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The iron-based superconductors (IBSs) of the recently discovered 1144 class, unlike many other IBSs, display superconductivity in their stoichiometric form and are intrinsically hole doped. The effects of chemical substitutions with electron donors are thus particularly interesting to investigate. Here, we study the effect of Co substitution in the Fe site of CaKFe$_4$As$_4$ single crystals on the critical temperature, on the energy gaps, and on the superfluid density by using transport, point-contact Andreev-reflection spectroscopy (PCARS), and London penetration depth measurements. The pristine compound (T$_\mathrm{c}\simeq$ 36 K) shows two isotropic gaps whose amplitudes ($\Delta_1$ = 1.4$-$3.9 meV and $\Delta_2$ = 5.2$-$8.5 meV) are perfectly compatible with those reported in the literature. Upon Co doping (up to $\approx$7% Co), T$_\mathrm{c}$ decreases down to $\simeq$ 20 K, the spin-vortex-crystal order appears, and the low-temperature superfluid density is gradually suppressed. PCARS and London penetration depth measurements perfectly agree in demonstrating that the nodeless multigap structure is robust upon Co doping, while the gap amplitudes decrease as a function of T$_\mathrm{c}$ in a linear way with almost constant values of the gap ratios 2$\Delta_i$/k$_\mathrm{B}$T$_\mathrm{c}$., Comment: 11 pages, 4 figures

FIR and submm observations have established the fundamental role of dust-obscured star formation in the assembly of stellar mass over the past 12 billion years. At z between 2 and 4, the bulk of star formation is enshrouded in dust, and dusty star forming galaxies (DSFGs) contain about half of the total stellar mass density. Star formation develops in dense molecular clouds, and is regulated by a complex interplay between all the ISM components that contribute to the energy budget of a galaxy: gas, dust, cosmic rays, interstellar electromagnetic fields, gravitational field, dark matter. Molecular gas is the actual link between star forming gas and its complex environment, providing by far the richest amount of information about the star formation process. However, molecular lines interpretation requires complex modeling of astrochemical networks, which regulate the molecular formation and establishes molecular abundances in a cloud, and a modeling of the physical conditions of the gas in which molecular energy levels become populated. This paper critically reviews the main astrochemical parameters needed to get predictions about molecular signals in DSFGs. We review the current knowledge and the open questions about the interstellar medium of DSFGs, outlying the key role of molecular gas as a tracer and shaper of the star formation process., Comment: Review paper, 34 pages, accepted for publication on MPDI Galaxies special issue "Molecular gas in Dusty Star-Forming Galaxies at the Cosmic Noon"

In this paper, we present GRASP, a novel graph generative model based on 1) the spectral decomposition of the graph Laplacian matrix and 2) a diffusion process. Specifically, we propose to use a denoising model to sample eigenvectors and eigenvalues from which we can reconstruct the graph Laplacian and adjacency matrix. Our permutation invariant model can also handle node features by concatenating them to the eigenvectors of each node. Using the Laplacian spectrum allows us to naturally capture the structural characteristics of the graph and work directly in the node space while avoiding the quadratic complexity bottleneck that limits the applicability of other methods. This is achieved by truncating the spectrum, which as we show in our experiments results in a faster yet accurate generative process. An extensive set of experiments on both synthetic and real world graphs demonstrates the strengths of our model against state-of-the-art alternatives.

