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CMS-PRF-14-001 ; CERN-EP-2017-110

Particle-flow reconstruction and global event description with the CMS detector

CMS Collaboration

15 June 2017

JINST 12 (2017) P10003

Abstract: The CMS apparatus was identified, a few years before the start of the LHC operation at CERN, to feature properties well suited to particle-flow (PF) reconstruction: a highly-segmented tracker, a fine-grained electromagnetic calorimeter, a hermetic hadron calorimeter, a strong magnetic field, and an excellent muon spectrometer. A fully-fledged PF reconstruction algorithm tuned to the CMS detector was therefore developed and has been consistently used in physics analyses for the first time at a hadron collider. For each collision, the comprehensive list of final-state particles identified and reconstructed by the algorithm provides a global event description that leads to unprecedented CMS performance for jet and hadronic $\tau$ decay reconstruction, missing transverse momentum determination, and electron and muon identification. This approach also allows particles from pileup interactions to be identified and enables efficient pileup mitigation methods. The data collected by CMS at a centre-of-mass energy of 8 TeV show excellent agreement with the simulation and confirm the superior PF performance at least up to an average of 20 pileup interactions.

Links: e-print arXiv:1706.04965 [physics.ins-det] (PDF) ; CDS record ; inSPIRE record ; CADI line (restricted) ;

Figures
png pdf	Figure 1: A sketch of the specific particle interactions in a transverse slice of the CMS detector, from the beam interaction region to the muon detector. The muon and the charged pion are positively charged, and the electron is negatively charged.
png pdf	Figure 2: Event display of an illustrative jet made of five particles only in the $(x,y)$ view (upper panel), and in the ($\eta,\varphi $) view on the ECAL surface (lower left) and the HCAL surface (lower right). In the top view, these two surfaces are represented as circles centred around the interaction point. The $\mathrm {K}^0_\mathrm {L}$, the $\pi ^-$, and the two photons from the $\pi ^0$ decay are detected as four well-separated ECAL clusters denoted $\mathrm {E}_{1,2,3,4}$. The $\pi ^+$ does not create a cluster in the ECAL. The two charged pions are reconstructed as charged-particle tracks $\mathrm {T}_{1,2}$, appearing as vertical solid lines in the ($\eta,\varphi $) views and circular arcs in the $(x,y)$ view. These tracks point towards two HCAL clusters $\mathrm {H}_{1,2}$. In the bottom views, the ECAL and HCAL cells are represented as squares, with an inner area proportional to the logarithm of the cell energy. Cells with an energy larger than those of the neighbouring cells are shown in dark grey. In all three views, the cluster positions are represented by dots, the simulated particles by dashed lines, and the positions of their impacts on the calorimeter surfaces by various open markers.
png pdf	Figure 2-a: Event display of an illustrative jet made of five particles only in the $(x,y)$ view. The ECAL and HCAL surfaces are represented as circles centred around the interaction point. The $\mathrm {K}^0_\mathrm {L}$, the $\pi ^-$, and the two photons from the $\pi ^0$ decay are detected as four well-separated ECAL clusters denoted $\mathrm {E}_{1,2,3,4}$. The $\pi ^+$ does not create a cluster in the ECAL. The two charged pions are reconstructed as charged-particle tracks $\mathrm {T}_{1,2}$, appearing as vertical solid lines in the ($\eta,\varphi $) views and circular arcs in the $(x,y)$ view. These tracks point towards two HCAL clusters $\mathrm {H}_{1,2}$.
png pdf	Figure 2-b: Event display of an illustrative jet made of five particles only in the ($\eta,\varphi $) view on the ECAL surface. The $\mathrm {K}^0_\mathrm {L}$, the $\pi ^-$, and the two photons from the $\pi ^0$ decay are detected as four well-separated ECAL clusters denoted $\mathrm {E}_{1,2,3,4}$. The $\pi ^+$ does not create a cluster in the ECAL. The ECAL cells are represented as squares, with an inner area proportional to the logarithm of the cell energy. Cells with an energy larger than those of the neighbouring cells are shown in dark grey. In all three views, the cluster positions are represented by dots, the simulated particles by dashed lines, and the positions of their impacts on the calorimeter surfaces by various open markers.
png pdf	Figure 2-c: Event display of an illustrative jet made of five particles only in the ($\eta,\varphi $) view on the HCAL surface. The two charged pions are reconstructed as charged-particle tracks $\mathrm {T}_{1,2}$, appearing as vertical solid lines in the ($\eta,\varphi $) views and circular arcs in the $(x,y)$ view. These tracks point towards two HCAL clusters $\mathrm {H}_{1,2}$. The HCAL cells are represented as squares, with an inner area proportional to the logarithm of the cell energy. Cells with an energy larger than those of the neighbouring cells are shown in dark grey. In all three views, the cluster positions are represented by dots, the simulated particles by dashed lines, and the positions of their impacts on the calorimeter surfaces by various open markers.
png pdf	Figure 3: Total thickness $t$ of the inner tracker material expressed in units of interaction lengths $\lambda _l$ (left) and radiation lengths $X_0$ (right), as a function of the pseudorapidity $\eta $. The acronyms TIB, TID, TOB, and TEC stand for "tracker inner barrel'', "tracker inner disks'', "tracker outer barrel'', and "tracker endcaps'', respectively. The two figures are taken from Ref. [18].
png pdf	Figure 3-a: Total thickness $t$ of the inner tracker material expressed in units of interaction lengths $\lambda _l$, as a function of the pseudorapidity $\eta $. The acronyms TIB, TID, TOB, and TEC stand for "tracker inner barrel'', "tracker inner disks'', "tracker outer barrel'', and "tracker endcaps'', respectively. The figure is taken from Ref. [18].
png pdf	Figure 3-b: Total thickness $t$ of the inner tracker material expressed in units of radiation lengths $X_0$, as a function of the pseudorapidity $\eta $. The acronyms TIB, TID, TOB, and TEC stand for "tracker inner barrel'', "tracker inner disks'', "tracker outer barrel'', and "tracker endcaps'', respectively. The figure is taken from Ref. [18].
