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Superclusters are the most massive structures in the universe. To what degree they are actually bound against an accelerating expansion of the background is of significant cosmological and astrophysical interest. In this study, we introduce a cross matched set of superclusters from the SLOW constrained simulations of the local (z<0.05) universe. Identifying the superclusters provides estimates on the efficacy of the constraints in reproducing the local large-scale structure accurately. The simulated counterparts can help identifying possible future observational targets containing interesting features such as bridges between pre-merging and merging galaxy clusters and collapsing filaments and provide comparisons for current observations. By determining the collapse volumes for the superclusters we further elucidate the dynamics of cluster-cluster interactions in those regions. Using catalogs of local superclusters and the most massive simulated clusters, we search for counterparts of supercluster members of six regions. We evaluate the significance of these detections by comparing their geometries to supercluster regions in random simulations. We then run an N-body version of the simulation into the far future and determine which of the member clusters are gravitationally bound to the host superclusters. Furthermore we compute masses and density contrasts for the collapse regions. We demonstrate the SLOW simulation of the local universe to accurately reproduce local supercluster regions in mass of their members and three-dimensional geometrical arrangement. We furthermore find the bound regions of the local superclusters consistent in size and density contrast with previous theoretical studies. This will allow to connect future numerical zoom-in studies of the clusters to the large scale environments and specifically the supercluster environments these local galaxy clusters evolve in., Comment: 18 pages, 20 figures, submitted to A&A

We present a study on the inference of cosmological and astrophysical parameters using stacked galaxy cluster profiles. Utilizing the CAMELS-zoomGZ simulations, we explore how various cluster properties--such as X-ray surface brightness, gas density, temperature, metallicity, and Compton-y profiles--can be used to predict parameters within the 28-dimensional parameter space of the IllustrisTNG model. Through neural networks, we achieve a high correlation coefficient of 0.97 or above for all cosmological parameters, including $\Omega_{\rm m}$, $H_0$, and $\sigma_8$, and over 0.90 for the remaining astrophysical parameters, showcasing the effectiveness of these profiles for parameter inference. We investigate the impact of different radial cuts, with bins ranging from $0.1R_{200c}$ to $0.7R_{200c}$, to simulate current observational constraints. Additionally, we perform a noise sensitivity analysis, adding up to 40\% Gaussian noise (corresponding to signal-to-noise ratios as low as 2.5), revealing that key parameters such as $\Omega_{\rm m}$, $H_0$, and the IMF slope remain robust even under extreme noise conditions. We also compare the performance of full radial profiles against integrated quantities, finding that profiles generally lead to more accurate parameter inferences. Our results demonstrate that stacked galaxy cluster profiles contain crucial information on both astrophysical processes within groups and clusters and the underlying cosmology of the universe. This underscores their significance for interpreting the complex data expected from next-generation surveys and reveals, for the first time, their potential as a powerful tool for parameter inference., Comment: Submitted to ApJ

We propose a data-driven approach using a Restricted Boltzmann Machine (RBM) to solve the Schr\"odinger equation in configuration space. Traditional Configuration Interaction (CI) methods, while powerful, are computationally expensive due to the large number of determinants required. Our approach leverages RBMs to efficiently identify and sample the most significant determinants, accelerating convergence and reducing computational cost. This method achieves up to 99.99\% of the correlation energy even by four orders of magnitude less determinants compared to full CI calculations and up to two orders of magnitude less than previous state of the art works. Additionally, our study demonstrate that the RBM can learn the underlying quantum properties, providing more detail insights than other methods . This innovative data-driven approach offers a promising tool for quantum chemistry, enhancing both efficiency and understanding of complex systems., Comment: Preprint to be submitted to Computer Physics Communications

(abridged) We analyze the emission of axion quark nuggets in a large sample of 161 simulated galaxy clusters using the SLOW simulation. These clusters are divided into a sub-sample of 150 galaxy clusters, ordered in five mass bins ranging from $0.8$ to $31.7 \times 10^{14} \,M_\odot$, along with 11 cross-identified galaxy clusters from observations. We investigate dark matter-baryonic matter interactions in galaxy clusters in their present stage at redshift $z=0$ by assuming all dark matter consists of axion quark nuggets. The resulting electromagnetic signatures are compared to thermal Bremsstrahlung and non-thermal cosmic ray synchrotron emission in each galaxy cluster. We further investigate individual frequency bands imitating the observable range of the WMAP, Planck, Euclid, and XRISM telescopes for the most promising cross-identified galaxy clusters hosting detectable signatures of axion quark nugget emission. We propose that the Fornax and Virgo clusters represent the most promising candidates to search for axion quark nugget emission signatures., Comment: 22 pages, 14 figures

This is the second paper in a series presenting the results from a 500 $h^{-1}$Mpc large constrained hydro-dynamical simulation of the local Universe (SLOW). The initial conditions are based on peculiar velocities derived from the CosmicFlows-2 catalogue. The inclusion of galaxy formation treatment, allows to directly predict observable properties of the Intra-Cluster Medium (ICM) within galaxy clusters. Comparing the properties of observed galaxy clusters within the local Universe with the properties of their simulated counterparts, enables us to assess the effectiveness of the initial condition constraints in accurately replicating the non-linear properties of the largest, collapsed objects within the simulation. Based on the combination of several, publicly available surveys, we identified 45 local Universe galaxy clusters in SLOW, including the 13 most massive from the Planck SZ catalog and 70% of those with $M_{500} > 2\times 10^{14}$ M$_{\odot}$. We then derived the probability of the cross identification based on mass, X-ray luminosity, temperature and Compton-y by comparing it to a random selection. In relation to previous constrained simulations of the local volume, we found in SLOW a much larger amount of replicated galaxy clusters, where their simulation based mass prediction falls within the uncertainties of the observational mass estimates. Comparing the median observed and simulated masses of our cross identified sample allows to independently deduce a hydrostatic mass bias of $(1-b)\approx0.87$. The SLOW constrained simulation of the local Universe faithfully reproduces numerous fundamental characteristics of the galaxy clusters within our local neighbourhood, opening a new avenue for studying the formation and evolution of a large set of individual galaxy clusters as well as testing our understanding of physical processes governing the ICM., Comment: Submitted to Astronomy & Astrophysics