Fostered by upcoming data from new generation observational campaigns, we are about to enter a new era for the study of how galaxies form and evolve. The unprecedented quantity of data that will be collected, from distances only marginally grasped up to now, will require analysis tools designed to target the specific physical peculiarities of the observed sources and handle extremely large datasets. One powerful method to investigate the complex astrophysical processes that govern the properties of galaxies is to model their observed spectral energy distribution (SED) at different stages of evolution and times throughout the history of the Universe. To address these challenges, we have developed GalaPy, a new library for modelling and fitting SEDs of galaxies from the X-ray to the radio band, as well as the evolution of their components and dust attenuation/reradiation. GalaPy incorporates both empirical and physically-motivated star formation histories, state-of-the-art single stellar population synthesis libraries, a two-component dust model for attenuation, an age-dependent energy conservation algorithm to compute dust reradiation, and additional sources of stellar continuum such as synchrotron, nebular/free-free emission and X-ray radiation from low and high mass binary stars. GalaPy has a hybrid implementation that combines the high performance of compiled C++ with the flexibility of Python, and exploits an object-oriented design. It generates models on the fly without relying on templates, and exploits fully Bayesian parameter space sampling. In this first work, we introduce the project and showcase the photometric SED fitting tools already available to users. The library is available on the Python Package Index (PyPI) and comes with extensive online documentation and tutorials., Comment: 41 pages, 31 figures, 7 tables, to be published on A&A, links to documentation and PyPI available in the PDF, comments are very welcome

Neutron star (NS) mergers where both stars have negligible spins are commonly considered as the most likely ``standard'' case. In globular clusters, however, the majority of NSs have been spun up to millisecond (ms) periods and, based on observed systems, we estimate that a non-negligible fraction of all double NS mergers ($\sim 4\pm2\;\%$) contains one component with a spin of a (few) ms. We use the Lagrangian numerical relativity code \SpB to simulate mergers where one star has no spin and the other has a dimensionless spin parameter of $\chi=0.5$. Such mergers exhibit several distinct signatures compared to irrotational cases. They form only one, very pronounced spiral arm and they dynamically eject an order of magnitude more mass of unshocked material at the original, very low electron fraction. One can therefore expect particularly bright, red kilonovae. Overall, the spinning case collisions are substantially less violent and they eject smaller amounts of shock-generated semi-relativistic material. Therefore, the ejecta produce a weaker blue/UV kilonova {\em precursor} signal, but -- since the total amount is larger -- brighter kilonova {\em afterglows} months after the merger. The spinning cases also have significantly more fallback accretion and thus could power late-time X-ray flares. Since the post-merger remnant loses energy and angular momentum significantly less efficiently to gravitational waves, such systems can delay a potential collapse to a black hole and are therefore candidates for merger-triggered gamma-ray bursts with longer emission time scales., Comment: 21 pages, 18 figures; final version, accepted for publication in MNRAS

We present version 1.0 of our Lagrangian Numerical Relativity code SPHINCS_BSSN. This code evolves the full set of Einstein equations, but contrary to other Numerical Relativity codes, it evolves the matter fluid via Lagrangian particles in the framework of a high-accuracy version of Smooth Particle Hydrodynamics (SPH). The major new elements introduced here are: i) a new method to map the stress--energy tensor (known at the particles) to the spacetime mesh, based on a local regression estimate; ii) additional measures that ensure the robust evolution of a neutron star through its collapse to a black hole; and iii) further refinements in how we place the SPH particles for our initial data. The latter are implemented in our code SPHINCS_ID which now, in addition to LORENE, can also couple to initial data produced by the initial data library FUKA. We discuss several simulations of neutron star mergers performed with SPHINCS_BSSN_v1.0, including irrotational cases with and without prompt collapse and a system where only one of the stars has a large spin ($\chi = 0.5)$., Comment: 42 pages, 17 figures

We report ALMA high-resolution observations of water emission lines $p-{\rm{H_2O}} (2_{02}-1_{11}$), $o-{\rm{H_2O}} (3_{21}-3_{12})$, $p-{\rm{H_2O}} (4_{22}-4_{13})$, in the strongly lensed galaxy HATLASJ113526.2-01460 at redshift z $\sim$ 3.1. From the lensing-reconstructed maps of water emission and line profiles, we infer the general physical properties of the ISM in the molecular clouds where the lines arise. We find that the water vapor lines $o-{\rm{H_2O}} (3_{21}-3_{12})$, $p-{\rm{H_2O}} (4_{22}-4_{13})$ are mainly excited by FIR pumping from dust radiation in a warm and dense environment, with dust temperatures ranging from 70 K to $\sim 100$ K, as suggested by the line ratios. The $p-{\rm{H_2O}} (2_{02}-1_{11})$ line instead, is excited by a complex interplay between FIR pumping and collisional excitation in the dense core of the star-forming region. This scenario is also supported by the detection of the medium-level excitation of CO resulting in the line emission CO (J=8-7). Thanks to the unprecedented high resolution offered by the combination of ALMA capabilities and gravitational lensing, we discern the different phases of the ISM and locate the hot molecular clouds into a physical scale of $\sim$ 500 pc. We discuss the possibility of J1135 hosting an AGN in its accretion phase. Finally, we determine the relation between the water emission lines and the total IR luminosity of J1135, as well as the SFR as a function of water emission intensities, comparing the outcomes to local and high-$z$ galactic samples from the literature., Comment: 22 pages, 10 figures, final version to be published in Astrophysical Journal