png pdf	Figure 4: Efficiency (left) and misreconstruction rate (right) of the global combinatorial track finder (black squares); and of the iterative tracking method (green triangles: prompt iterations based on seeds with at least one hit in the pixel detector; red circles: all iterations, including those with displaced seeds), as a function of the track $ {p_{\mathrm {T}}} $, for charged hadrons in multijet events without pileup interactions. Only tracks with $ {| \eta | } < $ 2.5 are considered in the efficiency and misreconstruction rate determination. The efficiency is displayed for tracks originating from within 3.5 cm of the beam axis and $\pm$30 cm of the nominal centre of CMS along the beam axis.
png pdf	Figure 4-a: Efficiency of the global combinatorial track finder (black squares); and of the iterative tracking method (green triangles: prompt iterations based on seeds with at least one hit in the pixel detector; red circles: all iterations, including those with displaced seeds), as a function of the track $ {p_{\mathrm {T}}} $, for charged hadrons in multijet events without pileup interactions. Only tracks with $ {| \eta | } < $ 2.5 are considered. The efficiency is displayed for tracks originating from within 3.5 cm of the beam axis and $\pm$30 cm of the nominal centre of CMS along the beam axis.
png pdf	Figure 4-b: Misreconstruction rate of the global combinatorial track finder (black squares); and of the iterative tracking method (green triangles: prompt iterations based on seeds with at least one hit in the pixel detector; red circles: all iterations, including those with displaced seeds), as a function of the track $ {p_{\mathrm {T}}} $, for charged hadrons in multijet events without pileup interactions. Only tracks with $ {| \eta | } < $ 2.5 are considered.
png pdf	Figure 5: Maps of nuclear interaction vertices for data collected by CMS in 2011 at $\sqrt {s} = $ 7 TeV, corresponding to an integrated luminosity of 1 nb$^{-1}$, in the longitudinal (left) and transverse (right) cross sections of the inner part of the tracker, exhibiting its structure in concentric layers around the beam axis. The different shades of grey indicate the reconstructed nuclear interaction probability per event and per unit of surface, the darker the larger.
png pdf	Figure 5-a: Map of nuclear interaction vertices for data collected by CMS in 2011 at $\sqrt {s} = $ 7 TeV, corresponding to an integrated luminosity of 1 nb$^{-1}$, in the longitudinal cross section of the inner part of the tracker. The different shades of grey indicate the reconstructed nuclear interaction probability per event and per unit of surface, the darker the larger.
png pdf	Figure 5-b: Map of nuclear interaction vertices for data collected by CMS in 2011 at $\sqrt {s} = $ 7 TeV, corresponding to an integrated luminosity of 1 nb$^{-1}$, in the transverse cross section of the inner part of the tracker, exhibiting its structure in concentric layers around the beam axis. The different shades of grey indicate the reconstructed nuclear interaction probability per event and per unit of surface, the darker the larger.
png pdf	Figure 6: Left: Electron seeding efficiency for electrons (triangles) and pions (circles) as a function of $ {p_{\mathrm {T}}} $, from a simulated event sample enriched in b quark jets with $ {p_{\mathrm {T}}} $ between 80 and 170 GeV, and with at least one semileptonic b hadron decay. Both the efficiencies for ECAL-based seeding only (hollow symbols) and with the tracker-based seeding added (solid symbols) are displayed. Right: Absolute efficiency gain from the tracker-based seeding for electrons from Z boson decays as a function of $ {p_{\mathrm {T}}} $.
png pdf	Figure 6-a: Electron seeding efficiency for electrons (triangles) and pions (circles) as a function of $ {p_{\mathrm {T}}} $, from a simulated event sample enriched in b quark jets with $ {p_{\mathrm {T}}} $ between 80 and 170 GeV, and with at least one semileptonic b hadron decay. Both the efficiencies for ECAL-based seeding only (hollow symbols) and with the tracker-based seeding added (solid symbols) are displayed.
png pdf	Figure 6-b: Absolute efficiency gain from the tracker-based seeding for electrons from Z boson decays as a function of $ {p_{\mathrm {T}}} $.
png pdf	Figure 7: Photon pair invariant mass distribution in the barrel ($ {| \eta | } <$ 1.0) for the simulation (left) and the data (right). The $\pi ^0$ signal is modelled by a Gaussian (red curve) and the background by an exponential function (blue curve). The Gaussian mean value (vertical dashed line) and its standard deviation are denoted $m^\text {fit}$ and $\sigma _\mathrm {m}$, respectively.
png pdf	Figure 7-a: Photon pair invariant mass distribution in the barrel ($ {| \eta | } <$ 1.0) for the simulation. The $\pi ^0$ signal is modelled by a Gaussian (red curve) and the background by an exponential function (blue curve). The Gaussian mean value (vertical dashed line) and its standard deviation are denoted $m^\text {fit}$ and $\sigma _\mathrm {m}$, respectively.
png pdf	Figure 7-b: Photon pair invariant mass distribution in the barrel ($ {| \eta | } <$ 1.0) for the data. The $\pi ^0$ signal is modelled by a Gaussian (red curve) and the background by an exponential function (blue curve). The Gaussian mean value (vertical dashed line) and its standard deviation are denoted $m^\text {fit}$ and $\sigma _\mathrm {m}$, respectively.
png pdf	Figure 8: Left: Calibration coefficients obtained from single hadrons in the barrel as a function of their true energy $E$, for hadrons depositing energy only in the HCAL (blue triangles), and for hadrons depositing energy in both the ECAL and HCAL, for the ECAL (red circles) and for the HCAL (green squares) clusters. Right: Relative raw (blue) and calibrated (red) energy response (dashed curves and triangles) and resolution (full curves and circles) for single hadrons in the barrel, as a function of their true energy $E$. Here the raw (calibrated) response and resolution are obtained by a Gaussian fit to the distribution of the relative difference between the raw (calibrated) calorimetric energy and the true hadron energy.
png pdf	Figure 8-a: Calibration coefficients obtained from single hadrons in the barrel as a function of their true energy $E$, for hadrons depositing energy only in the HCAL (blue triangles), and for hadrons depositing energy in both the ECAL and HCAL, for the ECAL (red circles) and for the HCAL (green squares) clusters.
