Search

Barchans are eolian dunes of crescent shape found on Earth, Mars and other celestial bodies. Among the different types of barchan-barchan interaction, there is one, known as chasing, in which the dunes remain close but without touching each other. In this paper, we investigate the origins of this barchan-barchan dune repulsion by carrying out grain-scale numerical computations in which a pair of granular heaps is deformed by the fluid flow into barchan dunes that interact with each other. In our simulations, data such as position, velocity and resultant force are computed for each individual particle at each time step, allowing us to measure details of both the fluid and grains that explain the repulsion. We show the trajectories of grains, time-average resultant forces, and mass balances for each dune, and that the downstream barchan shrinks faster than the upstream one, keeping, thus, a relatively high velocity although in the wake of the upstream barchan. In its turn, this fast shrinkage is caused by the flow disturbance, which induces higher erosion on the downstream barchan and its circumvention by grains leaving the upstream dune. Our results help explaining the mechanisms behind the distribution of barchans in dune fields found on Earth and Mars., Comment: An edited version of this paper was published by AGU. Copyright (2024) American Geophysical Union. Lima, N.C., Assis, W.R., Alvarez, C.A., Franklin, E.M., Barchan-barchan dune repulsion investigated at the grain scale. Journal of Geophysical Research: Earth Surface, 129, e2024JF007741, 2024. To view the published open abstract, go to https://doi.org/10.1029/2024JF007741

The energy spectra of particles produced from dark matter (DM) annihilation or decay are one of the fundamental ingredients to calculate the predicted fluxes of cosmic rays and radiation searched for in indirect DM detection. We revisit the calculation of the source spectra for annihilating and decaying DM using the Vincia shower algorithm in Pythia to include QED and QCD final state radiation and diagrams for the Electroweak (EW) corrections with massive bosons, not present in the default Pythia shower model. We take into account the spin information of the particles during the entire EW shower and the off-shell contributions from massive gauge bosons. Furthermore, we perform a dedicated tuning of the Vincia and Pythia parameters to LEP data on the production of pions, photons, and hyperons at the $Z$ resonance and discuss the underlying uncertainties. To enable the use of our results in DM studies, we provide the tabulated source spectra for the most relevant cosmic messenger particles, namely antiprotons, positrons, $\gamma$ rays and the three neutrino flavors, for all the fermionic and bosonic channels and DM masses between 5 GeV and 100 TeV, on https://github.com/ajueid/CosmiXs.git., Comment: 39 pages, 14 figures, 4 tables

When the COVID-19 pandemic first emerged in early 2020, healthcare and bureaucratic systems worldwide were caught off guard and largely unprepared to deal with the scale and severity of the outbreak. In Italy, this led to a severe underreporting of infections during the first wave of the spread. The lack of accurate data is critical as it hampers the retrospective assessment of nonpharmacological interventions, the comparison with the following waves, and the estimation and validation of epidemiological models. In particular, during the first wave, reported cases of new infections were strikingly low if compared with their effects in terms of deaths, hospitalizations and intensive care admissions. In this paper, we observe that the hospital admissions during the second wave were very well explained by the convolution of the reported daily infections with an exponential kernel. By formulating the estimation of the actual infections during the first wave as an inverse problem, its solution by a regularization approach is proposed and validated. In this way, it was possible to computed corrected time series of daily infections for each age class. The new estimates are consistent with the serological survey published in June 2020 by the National Institute of Statistics (ISTAT) and can be used to speculate on the total number of infections occurring in Italy during 2020, which appears to be about double the number officially recorded., Comment: 19 pages, 4 main figures, 2 supplementary figures, 2 tables

The singlet scalar Higgs portal model provides one of the simplest explanations of dark matter in our Universe. Its Higgs resonant region, $m_\text{DM}\approx m_h/2$, has gained particular attention, being able to reconcile the tension between the relic density measurement and direct detection constraints. Interestingly, this region is also preferred as an explanation of the Fermi-LAT $\gamma$-ray Galactic center excess. We perform a detailed study of this model using $\gamma$-ray data from the Galactic center and from dwarf spheroidal galaxies and combine them with cosmic-ray antiproton data from the AMS-02 experiment that shows a compatible excess. In the calculation of the relic density, we take into account effects of early kinetic decoupling relevant for resonant annihilation. The model provides excellent fits to the astrophysical data either in the case the dark matter candidate constitutes all or a subdominant fraction of the observed relic density. We show projections for future direct detection and collider experiments to probe these scenarios., Comment: 20 pages, 14 figures. For the SingletScalar_DM package, see https://github.com/dimauromattia/SingletScalar_DM

