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The ALICE Collaboration reports a search for jet quenching effects in high-multiplicity (HM) proton−proton collisions at s√ = 13 TeV, using the semi-inclusive azimuthal-difference distribution Δφ of charged-particle jets recoiling from a high transverse momentum (high-pT,trig) trigger hadron. Jet quenching may broaden the Δφ distribution measured in HM events compared to that in minimum bias (MB) events. The measurement employs a pT,trig-differential observable for data-driven suppression of the contribution of multiple partonic interactions, which is the dominant background. While azimuthal broadening is indeed observed in HM compared to MB events, similar broadening for HM events is observed for simulations based on the PYTHIA 8 Monte Carlo generator, which does not incorporate jet quenching. We elucidate the origin of the broadening by comparing biases induced by HM selection in the data and simulations, and discuss its implications for the study of jet quenching in small collision systems.
The first measurements of K∗(892)0 resonance production as a function of charged-particle multiplicity in Xe−Xe collisions at sNN−−−√= 5.44 TeV and pp collisions at s√= 5.02 TeV using the ALICE detector are presented. The resonance is reconstructed at midrapidity (|y|<0.5) using the hadronic decay channel K∗0→K±π∓. Measurements of transverse-momentum integrated yield, mean transverse-momentum, nuclear modification factor of K∗0, and yield ratios of resonance to stable hadron (K∗0/K) are compared across different collision systems (pp, p−Pb, Xe−Xe, and Pb−Pb) at similar collision energies to investigate how the production of K∗0 resonances depends on the size of the system formed in these collisions. The hadronic rescattering effect is found to be independent of the size of colliding systems and mainly driven by the produced charged-particle multiplicity, which is a proxy of the volume of produced matter at the chemical freeze-out. In addition, the production yields of K∗0 in Xe−Xe collisions are utilized to constrain the dependence of the kinetic freeze-out temperature on the system size using HRG-PCE model.
Long- and short-range correlations for pairs of charged particles are studied via two-particle angular correlations in pp collisions at s√=13 TeV and p−Pb collisions at sNN−−−√=5.02 TeV. The correlation functions are measured as a function of relative azimuthal angle Δφ and pseudorapidity separation Δη for pairs of primary charged particles within the pseudorapidity interval |η|<0.9 and the transverse-momentum interval 1<pT<4 GeV/c. Flow coefficients are extracted for the long-range correlations (1.6<|Δη|<1.8) in various high-multiplicity event classes using the low-multiplicity template fit method. The method is used to subtract the enhanced yield of away-side jet fragments in high-multiplicity events. These results show decreasing flow signals toward lower multiplicity events. Furthermore, the flow coefficients for events with hard probes, such as jets or leading particles, do not exhibit any significant changes compared to those obtained from high-multiplicity events without any specific event selection criteria. The results are compared with hydrodynamic-model calculations, and it is found that a better understanding of the initial conditions is necessary to describe the results, particularly for low-multiplicity events.
Modification of charged-particle jets in event-shape engineered Pb–Pb collisions at √sNN = 5.02 TeV
(2023)
Charged-particle jet yields have been measured in semicentral Pb−Pb collisions at center-of-mass energy per nucleon-nucleon collision sNN−−−√=5 TeV with the ALICE detector at the LHC. These yields are reported as a function of the jet transverse momentum, and further classified by their angle with respect to the event plane and the event shape, characterized by ellipticity, in an effort to study the path-length dependence of jet quenching. Jets were reconstructed at midrapidity from charged-particle tracks using the anti-kT algorithm with resolution parameters R= 0.2 and 0.4, with event-plane angle and event-shape values determined using information from forward scintillating detectors. The results presented in this letter show that, in semicentral Pb−Pb collisions, there is no significant difference between jet yields in predominantly isotropic and elliptical events. However, out-of-plane jets are observed to be more suppressed than in-plane jets. Further, this relative suppression is greater for low transverse momentum (< 50 GeV/c) R= 0.2 jets produced in elliptical events, with out-of-plane to in-plane jet-yield ratios varying up to 5.2σ between different event-shape classes. These results agree with previous studies indicating that jets experience azimuthally anisotropic suppression when traversing the QGP medium, and can provide additional constraints on the path-length dependence of jet energy loss.