png pdf	Figure 8-b: Relative raw (blue) and calibrated (red) energy response (dashed curves and triangles) and resolution (full curves and circles) for single hadrons in the barrel, as a function of their true energy $E$. Here the raw (calibrated) response and resolution are obtained by a Gaussian fit to the distribution of the relative difference between the raw (calibrated) calorimetric energy and the true hadron energy.
png pdf	Figure 9: Jet reconstruction in a simulated dijet event. The particles clustered in the two PF jets are displayed with a thicker line. For clarity, particles with $ {p_{\mathrm {T}}} < $ 1 GeV are not shown. The PF jet ${{\vec p}_{\mathrm {T}}}$, indicated as a radial line, is compared to the ${{\vec p}_{\mathrm {T}}}$ of the corresponding generated (Ref) and calorimeter (Calo) jets. In all cases, the four-momentum of the jet is obtained by summing the four-momenta of its constituents, and no jet energy correction is applied.
png pdf	Figure 10: Jet angular resolution in the barrel (left) and endcap (right) regions, as a function of the $ {p_{\mathrm {T}}} $ of the reference jet. The $\varphi $ resolution is expressed in radians.
png pdf	Figure 10-a: Jet angular resolution in the barrel region, as a function of the $ {p_{\mathrm {T}}} $ of the reference jet. The $\varphi $ resolution is expressed in radians.
png pdf	Figure 10-b: Jet angular resolution in the endcap region, as a function of the $ {p_{\mathrm {T}}} $ of the reference jet. The $\varphi $ resolution is expressed in radians.
png pdf	Figure 11: Distribution of the ratio between the reconstructed and reference transverse momenta, $\Sigma p_{\mathrm {T},i} / \Sigma p_{\mathrm {T},i}^\text {Ref}$, for charged hadrons (top left), photons (top right), neutral hadrons (bottom left), and for all neutral particles in Ref jets with no photon (bottom right). The Ref jet is required to have at least 10% of its $ {p_{\mathrm {T}}} $ carried by particles of type $i$, and to be located in the barrel.
png pdf	Figure 11-a: Distribution of the ratio between the reconstructed and reference transverse momenta, $\Sigma p_{\mathrm {T},i} / \Sigma p_{\mathrm {T},i}^\text {Ref}$, for charged hadrons. The Ref jet is required to have at least 10% of its $ {p_{\mathrm {T}}} $ carried by particles of type $i$, and to be located in the barrel.
png pdf	Figure 11-b: Distribution of the ratio between the reconstructed and reference transverse momenta, $\Sigma p_{\mathrm {T},i} / \Sigma p_{\mathrm {T},i}^\text {Ref}$, for photons. The Ref jet is required to have at least 10% of its $ {p_{\mathrm {T}}} $ carried by particles of type $i$, and to be located in the barrel.
png pdf	Figure 11-c: Distribution of the ratio between the reconstructed and reference transverse momenta, $\Sigma p_{\mathrm {T},i} / \Sigma p_{\mathrm {T},i}^\text {Ref}$, for neutral hadrons. The Ref jet is required to have at least 10% of its $ {p_{\mathrm {T}}} $ carried by particles of type $i$, and to be located in the barrel.
png pdf	Figure 11-d: Distribution of the ratio between the reconstructed and reference transverse momenta, $\Sigma p_{\mathrm {T},i} / \Sigma p_{\mathrm {T},i}^\text {Ref}$, for all neutral particles in Ref jets with no photon. The Ref jet is required to have at least 10% of its $ {p_{\mathrm {T}}} $ carried by particles of type $i$, and to be located in the barrel.
png pdf	Figure 12: Jet response as a function of $\eta ^\text {Ref}$ for the range 80 $ < {p_\mathrm {T}^\text {Ref}} < $ 120 GeV (top) and as a function of ${p_\mathrm {T}^\text {Ref}}$ in the barrel (left) and in the endcap (right) regions.
png pdf	Figure 12-a: Jet response as a function of $\eta ^\text {Ref}$ for the range 80 $ < {p_\mathrm {T}^\text {Ref}} < $ 120 GeV.
png pdf	Figure 12-b: Jet response as a function of ${p_\mathrm {T}^\text {Ref}}$ in the barrel region.
png pdf	Figure 12-c: Jet response as a function of ${p_\mathrm {T}^\text {Ref}}$ in the endcap region.
png pdf	Figure 13: Jet energy resolution as a function of ${p_\mathrm {T}^\text {Ref}}$ in the barrel (left) and in the endcap (right) regions. The lines, added to guide the eye, correspond to fitted functions with ad hoc parametrizations.
png pdf	Figure 13-a: Jet energy resolution as a function of ${p_\mathrm {T}^\text {Ref}}$ in the barrel region. The lines, added to guide the eye, correspond to fitted functions with ad hoc parametrizations.
png pdf	Figure 13-b: Jet energy resolution as a function of ${p_\mathrm {T}^\text {Ref}}$ in the endcap region. The lines, added to guide the eye, correspond to fitted functions with ad hoc parametrizations.
png pdf	Figure 14: Absolute difference in jet energy response between quark and gluon jets as a function of ${p_\mathrm {T}^\text {Ref}}$ for Calo jets (left) and PF jets (right).
png pdf	Figure 14-a: Absolute difference in jet energy response between quark and gluon jets as a function of ${p_\mathrm {T}^\text {Ref}}$ for Calo jets.
png pdf	Figure 14-b: Absolute difference in jet energy response between quark and gluon jets as a function of ${p_\mathrm {T}^\text {Ref}}$ for PF jets.
png pdf	Figure 15: Relative ${ {p_{\mathrm {T}}} ^\text {miss}}$ resolution and resolution on the ${{\vec p}_{\mathrm {T}}}^{\, \text{miss}} $ direction as a function of $ {p_\text {T,Ref}^\text {miss}} $ for a simulated $ {\mathrm{ t } {}\mathrm{ \bar{t} } } $ sample.
png pdf	Figure 15-a: Relative ${ {p_{\mathrm {T}}} ^\text {miss}}$ resolution as a function of $ {p_\text {T,Ref}^\text {miss}} $ for a simulated $ {\mathrm{ t } {}\mathrm{ \bar{t} } } $ sample.
png pdf	Figure 15-b: Resolution on the ${{\vec p}_{\mathrm {T}}}^{\, \text{miss}} $ direction as a function of $ {p_\text {T,Ref}^\text {miss}} $ for a simulated $ {\mathrm{ t } {}\mathrm{ \bar{t} } } $ sample.
