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This collection of perspective pieces captures recent advancements and reflections from a dynamic research community dedicated to bridging quantum gravity, hydrodynamics, and emergent cosmology. It explores four key research areas: (a) the interplay between hydrodynamics and cosmology, including analog gravity systems; (b) phase transitions, continuum limits and emergent geometry in quantum gravity; (c) relational perspectives in gravity and quantum gravity; and (d) the emergence of cosmological models rooted in quantum gravity frameworks. Each contribution presents the distinct perspectives of its respective authors. Additionally, the introduction by the editors proposes an integrative view, suggesting how these thematic units could serve as foundational pillars for a novel theoretical cosmology framework termed "hydrodynamics on superspace"., Comment: 37 + 23 pages

Previous investigations on the renormalizability properties of Lorentz-violating Yang-Mills (LVYM) theories in the Landau gauge have pointed out the necessity of the inclusion of a mass-like term for the gauge fields. If one aims at generalizing the theory to a more complicated gauge, such a mass-like term can bring severe issues regarding gauge-dependence of correlation functions of observables. We propose a Lorentz-violating Yang-Mills theory supplemented by a gauge-invariant mass term which generalizes the model in the Landau gauge. We discuss the foundational aspects of the model and highlight how it simplifies in the Landau gauge. Following the recent literature in pure Yang-Mills theories, we remark how the proposed massive extension of LVYM theories can be used as an effective model to access infrared properties within perturbation theory. Finally, we present a BRST-invariant formulation of the so-called Refined Gribov-Zwanziger action in the presence of Lorentz-violating terms in linear covariant gauges and the underlying tree-level gauge-field propagator which is enriched by the non-perturbative information carried by the elimination of Gribov copies., Comment: 14 pages, no figures

The large-scale linear halo bias encodes the relation between the clustering of dark-matter (DM) halos and that of the underlying matter density field. Although the primary dependence of bias on halo mass is well understood in the context of structure formation, the physical origins of the multiple additional relations at fixed halo mass, commonly known as secondary halo bias, have not been fully elucidated. Of particular relevance is the secondary dependence on halo assembly history, known as halo assembly bias. Our goal is to determine whether the properties of the initial regions from which $z=0$ halos originate produce any secondary bias at $z=0$. By analyzing these initial dependencies in connection with halo assembly bias, we intend to provide insight on the physical origins of the effect. To this end, we select halos at $z=0$ in the IllustrisTNG DM-only simulation and trace back the positions and velocities of their DM particles to $z=12$. The resulting initial regions are characterized according to several shape-related and kinematic properties. The secondary bias signal produced by these properties at $z=0$ is measured using an object-by-object bias estimator, which offers significant analytical advantages. We show that, when split by the properties of their initial DM clouds, $z=0$ halos display significant secondary bias, clearly exceeding the amplitude of the well-known halo assembly bias signal produced by concentration and age. The maximum bias segregation is measured for cloud velocity dispersion and radial velocity, followed by cloud concentration, sphericity, ellipticity and triaxiality. We further show that both velocity dispersion and radial velocity are also the properties of the initial clouds that most strongly correlate with halo age and concentration at fixed halo mass. Our results highlight the importance of linear effects in shaping halo assembly bias., Comment: 9 pages, 5 figures. Submitted to A&A, comments welcome