The iron-based compounds of the so-called 12442 family are very peculiar in various respects. They originate from the intergrowth of 122 and 1111 building blocks, display a large in-plane vs. out-of-plane anisotropy, possess double layers of FeAs separated by insulating layers, and are generally very similar to double-layer cuprates. Moreover, they are stoichiometric superconductors because of an intrinsic hole doping. Establishing their superconducting properties, and in particular the symmetry of the order parameter, is thus particularly relevant in order to understand to what extent these compounds can be considered as the iron-based counterpart of cuprates. In this work we review the results of various techniques from the current literature and compare them with ours, obtained in Rb-12442 by combining point-contact Andreev-reflection spectroscopy and coplanar waveguide resonator measurements of the superfluid density. It turns out that the compound possesses at least two gaps, one of which is certainly nodal. The compatibility of this result with the theoretically allowed gap structures, as well as with the other results in literature, is discussed in detail., Comment: 16 pages, 12 figures

Polarisation Filter Array (PFA) cameras allow the analysis of light polarisation state in a simple and cost-effective manner. Such filter arrays work as the Bayer pattern for colour cameras, sharing similar advantages and drawbacks. Among the others, the raw image must be demosaiced considering the local variations of the PFA and the characteristics of the imaged scene. Non-linear effects, like the cross-talk among neighbouring pixels, are difficult to explicitly model and suggest the potential advantage of a data-driven learning approach. However, the PFA cannot be removed from the sensor, making it difficult to acquire the ground-truth polarization state for training. In this work we propose a novel CNN-based model which directly demosaics the raw camera image to a per-pixel Stokes vector. Our contribution is twofold. First, we propose a network architecture composed by a sequence of Mosaiced Convolutions operating coherently with the local arrangement of the different filters. Second, we introduce a new method, employing a consumer LCD screen, to effectively acquire real-world data for training. The process is designed to be invariant by monitor gamma and external lighting conditions. We extensively compared our method against algorithmic and learning-based demosaicing techniques, obtaining a consistently lower error especially in terms of polarisation angle.

We calculated the critical temperature in the framework of s+-+d-wave multiband Eliashberg theory. We have solved these equations numerically to see at what values of the input parameters there is a solution with a non-zero critical temperature and what is the symmetry of the order parameter of this solution. For our model we consider the pnictide case with simplifications that allow us to obtain the most general possible information. For selected and representative cases in which the order parameter has s+-+d symmetry, we calculated the superconducting density of states, the temperature dependence of the gaps, and the superfluid density so that comparison with experimental data can be made. Finally, we show that such a system has only a twofold in-plane symmetry and undergoes a transition from nodal to fully gapped with increasing temperature., Comment: 8 Figures, accepted on Physica C

Niobium is one of the most studied superconductors, both theoretically and experimentally. It is tremendously important for applications, and it has the highest superconducting transition temperature, $T_{c}=9.33$ K, of all pure metals. In addition to power applications in alloys, pure niobium is used for sensitive magneto-sensing, radio-frequency cavities, and, more recently, as circuit metallization layers in superconducting qubits. A detailed understanding of its electronic and superconducting structure, especially its normal and superconducting state anisotropies, is crucial for mitigating the loss of quantum coherence in such devices. Recently, a microscopic theory of the anisotropic properties of niobium with the disorder was put forward. To verify theoretical predictions, we studied the effect of disorder produced by 3.5 MeV proton irradiation of thin Nb films grown by the same team and using the same protocols as those used in transmon qubits. By measuring the superconducting transition temperature and upper critical fields, we show a clear suppression of $T_{c}$ by potential (non-magnetic) scattering, which is directly related to the anisotropic order parameter. We obtain a very close quantitative agreement between the theory and the experiment.

