pm\ $ 0.024 (stat) $ \pm\ $ 0.059 (syst). Similar results for the excited states indicate a sequential suppression pattern, such that $R_{\mathrm{pPb}}(\Upsilon(1S)) &gt; R_{\mathrm{pPb}}(\Upsilon(2S)) &gt; R_{\mathrm{pPb}}(\Upsilon(3S))$. The suppression is much less pronounced in pPb than in PbPb collisions, and independent of transverse momentum $p^{\Upsilon}_{T}$ and center-of-mass rapidity $y^{\Upsilon}_{CM}$ of the individual $\Upsilon$ state in the studied range $p^{\Upsilon}_{T} &lt; $ 30 GeV/c and $|y^{\Upsilon}_{CM}| &lt; $ 1.93. Models that incorporate sequential suppression of bottomonia in pPb collisions are in better agreement with the data than those which only assume initial-state modifications.
DOI: 10.17182/hepdata.88291.v1/t15
title: "Table 15" of "Nuclear modification of $\Upsilon$ states in pPb collisions at $\sqrt{s_\mathrm{NN}}$ = 5.02 TeV"

description: Nuclear modification factor of Y(3S) as a function of pT. The global uncertainty arises from the integrated luminosity uncertainties in pPb and pp collisions.
description: Production cross sections of $\Upsilon(1S)$, $\Upsilon(2S)$, and $\Upsilon(3S)$ states decaying into $\mu^{+}\mu^{-}$ in proton-lead (pPb) collisions are reported using data collected by the CMS experiment at $\sqrt{s_{_{\mathrm{NN}}}} = $ 5.02 TeV. A comparison is made with corresponding cross sections obtained with pp data measured at the same collision energy and scaled by the Pb nucleus mass number. The nuclear modification factor for $\Upsilon(1S)$ is found to be $R_{\mathrm{pPb}}(\Upsilon(1S)) = $ 0.806 $ \pm\ $ 0.024 (stat) $ \pm\ $ 0.059 (syst). Similar results for the excited states indicate a sequential suppression pattern, such that $R_{\mathrm{pPb}}(\Upsilon(1S)) &gt; R_{\mathrm{pPb}}(\Upsilon(2S)) &gt; R_{\mathrm{pPb}}(\Upsilon(3S))$. The suppression is much less pronounced in pPb than in PbPb collisions, and independent of transverse momentum $p^{\Upsilon}_{T}$ and center-of-mass rapidity $y^{\Upsilon}_{CM}$ of the individual $\Upsilon$ state in the studied range $p^{\Upsilon}_{T} &lt; $ 30 GeV/c and $|y^{\Upsilon}_{CM}| &lt; $ 1.93. Models that incorporate sequential suppression of bottomonia in pPb collisions are in better agreement with the data than those which only assume initial-state modifications.
DOI: 10.17182/hepdata.88291.v1/t17
title: "Table 17" of "Nuclear modification of $\Upsilon$ states in pPb collisions at $\sqrt{s_\mathrm{NN}}$ = 5.02 TeV"

description: Nuclear modification factor of Y(2S) as a function of $y^{Y}_{CM}$. The global uncertainty arises from the integrated luminosity uncertainties in pPb and pp collisions.
description: Production cross sections of $\Upsilon(1S)$, $\Upsilon(2S)$, and $\Upsilon(3S)$ states decaying into $\mu^{+}\mu^{-}$ in proton-lead (pPb) collisions are reported using data collected by the CMS experiment at $\sqrt{s_{_{\mathrm{NN}}}} = $ 5.02 TeV. A comparison is made with corresponding cross sections obtained with pp data measured at the same collision energy and scaled by the Pb nucleus mass number. The nuclear modification factor for $\Upsilon(1S)$ is found to be $R_{\mathrm{pPb}}(\Upsilon(1S)) = $ 0.806 $ \pm\ $ 0.024 (stat) $ \pm\ $ 0.059 (syst). Similar results for the excited states indicate a sequential suppression pattern, such that $R_{\mathrm{pPb}}(\Upsilon(1S)) &gt; R_{\mathrm{pPb}}(\Upsilon(2S)) &gt; R_{\mathrm{pPb}}(\Upsilon(3S))$. The suppression is much less pronounced in pPb than in PbPb collisions, and independent of transverse momentum $p^{\Upsilon}_{T}$ and center-of-mass rapidity $y^{\Upsilon}_{CM}$ of the individual $\Upsilon$ state in the studied range $p^{\Upsilon}_{T} &lt; $ 30 GeV/c and $|y^{\Upsilon}_{CM}| &lt; $ 1.93. Models that incorporate sequential suppression of bottomonia in pPb collisions are in better agreement with the data than those which only assume initial-state modifications.
DOI: 10.3535/6xk-2tq-6ad
title: Jodis putata (Linnaeus, 1758)

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.3535/pmd-v1n-wbp
title: Selenia tetralunaria (Hufnagel, 1767)

description: Digital Specimen for the physical specimen hosted at Finnish Museum of Natural History.
DOI: 10.17182/hepdata.116281.v1/t7
title: "Figure 4 - top right, high pt DeepTau vs jet WPs" of "Identification of hadronic tau lepton decays using a deep neural network"