From small seeds falling from trees to asteroids colliding with planets and moons, the impact of projectiles onto granular targets occurs in nature at different scales. In this paper, we investigate open questions in the mechanics of granular cratering, in particular the forces acting on the projectile, and the roles of granular packing, grain-grain friction and projectile spin. For that, we carried out DEM (discrete element method) computations of the impact of solid projectiles on a cohesionless granular medium, where we varied the projectile and grain properties (diameter, density, friction and packing fraction) for different available energies (within relatively small values). We found that a denser region forms below the projectile, pushing it back and causing its rebound by the end of its motion, and that solid friction affects considerably the crater morphology. Besides, we show that the penetration length increases with the initial spin of the projectile, and that differences in initial packing fractions can engender the diversity of scaling laws found in the literature. Finally, we propose an ad hoc scaling that collapsed our data for the penetration length and can perhaps unify existing correlations. Our results provide new insights into the formation of craters in granular matter., Comment: Accepted manuscript for Physical Review E, 105, 034903, (2022), DOI: 10.1103/PhysRevE.107.044901, https://doi.org/10.1103/PhysRevE.107.044901

Clusters of galaxies are the largest gravitationally-bound systems in the Universe. Their dynamics are dominated by dark matter (DM), which makes them among the best targets for indirect DM searches. We analyze 12 years of data collected by the Fermi Large Area Telescope (Fermi-LAT) in the direction of 49 clusters of galaxies selected for their proximity to the Earth and their high X-ray flux, which makes them the most promising targets. We first create physically motivated models for the DM density around each cluster considering different assumptions for the substructure distribution. Then we perform a combined search for a $\gamma$-ray signal in the {\it Fermi}-LAT data between 500 MeV and 1 TeV. We find a signal of $\gamma$ rays potentially associated with DM that is at a statistical significance of $2.5\sigma-3.0\sigma$ when considering a slope for the subhalo mass distribution $\alpha=1.9$ and minimum mass of $M_{\rm{min}}=10^{-6}$ $M_{\odot}$. The best-fit DM mass and annihilation cross-sections for a $b\bar{b}$ annihilation channel are $m_{\chi}=40-60$ GeV and $\langle \sigma v \rangle = (2-4) \times 10^{-25}$ cm$^3$/s. When we consider $\alpha=2.0$ and $M_{\rm{min}}=10^{-9}$ $M_{\odot}$, the best-fit of the cross section reduces to $\langle \sigma v \rangle = (4-10) \times 10^{-26}$ cm$^3$/s. For both DM substructure models there is a tension between the values of $\langle \sigma v \rangle$ that we find and the upper limits obtained with the non-detection of a $\gamma$-ray flux from Milky Way dwarf spheroidal galaxies. This signal is thus more likely associated with $\gamma$ rays produced in the intracluster region by cosmic rays colliding with gas and photon fields., Comment: 27 Pages, 13 Figures. Accepted for publication in the PRD journal

Sub-GeV dark matter particles can annihilate or decay producing e^\pm pairs which upscatter the low-energy photon fields in the Galaxy and generate an X-ray emission (via the Inverse Compton effect). Using X-ray data from Xmm-Newton, Integral, NuStar and Suzaku, we derive new constraints on this class of dark matter (DM). In the annihilation case, our new bounds are the strongest available for DM masses above 180 MeV, reaching < 10^-28 cm^3/s for m_DM ~ 1 GeV. In the decay case, our bounds are the strongest to date over a large fraction of the considered mass range, constraining tau > 10^28 s for m_DM ~ 1 GeV and improving by up to 3 orders of magnitude upon existing limits., Comment: 16 pages, 9 figures

We reconstruct the extra-galactic gamma-ray source-count distribution, or $dN/dS$, of resolved and unresolved sources by adopting machine learning techniques. Specifically, we train a convolutional neural network on synthetic 2-dimensional sky-maps, which are built by varying parameters of underlying source-counts models and incorporate the Fermi-LAT instrumental response functions. The trained neural network is then applied to the Fermi-LAT data, from which we estimate the source count distribution down to flux levels a factor of 50 below the Fermi-LAT threshold. We perform our analysis using 14 years of data collected in the $(1,10)$ GeV energy range. The results we obtain show a source count distribution which, in the resolved regime, is in excellent agreement with the one derived from catalogued sources, and then extends as $dN/dS \sim S^{-2}$ in the unresolved regime, down to fluxes of $5 \cdot 10^{-12}$ cm$^{-2}$ s$^{-1}$. The neural network architecture and the devised methodology have the flexibility to enable future analyses to study the energy dependence of the source-count distribution., Comment: 26 pages + Appendix, 28 figures