png pdf	Figure 16: Left: Efficiency to reconstruct electrons from b hadron decays (signal) versus the probability to misidentify a hadron as an electron (background). The solid, long-dashed, and short-dashed lines refer to electrons and hadrons with $ {p_{\mathrm {T}}} $ larger than 15, within $ [7,15]$, and lower than $7 GeV $, respectively. The curves correspond to a threshold scan on the BDT classifier score for ECAL-based seeded electrons and for tracker- or ECAL-based seeded electrons. Right: Absolute gain in reconstruction and identification efficiency provided by the tracker-based seeding procedure for two working points (WP) corresponding to different values of the threshold on the BDT classifier score. The solid line corresponds to the value used in the $\mathrm{ H } \to {\mathrm{ Z } } {\mathrm{ Z } } \to 4 \mathrm{ e } $ analyses and the dashed line to the value typically used in analyses of single-electron final states. In all cases, the classifier score of the BDT trained for electrons selected without any trigger requirement is used.
png pdf	Figure 16-a: Efficiency to reconstruct electrons from b hadron decays (signal) versus the probability to misidentify a hadron as an electron (background). The solid, long-dashed, and short-dashed lines refer to electrons and hadrons with $ {p_{\mathrm {T}}} $ larger than 15, within $ [7,15]$, and lower than $7 GeV $, respectively. The curves correspond to a threshold scan on the BDT classifier score for ECAL-based seeded electrons and for tracker- or ECAL-based seeded electrons.
png pdf	Figure 16-b: Absolute gain in reconstruction and identification efficiency provided by the tracker-based seeding procedure for two working points (WP) corresponding to different values of the threshold on the BDT classifier score. The solid line corresponds to the value used in the $\mathrm{ H } \to {\mathrm{ Z } } {\mathrm{ Z } } \to 4 \mathrm{ e } $ analyses and the dashed line to the value typically used in analyses of single-electron final states. In all cases, the classifier score of the BDT trained for electrons selected without any trigger requirement is used.
png pdf	Figure 17: Efficiency for different algorithms (PF, soft, and tight) to identify a simulated muon track that has been reconstructed as a tracker muon, as a function of the $ {p_{\mathrm {T}}} $ of the reconstructed track. From top left to bottom right the efficiency of the three identification algorithms is shown for prompt muons, for muons from heavy-flavour decays, for muons from light-flavour decays, and for misidentified hadrons.
png pdf	Figure 17-a: Efficiency for different algorithms (PF, soft, and tight) to identify a simulated muon track that has been reconstructed as a tracker muon, as a function of the $ {p_{\mathrm {T}}} $ of the reconstructed track. Here, the efficiency of the three identification algorithms is shown for prompt muons.
png pdf	Figure 17-b: Efficiency for different algorithms (PF, soft, and tight) to identify a simulated muon track that has been reconstructed as a tracker muon, as a function of the $ {p_{\mathrm {T}}} $ of the reconstructed track. Here, the efficiency of the three identification algorithms is shown
png pdf	Figure 17-c: Efficiency for different algorithms (PF, soft, and tight) to identify a simulated muon track that has been reconstructed as a tracker muon, as a function of the $ {p_{\mathrm {T}}} $ of the reconstructed track. Here, the efficiency of the three identification algorithms is shown for muons from light-flavour decays.
png pdf	Figure 17-d: Efficiency for different algorithms (PF, soft, and tight) to identify a simulated muon track that has been reconstructed as a tracker muon, as a function of the $ {p_{\mathrm {T}}} $ of the reconstructed track. Here, the efficiency of the three identification algorithms is shown for misidentified hadrons.
png pdf	Figure 18: Isolation efficiency for muons from $\mathrm{ W } $ boson decays versus isolation efficiency for muons from secondary decays, both obtained from simulated ${\mathrm{ t } {}\mathrm{ \bar{t} } } $ events, as a function of the threshold on the isolation for the detector- and particle-based methods. The efficiencies are shown for two choices of the maximum $\Delta R$ (isolation cone size): 0.3 and 0.5.
png pdf	Figure 19: Ratio of reconstructed-to-generator level $ {p_{\mathrm {T}}} $ for genuine $ {\tau _\mathrm {h}} $ (left), and for quark and gluon jets that pass the $ {\tau _\mathrm {h}} $ identification criteria (right), for different intervals in generator level $ {p_{\mathrm {T}}} $. In the PF $\tau $ case, the $ {\tau _\mathrm {h}} $ candidates are reconstructed by the HPS algorithm and required to pass the loose isolation working point. In the Calo $\tau $ case, they are reconstructed solely with the calorimeters and required to pass the $ {\tau _\mathrm {h}} $ identification criteria. The generator level $ {p_{\mathrm {T}}} $ is taken to be either that of the $ {\tau _\mathrm {h}} $ or that of the jet. For comparison, the ratio is also shown for the closest PF jet in the $(\eta, \varphi )$ plane.
png pdf	Figure 19-a: Ratio of reconstructed-to-generator level $ {p_{\mathrm {T}}} $ for genuine $ {\tau _\mathrm {h}} $ for 20 $ < {p_{\mathrm {T}}} < $ 100 GeV at generator level. In the PF $\tau $ case, the $ {\tau _\mathrm {h}} $ candidates are reconstructed by the HPS algorithm and required to pass the loose isolation working point. In the Calo $\tau $ case, they are reconstructed solely with the calorimeters and required to pass the $ {\tau _\mathrm {h}} $ identification criteria. The generator level $ {p_{\mathrm {T}}} $ is taken to be either that of the $ {\tau _\mathrm {h}} $ or that of the jet. For comparison, the ratio is also shown for the closest PF jet in the $(\eta, \varphi )$ plane.