The connection between galaxies and dark matter halos encompasses a range of processes and play a pivotal role in our understanding of galaxy formation and evolution. Traditionally, this link has been established through physical or empirical models. Machine learning techniques are adaptable tools that handle high-dimensional data and grasp associations between numerous attributes. In particular, probabilistic models capture the stochasticity inherent to these complex relations. We compare different probabilistic machine learning methods to model the uncertainty in the halo-galaxy connection and efficiently generate galaxy catalogs that faithfully resemble the reference sample by predicting joint distributions of central galaxy properties conditioned to their host halo features. The analysis is based on the IllustrisTNG300 simulation. The methods model the distributions in different ways. We compare a multilayer perceptron that predicts the parameters of a multivariate Gaussian distribution, a multilayer perceptron classifier, and the method of normalizing flows. The classifier predicts the parameters of a Categorical distribution, which are defined in a high-dimensional parameter space through a Voronoi cell-based hierarchical scheme. We evaluate the model's performances under various sample selections based on halo properties. The three methods exhibit comparable results, with normalizing flows showing the best performance in most scenarios. The models reproduce the main features of galaxy properties distributions with high-fidelity and reproduce the results obtained with traditional, deterministic, estimators. Our results also indicate that different halos and galaxy populations are subject to varying degrees of stochasticity, which has relevant implications for studies of large-scale structure., Comment: 14 pages, 8 figures, 6 tables

Short gamma-ray bursts (GRBs) are explosive transients caused by binary mergers of compact objects containing at least one neutron star. Multi-wavelength afterglow observations provide constraints on the physical parameters of the jet, its surrounding medium, and the microphysics of the enhanced magnetic fields and accelerated electrons in the blast wave at the front of the jet. The synchrotron radio emission can be tracked for much longer than in other spectral regimes, and it can pin down the evolution of the spectral peak. We present the results of a systematic observing campaign of eight short GRBs with the MeerKAT radio telescope. Additionally, we present observations of four of these short GRBs using the ATCA radio telescope and two of these short GRBs with the e-MERLIN radio telescope. Using these results we report one possible detection of a short GRB afterglow from GRB 230217A and deep upper limits for the rest of our short GRB observations. We use these observations to place constraints on some of the physical parameters, in particular those related to electron acceleration, the circumburst density, and gamma-ray energy efficiency. We discuss how deeper observations with new and upgraded telescopes should be able to determine if the gamma-ray efficiency differs between long and short GRBs. We also report detections of the likely host galaxies for four of the eight GRBs and upper limits for another GRB, increasing the number of detected host galaxies in the radio with implications for the star formation rate in these galaxies., Comment: to be published in the Monthly Notices of the Royal Astronomical Society

When constructing galaxy mock catalogs based on suits of dark matter halo catalogs generated with approximated, calibrated or machine-learning approaches, the assignment of intrinsic properties for such tracers is a step of paramount relevance, given that these can shape the abundance of mock galaxy cluster and the spatial distribution of mock galaxies. We explore the possibility to assign properties of dark matter halos within the context of calibrated/learning approaches, explicitly using clustering information. The goal is to retrieve the correct signal of primary and secondary large-effective bias as a function of properties reconstructed solely based on phase-space properties of the halo distribution and dark matter density field. The algorithm reconstructs a set halo properties (such as virial mass, maximum circular velocity, concentration and spin) constraint to reproduce both primary and secondary (or assembly) bias. The key ingredients of the algorithm are the implementation of individually-assigned large-scale effective bias, a multi-scale approach to account for halo exclusion and a hierarchical assignment of halo properties. The method facilitates the assignment of halo properties aiming at replicating the large-scale effective bias, both primary and secondary. This improves over over previous methods found in the literature, especially at the high mass population. We have designed an strategy to reconstruct the main properties of dark matter halos obtained by calibrated/learning algorithms, in a way that the expected one and two-point statistics (on large scales) replicates the signal from detailed N-body simulations. We encourage the application of this strategy (or the implementation of our algorithm) for the generation of mock catalogs of dark matter halos based on approximated methods., Comment: 10 Pages, 6 Figures. Accepted for publication at A&A

