Search

We propose a new observable derived from a centrality-dependent scaling of transverse particle spectra. By removing the global scales of total particle number and mean transverse momentum, we isolate the shape of the spectrum. In hydrodynamic simulations, while the multiplicity and mean transverse momentum fluctuate significantly, the scaled spectrum is found to be almost constant even at an event-by-event level and after resonance decays. This universality survives when averaging over events in each centrality bin before scaling. We then investigate the presence of this scaling in experimental data from the ALICE collaboration in Pb-Pb, Xe-Xe, and p-Pb collisions. We find a remarkable universality in the experimentally observed scaled spectra at low transverse momentum, compatible with hydrodynamic predictions. The data show a minor breaking of universality at large transverse momentum and hints of evolution with the system size that are not seen in simulations. Our results motivate further theoretical and experimental investigations of this new observable to bring to light the collective and non-collective behavior encoded in the transverse particle spectrum of different collision systems., Comment: V1: 6 pages, 3 figures

Metastable nanostructures are kinetically trapped in local energy minima featuring intriguing surface and material properties. To unleash their potential, there is a need for non-equilibrium processes capable of stabilizing a large range of crystal phases outside thermodynamic equilibrium conditions by closely and flexibly controlling atomic reactant composition, spatial temperature distribution and residence time. Here, we demonstrate the capture of metastable pseudo-binary metal oxides at room temperature with scalable combustion-aerosol processes. By a combination of X-ray diffraction, electron microscopy and online flame characterization, we investigate the occurrence of metastable CoCu$_2$O$_3$ with controlled crystal size (4-16 nm) over thermodynamically stable CuO and Co$_3$O$_4$. Immediate practical impact is demonstrated by exceptional sensing and catalytic performance for air pollutant detection (e.g., 15 parts-per-billion benzene). This approach can be extended to various binary, ternary and high entropy oxides with even more components to access novel materials also promising for actuators, energy storage or solar cells.

PLATO (PLAnetary Transits and Oscillations of stars) is ESA's M3 mission designed to detect and characterise extrasolar planets and perform asteroseismic monitoring of a large number of stars. PLATO will detect small planets (down to <2 R_(Earth)) around bright stars (<11 mag), including terrestrial planets in the habitable zone of solar-like stars. With the complement of radial velocity observations from the ground, planets will be characterised for their radius, mass, and age with high accuracy (5 %, 10 %, 10 % for an Earth-Sun combination respectively). PLATO will provide us with a large-scale catalogue of well-characterised small planets up to intermediate orbital periods, relevant for a meaningful comparison to planet formation theories and to better understand planet evolution. It will make possible comparative exoplanetology to place our Solar System planets in a broader context. In parallel, PLATO will study (host) stars using asteroseismology, allowing us to determine the stellar properties with high accuracy, substantially enhancing our knowledge of stellar structure and evolution. The payload instrument consists of 26 cameras with 12cm aperture each. For at least four years, the mission will perform high-precision photometric measurements. Here we review the science objectives, present PLATO's target samples and fields, provide an overview of expected core science performance as well as a description of the instrument and the mission profile at the beginning of the serial production of the flight cameras. PLATO is scheduled for a launch date end 2026. This overview therefore provides a summary of the mission to the community in preparation of the upcoming operational phases.

GFlowNets are a promising alternative to MCMC sampling for discrete compositional random variables. Training GFlowNets requires repeated evaluations of the unnormalized target distribution or reward function. However, for large-scale posterior sampling, this may be prohibitive since it incurs traversing the data several times. Moreover, if the data are distributed across clients, employing standard GFlowNets leads to intensive client-server communication. To alleviate both these issues, we propose embarrassingly parallel GFlowNet (EP-GFlowNet). EP-GFlowNet is a provably correct divide-and-conquer method to sample from product distributions of the form $R(\cdot) \propto R_1(\cdot) ... R_N(\cdot)$ -- e.g., in parallel or federated Bayes, where each $R_n$ is a local posterior defined on a data partition. First, in parallel, we train a local GFlowNet targeting each $R_n$ and send the resulting models to the server. Then, the server learns a global GFlowNet by enforcing our newly proposed \emph{aggregating balance} condition, requiring a single communication step. Importantly, EP-GFlowNets can also be applied to multi-objective optimization and model reuse. Our experiments illustrate the EP-GFlowNets's effectiveness on many tasks, including parallel Bayesian phylogenetics, multi-objective multiset, sequence generation, and federated Bayesian structure learning., Comment: Accepted to ICML 2024