We present high-resolution ($\lesssim0.1$arcsec) ALMA observations of the strongly-lensed galaxy HATLASJ113526.2-01460 at redshift $z\sim3.1$ discovered in the Gama 12$^{\rm th}$ field of the Herschel-ATLAS survey. The gravitationally lensed system is remarkably peculiar in that neither the background source nor the foreground lens show a clearly detected optical/NIR emission. We perform accurate lens modeling and source morphology reconstruction in three different (sub-)mm continuum bands, and in the C[II] and CO(8-7) spectral lines. The modeling indicates a foreground lensing (likely elliptical) galaxy with mass $\gtrsim10^{11}\, M_\odot$ at $z\gtrsim1.5$, while the source (sub-)mm continuum and line emissions are amplified by factors $\mu\sim6-13$. We estimate extremely compact sizes $\lesssim0.5$ kpc for the star-forming region and $\lesssim 1$ kpc for the gas component, with no clear evidence of rotation or of ongoing merging events. We perform broadband SED-fitting and retrieve the intrinsic de-magnified physical properties of the source, which is found to feature a very high star-formation rate $\gtrsim10^3\, M_\odot$ yr$^{-1}$, that given the compact sizes is on the verge of the Eddington limit for starbursts; the radio luminosity at 6 cm from available EVLA observations is consistent with the star-formation activity. The galaxy is found to be extremely rich in gas $\sim10^{11}\, M_\odot$ and dust $\gtrsim10^9\, M_\odot$. The stellar content $\lesssim10^{11}\, M_\odot$ places the source well above the main sequence of starforming galaxies, indicating that the starburst is rather young with estimated age $\sim10^8$ yr. Our results indicate that the overall properties of HATLASJ113526.2-01460 are consistently explained by in-situ galaxy formation and evolution scenarios., Comment: 17 Pages, 12 Figures, 7 Tables. Submitted to ApJ

To date, essentially all simulation codes that solve the full set of Einstein's equations are performed in the framework of Eulerian hydrodynamics. The exception is our recently developed Numerical Relativity code SPHINCS_BSSN which solves the commonly used BSSN formulation of the Einstein equations on a structured mesh and the matter equations via Lagrangian particles. We show here, for the first time, SPHINCS_BSSN neutron star merger simulations with piecewise polytropic approximations to four nuclear matter equations of state. In this set of neutron star merger simulations we focus on perfectly symmetric binary systems that are irrotational and have 1.3 $M_\odot$ masses. We introduce some further methodological refinements (a new way of steering dissipation, an improved particle-mesh mapping) and we explore the impact of the exponent that enters in the calculation of the thermal pressure contribution. We find that it leaves a noticeable imprint on the gravitational wave amplitude (calculated via both quadrupole approximation and the $\Psi_4$-formalism) and has a noticeable impact on the amount of dynamic ejecta. Consistent with earlier findings, we only find a few times $10^{-3}$ \Msun as dynamic ejecta in the studied equal mass binary systems, with softer equations of state (which are more prone to shock formation) ejecting larger amounts of matter. In all of the cases, we see a credible high-velocity ($\sim0.5 .. 0.7c$) ejecta component of $\sim 10^{-4}$ \Msun that is launched at contact from the interface between the two neutron stars. Such a high-velocity component has been suggested to produce an early, blue precursor to the main kilonova emission and it could also potentially cause a kilonova afterglow., Comment: 36 pages, accepted for publication in Symmetry