We investigate low-mass central galaxies with Mstar = $10^{9.5}-10^{10}$ Msun/h, located near massive groups and galaxy clusters using the TNG300 and MDPL2-SAG simulations. We set out to study their evolution, aiming to find hints about the large-scale conformity signal they produce. We also use a control sample of low-mass central galaxies located far away from massive structures. For both samples, we find a sub-population of galaxies that were accreted by another halo in the past but are now considered central galaxies; we refer to these objects as former satellites. The fraction of former satellites is higher for quenched central galaxies near massive systems: 45% in TNG300 and 17% in MDPL2-SAG. Our results in TNG300 show that former satellites were typically hosted by massive dark matter halos (M200 $\geq 10^{13}$ Msun/h) at z$\sim$0.3, followed by a drop in halo mass at lower redshifts. In addition, we find a strong drop in the total gas mass at z$\leq$1 for quenched central galaxies near galaxy groups and clusters produced by these former satellites as well. By removing former satellites, the evolution of quenched central galaxies is fairly similar to those of the quenched control galaxies, showing small differences at low-z. For MDPL2-SAG, former satellites were hosted by less massive halos, with a mean halo mass around $10^{11}$ Msun/h, and the evolution remains equal before and after removing former satellites. We also measure the two-halo conformity, i.e., the correlation in the specific SFR between low-mass central galaxies and their neighbors at Mpc scales, and how former satellites contribute to the signal at z=0, 0.3, and 1. The conformity signal decreases from z=0 to z=1 in MDPL2-SAG but it increases in TNG300. However, after removing former satellites in TNG300, the signal is strongly reduced but almost does not change at z$\leq$0.3, and it disappears at z=1 (abridged)., Comment: 16 pages, 13 figures, submitted to A&A

The Refined Gribov-Zwanziger (RGZ) action in the Landau gauge provides a local and renormalizable framework to account for the existence of infinitesimal Gribov copies in the path integral together with other relevant infrared effects such as the formation of condensates. The properties of the tree-level gluon propagator obtained in this setup has been thoroughly investigated over the past decade. It accommodates important properties seen in lattice simulations such as a finite value at vanishing momentum and positivity violation. Yet a comprehensive study about the stability of such properties against quantum corrections was lacking. In this work, we compute the gluon propagator in the RGZ scenario at one-loop order and implement an appropriate renormalization scheme in order to compare our findings with lattice data. Remarkably, the qualitative properties of the tree-level gluon propagator are preserved. In particular, the fits with lattice data show evidence for positivity violation and the existence of complex poles for SU(2) and SU(3) gauge groups. We comment on the results for the ghost propagator as well., Comment: 12 pages (including refs.); 6 figures

We describe pentaquarks as baryo-charmonia with a color octet $c\bar{c}$ core bonded to a color octet three-quark system. Fermi statistics of the light quark cloud allows to describe two pentaquark triplets: a lower one, well supported by experiment, and a higher one with strangeness. For the time being, the lowest line of the strange triplet has been experimentally identified in a $3\sigma$ peak. Data also suggest (at least) two different production mechanisms for pentaquarks. We show how this can be described in the proposed scheme., Comment: 14 pages, 2 figures

Computational models are an essential tool for the design, characterization, and discovery of novel materials. Hard computational tasks in materials science stretch the limits of existing high-performance supercomputing centers, consuming much of their simulation, analysis, and data resources. Quantum computing, on the other hand, is an emerging technology with the potential to accelerate many of the computational tasks needed for materials science. In order to do that, the quantum technology must interact with conventional high-performance computing in several ways: approximate results validation, identification of hard problems, and synergies in quantum-centric supercomputing. In this paper, we provide a perspective on how quantum-centric supercomputing can help address critical computational problems in materials science, the challenges to face in order to solve representative use cases, and new suggested directions., Comment: 65 pages, 15 figures; comments welcome

We measure the signal of secondary halo bias as a function of a variety of intrinsic and environmental halo properties, and characterize its statistical significance as a function of cosmological redshift. Using fixed and paired $N$-body simulations of dark-matter halos -- the UNIT simulation -- with masses above $10^{11}M_{\odot}h^{-1}$ identified over a wide range of cosmological redshifts (0