Barchans are crescent-shaped dunes commonly found in diverse environments and scales: from the 10-cm-long barchans found under water to the 1-km-long barchans on Mars, passing by the 100-m-long dunes on Earth's deserts. Although ubiquitous in nature, there is a lack of grain-scale computations of the growth and evolution of those bedforms. In this paper, we investigate the values of grain properties (coefficients of sliding friction, rolling friction and restitution) necessary to carry out numerical simulations of subaqueous barchans with CFD-DEM (computational fluid dynamics - discrete element method), and how the values of those coefficients change the barchan dynamics. We made use of LES (large eddy simulation) for the fluid, varied the coefficients of sliding friction, rolling friction and restitution in the DEM, and compared the outputs with experiments. We show: (i) for the case of glass spheres, the values of coefficients for correctly obtaining the dune morphology, timescales, trajectories of individual grains, and forces experienced by grains; (ii) the LES meshes allowing computations of bedload while capturing the main disturbances of the fluid flow; (iii) how different values of coefficients affect the morphology of barchans; and (iv) that spheres with higher coefficients of rolling friction can be used for simulating barchans consisting of angular grains. Our results represent a significant step for performing simulations that capture, at the same time, details of the fluid flow (large eddies) and grains' motion (individual particles)., Comment: This article may be downloaded for personal use only. Any other use requires prior permission of the author and AIP Publishing. This article appeared in Phys. Fluids 34, 123320 (2022) and may be found at https://doi.org/10.1063/5.0121810

We consider the cosmology and phenomenology of a dark photon portal to a simple dark sector consisting of a single, light, fermionic dark matter particle species with mass in the MeV range. We entertain three possible kinetic mixing structures of a new Abelian gauge group U(1)$_{\text{dark}}$ with the visible sector through U(1)$_{\text{e.m.}}$, U(1)$_{\text{Y}}$ and T[SU(2)$_{\text{L}}$]. We assume the dark photon to be massive and around the MeV scale, thus close to the mass scale of the dark matter candidate. We compute the dark matter relic density via freeze-out and freeze-in, entertaining the additional possibilities of (i) a late inflationary period that could dilute the dark matter yield of heavy candidates, and (ii) additional production modes, for models with under-abundant thermal production. We explore the parameter space compatible with a variety of experimental and astrophysical bounds, and discuss prospects for discovery with new CMB probes and MeV gamma-ray telescopes.

A general theory for the melting of two dimensional solids explaining the universal and non-universal properties is an open problem up to date. Although the celebrated KTHNY theory have been able to predict the critical properties of the melting transition in a variety cases, it is already known that it is not able to capture the occurrence of first order transitions observed in certain systems as well as it doesn't provide a clear way to calculate the melting temperature for a specific model. In the present work we have developed an analytical method that combines Self Consistent Variational Approximation with the Renormalization Group in order to deal simultaneously with the phonon fluctuations and the topological defects present in the melting process of two dimensional crystals. The method was applied with impressive success to the study of the phase diagram of the Gaussian-core model, capturing not only the reentrant feature of its 2D solid phase, but also the related critical temperatures as a function of the density in quantitative detail. The developed method can be directly applied to study the melting of any hexagonal simple crystal formed by particles interacting through any finite pairwise interaction potential. Additionally, it has the potential to explain the occurrence of first order transitions in the melting process of two dimensional crystals.