Results on the transverse spherocity dependence of light-flavor particle production (π, K, p, ϕ, K∗0, K0S, Λ, Ξ) at midrapidity in high-multiplicity pp collisions at s√=13 TeV were obtained with the ALICE apparatus. The transverse spherocity estimator (SpT=1O) categorizes events by their azimuthal topology. Utilizing narrow selections on SpT=1O, it is possible to contrast particle production in collisions dominated by many soft initial interactions with that observed in collisions dominated by one or more hard scatterings. Results are reported for two multiplicity estimators covering different pseudorapidity regions. The SpT=1O estimator is found to effectively constrain the hardness of the events when the midrapidity (|η|<0.8) estimator is used. The production rates of strange particles are found to be slightly higher for soft isotropic topologies, and severely suppressed in hard jet-like topologies. These effects are more pronounced for hadrons with larger mass and strangeness content, and observed when the topological selection is done within a narrow multiplicity interval. This demonstrates that an important aspect of the universal scaling of strangeness enhancement with final-state multiplicity is that high-multiplicity collisions are dominated by soft, isotropic processes. On the contrary, strangeness production in events with jet-like processes is significantly reduced. The results presented in this article are compared with several QCD-inspired Monte Carlo event generators. Models that incorporate a two-component phenomenology, either through mechanisms accounting for string density, or thermal production, are able to describe the observed strangeness enhancement as a function of SpT=1O.
A central motivation for the development of x-ray free-electron lasers has been the prospect of time-resolved single-molecule imaging with atomic resolution. Here, we show that x-ray photoelectron diffraction—where a photoelectron emitted after x-ray absorption illuminates the molecular structure from within—can be used to image the increase of the internuclear distance during the x-ray-induced fragmentation of an O2 molecule. By measuring the molecular-frame photoelectron emission patterns for a two-photon sequential K-shell ionization in coincidence with the fragment ions, and by sorting the data as a function of the measured kinetic energy release, we can resolve the elongation of the molecular bond by approximately 1.2 a.u. within the duration of the x-ray pulse. The experiment paves the road toward time-resolved pump-probe photoelectron diffraction imaging at high-repetition-rate x-ray free-electron lasers.
SARS-CoV-2 and stroke characteristics: a report from the Multinational COVID-19 Stroke Study Group
(2020)
Background: Stroke is reported as a consequence of SARS-CoV-2 infection. However, there is a lack of regarding comprehensive stroke phenotype and characteristics
Methods: We conducted a multinational observational study on features of consecutive acute ischemic stroke (AIS), intracranial hemorrhage (ICH), and cerebral venous or sinus thrombosis (CVST) among SARS-CoV-2 infected patients. We further investigated the association of demographics, clinical data, geographical regions, and countries’ health expenditure among AIS patients with the risk of large vessel occlusion (LVO), stroke severity as measured by National Institute of Health stroke scale (NIHSS), and stroke subtype as measured by the TOAST criteria. Additionally, we applied unsupervised machine learning algorithms to uncover possible similarities among stroke patients.
