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Institute
- Frankfurt Institute for Advanced Studies (FIAS) (1580) (remove)
Measurements of the production cross sections of prompt D0, D+, D∗+, D+s, Λ+c, and Ξ+c charm hadrons at midrapidity in proton−proton collisions at s√=13 TeV with the ALICE detector are presented. The D-meson cross sections as a function of transverse momentum (pT) are provided with improved precision and granularity. The ratios of pT-differential meson production cross sections based on this publication and on measurements at different rapidity and collision energy provide a constraint on gluon parton distribution functions at low values of Bjorken-x (10−5−10−4). The measurements of Λ+c (Ξ+c) baryon production extend the measured pT intervals down to pT=0(3)~GeV/c. These measurements are used to determine the charm-quark fragmentation fractions and the cc¯¯ production cross section at midrapidity (|y|<0.5) based on the sum of the cross sections of the weakly-decaying ground-state charm hadrons D0, D+, D+s, Λ+c, Ξ0c and, for the first time, Ξ+c, and of the strongly-decaying J/psi mesons. The first measurements of Ξ+c and Σ0,++c fragmentation fractions at midrapidity are also reported. A significantly larger fraction of charm quarks hadronising to baryons is found compared to e+e− and ep collisions. The cc¯¯ production cross section at midrapidity is found to be at the upper bound of state-of-the-art perturbative QCD calculations.
The Chiral Magnetic Wave (CMW) phenomenon is essential to provide insights into the strong interaction in QCD, the properties of the quark-gluon plasma, and the topological characteristics of the early universe, offering a deeper understanding of fundamental physics in high-energy collisions. Measurements of the charge-dependent anisotropic flow coefficients are studied in Pb-Pb collisions at center-of-mass energy per nucleon-nucleon collision sNN−−−√= 5.02 TeV to probe the CMW. In particular, the slope of the normalized difference in elliptic (v2) and triangular (v3) flow coefficients of positively and negatively charged particles as a function of their event-wise normalized number difference, is reported for inclusive and identified particles. The slope rNorm3 is found to be larger than zero and to have a magnitude similar to rNorm2, thus pointing to a large background contribution for these measurements. Furthermore, rNorm2 can be described by a blast wave model calculation that incorporates local charge conservation. In addition, using the event shape engineering technique yields a fraction of CMW (fCMW) contribution to this measurement which is compatible with zero. This measurement provides the very first upper limit for fCMW, and in the 10-60% centrality interval it is found to be 26% (38%) at 95% (99.7%) confidence level.
Measurements of charged-particle production in pp, p−Pb, and Pb−Pb collisions in the toward, away, and transverse regions with the ALICE detector are discussed. These regions are defined event-by-event relative to the azimuthal direction of the charged trigger particle, which is the reconstructed particle with the largest transverse momentum (ptrigT) in the range 8<ptrigT<15 GeV/c. The toward and away regions contain the primary and recoil jets, respectively; both regions are accompanied by the underlying event (UE). In contrast, the transverse region perpendicular to the direction of the trigger particle is dominated by the so-called UE dynamics, and includes also contributions from initial- and final-state radiation. The relative transverse activity classifier, RT=NTch/⟨NTch⟩, is used to group events according to their UE activity, where NTch is the charged-particle multiplicity per event in the transverse region and ⟨NTch⟩ is the mean value over the whole analysed sample. The energy dependence of the RT distributions in pp collisions at s√=2.76, 5.02, 7, and 13 TeV is reported, exploring the Koba-Nielsen-Olesen (KNO) scaling properties of the multiplicity distributions. The first measurements of charged-particle pT spectra as a function of RT in the three azimuthal regions in pp, p−Pb, and Pb−Pb collisions at sNN−−−√=5.02 TeV are also reported. Data are compared with predictions obtained from the event generators PYTHIA 8 and EPOS LHC. This set of measurements is expected to contribute to the understanding of the origin of collective-like effects in small collision systems (pp and p−Pb).
