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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).
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.
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.
A considerable effort has been dedicated recently to the construction of generic equations of state (EOSs) for matter in neutron stars. The advantage of these approaches is that they can provide model-independent information on the interior structure and global properties of neutron stars. Making use of more than 106 generic EOSs, we assess the validity of quasi-universal relations of neutron-star properties for a broad range of rotation rates, from slow rotation up to the mass-shedding limit. In this way, we are able to determine with unprecedented accuracy the quasi-universal maximum-mass ratio between rotating and nonrotating stars and reveal the existence of a new relation for the surface oblateness, i.e., the ratio between the polar and equatorial proper radii. We discuss the impact that our findings have on the imminent detection of new binary neutron-star mergers and how they can be used to set new and more stringent limits on the maximum mass of nonrotating neutron stars, as well as to improve the modeling of the X-ray emission from the surface of rotating stars.
The production of K∗(892)± meson resonance is measured at midrapidity (|y|<0.5) in Pb−Pb collisions at √sNN=5.02 TeV using the ALICE detector at the CERN Large Hadron Collider. The resonance is reconstructed via its hadronic decay channel K∗(892)±→K0Sπ±. The transverse momentum distributions are obtained for various centrality intervals in the pT range of 0.4−16 GeV/c . Measurements of integrated yields, mean transverse momenta, and particle yield ratios are reported and found to be consistent with previous ALICE measurements for K∗(892)0 within uncertainties. The pT-integrated yield ratio 2K∗(892)±/(K++K−) in central Pb−Pb collisions shows a significant suppression at a level of 9.3σ relative to pp collisions. Thermal model calculations result in an overprediction of the particle yield ratio. Although both hadron resonance gas in partial chemical equilibrium (HRG-PCE) and music + smash simulations consider the hadronic phase, only HRG-PCE accurately represents the measurements, whereas music + smash simulations tend to overpredict the particle yield ratio. These observations, along with the kinetic freeze-out temperatures extracted from the yields measured for light-flavored hadrons using the HRG-PCE model, indicate a finite hadronic phase lifetime, which decreases with increasing collision centrality percentile. The pT-differential yield ratios 2K∗(892)±/(K++K−) and 2K∗(892)±/(π++π−) are presented and compared with measurements in pp collisions at √s=5.02 TeV. Both pa rticle ratios are found to be suppressed by up to a factor of five at pT<2.0 GeV/c in central Pb−Pb collisions and are qualitatively consistent with expectations for rescattering effects in the hadronic phase. The nuclear modification factor (RAA) shows a smooth evolution with centrality and is found to be below unity at pT>8 GeV/c, consistent with measurements for other light-flavored hadrons. The smallest values are observed in most central collisions, indicating larger energy loss of partons traversing the dense medium.
The inclusive production of the charm-strange baryon Ω0c is measured for the first time via its semileptonic decay into Ω−e+νe at midrapidity (|y| < 0.8) in proton–proton (pp) collisions at the centre-of-mass energy √s = 13 TeV with the ALICE detector at the LHC. The transverse momentum (pT) differential cross section multiplied by the branching ratio is presented in the interval 2 < pT < 12 GeV/c. The branching-fraction ratio BR(Ω0c → Ω−e+νe)/BR(Ω0c → Ω−π+) is measured to be 1.12 ± 0.22 (stat.) ± 0.27 (syst.). Comparisons with other experimental measurements, as well as with theoretical calculations, are presented.
We report results on an elastic cross section measurement in proton–proton collisions at a center-of-mass energy √𝑠 = 510 GeV, obtained with the Roman Pot setup of the STAR experiment at the Relativistic Heavy Ion Collider (RHIC). The elastic differential cross section is measured in the four-momentum transfer squared range 0.23 ≤ −𝑡 ≤ 0.67 GeV2. This is the only measurement of the proton-proton elastic cross section in this 𝑡 range for collision energies above the Intersecting Storage Rings (ISR) and below the Large Hadron Collider (LHC) colliders. We find that a constant slope 𝐵 does not fit the data in the aforementioned 𝑡 range, and we obtain a much better fit using a second-order polynomial for 𝐵(𝑡). This is the first measurement below the LHC energies for which the non-constant behavior 𝐵(𝑡) is observed. The 𝑡 dependence of 𝐵 is also determined using six subintervals of 𝑡 in the STAR measured 𝑡 range, and is in good agreement with the phenomenological models. The measured elastic differential cross section d𝜎∕dt agrees well with the results obtained at √𝑠 = 540 GeV for proton–antiproton collisions by the UA4 experiment. We also determine that the integrated elastic cross section within the STAR 𝑡-range is 𝜎f id el = 462.1 ± 0.9(stat.) ± 1.1(syst.) ± 11.6(scale) 𝜇b.