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The ALICE Collaboration reports the first fully-corrected measurements of the N-subjettiness observable for track-based jets in heavy-ion collisions. This study is performed using data recorded in pp and Pb−Pb collisions at centre-of-mass energies of √s=7 TeV and √sNN=2.76\,TeV, respectively. In particular the ratio of 2-subjettiness to 1-subjettiness, τ2/τ1, which is sensitive to the rate of two-pronged jet substructure, is presented. Energy loss of jets traversing the strongly interacting medium in heavy-ion collisions is expected to change the rate of two-pronged substructure relative to vacuum. The results are presented for jets with a resolution parameter of R=0.4 and charged jet transverse momentum of 40≤pT,jet≤60 GeV/c, which constitute a larger jet resolution and lower jet transverse momentum interval than previous measurements in heavy-ion collisions. This has been achieved by utilising a semi-inclusive hadron-jet coincidence technique to suppress the larger jet combinatorial background in this kinematic region. No significant modification of the τ2/τ1 observable for track-based jets in Pb--Pb collisions is observed relative to vacuum PYTHIA6 and PYTHIA8 references at the same collision energy. The measurements of τ2/τ1, together with the splitting aperture angle ΔR, are also performed in pp collisions at √s=7 TeV for inclusive jets. These results are compared with PYTHIA calculations at √s=7 TeV, in order to validate the model as a vacuum reference for the Pb−Pb centre-of-mass energy. The PYTHIA references for τ2/τ1 are shifted to larger values compared to the measurement in pp collisions. This hints at a reduction in the rate of two-pronged jets in Pb--Pb collisions compared to pp collisions.
The most precise measurements to date of the 3ΛH lifetime τ and Λ separation energy BΛ are obtained using the data sample of Pb-Pb collisions at √sNN = 5.02 TeV collected by ALICE at the LHC. The 3ΛH is reconstructed via its charged two-body mesonic decay channel (3ΛH → 3He + π− and the charge-conjugate process). The measured values τ = [253 ± 11 (stat) ± 6 (syst)] ps and BΛ = [102 ± 63 (stat) ± 67 (syst)] keV are compatible with predictions from effective field theories and confirm that the 3ΛH structure is consistent with a weakly bound system.
The most precise measurements to date of the 3ΛH lifetime τ and Λ separation energy BΛ are obtained using the data sample of Pb-Pb collisions at √= 5.02 TeV collected by ALICE at the LHC. The 3ΛH is reconsNN structed via its charged two-body mesonic decay channel (3ΛH→ 3He + π− and the charge-conjugate process). The measured values τ=[253±11 (stat.)±6 (syst.)] ps and BΛ=[102±63 (stat.)±67 (syst.)] keV are compatible with predictions from effective field theories and confirm that the 3ΛH structure is consistent with a weakly-bound system.
W±-boson production in p–Pb collisions at √sNN = 8.16 TeV and Pb–Pb collisions at √sNN = 5.02 TeV
(2023)
The production of the W± bosons measured in p–Pb collisions at a centreof-mass energy per nucleon–nucleon collision √sNN = 8.16 TeV and Pb–Pb collisions at √sNN = 5.02 TeV with ALICE at the LHC is presented. The W± bosons are measured via their muonic decay channel, with the muon reconstructed in the pseudorapidity region −4 < ηµ lab < −2.5 with transverse momentum p µ T > 10 GeV/c. While in Pb–Pb collisions the measurements are performed in the forward (2.5 < yµ cms < 4) rapidity region, in p–Pb collisions, where the centre-of-mass frame is boosted with respect to the laboratory frame, the measurements are performed in the backward (−4.46 < yµ cms < −2.96) and forward (2.03 < yµ cms < 3.53) rapidity regions. The W− and W+ production cross sections, leptoncharge asymmetry, and nuclear modification factors are evaluated as a function of the muon rapidity. In order to study the production as a function of the p–Pb collision centrality, the production cross sections of the W− and W+ bosons are combined and normalised to the average number of binary nucleon–nucleon collision hNcolli. In Pb–Pb collisions, the same measurements are presented as a function of the collision centrality. Study of the binary scaling of the W±-boson cross sections in p–Pb and Pb–Pb collisions is also reported. The results are compared with perturbative QCD calculations, with and without nuclear modifications of the Parton Distribution Functions (PDFs), as well as with available data at the LHC. Significant deviations from the theory expectations are found in the two collision systems, indicating that the measurements can provide additional constraints for the determination of nuclear PDFs and in particular of the light-quark distributions.