We investigate numerically how the motion of an intruder within a two-dimensional granular system affects its structure and produces drag on the intruder. We made use of discrete numerical simulations in which a larger disk (intruder) is driven at constant speed amid smaller disks confined in a rectangular cell. By varying the intruder's velocity and the basal friction, we obtained the resultant force on the intruder and the instantaneous network of contact forces, which we analyze at both the cell and grain scales. We found that there is a bearing network that percolates forces from the intruder toward the walls, being responsible for jammed regions and high values of the drag force, and a dissipative network that percolates small forces within the grains, in agreement with previous experiments on compressed granular systems. In addition, we found the anisotropy levels of the contact network for different force magnitudes and regions, that the force network can reach regions far downstream of the intruder by the end of the intruder's motion, that the extent of the force network decreases with decreasing the basal friction, and that the void region (cavity) that appears downstream the intruder tends to disappear for lower values of the basal friction. Interestingly, our results show that grains within the bearing chains creep while the chains break, revealing the mechanism by which bearing chains collapse., Comment: Accepted manuscript for Physical Review E, 105, 034903, (2022), DOI: 10.1103/PhysRevE.105.034903, https://doi.org/10.1103/PhysRevE.105.034903

We analyze the angular power spectrum (APS) of the unresolved gamma-ray background (UGRB) emission and combine it with the measured properties of the resolved gamma-ray sources of the Fermi-LAT 4FGL catalog. Our goals are to dissect the composition of the gamma-ray sky and to establish the relevance of different classes of source populations of active galactic nuclei in determining the observed size of the UGRB anisotropy, especially at low energies. We find that, under physical assumptions for the spectral energy dispersion, i.e. by using the 4FGL catalog data as a prior, two populations are required to fit APS data, namely flat spectrum radio quasars (FSRQs) at low energies and BL Lacs (BLLs) at higher energies. The inferred luminosity functions agree well with the extrapolation of the FSRQ and BLL ones obtained from the 4FLG catalog. We use these luminosity functions to calculate the UGRB intensity from blazars, finding a contribution of 20% at 1GeV and 30% above 10 GeV. Finally, bounds on an additional gamma-ray emission due to annihilating dark matter are derived., Comment: 23 pages, 13 figures, v2: updated to the published version, results are unchanged

The problem of forecasting the whole 24 profile of the Italian electric load is addressed as a multitask learning problem, whose complexity is kept under control via alternative regularization methods. In view of the quarter-hourly samplings, 96 predictors are used, each of which linearly depends on 96 regressors. The 96x96 matrix weights form a 96x96 matrix, that can be seen and displayed as a surface sampled on a square domain. Different regularization and sparsity approaches to reduce the degrees of freedom of the surface were explored, comparing the obtained forecasts with those of the Italian Transmission System Operator Terna. Besides outperforming Terna in terms of quarter-hourly mean absolute percentage error and mean absolute error, the prediction residuals turned out to be weakly correlated with Terna, which suggests that further improvement could ensue from forecasts aggregation. In fact, the aggregated forecasts yielded further relevant drops in terms of quarter-hourly and daily mean absolute percentage error, mean absolute error and root mean square error (up to 30%) over the three test years considered.

This paper investigates numerically the hydraulic conveying of solids through a 90$^{\circ}$ elbow that changes the flow direction from horizontal to vertical, in the very-narrow case where the ratio of pipe to particle diameters is less than 5. We performed resolved CFD-DEM (computational fluid dynamics - discrete element method) computations, in which we made use of the IB (immersed boundary) method of the open-source code CFDEM. We investigate the effects of the water flow and particle injection rate on the transport rate and sedimentation by tracking the granular structures appearing in the pipe, the motion of individual particles, and the contact network of settled particles. We found the saturated transport rate for each water velocity and that a large number of particles settle in the elbow region for smaller velocities, forming a crystal-like lattice that persists in time, and we propose a procedure to mitigate the problem., Comment: This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/. Accepted Manuscript for Powder Technology, v. 395, p. 811-821, 2022, DOI:10.1016/j.powtec.2021.10.022

Many countries are managing COVID-19 epidemic by switching between lighter and heavier restrictions. While an open-close and a close-open cycle have comparable socio-economic costs, the former leads to a much heavier burden in terms of deaths and pressure on the healthcare system. An empirical demonstration of the toll ensuing from procrastination was recently observed in Israel, where both cycles were enforced from late August to mid-December 2020, yielding some 1,600 deaths with open-close compared to 440 with close-open., Comment: 14 pages, 5 figures

Despite the progress in medical care, combined population-wide interventions (such as physical distancing, testing and contact tracing) are still crucial to manage the SARS-CoV-2 pandemic, aggravated by the emergence of new highly transmissible variants. We combine the compartmental SIDARTHE model, predicting the course of COVID-19 infections, with a new data-based model that projects new cases onto casualties and healthcare system costs. Based on the Italian case study, we outline several scenarios: mass vaccination campaigns with different paces, different transmission rates due to new variants, and different enforced countermeasures, including the alternation of opening and closure phases. Our results demonstrate that non-pharmaceutical interventions (NPIs) have a higher impact on the epidemic evolution than vaccination, which advocates for the need to keep containment measures in place throughout the vaccination campaign. We also show that, if intermittent open-close strategies are adopted, deaths and healthcare system costs can be drastically reduced, without any aggravation of socioeconomic losses, as long as one has the foresight to start with a closing phase rather than an opening one.