In this paper, we investigate the continual learning of Vision Transformers (ViT) for the challenging exemplar-free scenario, with special focus on how to efficiently distill the knowledge of its crucial self-attention mechanism (SAM). Our work takes an initial step towards a surgical investigation of SAM for designing coherent continual learning methods in ViTs. We first carry out an evaluation of established continual learning regularization techniques. We then examine the effect of regularization when applied to two key enablers of SAM: (a) the contextualized embedding layers, for their ability to capture well-scaled representations with respect to the values, and (b) the prescaled attention maps, for carrying value-independent global contextual information. We depict the perks of each distilling strategy on two image recognition benchmarks (CIFAR100 and ImageNet-32) -- while (a) leads to a better overall accuracy, (b) helps enhance the rigidity by maintaining competitive performances. Furthermore, we identify the limitation imposed by the symmetric nature of regularization losses. To alleviate this, we propose an asymmetric variant and apply it to the pooled output distillation (POD) loss adapted for ViTs. Our experiments confirm that introducing asymmetry to POD boosts its plasticity while retaining stability across (a) and (b). Moreover, we acknowledge low forgetting measures for all the compared methods, indicating that ViTs might be naturally inclined continual learner, Comment: Accepted at Continual Learning Workshop (CVPR 2022)

We present the first neutron star merger simulations performed with the newly developed Numerical Relativity code SPHINCS_BSSN. This code evolves the spacetime on a mesh using the BSSN formulation, but matter is evolved via Lagrangian particles according to a high-accuracy version of general-relativistic Smooth Particle Hydrodynamics (SPH). Our code contains a number of new methodological elements compared to other Numerical Relativity codes. The main focus here is on the new elements that were introduced to model neutron star mergers. These include a) a refinement (fixed in time) of the spacetime-mesh, b) corresponding changes in the particle--mesh mapping algorithm and c) a novel way to construct SPH initial data for binary systems via the recently developed "Artificial Pressure Method." This latter method makes use of the spectral initial data produced by the library LORENE, and is implemented in a new code called SPHINCS_ID. While our main focus is on introducing these new methodological elements and documenting the current status of SPHINCS_BSSN, we also show as a first application a set of neutron star merger simulations employing "soft" ($\Gamma=2.00$) and "stiff" ($\Gamma=2.75$) polytropic equations of state., Comment: 29 pages, 14 figures; small wording changes after referee reports

We report on the investigation of the magnetic superconductor EuFe$_{2}$(As$_{0.7}$P$_{0.3}$)$_{2}$ based on muon-spin spectroscopy and ac magnetic susceptibility ($\chi$) measurements. The dependence of the internal field at the muon site on temperature is indicative of a ferromagnetic ordering of Eu$^{2+}$ magnetic moments and only the conventional magnon scattering governs the longitudinal relaxation rate at low temperatures. At the same time, we observe a rich phenomenology for the imaginary component of the susceptibility $\chi^{\prime\prime}$ by means of both standard ac susceptibility and a novel technique based on a microwave coplanar waveguide resonator. In particular, we detect activated trends for several features in $\chi^{\prime\prime}$ over frequencies spanning ten orders of magnitude. We interpret our results in terms of the complex dynamics of vortices and antivortices influenced by the underlying structure of magnetic domains., Comment: 8 pages, 7 figures

The 12442 compounds are a recently discovered family of iron-based superconductors, that share several features with the cuprates due to their strongly anisotropic structure, but are so far poorly understood. Here, we report on the gap structure and anisotropy of RbCa2(Fe1-xNix)4As4F2 single crystals, investigated by a combination of directional point-contact Andreev-reflection spectroscopy and coplanar waveguide resonator measurements. Two gaps were identified, with clear signatures of d-wave-like nodal structures which persist upon Ni doping, well described by a two-band d-d state with symmetry-imposed nodes. A large London penetration depth anisotropy was revealed, weakly dependent on temperature and fully compatible with the d-d model., Comment: 9 pages, 4 figures