Objectives: This study identified potential general influencing factors for a mathematical prediction of implant stability quotient (ISQ) values in clinical practice.
Methods: We collected the ISQ values of 557 implants from 2 different brands (SICace and Osstem) placed by 2 surgeons in 336 patients. Surgeon 1 placed 329 SICace implants, and surgeon 2 placed 113 SICace implants and 115 Osstem implants. ISQ measurements were taken at T1 (immediately after implant placement) and T2 (before dental restoration). A multivariate linear regression model was used to analyze the influence of the following 11 candidate factors for stability prediction: sex, age, maxillary/mandibular location, bone type, immediate/delayed implantation, bone grafting, insertion torque, I-stage or II-stage healing pattern, implant diameter, implant length and T1-T2 time interval.
Results: The need for bone grafting as a predictor significantly influenced ISQ values in all three groups at T1 (weight coefficients ranging from -4 to -5). In contrast, implant diameter consistently influenced the ISQ values in all three groups at T2 (weight coefficients ranging from 3.4 to 4.2). Other factors, such as sex, age, I/II-stage implantation and bone type, did not significantly influence ISQ values at T2, and implant length did not significantly influence ISQ values at T1 or T2.
Conclusions: These findings provide a rational basis for mathematical models to quantitatively predict the ISQ values of implants in clinical practice.
A primordial state of matter consisting of free quarks and gluons that existed in the early universe a few microseconds after the Big Bang is also expected to form in high-energy heavy-ion collisions. Determining the equation of state (EoS) of such a primordial matter is the ultimate goal of high-energy heavy-ion experiments. Here we use supervised learning with a deep convolutional neural network to identify the EoS employed in the relativistic hydrodynamic simulations of heavy ion collisions. High-level correlations of particle spectra in transverse momentum and azimuthal angle learned by the network act as an effective EoS-meter in deciphering the nature of the phase transition in quantum chromodynamics. Such EoS-meter is model-independent and insensitive to other simulation inputs including the initial conditions for hydrodynamic simulations.
In this proceeding we review our recent work using supervised learning with a deep convolutional neural network (CNN) to identify the QCD equation of state (EoS) employed in hydrodynamic modeling of heavy-ion collisions given only final-state particle spectra ρ(pT, Ф). We showed that there is a traceable encoder of the dynamical information from phase structure (EoS) that survives the evolution and exists in the final snapshot, which enables the trained CNN to act as an effective “EoS-meter” in detecting the nature of the QCD transition.
The state-of-the-art pattern recognition method in machine learning (deep convolution neural network) is used to identify the equation of state (EoS) employed in the relativistic hydrodynamic simulations of heavy ion collisions. High-level correlations of particle spectra in transverse momentum and azimuthal angle learned by the network act as an effective EoS-meter in deciphering the nature of the phase transition in QCD. The EoS-meter is model independent and insensitive to other simulation inputs including the initial conditions and shear viscosity for hydrodynamic simulations. Through this study we demonstrate that there is a traceable encoder of the dynamical information from the phase structure that survives the evolution and exists in the final snapshot of heavy ion collisions and one can exclusively and effectively decode these information from the highly complex final output with machine learning when traditional methods fail. Besides the deep neural network, the performance of traditional machine learning classifiers are also provided.
In this proceeding, we review our recent work using deep convolutional neural network (CNN) to identify the nature of the QCD transition in a hybrid modeling of heavy-ion collisions. Within this hybrid model, a viscous hydrodynamic model is coupled with a hadronic cascade “after-burner”. As a binary classification setup, we employ two different types of equations of state (EoS) of the hot medium in the hydrodynamic evolution. The resulting final-state pion spectra in the transverse momentum and azimuthal angle plane are fed to the neural network as the input data in order to distinguish different EoS. To probe the effects of the fluctuations in the event-by-event spectra, we explore different scenarios for the input data and make a comparison in a systematic way. We observe a clear hierarchy in the predictive power when the network is fed with the event-by-event, cascade-coarse-grained and event-fine-averaged spectra. The carefully-trained neural network can extract high-level features from pion spectra to identify the nature of the QCD transition in a realistic simulation scenario.