We introduce a dually-weighted multi-matrix model that for a suitable choice of weights reproduce two-dimensional Causal Dynamical Triangulations (CDT) coupled to the Ising model. When Ising degrees of freedom are removed, this model corresponds to the CDT-matrix model introduced by Benedetti and Henson [Phys. Lett. B 678, 222 (2009)]. We present exact as well as approximate results for the Gaussian averages of characters of a Hermitian matrix $A$ and $A^2$ for a given representation and establish the present limitations that prevent us to solve the model analytically. This sets the stage for the formulation of more sophisticated matter models coupled to two-dimensional CDT as dually weighted multi-matrix models providing a complementary view to the standard simplicial formulation of CDT-matter models., Comment: 48 pages, 19 figures

For low-mass haloes, the physical origins of halo assembly bias have been linked to the slowdown of accretion due to tidal forces, which are expected to be more dominant in some cosmic-web environments as compared to others. In this work, we use publicly available data from the application of the Discrete Persistent Structures Extractor (DisPerSE) to the IllustrisTNG magnetohydrodynamical simulation to investigate the dependence of the related galaxy assembly bias effect on the cosmic web. We first show that, at fixed halo mass, the galaxy population displays significant low-mass secondary bias when split by distance to DisPerSE critical points representing nodes ($d_{\rm node}$), filaments ($d_{\rm skel}$), and saddles ($d_{\rm sadd}$), with objects closer to these features being more tightly clustered. The secondary bias produced by some of these parameters exceeds the assembly bias signal considerably at some mass ranges, particularly for $d_{\rm sadd}$. We also demonstrate that the assembly bias signal is reduced significantly when clustering is conditioned to galaxies being close or far from these critical points. The maximum attenuation is measured for galaxies close to saddle points, where less than 35$\%$ of the signal remains. Conversely, objects near voids preserve a fairly pristine effect (almost 85$\%$ of the signal). Our analysis confirms the important role played by the tidal field in shaping assembly bias, but they are also consistent with the signal being the result of different physical mechanisms. Our work introduces some new aspects of secondary bias where the predictions from hydrodynamical simulations can be directly tested with observational data., Comment: 12 pages, 7 figures. Submitted to MNRAS, comments welcome

We determine the contribution of long-range pion interactions to the $X(3872)$ dynamics, assuming it is a loosely bound $D^0 \bar{D}^{*0}$ molecule. Our result is based on the distorted wave Born approximation in non-relativistic quantum mechanics. Despite their long-range nature, we find that pion interactions cannot produce a large and negative effective range. Nonetheless, they introduce imaginary parts. In particular, they contribute to the total decay width of the $X(3872)$ with a term associated with, but not precisely corresponding to, the $D^*$ width. Our approach can also be applied to the recently discovered $T_{cc}^+$ states., Comment: 9 pages, 1 figure. v2: minor changes. Comparison with ref. [26] improved

We study the mechanism of chiral symmetry breaking for fermionic systems in a gravitational background with curvature and torsion. The analysis is based on a scale-dependent effective potential derived from a bosonized version of the Nambu-Jona-Lasino model in a Riemann-Cartan background. We have investigated the fate of chiral symmetry in two different regimes. First, to gain some intuition on the combined effect of curvature and torsion, we investigate the regime of weak curvature and torsion. However, this regime does not access the deep infrared limit, which is essential to answer questions related to the mechanism of gravitational catalysis in fermionic systems. Second, we look at the regime of vanishing curvature and homogeneous torsion. In this case, although we cannot probe the combined effects of curvature and torsion, we can access the deep infrared contributions of the background torsion to the mechanism of chiral symmetry breaking. Our main finding is that, in the scenario where only torsion is present, there is no indication of a mechanism of gravitational catalysis., Comment: 18 pages, 2 figures; typos corrected and Eqs. (14), (15), (20) and (21) added. Version matches the published one in PRD