png pdf	Figure 19-b: Ratio of reconstructed-to-generator level $ {p_{\mathrm {T}}} $ for quark and gluon jets that pass the $ {\tau _\mathrm {h}} $ identification criteria for 20 $ < {p_{\mathrm {T}}} < $ 100 GeV at generator level. In the PF $\tau $ case, the $ {\tau _\mathrm {h}} $ candidates are reconstructed by the HPS algorithm and required to pass the loose isolation working point. In the Calo $\tau $ case, they are reconstructed solely with the calorimeters and required to pass the $ {\tau _\mathrm {h}} $ identification criteria. The generator level $ {p_{\mathrm {T}}} $ is taken to be either that of the $ {\tau _\mathrm {h}} $ or that of the jet. For comparison, the ratio is also shown for the closest PF jet in the $(\eta, \varphi )$ plane.
png pdf	Figure 19-c: Ratio of reconstructed-to-generator level $ {p_{\mathrm {T}}} $ for genuine $ {\tau _\mathrm {h}} $ for 100 $ < {p_{\mathrm {T}}} < $ 200 GeV at generator level. In the PF $\tau $ case, the $ {\tau _\mathrm {h}} $ candidates are reconstructed by the HPS algorithm and required to pass the loose isolation working point. In the Calo $\tau $ case, they are reconstructed solely with the calorimeters and required to pass the $ {\tau _\mathrm {h}} $ identification criteria. The generator level $ {p_{\mathrm {T}}} $ is taken to be either that of the $ {\tau _\mathrm {h}} $ or that of the jet. For comparison, the ratio is also shown for the closest PF jet in the $(\eta, \varphi )$ plane.
png pdf	Figure 19-d: Ratio of reconstructed-to-generator level $ {p_{\mathrm {T}}} $ for quark and gluon jets that pass the $ {\tau _\mathrm {h}} $ identification criteria for 100 $ < {p_{\mathrm {T}}} < $ 200 GeV at generator level. In the PF $\tau $ case, the $ {\tau _\mathrm {h}} $ candidates are reconstructed by the HPS algorithm and required to pass the loose isolation working point. In the Calo $\tau $ case, they are reconstructed solely with the calorimeters and required to pass the $ {\tau _\mathrm {h}} $ identification criteria. The generator level $ {p_{\mathrm {T}}} $ is taken to be either that of the $ {\tau _\mathrm {h}} $ or that of the jet. For comparison, the ratio is also shown for the closest PF jet in the $(\eta, \varphi )$ plane.
png pdf	Figure 19-e: Ratio of reconstructed-to-generator level $ {p_{\mathrm {T}}} $ for genuine $ {\tau _\mathrm {h}} $ for $ {p_{\mathrm {T}}} > $ 200 GeV at generator level. In the PF $\tau $ case, the $ {\tau _\mathrm {h}} $ candidates are reconstructed by the HPS algorithm and required to pass the loose isolation working point. In the Calo $\tau $ case, they are reconstructed solely with the calorimeters and required to pass the $ {\tau _\mathrm {h}} $ identification criteria. The generator level $ {p_{\mathrm {T}}} $ is taken to be either that of the $ {\tau _\mathrm {h}} $ or that of the jet. For comparison, the ratio is also shown for the closest PF jet in the $(\eta, \varphi )$ plane.
png pdf	Figure 19-f: Ratio of reconstructed-to-generator level $ {p_{\mathrm {T}}} $ for quark and gluon jets that pass the $ {\tau _\mathrm {h}} $ identification criteria for $ {p_{\mathrm {T}}} > $ 200 GeV at generator level. In the PF $\tau $ case, the $ {\tau _\mathrm {h}} $ candidates are reconstructed by the HPS algorithm and required to pass the loose isolation working point. In the Calo $\tau $ case, they are reconstructed solely with the calorimeters and required to pass the $ {\tau _\mathrm {h}} $ identification criteria. The generator level $ {p_{\mathrm {T}}} $ is taken to be either that of the $ {\tau _\mathrm {h}} $ or that of the jet. For comparison, the ratio is also shown for the closest PF jet in the $(\eta, \varphi )$ plane.
png pdf	Figure 20: Efficiency of the $ {\tau _\mathrm {h}} $ identification versus misidentification probability for quark and gluon jets. The efficiency is measured for $ {\tau _\mathrm {h}} $ produced at low $ {p_{\mathrm {T}}} $ in simulated $\mathrm{ Z } / {\gamma ^{*}} \to \tau \tau $ events (left), and at high $ {p_{\mathrm {T}}} $ in the decay of a heavy particle $\mathrm{ H } $(3.2 TeV) $\to \tau \tau $ events (right). The misidentification probability is measured for quark and gluon jets in simulated multijet events. The line is obtained by varying the threshold on the absolute isolation for PF $ {\tau _\mathrm {h}} $ identified with the HPS algorithm. On this curve, the three points indicate the loose, medium and tight isolation working points. The performance of the calorimeter-based $ {\tau _\mathrm {h}} $ identification is depicted by a square away from the line.
png pdf	Figure 20-a: Efficiency of the $ {\tau _\mathrm {h}} $ identification versus misidentification probability for quark and gluon jets. The efficiency is measured for $ {\tau _\mathrm {h}} $ produced at low $ {p_{\mathrm {T}}} $ in simulated $\mathrm{ Z } / {\gamma ^{*}} \to \tau \tau $ events. The misidentification probability is measured for quark and gluon jets in simulated multijet events. The line is obtained by varying the threshold on the absolute isolation for PF $ {\tau _\mathrm {h}} $ identified with the HPS algorithm. On this curve, the three points indicate the loose, medium and tight isolation working points. The performance of the calorimeter-based $ {\tau _\mathrm {h}} $ identification is depicted by a square away from the line.
png pdf	Figure 20-b: Efficiency of the $ {\tau _\mathrm {h}} $ identification versus misidentification probability for quark and gluon jets. The efficiency is measured for $ {\tau _\mathrm {h}} $ produced at high $ {p_{\mathrm {T}}} $ in the decay of a heavy particle $\mathrm{ H } $(3.2 TeV) $\to \tau \tau $ events. The misidentification probability is measured for quark and gluon jets in simulated multijet events. The line is obtained by varying the threshold on the absolute isolation for PF $ {\tau _\mathrm {h}} $ identified with the HPS algorithm. On this curve, the three points indicate the loose, medium and tight isolation working points. The performance of the calorimeter-based $ {\tau _\mathrm {h}} $ identification is depicted by a square away from the line.