Research on aging processes of Gas Electron Multipliers (GEMs) is important to obtain insights on how to increase the detector's longevity, stability, and performance, as highlighted in the latest developments roadmap by the European Council of Future Accelerators (ECFA). One key aspect of the aging process is the deposit formation on the electrodes surfaces. In this work, through the analysis of the molecular content on the surface of a used GEM, we provide evidence for polyimide redeposition as a source of organic material contributing to the formation of insulating layers on the electrodes, which eventually lead to sparks and detector failure. Furthermore, we show that chromium, used to promote adhesion between copper and polyimide, in the device undergoes a diffusion process, effectively blurring the layered structure. We demonstrate the significance of surface-sensitive chemical analysis to investigate the surface deposits on electrodes of gaseous detectors and our results reveal the necessity of standardization and more stringent study protocols., Comment: 19 pages and 10 figures

Particle production yields measured in central Au-Au collision at RHIC are obtained with free Fermi and Bose gases and also with a replacement of these statistics by non-extensive statistics. For the latter calculation, a set of different parameters was used with values of the Tsallis parameter $q$ chosen between 1.01 and 1.25, with 1.16 generating the best agreement with experimental data, an indication that non-extensive statistics may be one of the underlying features in heavy ion-collisions., Comment: 7 pages, 3 figures, 1 table

Heavy-ion collisions, such as Pb-Pb or p-Pb, produce extreme conditions in temperature and density that make the hadronic matter transition to a new state, called quark-gluon plasma (QGP). Simulations of heavy-ion collisions provide a way to improve our understanding of the QGP's properties. These simulations are composed of a hybrid description that results in final observables in agreement with accelerators like LHC and RHIC. However, recent works pointed out that these hydrodynamic simulations can display acausal behavior during the evolution in certain regions, indicating a deviation from a faithful representation of the underlying QCD dynamics. To pursue a better understanding of this problem and its consequences, this work simulated two different collision systems, Pb-Pb and p-Pb at $\sqrt{s_{NN}} = 5.02$ TeV. In this context, our results show that causality violation, even though always present, typically occurs on a small part of the system, quantified by the total energy fraction residing in the acausal region. In addition, the acausal behavior can be reduced with changes in the pre-hydrodynamic factors and the definition of the bulk-viscous relaxation time. Since these aspects are fairly arbitrary in current simulation models, without solid guidance from the underlying theory, it is reasonable to use the disturbing presence of acausal behavior in current simulations to guide improvements towards more realistic modeling. While this work does not solve the acausality problem, it sheds more light on this issue and also proposes a way to solve this problem in simulations of heavy-ion collisions., Comment: 15 pages, 17 figures

Structure learning is the crux of causal inference. Notably, causal discovery (CD) algorithms are brittle when data is scarce, possibly inferring imprecise causal relations that contradict expert knowledge -- especially when considering latent confounders. To aggravate the issue, most CD methods do not provide uncertainty estimates, making it hard for users to interpret results and improve the inference process. Surprisingly, while CD is a human-centered affair, no works have focused on building methods that both 1) output uncertainty estimates that can be verified by experts and 2) interact with those experts to iteratively refine CD. To solve these issues, we start by proposing to sample (causal) ancestral graphs proportionally to a belief distribution based on a score function, such as the Bayesian information criterion (BIC), using generative flow networks. Then, we leverage the diversity in candidate graphs and introduce an optimal experimental design to iteratively probe the expert about the relations among variables, effectively reducing the uncertainty of our belief over ancestral graphs. Finally, we update our samples to incorporate human feedback via importance sampling. Importantly, our method does not require causal sufficiency (i.e., unobserved confounders may exist). Experiments with synthetic observational data show that our method can accurately sample from distributions over ancestral graphs and that we can greatly improve inference quality with human aid.

This work presents a reaction mechanism for oxygen plasmas, i.e. a set of reactions and corresponding rate coefficients that are validated against benchmark experiments. The kinetic scheme is validated in a DC glow discharge for gas pressures of 0.2-10 Torr and currents of 10-40 mA, using the 0D LisbOn KInetics (LoKI) simulation tool and available experimental data. The comparison comprises not only the densities of the main species in the discharge - $\mathrm{O_2(X^3\Sigma_g^-)}$, $\mathrm{O_2(a^1\Delta_g)}$, $\mathrm{O_2(b^1\Sigma_g^+)}$ and $\mathrm{O(^3P)}$ - but also the self-consistent calculation of the reduced electric field and the gas temperature. The main processes involved in the creation and destruction of these species are identified. Moreover, the results show that the oxygen atoms play a dominant role in gas heating, via recombination at the wall and quenching of $\mathrm{O_2(X^3\Sigma_g^-,v)}$ vibrations and $\mathrm{O_2}$ electronically-excited states. It is argued that the development and validation of kinetic schemes for plasma chemistry should adopt a paradigm based on the comparison against standard validation tests, as it is done in electron swarm validation of cross sections.