Intelligent Transportation Systems are producing tons of hardly manageable traffic data, which motivates the use of Machine Learning (ML) for data-driven applications, such as Traffic Forecasting (TF). TF is gaining relevance due to its ability to mitigate traffic congestion by forecasting future traffic states. However, TF poses one big challenge to the ML paradigm, known as the Model Selection Problem (MSP): deciding the most suitable combination of data preprocessing techniques and ML method for traffic data collected under different transportation circumstances. In this context, Automated Machine Learning (AutoML), the automation of the ML workflow from data preprocessing to model validation, arises as a promising strategy to deal with the MSP in problem domains wherein expert ML knowledge is not always an available or affordable asset, such as TF. Various AutoML frameworks have been used to approach the MSP in TF. Most are based on online optimisation processes to search for the best-performing pipeline on a given dataset. This online optimisation could be complemented with meta-learning to warm-start the search phase and/or the construction of ensembles using pipelines derived from the optimisation process. However, given the complexity of the search space and the high computational cost of tuning-evaluating pipelines generated, online optimisation is only beneficial when there is a long time to obtain the final model. Thus, we introduce AutoEn, which is a simple and efficient method for automatically generating multi-classifier ensembles from a predefined set of ML pipelines. We compare AutoEn against Auto-WEKA and Auto-sklearn, two AutoML methods commonly used in TF. Experimental results demonstrate that AutoEn can lead to better or more competitive results in the general-purpose domain and in TF.

We study compact hidden charm pentaquarks in the Born-Oppenheimer approximation, previously introduced for tetraquarks, assuming the heavy pair to be in a color octet. We show that Fermi statistics applied to the complex of the three light quarks, also in color octet, requires S-wave pentaquark ground states to consist of three octets of flavour-SU(3)_f, two with spin 1/2 and one with spin 3/2, in line with the observed, strangeness S=0,-1, spectrum. Additional lines corresponding to decays into J/\psi + Sigma and J/\psi + Xi are predicted. In the language of non-relativistic SU(6), ground state pentaquarks form either a 56 or a 20 representation, distinguished by presence or absence of pentaquarks decaying in the spin 3/2 decuplet, e.g. in J/\psi + Delta^++. Observation of a strangeness S=-2 or isospin I=3/2 pentaquarks would be a clear signature of compact, QCD bound pentaquarks., Comment: 15 pages, 2 figures

The Refined Gribov-Zwanziger (RGZ) action in the Landau gauge accounts for the existence of infinitesimal Gribov copies as well as the dynamical formation of condensates in the infrared of Euclidean Yang-Mills theories. We couple scalar fields to the RGZ action and compute the one-loop scalar propagator in the adjoint representation of the gauge group. We compare our findings with existing lattice data. The fate of BRST symmetry in this model is discussed, and we provide a comparison to a previous proposal for a non-minimal coupling between matter and the RGZ action. We find good agreement with the lattice data of the scalar propagator for the values of the mass parameters that fit the RGZ gluon propagator to the lattice. This suggests that the non-perturbative information carried by the gluon propagator in the RGZ framework provides a suitable mechanism to reproduce the behavior of correlation functions of colored matter fields in the infrared., Comment: 18 pages + refs.; 6 figures; Matches the journal version

The relationship between galaxies and haloes is central to the description of galaxy formation, and a fundamental step towards extracting precise cosmological information from galaxy maps. However, this connection involves several complex processes that are interconnected. Machine Learning methods are flexible tools that can learn complex correlations between a large number of features, but are traditionally designed as deterministic estimators. In this work, we use the IllustrisTNG300-1 simulation and apply neural networks in a binning classification scheme to predict probability distributions of central galaxy properties, namely stellar mass, colour, specific star formation rate, and radius, using as input features the halo mass, concentration, spin, age, and the overdensity on a scale of 3 $h^{-1}$ Mpc. The model captures the intrinsic scatter in the relation between halo and galaxy properties, and can thus be used to quantify the uncertainties related to the stochasticity of the galaxy properties with respect to the halo properties. In particular, with our proposed method, one can define and accurately reproduce the properties of the different galaxy populations in great detail. We demonstrate the power of this tool by directly comparing traditional single-point estimators and the predicted joint probability distributions, and also by computing the power spectrum of a large number of tracers defined on the basis of the predicted colour-stellar mass diagram. We show that the neural networks reproduce clustering statistics of the individual galaxy populations with excellent precision and accuracy., Comment: 12 pages, 7 figures