Diffuse filaments connect galaxy clusters to form the cosmic web. Detecting these filaments could yield information on the magnetic field strength, cosmic ray population and temperature of intercluster gas, yet, the faint and large-scale nature of these bridges makes direct detections very challenging. Using multiple independent all-sky radio and X-ray maps we stack pairs of luminous red galaxies as tracers for cluster pairs. For the first time, we detect an average surface brightness between the clusters from synchrotron (radio) and thermal (X-ray) emission with $\gtrsim 5\sigma$ significance, on physical scales larger than observed to date ($\geq 3\,$Mpc). We obtain a synchrotron spectral index of $\alpha \simeq -1.0$ and estimates of the average magnetic field strength of $ 30 \leq B \leq 60 \,$nG, derived from both equipartition and Inverse Compton arguments, implying a 5 to 15$\,$per cent degree of field regularity when compared with Faraday rotation measure estimates. While the X-ray detection is inline with predictions, the average radio signal comes out higher than predicted by cosmological simulations and dark matter annihilation and decay models. This discovery demonstrates that there are connective structures between mass concentrations that are significantly magnetised, and the presence of sufficient cosmic rays to produce detectable synchrotron radiation., Comment: 21 pages, 3 tables, 14 figures. Accepted by MNRAS May 2021

This paper presents an experimental and numerical investigation of solid-liquid fluidized beds consisting of bonded spheres in very narrow tubes, i.e., when the ratio between the tube and grain diameters is small. In narrow beds, high confinement effects have proved to induce crystallization, jamming and different patterns, which can be intensified or modified if some grains are bonded together. In order to investigate that, we produced duos and trios of bonded aluminum spheres with diameter of 4.8 mm, and formed beds consisting either of 150-300 duos or 100-200 trios in a 25.4 mm-ID pipe, which were submitted to water velocities above those necessary for fluidization. For the experiments, we filmed the bed with high-speed and conventional cameras and processed the images, obtaining measurements at both the bed and grain scales. For the numerical part, we computed the bed evolution for the same conditions with a CFD-DEM (computational fluid dynamics - discrete element method) code. Our results show distinct motions for individual duos and trios, and different structures within the bed. We also found that jamming may occur suddenly for trios, where even the microscopic motion (fluctuation at the grain scale) stops, calling into question the fluidization conditions for those cases., Comment: This article may be downloaded for personal use only. Any other use requires prior permission of the author and AIP Publishing. This article appeared in Phys. Fluids 33, 023303 (2021) and may be found at https://doi.org/10.1063/5.0035718

The extragalactic background light is comprised of the cumulative radiation from all galaxies across the history of the universe. The angular power spectrum of the anisotropies of such a background at near-infrared (IR) frequencies lacks of a complete understanding and shows a robust excess which cannot be easily explained with known sources. Dark matter in the form of axion-like particles (ALPs) with a mass around the electronvolt will decay into two photons with wavelengths in the near-IR band, possibly contributing to the background intensity. We compute the near-IR background angular power spectrum including emissions from galaxies, as well as the contributions from the intra-halo light and ALP decay, and compare it to measurements from the Hubble Space Telescope and Spitzer. We find that the preferred values for the ALP mass and ALP-photon coupling to explain the excess are in tension with star cooling data and observations of dwarf spheroidal galaxies., Comment: Appendices added; version published in JCAP

Light Dark Matter (DM), defined here as having a mass between 1 MeV and about 1 GeV, is an interesting possibility both theoretically and phenomenologically, at one of the frontiers of current progress in the field of DM searches. Its indirect detection via gamma-rays is challenged by the scarcity of experiments in the MeV-GeV region. We look therefore at lower-energy X-ray data from the Integral telescope, and compare them with the predicted DM flux. We derive bounds which are competitive with existing ones from other techniques. Crucially, we include the contribution from inverse Compton scattering on galactic radiation fields and the CMB, which leads to much stronger constraints than in previous studies for DM masses above 20 MeV., Comment: 16 pp+refs, 7 figs. v2: added comments on radiative decays and positron in-flight annihilation. Results unchanged. v3: comments added, on astrophysical uncertainties, on more aggressive bounds including background and on justifying adopted approximations. Results unchanged. v4: figure on astrophysical uncertainties added. Matches PRD version. v5: typo in the units of plots in fig. 2 and 3 fixed