png pdf	Figure 21: Identification efficiency for genuine $ {\tau _\mathrm {h}} $ (left), and $ {\tau _\mathrm {h}} $ misidentification probability for quark and gluon jets (right). Low-$ {p_{\mathrm {T}}} $ $ {\tau _\mathrm {h}} $ are obtained from simulated $\mathrm{ Z } / {\gamma ^{*}} \to \tau \tau $ events and high-$ {p_{\mathrm {T}}} $ $ {\tau _\mathrm {h}} $ from simulated $\mathrm{ H } $(3.2 TeV)$ \to \tau \tau $ events. Quark and gluon jets are obtained from simulated QCD multijet events. The $ {\tau _\mathrm {h}} $ are required to be reconstructed by the HPS (PF) algorithm, to have $ {p_{\mathrm {T}}} > $ 20 GeV and $ {| \eta | } < $ 2.3, and to satisfy the loose $ {\tau _\mathrm {h}} $ identification criteria.
png pdf	Figure 21-a: Identification efficiency for genuine $ {\tau _\mathrm {h}} $. Low-$ {p_{\mathrm {T}}} $ $ {\tau _\mathrm {h}} $ are obtained from simulated $\mathrm{ Z } / {\gamma ^{*}} \to \tau \tau $ events and high-$ {p_{\mathrm {T}}} $ $ {\tau _\mathrm {h}} $ from simulated $\mathrm{ H } $(3.2 TeV)$ \to \tau \tau $ events. Quark and gluon jets are obtained from simulated QCD multijet events. The $ {\tau _\mathrm {h}} $ are required to be reconstructed by the HPS (PF) algorithm, to have $ {p_{\mathrm {T}}} > $ 20 GeV and $ {| \eta | } < $ 2.3, and to satisfy the loose $ {\tau _\mathrm {h}} $ identification criteria.
png pdf	Figure 21-b: $ {\tau _\mathrm {h}} $ misidentification probability for quark and gluon jets. Low-$ {p_{\mathrm {T}}} $ $ {\tau _\mathrm {h}} $ are obtained from simulated $\mathrm{ Z } / {\gamma ^{*}} \to \tau \tau $ events and high-$ {p_{\mathrm {T}}} $ $ {\tau _\mathrm {h}} $ from simulated $\mathrm{ H } $(3.2 TeV)$ \to \tau \tau $ events. Quark and gluon jets are obtained from simulated QCD multijet events. The $ {\tau _\mathrm {h}} $ are required to be reconstructed by the HPS (PF) algorithm, to have $ {p_{\mathrm {T}}} > $ 20 GeV and $ {| \eta | } < $ 2.3, and to satisfy the loose $ {\tau _\mathrm {h}} $ identification criteria.
png pdf	Figure 22: Left: Probability to find at HLT a jet with $ {p_{\mathrm {T}}} >40 GeV $ matching the jet reconstructed offline, as a function of the offline jet $ {p_{\mathrm {T}}} $. At the threshold, the curve is steeper for HLT PF jets (circles) than for HLT calorimeter jets (squares). Right: Probability to find a $ {\tau _\mathrm {h}} $ with $ {p_{\mathrm {T}}} >20 GeV $ at HLT matching the $ {\tau _\mathrm {h}} $ reconstructed and identified offline with the loose isolation working point.
png pdf	Figure 22-a: Probability to find at HLT a jet with $ {p_{\mathrm {T}}} >40 GeV $ matching the jet reconstructed offline, as a function of the offline jet $ {p_{\mathrm {T}}} $. At the threshold, the curve is steeper for HLT PF jets (circles) than for HLT calorimeter jets (squares).
png pdf	Figure 22-b: Probability to find a $ {\tau _\mathrm {h}} $ with $ {p_{\mathrm {T}}} >20 GeV $ at HLT matching the $ {\tau _\mathrm {h}} $ reconstructed and identified offline with the loose isolation working point.
png pdf	Figure 23: Jet energy composition in observed and simulated events as a function of $ {p_{\mathrm {T}}} $ (top left), $\eta $ (top right), and number of pileup interactions (bottom). The top panels show the measured and simulated energy fractions stacked, whereas the bottom panels show the difference between observed and simulated events. Charged hadrons associated with pileup vertices are denoted as charged PU hadrons.
png pdf	Figure 23-a: Jet energy composition in observed and simulated events as a function of $ {p_{\mathrm {T}}} $. The top panels show the measured and simulated energy fractions stacked, whereas the bottom panels show the difference between observed and simulated events. Charged hadrons associated with pileup vertices are denoted as charged PU hadrons.
png pdf	Figure 23-b: Jet energy composition in observed and simulated events as a function of $\eta $. The top panels show the measured and simulated energy fractions stacked, whereas the bottom panels show the difference between observed and simulated events. Charged hadrons associated with pileup vertices are denoted as charged PU hadrons.
png pdf	Figure 23-c: Jet energy composition in observed and simulated events as a function of the number of pileup interactions. The top panels show the measured and simulated energy fractions stacked, whereas the bottom panels show the difference between observed and simulated events. Charged hadrons associated with pileup vertices are denoted as charged PU hadrons.
png pdf	Figure 24: Jet $ {p_{\mathrm {T}}} $ resolution for PF+CHS jets (open markers) and PF jets (full markers) under three different pileup conditions (left), and jet energy resolution parameters (right). The jet $ {p_{\mathrm {T}}} $ resolution is shown as a function of $ {p_{\mathrm {T}}} ^\text {Ref}$. The jet energy resolution parameters are shown as a function of the number of pileup interactions $\mu $ times the jet area $A$ for PF jets and PF+CHS jets. The three resolution parameters are determined in bins of $\mu $ for various radius parameters $R$, and then averaged in bins of $\mu A$.
png pdf	Figure 24-a: Jet $ {p_{\mathrm {T}}} $ resolution for PF+CHS jets (open markers) and PF jets (full markers) under three different pileup conditions. The jet $ {p_{\mathrm {T}}} $ resolution is shown as a function of $ {p_{\mathrm {T}}} ^\text {Ref}$. The jet energy resolution parameters are shown as a function of the number of pileup interactions $\mu $ times the jet area $A$ for PF jets and PF+CHS jets. The three resolution parameters are determined in bins of $\mu $ for various radius parameters $R$, and then averaged in bins of $\mu A$.