The convolution operator at the core of many modern neural architectures can effectively be seen as performing a dot product between an input matrix and a filter. While this is readily applicable to data such as images, which can be represented as regular grids in the Euclidean space, extending the convolution operator to work on graphs proves more challenging, due to their irregular structure. In this paper, we propose to use graph kernels, i.e. kernel functions that compute an inner product on graphs, to extend the standard convolution operator to the graph domain. This allows us to define an entirely structural model that does not require computing the embedding of the input graph. Our architecture allows to plug-in any type of graph kernels and has the added benefit of providing some interpretability in terms of the structural masks that are learned during the training process, similarly to what happens for convolutional masks in traditional convolutional neural networks. We perform an extensive ablation study to investigate the model hyper-parameters' impact and show that our model achieves competitive performance on standard graph classification and regression datasets., Comment: IEEE Transactions on Neural Networks and Learning Systems (2024)

The design of machines and algorithms capable of learning in a dynamically changing environment has become an increasingly topical problem with the increase of the size and heterogeneity of data available to learning systems. As a consequence, the key issue of Continual Learning has become that of addressing the stability-plasticity dilemma of connectionist systems, as they need to adapt their model without forgetting previously acquired knowledge. Within this context, rehearsal-based methods i.e., solutions in where the learner exploits memory to revisit past data, has proven to be very effective, leading to performance at the state-of-the-art. In our study, we propose an analysis of the memory quantity/quality trade-off adopting various data reduction approaches to increase the number of instances storable in memory. In particular, we investigate complex instance compression techniques such as deep encoders, but also trivial approaches such as image resizing and linear dimensionality reduction. Our findings suggest that the optimal trade-off is severely skewed toward instance quantity, where rehearsal approaches with several heavily compressed instances easily outperform state-of-the-art approaches with the same amount of memory at their disposal. Further, in high memory configurations, deep approaches extracting spatial structure combined with extreme resizing (of the order of $8\times8$ images) yield the best results, while in memory-constrained configurations where deep approaches cannot be used due to their memory requirement in training, Extreme Learning Machines (ELM) offer a clear advantage., Comment: Accepted at IJCNN 2022

Static (DC) and dynamic (AC, at 14 MHz and 8 GHz) magnetic susceptibilities of single crystals of a ferromagnetic superconductor, $\textrm{EuFe}_{2}(\textrm{As}_{1-x}\textrm{P}_{x})_{2}$ (x = 0.23), were measured in pristine state and after different doses of 2.5 MeV electron or 3.5 MeV proton irradiation. The superconducting transition temperature, $T_{c}(H)$, shows an extraordinarily large decrease. It starts at $T_{c}(H=0)\approx24\:\textrm{K}$ in the pristine sample for both AC and DC measurements, but moves to almost half of that value after moderate irradiation dose. Our results suggest that in $\textrm{EuFe}_{2}(\textrm{As}_{1-x}\textrm{P}_{x})_{2}$ superconductivity is affected by local-moment ferromagnetism mostly via the spontaneous internal magnetic fields induced by the FM subsystem. Another mechanism is revealed upon irradiation where magnetic defects created in ordered $\text{Eu}^{2+}$ lattice act as efficient pairbreakers leading to a significant $T_{c}$ reduction upon irradiation compared to other 122 compounds. On the other hand, the exchange interactions seem to be weakly screened by the superconducting phase leading to a modest increase of $T_{m}$ (less than 1 K) after the irradiation drives $T_{c}$ to below $T_{m}$. The results suggest that FM and SC phases coexist microscopically in the same volume.

We propose a new fast fully unsupervised method to discover semantic patterns. Our algorithm is able to hierarchically find visual categories and produce a segmentation mask where previous methods fail. Through the modeling of what is a visual pattern in an image, we introduce the notion of "semantic levels" and devise a conceptual framework along with measures and a dedicated benchmark dataset for future comparisons. Our algorithm is composed by two phases. A filtering phase, which selects semantical hotsposts by means of an accumulator space, then a clustering phase which propagates the semantic properties of the hotspots on a superpixels basis. We provide both qualitative and quantitative experimental validation, achieving optimal results in terms of robustness to noise and semantic consistency. We also made code and dataset publicly available.