The FC Portugal 3D team is developed upon the structure of our previous Simulation league 2D/3D teams and our standard platform league team. Our research concerning the robot low-level skills is focused on developing behaviors that may be applied on real robots with minimal adaptation using model-based approaches. Our research on high-level soccer coordination methodologies and team playing is mainly focused on the adaptation of previously developed methodologies from our 2D soccer teams to the 3D humanoid environment and on creating new coordination methodologies based on the previously developed ones. The research-oriented development of our team has been pushing it to be one of the most competitive over the years (World champion in 2000 and Coach Champion in 2002, European champion in 2000 and 2001, Coach 2nd place in 2003 and 2004, European champion in Rescue Simulation and Simulation 3D in 2006, World Champion in Simulation 3D in Bremen 2006 and European champion in 2007, 2012, 2013, 2014 and 2015). This paper describes some of the main innovations of our 3D simulation league team during the last years. A new generic framework for reinforcement learning tasks has also been developed. The current research is focused on improving the above-mentioned framework by developing new learning algorithms to optimize low-level skills, such as running and sprinting. We are also trying to increase student contact by providing reinforcement learning assignments to be completed using our new framework, which exposes a simple interface without sharing low-level implementation details.

The extreme conditions of temperature and density produced in ultrarelativistic collisions of heavy nuclei facilitate the formation of the most fundamental fluid in the universe, the deconfined phase of Quantum Chromodynamics called quark-gluon plasma. Despite the extensive experimental evidence collected over the past decade of its production in colliding systems such as Au-Au and Pb-Pb, establishing quark-gluon plasma formation in the collision of smaller systems, such as p-Pb, remains an open question. In this study, we describe the evolution of matter formed in p-Pb collisions at 5.02 TeV using a state-of-the-art hybrid model based on viscous relativistic hydrodynamics. We investigate the thermodynamic properties of the medium and final state observables. Our findings are compared with experimental data and first-principles calculations derived from lattice quantum chromodynamics. The results support the formation of a collective phase of strongly interacting matter in high-multiplicity p-Pb collisions., Comment: 12 pages, 4 figures; Published version

We extend our previous investigation of the effects of pre-hydrodynamic evolution on final-state observables in heavy-ion collisions to smaller systems. We use a state-of-the-art hybrid model for the numerical simulations with optimal parameters obtained from a previous Bayesian study. By studying p-Pb collisions, we find that the effects due to the assumption of a conformal evolution in the pre-hydrodynamical stage are even more important in small systems. We also show that this effect depends on the time duration of the pre-equilibrium stage, which is further enhanced in small systems. Finally, we show that the recent proposal of a free-streaming with subluminal velocity for the pre-equilibrium stage, thus effectively breaking conformal invariance, can alleviate the contamination of final state observables. Our study further reinforces the need for moving beyond conformal approaches in pre-equilibrium dynamics modeling, especially when extracting transport coefficients from hybrid models in the high-precision era of heavy-ion collisions., Comment: 15 pages, 14 figures

Rutherford Backscattering Spectrometry (RBS) is an important technique providing elemental information of the near surface region of samples with high accuracy and robustness. However, this technique lacks throughput by the limited rate of data processing and is hardly routinely applied in research with a massive number of samples (i.e. hundreds or even thousands of samples). The situation is even worse for complex samples. If roughness or porosity is present in those samples the simulation of such structures is computationally demanding. Fortunately, Artificial Neural Networks (ANN) show to be a great ally for massive data processing of ion beam data. In this paper, we report the performance comparison of ANN against human evaluation and an automatic fit routine running on batch mode. 500 spectra of marker layers from the stellarator W7-X were used as study case. The results showed ANN as more accurate than humans and more efficient than automatic fits., Comment: 15 pages, 8 figures

With a growing demand for accurate ion beam analysis on a large number of samples, it becomes an issue of how to ensure the quality standards and consistency over hundreds or thousands of samples. In this sense, a virtual assistant that checks the data quality, emitting certificates of quality, is highly desired. Even the processing of a massive number of spectra is a problem regarding the consistency of the analysis. In this work, we report the design and first results of a virtual layer under implementation in our laboratory. It consists of a series of systems running in the cloud that perform the mentioned tasks and serves as a virtual assistant for member staff and users. We aim to bring the concept of the Internet of Things and artificial intelligence closer to the laboratory to support a new generation of instrumentation., Comment: 9 pages, 4 figures, 2019 Ion Beam Analysis Conference