png pdf	Figure 24-b: Jet $ {p_{\mathrm {T}}} $ resolution for PF+CHS jets (open markers) and PF jets (full markers) under three different jet energy resolution parameters. The jet $ {p_{\mathrm {T}}} $ resolution is shown as a function of $ {p_{\mathrm {T}}} ^\text {Ref}$. The jet energy resolution parameters are shown as a function of the number of pileup interactions $\mu $ times the jet area $A$ for PF jets and PF+CHS jets. The three resolution parameters are determined in bins of $\mu $ for various radius parameters $R$, and then averaged in bins of $\mu A$.
png pdf	Figure 25: Ratio of PF jet multiplicity with and without application of CHS, for hard jets, pileup jets, and soft jets, as a function of the reconstructed jet pseudorapidity. The uncertainty bands include both statistical uncertainties and uncertainties in the jet energy corrections.
png pdf	Figure 26: Spectrum of PF $ { {p_{\mathrm {T}}} ^\text {miss}} $ in a $\mathrm{ Z } \to \mu \mu $ data set [47]. The observed data are compared to simulated $\mathrm{ Z } \to \mu \mu $, diboson (VV), and ${\mathrm{ t } {}\mathrm{ \bar{t} } } $ plus single top quark events. The lower panel shows the ratio of data to simulation, with the uncertainty bars of the points including the statistical uncertainties of both observed and simulated events and the grey uncertainty band displaying the systematic uncertainty in the simulation. The last bin contains the overflow.
png pdf	Figure 27: Comparison of the average response of the parallel recoil component, $- < u_{\text{parallel}} > q_\mathrm {T}$, for the PF ${{\vec p}_{\mathrm {T}}}^{\, \text{miss}} $, No-PU PF ${{\vec p}_{\mathrm {T}}}^{\, \text{miss}} $, and MVA PF ${{\vec p}_{\mathrm {T}}}^{\, \text{miss}} $ (denoted as MVA Unity PF ${E_{\mathrm {T}}}$) algorithms as a function of $q_\mathrm {T}$, as determined in $\mathrm{ Z } \to \mu \mu $ events.
png pdf	Figure 28: Comparison of the resolutions of the parallel (left) and perpendicular (right) recoil components for the PF ${{\vec p}_{\mathrm {T}}}^{\, \text{miss}} $, No-PU PF ${{\vec p}_{\mathrm {T}}}^{\, \text{miss}} $, and MVA PF ${{\vec p}_{\mathrm {T}}}^{\, \text{miss}} $ algorithms as a function of the number of reconstructed vertices in $\mathrm{ Z } \to \mu \mu $ events [47]. The upper frame of each figure shows the resolution in observed events; the lower frame shows the ratio of data to simulation.
png pdf	Figure 28-a: Comparison of the resolutions of the parallel recoil components for the PF ${{\vec p}_{\mathrm {T}}}^{\, \text{miss}} $, No-PU PF ${{\vec p}_{\mathrm {T}}}^{\, \text{miss}} $, and MVA PF ${{\vec p}_{\mathrm {T}}}^{\, \text{miss}} $ algorithms as a function of the number of reconstructed vertices in $\mathrm{ Z } \to \mu \mu $ events [47]. The upper frame of the figure shows the resolution in observed events; the lower frame shows the ratio of data to simulation.
png pdf	Figure 28-b: Comparison of the resolutions of the perpendicular recoil components for the PF ${{\vec p}_{\mathrm {T}}}^{\, \text{miss}} $, No-PU PF ${{\vec p}_{\mathrm {T}}}^{\, \text{miss}} $, and MVA PF ${{\vec p}_{\mathrm {T}}}^{\, \text{miss}} $ algorithms as a function of the number of reconstructed vertices in $\mathrm{ Z } \to \mu \mu $ events [47]. The upper frame of the figure shows the resolution in observed events; the lower frame shows the ratio of data to simulation.
png pdf	Figure 29: Efficiency of the PF muon identification for muons from ${\mathrm{ Z } } $boson decays as a function of $ {p_{\mathrm {T}}} $ (top left), $\eta $ (top right), and $N_\text {vtx}$ (bottom).
png pdf	Figure 29-a: Efficiency of the PF muon identification for muons from ${\mathrm{ Z } } $boson decays as a function of $ {p_{\mathrm {T}}} $.
png pdf	Figure 29-b: Efficiency of the PF muon identification for muons from ${\mathrm{ Z } } $boson decays as a function of $\eta $.
png pdf	Figure 29-c: Efficiency of the PF muon identification for muons from ${\mathrm{ Z } } $boson decays as a function of $N_\text {vtx}$.
png pdf	Figure 30: Isolation efficiency for muons from ${\mathrm{ Z } } $ boson decays as a function of $ {p_{\mathrm {T}}} $ (left) and $N_\text {vtx}$ (right).
png pdf	Figure 30-a: Isolation efficiency for muons from ${\mathrm{ Z } } $ boson decays as a function of $ {p_{\mathrm {T}}} $.
png pdf	Figure 30-b: Isolation efficiency for muons from ${\mathrm{ Z } } $ boson decays as a function of $N_\text {vtx}$.
png pdf	Figure 31: Efficiency for hadronic $\tau $ decays to pass the loose, medium and tight working points of the HPS $ {\tau _\mathrm {h}} $ identification algorithm, as measured with the tag-and-probe technique in recorded and simulated $\mathrm{ Z } / {\gamma ^{*}} \to \tau \tau $ events [52]. The efficiency is presented as a function of $ {\tau _\mathrm {h}} $ $ {p_{\mathrm {T}}} $ (left), and as function of the reconstructed vertex multiplicity (right).
png pdf	Figure 31-a: Efficiency for hadronic $\tau $ decays to pass the loose, medium and tight working points of the HPS $ {\tau _\mathrm {h}} $ identification algorithm, as measured with the tag-and-probe technique in recorded and simulated $\mathrm{ Z } / {\gamma ^{*}} \to \tau \tau $ events [52]. The efficiency is presented as a function of $ {\tau _\mathrm {h}} $ $ {p_{\mathrm {T}}} $.