The increasing penetration of intermittent distributed energy resources in power networks calls for novel planning and control methodologies which hinge on detailed knowledge of the grid. However, reliable information concerning the system topology and parameters may be missing or outdated for temporally varying electric distribution networks. This paper proposes an online learning procedure to estimate the network admittance matrix capturing topological information and line parameters. We start off by providing a recursive identification algorithm exploiting phasor measurements of voltages and currents. With the goal of accelerating convergence, we subsequently complement our base algorithm with a design-of-experiment procedure which maximizes the information content of data at each step by computing optimal voltage excitations. Our approach improves on existing techniques, and its effectiveness is substantiated by numerical studies on realistic testbeds.

In this study, we present an innovative technique for speaker adaptation in order to improve the accuracy of segmentation with application to unit-selection Text-To-Speech (TTS) systems. Unlike conventional techniques for speaker adaptation, which attempt to improve the accuracy of the segmentation using acoustic models that are more robust in the face of the speaker's characteristics, we aim to use only context dependent characteristics extrapolated with linguistic analysis techniques. In simple terms, we use the intuitive idea that context dependent information is tightly correlated with the related acoustic waveform. We propose a statistical model, which predicts correcting values to reduce the systematic error produced by a state-of-the-art Hidden Markov Model (HMM) based speech segmentation. Our approach consists of two phases: (1) identifying context-dependent phonetic unit classes (for instance, the class which identifies vowels as being the nucleus of monosyllabic words); and (2) building a regression model that associates the mean error value made by the ASR during the segmentation of a single speaker corpus to each class. The success of the approach is evaluated by comparing the corrected boundaries of units and the state-of-the-art HHM segmentation against a reference alignment, which is supposed to be the optimal solution. In conclusion, our work supplies a first analysis of a model sensitive to speaker-dependent characteristics, robust to defective and noisy information, and a very simple implementation which could be utilized as an alternative to either more expensive speaker-adaptation systems or of numerous manual correction sessions., Comment: isbn:978-88-7870-619-4

The global contribution of unresolved gamma-ray point sources to the extragalactic gamma-ray background has been recently measured down to gamma-ray fluxes lower than those reached with standard source detection techniques, and by employing the statistical properties of the observed gamma-ray counts. We investigate and exploit the complementarity of the information brought by the one-point statistics of photon counts (using more than 10 years of Fermi-LAT data) and by the recent measurement of the angular power spectrum of the unresolved gamma-ray background (based on 8 years of Fermi-LAT data). We determine, under the assumption that the source-count distribution of the brightest unresolved objects is dominated by blazars, their gamma-ray luminosity function and spectral energy distribution down to fluxes almost two orders of magnitude smaller than the threshold for detecting resolved sources. The different approaches provide consistent predictions for the gamma-ray luminosity function of blazars, and they show a significant complementarity., Comment: Matches the published version; extended discussion on detection efficiency, 4FGL fit improved, comments added. Main results and conclusions unchanged

Neutral hydrogen (HI) intensity mapping traces the large-scale distribution of matter in the Universe and therefore should correlate with the gamma-ray emission originated from particle dark matter annihilation or from active galactic nuclei and star-forming galaxies, since the related processes occur in the same cosmic structures hosting HI. In this paper, we derive the cross-correlation signal between the brightness temperature of the 21-cm line emission of the HI spin-flip transition in the Universe and the unresolved gamma-ray background. Specifically, we derive forecasts for the cross-correlation signal by focussing on the opportunities offered by the combination of the Fermi-Large Area Telescope (LAT) gamma-ray sensitivity with the expectations of the HI intensity mapping measurements from future radio telescopes, for which we concentrate on the Square Kilometre Array (SKA) and MeerKAT, one of its precursors. We find that the combination of MeerKAT with the current Fermi-LAT statistics has the potential to provide a first hint of the cross-correlation signal originated by astrophysical sources, with a signal-to-noise ratio (SNR) of 3.7. With SKA Phase 1 and SKA Phase 2, the SNR is predicted to increase up to 5.7 and 8.2, respectively. The bounds on dark matter properties attainable with SKA combined with the current statistics of Fermi-LAT are predicted to be comparable to those obtained from other techniques able to explore the unresolved components of the gamma-ray background. The enhanced capabilities of SKA Phase 2, combined with a future generation gamma-ray telescope with improved specifications, can allow us to investigate the whole mass window for weakly interacting massive particles up to the TeV scale., Comment: 22 pages, 12 figures