We study the applications of non-extensive Tsallis statistics to high energy and hadron physics. These applications include studies of $pp$ collisions, equation of state of QCD, as well as Bose-Einstein condensation. We also analyze the connections of Tsallis statistics with thermofractals, and address some of the conceptual aspects of the fractal approach, which are expressed in terms of the renormalization group equation and the self-energy corrections to the parton mass. We associate these well-known concepts with the origins of the fractal structure in the quantum field theory., Comment: 16 pages, 8 figures. Talk given by E.Megias at the 10th International Conference on New Frontiers in Physics (ICNFP 2021), 23 Aug - 7 Oct 2021, Kolymbari, Crete, Greece

Using multiple ion beam analysis measurements, or techniques, combined with self-consistent data processing, generally allows extracting more (or more accurate) information from the measurements than processing separately data from single measurements. Solving ambiguities, improving the final depth resolution, defining constraints and extending applicability are the main strengths of the data-fusion approach. It basically consists in formulating a multi-objective minimization problem that can be tackled by the adoption of the weighted-sum method. A simulation study is reported in order to evaluate the systematic error inserted in the analysis by the choice of a specific objective function, or even by the weights or normalization adopted in the weighted-sum method. We demonstrate that the bias of the analyzed objective functions asymptotically converges to the true value for better statistics. We also demonstrate that the joint analysis inherits the accuracy of the most accurate measurement, establishing a rank of information content, where some combinations of measurements are more valuable than others, i.e. when processed together they provide more information by means of a better constraint for the multi-objective optimization., Comment: 9 pages and 8 figures

Ion Beam Analysis (IBA) comprises a set of analytical techniques suited for material analysis, many of which are rather closely related. Self-consistent analysis of several IBA techniques takes advantage of this close relationship to combine different Ion Beam measurements in a unique model to obtain an improved characterization of the sample. This approach provides a powerful tool to obtain an unequivocal and reliable model of the sample, increasing confidence and reducing ambiguities. Taking advantage of the recognized reliability and quality of the simulations provided by SIMNRA, we developed a multi-process program for a self-consistent analysis based on SIMNRA calculations. MultiSIMNRA uses computational algorithms to minimize an objective function running multiple instances of SIMNRA. With four different optimization algorithms, the code can handle sample and setup parameters (including correlations and constraints), to find the set of parameters that best fits simultaneously all experimental data., Comment: 27 pages, 5 figures, research paper

In 2020, Brazil was the leading country in COVID-19 cases in Latin America, and capital cities were the most severely affected by the outbreak. Climates vary in Brazil due to the territorial extension of the country, its relief, geography, and other factors. Since the most common COVID-19 symptoms are related to the respiratory system, many researchers have studied the correlation between the number of COVID-19 cases with meteorological variables like temperature, humidity, rainfall, etc. Also, due to its high transmission rate, some researchers have analyzed the impact of human mobility on the dynamics of COVID-19 transmission. There is a dearth of literature that considers these two variables when predicting the spread of COVID-19 cases. In this paper, we analyzed the correlation between the number of COVID-19 cases and human mobility, and meteorological data in Brazilian capitals. We found that the correlation between such variables depends on the regions where the cities are located. We employed the variables with a significant correlation with COVID-19 cases to predict the number of COVID-19 infections in all Brazilian capitals and proposed a prediction method combining the Ensemble Empirical Mode Decomposition (EEMD) method with the Autoregressive Integrated Moving Average Exogenous inputs (ARIMAX) method, which we called EEMD-ARIMAX. After analyzing the results poor predictions were further investigated using a signal processing-based anomaly detection method. Computational tests showed that EEMD-ARIMAX achieved a forecast 26.73% better than ARIMAX. Moreover, an improvement of 30.69% in the average root mean squared error (RMSE) was noticed when applying the EEMD-ARIMAX method to the data normalized after the anomaly detection.