Satellite galaxies undergo a variety of physical processes when they are accreted by groups and clusters, often resulting in the loss of baryonic and dark matter (DM) mass. In this work, we evaluate the predictions from the IllustrisTNG hydrodynamical simulation regarding the evolution of the matter content of satellites, focusing on a population that are accreted at $z>1$ and retain their identity as satellites down to $z=0$. At fixed host halo mass, the amount of DM and stellar mass stripped depends mostly on the pericentric distance, $d_{\rm peri}$, here normalised by host halo virial radius. The closest encounters result in significant loss of DM, with subhaloes retaining between 20 and a few per cent of their $z=1$ mass. At fixed $d_{\rm peri}$, DM mass stripping seems more severe in lower mass haloes. Conversely, the average satellite in higher mass haloes has its stellar mass growth halted earlier, having lost a higher fraction of stellar mass by $z=0$. We also show that mass stripping has a strong impact on the quenched fractions. The IllustrisTNG boxes are qualitatively consistent in these predictions, with quantitative differences mostly originating from the distinct subhalo mass ranges covered by the boxes. Finally, we have identified DM-deficient systems in all TNG boxes. These objects are preferentially found in massive clusters ($M_{\rm host } \gtrsim 10^{13}$ M$_\odot$), had very close encounters with their central galaxies ($d_{\rm peri}\simeq0.05\, R_{\rm vir}$), and were accreted at high redshift ($z_{\rm infall} \gtrsim 1.4$), reinforcing the notion that tidal stripping is responsible for their remarkable lack of DM., Comment: 16 pages, 12 figures. Submitted to MNRAS, comments welcome

Tetraquarks QQq*q* are found to be described remarkably well with the Quantum Chromodynamics version of the Hydrogen bond, as treated with the Born-Oppenheimer approximation. We show the robustness of the method by computing the mass of the observed Tcc tetraquark following two different paths. Relying on this, we provide a prediction for the mass of the expected Tbb particle. The average sizes of tetraquarks are estimated to be approximately 3 - 5 GeV**-1. As a consequence hyperfine separations are not expected to be sizeable. We discussed possible reasons why LHCb has observed only one state in the DD* spectrum., Comment: 8 pages, 1 figure. Corrections made. Improved draft. To appear in PLB

Understanding the quantum nature of spacetime and gravity remains one of the most ambitious goals of theoretical physics. It promises to provide key new insights into fundamental particle theory, astrophysics, cosmology and the foundations of physics. Despite this common goal, the community of quantum gravity researchers is sometimes seen as divided into sub-communities working on different, mutually exclusive approaches. In practice however, recent years have shown the emergence of common techniques, results and physical ideas arising from different sub-communities, suggesting exciting new prospects for collaboration and interaction between traditionally distinct approaches. In this White Paper we discuss some of the common themes which have seen a growing interest from various directions, and argue that focusing on them will help the quantum gravity community as a whole towards shared objectives., Comment: Contribution to Snowmass 2021, 11 pages

Wetterich's equation provides a powerful tool for investigating the existence and universal properties of renormalization group fixed points exhibiting quantum scale invariance. Motivated by recent works on asymptotically safe scalar-tensor theories, we develop a novel approximation scheme which projects the functional renormalization group equation onto functions of the kinetic term. Applying this projection to scalars and gauge fields, our analysis identifies a new universality class with a very special spectrum of stability coefficients. The implications of our findings in the context of asymptotically safe gravity-matter systems are discussed., Comment: 10 pages, 3 figures