png pdf	Figure 31-b: Efficiency for hadronic $\tau $ decays to pass the loose, medium and tight working points of the HPS $ {\tau _\mathrm {h}} $ identification algorithm, as measured with the tag-and-probe technique in recorded and simulated $\mathrm{ Z } / {\gamma ^{*}} \to \tau \tau $ events [52]. The efficiency is presented as a function of the reconstructed vertex multiplicity.
png pdf	Figure 32: Probability for quark and gluon jets to pass the $ {\tau _\mathrm {h}} $ reconstruction and $ {\tau _\mathrm {h}} $ isolation criteria as a function of jet $ {p_{\mathrm {T}}} $ (left) and number of reconstructed vertices (right) [52]. The misidentification rates measured in QCD multijet data are compared to the simulation.
png pdf	Figure 32-a: Probability for quark and gluon jets to pass the $ {\tau _\mathrm {h}} $ reconstruction and $ {\tau _\mathrm {h}} $ isolation criteria as a function of jet $ {p_{\mathrm {T}}} $ [52]. The misidentification rates measured in QCD multijet data are compared to the simulation.
png pdf	Figure 32-b: Probability for quark and gluon jets to pass the $ {\tau _\mathrm {h}} $ reconstruction and $ {\tau _\mathrm {h}} $ isolation criteria as a function of number of reconstructed vertices [52]. The misidentification rates measured in QCD multijet data are compared to the simulation.
png pdf	Figure 33: Distribution of reconstructed $\tau $ decay mode (left) and of $ {\tau _\mathrm {h}} $ mass (right) in $\mathrm{ Z } / {\gamma ^{*}} \to \tau \tau $ events selected in data compared to the MC expectation. The $\mathrm{ Z } / {\gamma ^{*}} \to \tau \tau $ events are selected in the decay channel with a muon and a $ {\tau _\mathrm {h}} $. The $ {\tau _\mathrm {h}} $ is required to be reconstructed in one of the three allowed decay modes and to be isolated [52].
png pdf	Figure 33-a: Distribution of reconstructed $\tau $ decay mode in $\mathrm{ Z } / {\gamma ^{*}} \to \tau \tau $ events selected in data compared to the MC expectation. The $\mathrm{ Z } / {\gamma ^{*}} \to \tau \tau $ events are selected in the decay channel with a muon and a $ {\tau _\mathrm {h}} $. The $ {\tau _\mathrm {h}} $ is required to be reconstructed in one of the three allowed decay modes and to be isolated [52].
png pdf	Figure 33-b: Distribution of $ {\tau _\mathrm {h}} $ mass in $\mathrm{ Z } / {\gamma ^{*}} \to \tau \tau $ events selected in data compared to the MC expectation. The $\mathrm{ Z } / {\gamma ^{*}} \to \tau \tau $ events are selected in the decay channel with a muon and a $ {\tau _\mathrm {h}} $. The $ {\tau _\mathrm {h}} $ is required to be reconstructed in one of the three allowed decay modes and to be isolated [52].

Tables
png pdf	Table 1: Seeding configuration and targeted tracks of the ten tracking iterations. In the last column, $R$ is the targeted distance between the track production position and the beam axis.
png pdf	Table 2: Clustering parameters for the ECAL, the HCAL, and the preshower. All values result from optimizations based on the simulation of single photons, $\pi ^{0}$, $\mathrm {K}^{0}_\mathrm {L}$, and jets.
png pdf	Table 3: Branching fraction $\mathcal {B}$ of the main (negative) $\tau $ decay modes [50]. The generic symbol $ {\mathrm{h} ^-} $ represents a charged hadron, pion or kaon. In some cases, the decay products arise from an intermediate mesonic resonance.
png pdf	Table 4: Correlation between the reconstructed and generated decay modes, for $ {\tau _\mathrm {h}} $ produced in simulated $\mathrm{ Z } / {\gamma ^{*}} \to \tau \tau $ events. Reconstructed $ {\tau _\mathrm {h}} $ candidates are required to be matched to a generated $ {\tau _\mathrm {h}} $, to be reconstructed with $ {p_{\mathrm {T}}} > 20$ GeV and $ {| \eta | } < 2.3$ under one of the HPS decay modes, and to satisfy the loose isolation working point.

Summary

The CMS detector was designed 20 years ago to identify energetic and isolated leptons and photons and measure their momenta with high precision, to provide a calorimetric determination of jets and missing transverse momentum, and to efficiently tag b quark jets. The CMS detector turned out to feature properties well-suited for particle-flow (PF) reconstruction. For the first time in a hadron collider experiment, a PF algorithm aimed at identifying and reconstructing all final-state particles was implemented. The technical challenges posed by the complexity of proton-proton collisions and the amount of material in the tracker were overcome with the development of new, high-performance reconstruction algorithms in the different subdetectors, and of discriminating particle identification algorithms combining their information. The PF reconstruction computing time was kept under control both for offline data processing and for triggering the data acquisition, irrespective of the final state intricacy. The resulting global event description augmented the performance of all physics objects (efficiency, purity, response bias, energy and angular resolutions, etc.), thereby reducing the associated systematic biases and the need for a posteriori corrections. Knowledge of the detailed particle content of these physics objects enhanced the scope of many physics analyses. Excellent agreement was obtained between the simulation and the data recorded by CMS at a centre-of-mass energy of 8 TeV, thereby validating the use of PF reconstruction in real data-taking conditions. The PF approach also paved the way for particle-level pileup mitigation methods, the simplest of which have been presented in this paper for an average of 20 and up to 35 concurrent pileup interactions. Machine learning algorithms based on the detailed PF information were shown to preserve the improved physics object performance even in the presence of a large number of background particles produced in pileup interactions. The future CMS detector upgrades have been planned to provide optimal conditions for PF performance. In the first phase of the upgrade programme, a better and lighter pixel detector [64] will reduce the rate of misreconstructed charged-particle tracks, and the readout of multiple layers with low noise photodetectors in the hadron calorimeter [65] will improve the neutral-hadron identification, which currently limits the jet energy resolution. The second phase [66] will include a lighter and extended tracker (integrated into the level 1 trigger) and high-granularity endcap calorimeters, enhancing the PF capabilities for online and offline reconstruction. These detector evolutions, accompanied by the necessary PF software developments, should help to respond to the new challenges posed by the 200 pileup interactions foreseen at the LHC by the end of the next decade.

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