The connection between initial-state geometry and anisotropic flow can be quantified through a well-established mapping between $p_T$-integrated flow harmonics and cumulants of the initial transverse energy distribution. In this paper we successfully extend this mapping to also include $p_T$-differential flow. In doing so, we find that subleading principal components of anisotropic flow can reveal previously unobserved details of the hydrodynamic response, in both the linear and the nonlinear regimes. Most importantly, we show that they provide novel information on the small-scale structures present in the initial stage of relativistic heavy-ion collisions., Comment: 18 pages, 12 figures. Published version

We investigate the effects of pre-hydrodynamic evolution on final-state observables in heavy-ion collisions using state-of-the art event simulations coupled to different pre-hydrodynamic scenarios, which include the recently-developed effective kinetic transport theory evolution model KoMPoST. Flow observables are found to be insensitive to the details of pre-hydrodynamic evolution. The main effect we observe is in the $p_T$ spectra, particularly the mean transverse momentum. However, at least part of this effect is a consequence of the underlying conformal invariance assumption currently present in such approaches, which is known to be violated in the temperature regime probed in heavy-ion collisions. This assumption of early time conformal invariance leads to an artificially large out-of-equilibrium bulk pressure when switching from (conformal) pre-hydrodynamic evolution to hydrodynamics (using the non-conformal QCD equation of state), which in turn increases the transverse momentum. Our study indicates that a consistent treatment of pre-hydrodynamic evolution in heavy-ion collisions requires the use of non-conformal models of early time dynamics., Comment: 17 pages, 15 figures, 1 appendix

We propose a redefinition of the principal component analysis (PCA) of anisotropic flow that makes it more directly connected to fluctuations of the initial geometry of the system. Then, using state-of-the-art hydrodynamic simulations, we make an explicit connection between flow fluctuations and a cumulant expansion of the initial transverse geometry. In particular, we show that the second principal component of elliptic flow is generated by higher-order cumulants, and therefore probes smaller length scales of the initial state. With this information, it will be possible to put new constraints on properties of the early-time dynamics of a heavy-ion collision, including small-scale structure, as well as properties of the quark-gluon plasma., Comment: 4 pages, 2 figures, contribution to the proceedings of XXVIIIth International Conference on Ultrarelativistic Nucleus-Nucleus Collisions (Quark Matter 2019)

In this work, we have investigated anisotropic neutron stars in the framework of Rastall-Rainbow gravity. All our calculations were computed using the IU-FSU realistic equation of state (EoS), in which was considered two cases: standard nucleonic composition and the one with the eight lightest baryons. From the neutron star masses and radii obtained we conclude that anisotropic pressure has significant consequences on the structure of stellar objects. In particular, when anisotropy is considered within the general relativity framework, it significantly modifies the maximum stellar mass. On the other hand, when Rastall-Rainbow gravity and anisotropy are simultaneously considered, they provide the best results for mass and radius values, including important astrophysical objects such as the LMXB NGC 6397 and the extremely massive pulsar millisecond MSP J0740 + 6620. Although the expected inclusion of hyperons in the nuclear model reproduces stellar masses smaller than those produced by standard nucleonic matter, we shown that the hyperon puzzle problem can be solved by including anisotropic effects on compact stars in the context of the Rastall-Rainbow gravity.

In heavy-ion collisions, momentum-dependent pair correlations can be characterized by a principal component analysis (PCA), in which subleading modes are expected to reveal new information on flow fluctuations. However, we find that, as currently measured, these modes can be dominated by multiplicity fluctuations, which serve as an unwanted background. Here, we propose new PCA observables that are robust against multiplicity fluctuations and isolate novel sources of flow fluctuations, thus being suited to provide fresh insight into the initial stages of the system at small length scales., Comment: 9 pages, 4 figures. Published version

In this work we present a bag-type model within a non-extensive statistics applied to the description of the properties of a hadronic system with an underlying fractal structure. The non-extensive ideal gas inside the bag is determined by the grand canonical partition function from which pressure, energy and particle density as well as temperature and chemical potential are obtained for the hadronic system. These quantities are studied in the approximation of fixed mass for all bag constituents but also for discrete and continuum masses. In all cases, the freeze-out line, corresponding to the energy per particle equal to 1 GeV and the lines corresponding to a fractal structure inside the proton volume are obtained. Finally, the pressure on the bag surface of the proton is calculated and the resulting value $(0.135\, {\rm GeV})^4$ obtained., Comment: 19 pages, 16 figures

Ripples or corrugations are common phenomena observed in unpaved roads in less developed countries or regions. They cause several damages in vehicles leading to increased maintenance and product costs. In this paper, we present a computational study about the so-called washboard roads. Also, we study grain segregation on unpaved roads. Our simulations have been performed by the Discrete Element Method (DEM). In our model, the grains are regarded as soft disks. The grains are subjected to a gravitational field and both translational and rotational movements are allowed. The results show that wheels' of different sizes, weights and moving with different velocities can change corrugations amplitude and wavelength. Our results also show that some wavelength values are related to specific wheels' speed intervals. Segregation has been studied in roads formed by three distinct grain diameters distribution. We observed that the phenomenon is more evident for higher grain size dispersion.