The emergence of complex modulated structures in the magnetization pattern of thin films is a well-established experimental phenomenology caused by the frustrating effects of competing interactions. Using a coarse-grained version of the Ising ferromagnet with dipolar interactions, we develop a method that uses the information from the microscopic Hamiltonian to predict the specific topological phases present in the temperature-external magnetic field phase diagram. This is done by the combination of mean-field variational calculations and the renormalization group equations from the classical theory of two-dimensional melting. In this framework, we are able to distinguish when the orientational and translational symmetries are broken, discriminating between the ordered and disordered states of the system for all temperatures and fields. We observe that the reentrance developed by the $H$-$T$ phase diagrams in the regime of weak dipolar interactions is directly related with the appearance of anomalous topological transitions. These results motivate the realization of new experiments on magnetic thin films in order to explore the topological properties of the magnetic textures, allowing to identify new exotic phases in these materials., Comment: 11 pages, 5 figures

A different route to identification of time-invariant linear systems has been recently proposed which does not require committing to a specific parametric model structure. Impulse responses are described in a nonparametric Bayesian framework as zero-mean Gaussian processes. Their covariances are given by the so-called stable spline kernels encoding information on regularity and BIBO stability. In this paper, we demonstrate that these kernels also lead to a new family of radial basis functions kernels suitable to model system components subject to disturbances given by filtered white noise. This novel class, in cooperation with the stable spline kernels, paves the way to a new approach to solve missing data problems in both discrete and continuous-time settings. Numerical experiments show that the new technique may return models more predictive than those obtained by standard parametric Prediction Error Methods, also when these latter exploit the full data set.

We report the identification of a positive cross-correlation signal between the unresolved $\gamma$-ray emission, measured by the \emph{Fermi} Large Area Telescope, and four different galaxy cluster catalogues. The selected catalogues peak at low-redshift and span different frequency bands, including infrared, optical and X-rays. The signal-to-noise ratio of the detected cross-correlation amounts to 3.5 in the most significant case. We investigate and comment about its possible origin, in terms of compact $\gamma$-ray emission from AGNs inside clusters or diffuse emission from the intracluster medium. The analysis has been performed by introducing an accurate estimation of the cross-correlation power-spectrum covariance matrix, built with mock realisations of the gamma and galaxy cluster maps. Different methods to produce the mock realizations starting from the data maps have been investigated and compared, identifying suitable techniques which can be generalized to other cross-correlation studies., Comment: 24 pages, 17 figures

Since the science white paper of the Large High Altitude Air Shower Observatory (LHAASO) published on arXiv in 2019 [e-Print: 1905.02773 (astro-ph.HE)], LHAASO has completed the transition from a project to an operational gamma-ray astronomical observatory LHAASO is a new generation multi-component facility located in Daocheng, Sichuan province of China, at an altitude of 4410 meters. It aims at measuring with unprecedented sensitivity the spectrum, composition, and anisotropy of cosmic rays in the energy range between 10$^{12}$ and 10$^{18}$~eV, and acting simultaneously as a wide aperture (one stereoradiant) continuously operating gamma-ray telescope in the energy range between 10$^{11}$ and $10^{15}$~eV with the designed sensitivity of 1.3\% of the Crab Unit (CU) above 100 TeV. LHAASO's capability of measuring simultaneously different shower components (electrons, muons, and Cherenkov/fluorescence light), will allow it to investigate the origin, acceleration, and propagation of CR through measurement of the energy spectrum, elemental composition, and anisotropy with unprecedented resolution. The remarkable sensitivity of LHAASO will play a key role in CR physics and gamma-ray astronomy for a general and comprehensive exploration of the high energy universe and will allow important studies of fundamental physics (such as indirect dark matter search, Lorentz invariance violation, quantum gravity) and solar and heliospheric physics. The LHAASO Collaboration organized an editorial working group and finished all editorial work of this science book, to summarize the instrumental features and outline the prospects of scientific researches with the LHAASO experiment., Comment: This document is a collaborative effort, 185 pages, 110 figures