Measurements of charged-particle production in pp, p−Pb, and Pb−Pb collisions in the toward, away, and transverse regions with the ALICE detector are discussed. These regions are defined event-by-event relative to the azimuthal direction of the charged trigger particle, which is the reconstructed particle with the largest transverse momentum (ptrigT) in the range 8<ptrigT<15 GeV/c. The toward and away regions contain the primary and recoil jets, respectively; both regions are accompanied by the underlying event (UE). In contrast, the transverse region perpendicular to the direction of the trigger particle is dominated by the so-called UE dynamics, and includes also contributions from initial- and final-state radiation. The relative transverse activity classifier, RT=NTch/⟨NTch⟩, is used to group events according to their UE activity, where NTch is the charged-particle multiplicity per event in the transverse region and ⟨NTch⟩ is the mean value over the whole analysed sample. The energy dependence of the RT distributions in pp collisions at s√=2.76, 5.02, 7, and 13 TeV is reported, exploring the Koba-Nielsen-Olesen (KNO) scaling properties of the multiplicity distributions. The first measurements of charged-particle pT spectra as a function of RT in the three azimuthal regions in pp, p−Pb, and Pb−Pb collisions at sNN−−−√=5.02 TeV are also reported. Data are compared with predictions obtained from the event generators PYTHIA 8 and EPOS LHC. This set of measurements is expected to contribute to the understanding of the origin of collective-like effects in small collision systems (pp and p−Pb).
The elliptic flow (v2) of D0 mesons from beauty-hadron decays (non-prompt D0) was measured in midcentral (30-50%) Pb-Pb collisions at a centre-of-mass energy per nucleon pair sNN−−−√ = 5.02 TeV with the ALICE detector at the LHC. The D0 mesons were reconstructed at midrapidity (|y|<0.8) from their hadronic decay D0→K−π+, in the transverse momentum interval 2<pT<12 GeV/c. The result indicates a positive v2 for non-prompt D0 mesons with a significance of 2.7σ. The non-prompt D0-meson v2 is lower than that of prompt non-strange D mesons with 3.2σ significance in 2<pT<8 GeV/c, and compatible with the v2 of beauty-decay electrons. Theoretical calculations of beauty-quark transport in a hydrodynamically expanding medium describe the measurement within uncertainties.
Correlations in azimuthal angle extending over a long range in pseudorapidity between particles, usually called the "ridge" phenomenon, were discovered in heavy-ion collisions, and later found in pp and p−Pb collisions. In large systems, they are thought to arise from the expansion (collective flow) of the produced particles. Extending these measurements over a wider range in pseudorapidity and final-state particle multiplicity is important to understand better the origin of these long-range correlations in small-collision systems. In this Letter, measurements of the long-range correlations in p−Pb collisions at sNN−−−√=5.02 TeV are extended to a pseudorapidity gap of Δη∼8 between particles using the ALICE, forward multiplicity detectors. After suppressing non-flow correlations, e.g., from jet and resonance decays, the ridge structure is observed to persist up to a very large gap of Δη∼8 for the first time in p−Pb collisions. This shows that the collective flow-like correlations extend over an extensive pseudorapidity range also in small-collision systems such as p−Pb collisions. The pseudorapidity dependence of the second-order anisotropic flow coefficient, v2({\eta}), is extracted from the long-range correlations. The v2(η) results are presented for a wide pseudorapidity range of −3.1<η<4.8 in various centrality classes in p−Pb collisions. To gain a comprehensive understanding of the source of anisotropic flow in small-collision systems, the v2(η) measurements are compared to hydrodynamic and transport model calculations. The comparison suggests that the final-state interactions play a dominant role in developing the anisotropic flow in small-collision systems.
Highlights
• We present the first results of a deep learning model based on a convolutional neural network for earthquake magnitude estimation, using HR-GNSS displacement time series.
• The influence of different dataset configurations, such as station numbers, epicentral distances, signal duration, and earthquake size, were analyzed to figure out how the model can be adapted to various scenarios.
• The model was tested using real data from different regions and magnitudes, resulting in the best cases with 0.09 ≤ RMS ≤ 0.33.