description: Efficiency for quark and gluon jets to pass different tau identification discriminators versus the efficiency for genuine tau_h. The upper two plots are obtained with jets from the W+jets simulated sample and the lower two plots with jets from the tt sample. The left two plots include jets and genuine tau_h with pt &lt; 100 GeV, whereas the right two plots include those with pt &gt; 100 GeV. The working points are indicated as full circles. The efficiency for jets from the W+jets event sample, enriched in quark jets, to pass the discriminators is higher compared to jets from the tt event sample, which has a larger fraction of gluon and b-quark jets. The jet efficiency for a given tau_h efficiency is larger for jets and tau_h with pt &lt; 100 GeV than for those with pt &gt; 100 GeV. Compared with the previously used MVA discriminator, the DEEPTAU discriminator reduces the jet efficiency for a given tau_h efficiency by consistently more than a factor of 1.8, and by more at high tau_h efficiency. The additional gain at high pt comes from the inclusion of updated decay modes in the tau_h reconstruction, as illustrated by the curves for the previously used MVA discriminator but including reconstructed tau_h candidates with additional decay modes.
description: A new algorithm is presented to discriminate reconstructed hadronic decays of tau leptons (tau_h) that originate from genuine tau leptons in the CMS detector against tau_h candidates that originate from quark or gluon jets, electrons, or muons. The algorithm inputs information from all reconstructed particles in the vicinity of a tau_h candidate and employs a deep neural network with convolutional layers to efficiently process the inputs. This algorithm leads to a significantly improved performance compared with the previously used one. For example, the efficiency for a genuine tau_h to pass the discriminator against jets increases by 10-30% for a given efficiency for quark and gluon jets. Furthermore, a more efficient tau_h reconstruction is introduced that incorporates additional hadronic decay modes. The superior performance of the new algorithm to discriminate against jets, electrons, and muons and the improved tau_h reconstruction method are validated with LHC proton-proton collision data at sqrt(s) = 13 TeV.
DOI: 10.17182/hepdata.116281.v1/t8
title: "Figure 4 - top right, high pt MVA WPs" of "Identification of hadronic tau lepton decays using a deep neural network"

description: Efficiency for quark and gluon jets to pass different tau identification discriminators versus the efficiency for genuine tau_h. The upper two plots are obtained with jets from the W+jets simulated sample and the lower two plots with jets from the tt sample. The left two plots include jets and genuine tau_h with pt &lt; 100 GeV, whereas the right two plots include those with pt &gt; 100 GeV. The working points are indicated as full circles. The efficiency for jets from the W+jets event sample, enriched in quark jets, to pass the discriminators is higher compared to jets from the tt event sample, which has a larger fraction of gluon and b-quark jets. The jet efficiency for a given tau_h efficiency is larger for jets and tau_h with pt &lt; 100 GeV than for those with pt &gt; 100 GeV. Compared with the previously used MVA discriminator, the DEEPTAU discriminator reduces the jet efficiency for a given tau_h efficiency by consistently more than a factor of 1.8, and by more at high tau_h efficiency. The additional gain at high pt comes from the inclusion of updated decay modes in the tau_h reconstruction, as illustrated by the curves for the previously used MVA discriminator but including reconstructed tau_h candidates with additional decay modes.
description: A new algorithm is presented to discriminate reconstructed hadronic decays of tau leptons (tau_h) that originate from genuine tau leptons in the CMS detector against tau_h candidates that originate from quark or gluon jets, electrons, or muons. The algorithm inputs information from all reconstructed particles in the vicinity of a tau_h candidate and employs a deep neural network with convolutional layers to efficiently process the inputs. This algorithm leads to a significantly improved performance compared with the previously used one. For example, the efficiency for a genuine tau_h to pass the discriminator against jets increases by 10-30% for a given efficiency for quark and gluon jets. Furthermore, a more efficient tau_h reconstruction is introduced that incorporates additional hadronic decay modes. The superior performance of the new algorithm to discriminate against jets, electrons, and muons and the improved tau_h reconstruction method are validated with LHC proton-proton collision data at sqrt(s) = 13 TeV.
DOI: 10.17182/hepdata.116281.v1/t9
title: "Figure 4 - top right, high pt MVA updated Decay Mode WPs" of "Identification of hadronic tau lepton decays using a deep neural network"

description: Efficiency for quark and gluon jets to pass different tau identification discriminators versus the efficiency for genuine tau_h. The upper two plots are obtained with jets from the W+jets simulated sample and the lower two plots with jets from the tt sample. The left two plots include jets and genuine tau_h with pt &lt; 100 GeV, whereas the right two plots include those with pt &gt; 100 GeV. The working points are indicated as full circles. The efficiency for jets from the W+jets event sample, enriched in quark jets, to pass the discriminators is higher compared to jets from the tt event sample, which has a larger fraction of gluon and b-quark jets. The jet efficiency for a given tau_h efficiency is larger for jets and tau_h with pt &lt; 100 GeV than for those with pt &gt; 100 GeV. Compared with the previously used MVA discriminator, the DEEPTAU discriminator reduces the jet efficiency for a given tau_h efficiency by consistently more than a factor of 1.8, and by more at high tau_h efficiency. The additional gain at high pt comes from the inclusion of updated decay modes in the tau_h reconstruction, as illustrated by the curves for the previously used MVA discriminator but including reconstructed tau_h candidates with additional decay modes.
description: A new algorithm is presented to discriminate reconstructed hadronic decays of tau leptons (tau_h) that originate from genuine tau leptons in the CMS detector against tau_h candidates that originate from quark or gluon jets, electrons, or muons. The algorithm inputs information from all reconstructed particles in the vicinity of a tau_h candidate and employs a deep neural network with convolutional layers to efficiently process the inputs. This algorithm leads to a significantly improved performance compared with the previously used one. For example, the efficiency for a genuine tau_h to pass the discriminator against jets increases by 10-30% for a given efficiency for quark and gluon jets. Furthermore, a more efficient tau_h reconstruction is introduced that incorporates additional hadronic decay modes. The superior performance of the new algorithm to discriminate against jets, electrons, and muons and the improved tau_h reconstruction method are validated with LHC proton-proton collision data at sqrt(s) = 13 TeV.