It is several years since national research evaluation systems around the globe started making use of quantitative indicators to measure the performance of researchers. Nevertheless, the effects on these systems on the behavior of the evaluated researchers are still largely unknown. We attempt to shed light on this topic by investigating how Italian researchers reacted to the introduction in 2011 of national regulations in which key passages of professional careers are governed by bibliometric indicators. A new inwardness measure, able to gauge the degree of scientific self-referentiality of a country, is defined as the proportion of citations coming from the country itself compared to the total number of citations gathered by the country. Compared to the trends of the other G10 countries in the period 2000-2016, Italy's inwardness shows a net increase after the introduction of the new evaluation rules. Indeed, globally and also for a large majority of the research fields, Italy became the European country with the highest inwardness. Possible explanations are proposed and discussed, concluding that the observed trends are strongly suggestive of a generalized strategic use of citations, both in the form of author self-citations and of citation clubs. We argue that the Italian case offers crucial insights on the constitutive effects of evaluation systems. As such, it could become a paradigmatic case in the debate about the use of indicators in science-policy contexts.

Gas demand is made of three components: Residential, Industrial, and Thermoelectric Gas Demand. Herein, the one-day-ahead prediction of each component is studied, using Italian data as a case study. Statistical properties and relationships with temperature are discussed, as a preliminary step for an effective feature selection. Nine "base forecasters" are implemented and compared: Ridge Regression, Gaussian Processes, Nearest Neighbours, Artificial Neural Networks, Torus Model, LASSO, Elastic Net, Random Forest, and Support Vector Regression (SVR). Based on them, four ensemble predictors are crafted: simple average, weighted average, subset average, and SVR aggregation. We found that ensemble predictors perform consistently better than base ones. Moreover, our models outperformed Transmission System Operator (TSO) predictions in a two-year out-of-sample validation. Such results suggest that combining predictors may lead to significant performance improvements in gas demand forecasting.

In recent years, Twitter data related to political trends have tentatively been used to make predictions (poll) about several electoral events. Given $q$ candidates for an election and a time-series of Twitts (short messages), one can extract the $q$ mean trends and the $q(q+1)/2$ Twitt-to-Twitt correlations, and look for the statistical models that reproduce these data. On the base of several electoral events and assuming a stationary regime, we find out the following: i) the maximization of the entropy singles out a microscopic model (single-Twitt-level) that coincides with a $q$-state Potts model having suitable couplings and external fields to be determined via an inverse problem from the two sets of data; ii) correlations decay as $1/N_{eff}$, where $N_{eff}$ is a small fraction of the mean number of Twitts; iii) the simplest statistical models that reproduce these correlations are the multinomial distribution (MD), characterized by $q$ external fields, and the mean-field Potts model (MFP), characterized by one coupling; iv) remarkably, this coupling turns out to be always close to its critical value. This results in a MD or MFP model scenario that discriminates between cases in which polls are reliable and not reliable, respectively. More precisely, predictions based on polls should be avoided whenever the data maps to a MFP because anomalous large fluctuations (if $q=2$) or sudden jumps (if $q\geq 3$) in the trends might take place as a result of a second-order or a first-order phase transition of the MFP, respectively., Comment: 14 pages, 14 figures

Gas demand forecasting is a critical task for energy providers as it impacts on pipe reservation and stock planning. In this paper, the one-day-ahead forecasting of residential gas demand at country level is investigated by implementing and comparing five models: Ridge Regression, Gaussian Process (GP), k-Nearest Neighbour, Artificial Neural Network (ANN), and Torus Model. Italian demand data from 2007 to 2017 are used for training and testing the proposed algorithms. The choice of the relevant covariates and the most significant aspects of the pre-processing and feature extraction steps are discussed in-depth, lending particular attention to the role of one-day-ahead temperature forecasts. Our best model, in terms of Root Mean Squared Error (RMSE), is the ANN, closely followed by the GP. If the Mean Absolute Error (MAE) is taken as an error measure, the GP becomes the best model, although by a narrow margin. A main novel contribution is the development of a model describing the propagation of temperature errors to gas forecasting errors that is successfully validated on experimental data. Being able to predict the quantitative impact of temperature forecasts on gas forecasts could be useful in order to assess potential improvement margins associated with more sophisticated weather forecasts. On the Italian data, it is shown that temperature forecast errors account for some 18% of the mean squared error of gas demand forecasts provided by ANN.