We introduce a background gauge akin to the Landau-DeWitt gauge but deformed by the presence of a gauge parameter for the quantization of Euclidean Yang-Mills theories. In the limit where the background field vanishes, standard linear covariant gauges are recovered. This gauge allows for an explicit investigation of the effects of infinitesimal Gribov copies and their impact to background and gauge parameter dependence of physical correlators. Similarly to linear covariant gauges, the introduction of gauge-invariant dressed fields is essential to restore BRST symmetry. Hence, we construct a BRST symmetric action in linear covariant background gauges which eliminates regular infinitesimal Gribov copies in analogy to the recently introduced BRST invariant (refined) Gribov-Zwanziger action. The issue of background dependence and its relation to gauge parameter dependence is discussed in the light of non-perturbative effects driven by the elimination of Gribov copies., Comment: 14 pages

Electric vehicles (EVs) have been adopted in urban areas to reduce environmental pollution and global warming as a result of the increasing number of freight vehicles. However, there are still deficiencies in routing the trajectories of last-mile logistics that continue to impact social and economic sustainability. For that reason, in this paper, a hyper-heuristic (HH) approach called Hyper-heuristic Adaptive Simulated Annealing with Reinforcement Learning (HHASA$_{RL}$) is proposed. It is composed of a multi-armed bandit method and the self-adaptive Simulated Annealing (SA) metaheuristic algorithm for solving the problem called Capacitated Electric Vehicle Routing Problem (CEVRP). Due to the limited number of charging stations and the travel range of EVs, the EVs must require battery recharging moments in advance and reduce travel times and costs. The HH implemented improves multiple minimum best-known solutions and obtains the best mean values for some high-dimensional instances for the proposed benchmark for the IEEE WCCI2020 competition., Comment: 26 pages, 6 figures, 6 tables

The terrestrial water cycle links the soil and atmosphere moisture reservoirs through four fluxes: precipitation, evaporation, runoff and atmospheric moisture convergence. Each of these fluxes is essential for human and ecosystem well-being. However, predicting how the water cycle responds to changes in vegetation cover, remains a challenge (Lawrence and Vandecar, 2015; Ellison et al., 2017; te Wierik et al., 2021). Recently, rainfall was shown to decrease disproportionally with reduced forest transpiration following deforestation (Baudena et al., 2021). Here, combining these findings with the law of matter conservation, we show that in a sufficiently wet atmosphere forest transpiration can control atmospheric moisture convergence such that increased transpiration enhances atmospheric moisture import. Conversely, in a drier atmosphere increased transpiration reduces atmospheric moisture convergence and runoff. This previously unrecognized dichotomy can explain the seemingly random observations of runoff and soil moisture sometimes increasing and sometimes reducing in response to re-greening (e.g., Zheng et al., 2021). Evaluating the transition between the two regimes is crucial both for characterizing the risk posed by deforestation as well as for motivating and guiding global ecosystem restoration., Comment: 8 pages, 1 figure

Quantum error correction offers a promising path for performing quantum computations with low errors. Although a fully fault-tolerant execution of a quantum algorithm remains unrealized, recent experimental developments, along with improvements in control electronics, are enabling increasingly advanced demonstrations of the necessary operations for applying quantum error correction. Here, we perform quantum error correction on superconducting qubits connected in a heavy-hexagon lattice. The full processor can encode a logical qubit with distance three and perform several rounds of fault-tolerant syndrome measurements that allow the correction of any single fault in the circuitry. Furthermore, by using dynamic circuits and classical computation as part of our syndrome extraction protocols, we can exploit real-time feedback to reduce the impact of energy relaxation error in the syndrome and flag qubits. We show that the logical error varies depending on the use of a perfect matching decoder compared to a maximum likelihood decoder. We observe a logical error per syndrome measurement round as low as $\sim0.04$ for the matching decoder and as low as $\sim0.03$ for the maximum likelihood decoder. Our results suggest that more significant improvements to decoders are likely on the horizon as quantum hardware has reached a new stage of development towards fully fault-tolerant operations., Comment: 15 pages, 6 figures, 5 tables