Abstract
High-rate Global Navigation Satellite System (HR-GNSS) data can be highly useful for earthquake analysis as it provides continuous high-frequency measurements of ground motion. This data can be used to analyze diverse parameters related to the seismic source and to assess the potential of an earthquake to prompt strong motions at certain distances and even generate tsunamis. In this work, we present the first results of a deep learning model based on a convolutional neural network for earthquake magnitude estimation, using HR-GNSS displacement time series. The influence of different dataset configurations, such as station numbers, epicentral distances, signal duration, and earthquake size, were analyzed to figure out how the model can be adapted to various scenarios. We explored the potential of the model for global application and compared its performance using both synthetic and real data from different seismogenic regions. The performance of our model at this stage was satisfactory in estimating earthquake magnitude from synthetic data with 0.07 ≤ RMS ≤ 0.11. Comparable results were observed in tests using synthetic data from a different region than the training data, with RMS ≤ 0.15. Furthermore, the model was tested using real data from different regions and magnitudes, resulting in the best cases with 0.09 ≤ RMS ≤ 0.33, provided that the data from a particular group of stations had similar epicentral distance constraints to those used during the model training. The robustness of the DL model can be improved to work independently from the window size of the time series and the number of stations, enabling faster estimation by the model using only near-field data. Overall, this study provides insights for the development of future DL approaches for earthquake magnitude estimation with HR-GNSS data, emphasizing the importance of proper handling and careful data selection for further model improvements.
Residual connections have been proposed as an architecture-based inductive bias to mitigate the problem of exploding and vanishing gradients and increased task performance in both feed-forward and recurrent networks (RNNs) when trained with the backpropagation algorithm. Yet, little is known about how residual connections in RNNs influence their dynamics and fading memory properties. Here, we introduce weakly coupled residual recurrent networks (WCRNNs) in which residual connections result in well-defined Lyapunov exponents and allow for studying properties of fading memory. We investigate how the residual connections of WCRNNs influence their performance, network dynamics, and memory properties on a set of benchmark tasks. We show that several distinct forms of residual connections yield effective inductive biases that result in increased network expressivity. In particular, those are residual connections that (i) result in network dynamics at the proximity of the edge of chaos, (ii) allow networks to capitalize on characteristic spectral properties of the data, and (iii) result in heterogeneous memory properties. In addition, we demonstrate how our results can be extended to non-linear residuals and introduce a weakly coupled residual initialization scheme that can be used for Elman RNNs.
Determining the structure and mechanisms of all individual functional modules of cells at high molecular detail has often been seen as equal to understanding how cells work. Recent technical advances have led to a flush of high-resolution structures of various macromolecular machines, but despite this wealth of detailed information, our understanding of cellular function remains incomplete. Here, we discuss present-day limitations of structural biology and highlight novel technologies that may enable us to analyze molecular functions directly inside cells. We predict that the progression toward structural cell biology will involve a shift toward conceptualizing a 4D virtual reality of cells using digital twins. These will capture cellular segments in a highly enriched molecular detail, include dynamic changes, and facilitate simulations of molecular processes, leading to novel and experimentally testable predictions. Transferring biological questions into algorithms that learn from the existing wealth of data and explore novel solutions may ultimately unveil how cells work.
G protein-coupled receptors (GPCRs) play a crucial role in modulating physiological responses and serve as the main drug target. Specifically, salmeterol and salbutamol which are used for the treatment of pulmonary diseases, exert their effects by activating the GPCR β2-adrenergic receptor (β2AR). In our study, we employed coarse-grained molecular dynamics simulations with the Martini 3 force field to investigate the dynamics of drug molecules in membranes in presence and absence of β2AR. Our simulations reveal that in more than 50% of the flip-flop events the drug molecules use the β2AR surface to permeate the membrane. The pathway along the GPCR surface is significantly more energetically favorable for the drug molecules, which was revealed by umbrella sampling simulations along spontaneous flip-flop pathways. Furthermore, we assessed the behavior of drugs with intracellular targets, such as kinase inhibitors, whose therapeutic efficacy could benefit from this observation. In summary, our results show that β2AR surface interactions can significantly enhance membrane permeation of drugs, emphasizing their potential for consideration in future drug development strategies.