Results: Among the 136 tertiary centers of 32 countries who participated in this study, 71 centers from 17 countries had at least one eligible stroke patient. Out of 432 patients included, 323(74.8%) had AIS, 91(21.1%) ICH, and 18(4.2%) CVST. Among 23 patients with subarachnoid hemorrhage, 16(69.5%) had no evidence of aneurysm. A total of 183(42.4%) patients were women, 104(24.1%) patients were younger than 55 years, and 105(24.4%) patients had no identifiable vascular risk factors. Among 380 patients who had known interval onset of the SARS-CoV-2 and stroke, 144(37.8%) presented to the hospital with chief complaints of stroke-related symptoms, with asymptomatic or undiagnosed SARS-CoV-2 infection. Among AIS patients 44.5% had LVO; 10% had small artery occlusion according to the TOAST criteria. We observed a lower median NIHSS (8[3-17], versus 11 [5-17]; p=0.02) and higher rate of mechanical thrombectomy (12.4% versus 2%; p<0.001) in countries with middle to high-health expenditure when compared to countries with lower health expenditure. The unsupervised machine learning identified 4 subgroups, with a relatively large group with no or limited comorbidities.
Conclusions: We observed a relatively high number of young, and asymptomatic SARS-CoV-2 infections among stroke patients. Traditional vascular risk factors were absent among a relatively large cohort of patients. Among hospitalized patients, the stroke severity was lower and rate of mechanical thrombectomy was higher among countries with middle to high-health expenditure.
The Fire Modeling Intercomparison Project (FireMIP), phase 1: experimental and analytical protocols
(2016)
The important role of fire in regulating vegetation community composition and contributions to emissions of greenhouse gases and aerosols make it a critical component of dynamic global vegetation models and Earth system models. Over two decades of development, a wide variety of model structures and mechanisms have been designed and incorporated into global fire models, which have been linked to different vegetation models. However, there has not yet been a systematic examination of how these different strategies contribute to model performance. Here we describe the structure of the first phase of the Fire Model Intercomparison Project (FireMIP), which for the first time seeks to systematically compare a number of models. By combining a standardized set of input data and model experiments with a rigorous comparison of model outputs to each other and to observations, we will improve the understanding of what drives vegetation fire, how it can best be simulated, and what new or improved observational data could allow better constraints on model behavior. Here we introduce the fire models used in the first phase of FireMIP, the simulation protocols applied, and the benchmarking system used to evaluate the models. The works published in this journal are distributed under the Creative Commons Attribution 3.0 License. This license does not affect the Crown copy-right work, which is re-usable under the Open Government Licence (OGL). The Creative Commons Attribution 3.0 License and the OGL are interoperable and do not conflict with, reduce, or limit each other.
Biomass burning impacts vegetation dynamics, biogeochemical cycling, atmospheric chemistry, and climate, with sometimes deleterious socio-economic impacts. Under future climate projections it is often expected that the risk of wildfires will increase. Our ability to predict the magnitude and geographic pattern of future fire impacts rests on our ability to model fire regimes, using either well-founded empirical relationships or process-based models with good predictive skill. While a large variety of models exist today, it is still unclear which type of model or degree of complexity is required to model fire adequately at regional to global scales. This is the central question underpinning the creation of the Fire Model Intercomparison Project (FireMIP), an international initiative to compare and evaluate existing global fire models against benchmark data sets for present-day and historical conditions. In this paper we review how fires have been represented in fire-enabled dynamic global vegetation models (DGVMs) and give an overview of the current state of the art in fire-regime modelling. We indicate which challenges still remain in global fire modelling and stress the need for a comprehensive model evaluation and outline what lessons may be learned from FireMIP.
Biomass burning impacts vegetation dynamics, biogeochemical cycling, atmospheric chemistry, and climate, with sometimes deleterious socio-economic impacts. Under future climate projections it is often expected that the risk of wildfires will increase. Our ability to predict the magnitude and geographic pattern of future fire impacts rests on our ability to model fire regimes, either using well-founded empirical relationships or process-based models with good predictive skill. A large variety of models exist today and it is still unclear which type of model or degree of complexity is required to model fire adequately at regional to global scales. This is the central question underpinning the creation of the Fire Model Intercomparison Project - FireMIP, an international project to compare and evaluate existing global fire models against benchmark data sets for present-day and historical conditions. In this paper we summarise the current state-of-the-art in fire regime modelling and model evaluation, and outline what lessons may be learned from FireMIP.