The present paper assesses the efficacy of code live-load models in accurately estimating the vehicular loads transferred to bridge substructures, such as abutments, piers, and foundations. Realistic traffic vehicle data are represented using four Weigh-in-Motion databases, which provide an authentic representation of vehicle information, thus providing a realistic basis for the examination of the bridges studied. The evaluation includes various bridge models, such as single-span girder bridges and two-, three-, and four-span continuous pinned-support girder bridges. By analyzing exceedance rates, the study compares the extreme force values obtained for vehicles in the databases with those predicted by selected code live-load models. These exceedance rates are presented in spectra format, as a function of the span length. The significant variations observed in the exceedance rates highlight the need for improving existing code live-load models to achieve more accurate estimations of the forces transferred to bridge substructures. Such improvements would lead to more uniform reliability levels for any limit state, such as resistance, fatigue, serviceability, and cracking.

The entanglement production is key for many applications in the realm of quantum information, but so is the identification of processes that allow to create entanglement in a fast and sustained way. Most of the advances in this direction have been circumscribed to bipartite systems only, and the rate of entanglement in multipartite system has been much less explored.Here we contribute to the identification of processes that favor the fastest and sustained generation of tripartite entanglement in a class of 3-qubit GHZ-type states. By considering a three-party interaction Hamiltonian, we analyse the dynamics of the 3-tangle and the entanglement rate to identify the optimal local operations that supplement the Hamiltonian evolution in order to speed-up the generation of three-way entanglement, and to prevent its decay below a predetermined threshold value. The appropriate local operation that maximizes the speed at which a highly-entangled state is reached has the advantage of requiring access to only one of the qubits, yet depends on the actual state of the system. Other universal (state-independent) local operations are found that conform schemes to maintain a sufficiently high amount of 3-tangle. Our results expand our understanding of entanglement rates to multipartite systems, and offer guidance regarding the strategies that improve the efficiency in various quantum information processing tasks., Comment: 11 pages, 11 figures

It has been recently shown that a powerful way to constrain cosmological parameters from galaxy redshift surveys is to train graph neural networks to perform field-level likelihood-free inference without imposing cuts on scale. In particular, de Santi et al. (2023) developed models that could accurately infer the value of $\Omega_{\rm m}$ from catalogs that only contain the positions and radial velocities of galaxies that are robust to uncertainties in astrophysics and subgrid models. However, observations are affected by many effects, including 1) masking, 2) uncertainties in peculiar velocities and radial distances, and 3) different galaxy selections. Moreover, observations only allow us to measure redshift, intertwining galaxies' radial positions and velocities. In this paper we train and test our models on galaxy catalogs, created from thousands of state-of-the-art hydrodynamic simulations run with different codes from the CAMELS project, that incorporate these observational effects. We find that, although the presence of these effects degrades the precision and accuracy of the models, and increases the fraction of catalogs where the model breaks down, the fraction of galaxy catalogs where the model performs well is over 90 %, demonstrating the potential of these models to constrain cosmological parameters even when applied to real data., Comment: 39 pages, 25 figures. For the reference in the abstract (de Santi et al. 2023) see arXiv:2302.14101

Aims: Detecting diffuse synchrotron emission from the cosmic web is still a challenge for current radio telescopes. We aim to make predictions about the detectability of cosmic web filaments from simulations. Methods: We present the first cosmological magnetohydrodynamic simulation of a 500 $h^{-1} c$Mpc volume with an on-the-fly spectral cosmic ray (CR) model. This allows us to follow the evolution of populations of CR electrons and protons within every resolution element of the simulation. We modeled CR injection at shocks, while accounting for adiabatic changes to the CR population and high-energy-loss processes of electrons. The synchrotron emission was then calculated from the aged electron population, using the simulated magnetic field, as well as different models for the origin and amplification of magnetic fields. We used constrained initial conditions, which closely resemble the local Universe, and compared the results of the cosmological volume to a zoom-in simulation of the Coma cluster, to study the impact of resolution and turbulent reacceleration of CRs on the results. Results: We find a consistent injection of CRs at accretion shocks onto cosmic web filaments and galaxy clusters. This leads to diffuse emission from filaments of the order $S_\nu \approx 0.1 \: \mu$Jy beam$^{-1}$ for a potential LOFAR observation at 144~MHz, when assuming the most optimistic magnetic field model. The flux can be increased by up to two orders of magnitude for different choices of CR injection parameters. This can bring the flux within a factor of ten of the current limits for direct detection. We find a spectral index of the simulated synchrotron emission from filaments of $\alpha \approx$ -1.0 -- -1.5 in the LOFAR band., Comment: 26 pages, 18 figures, accepted for publication in A&A

When measuring the observed pressure, density or temperature profiles of the intracluster gas, and hence the mass of clusters of galaxies, projection effects or departures from the spherical symmetry hypothesis may induce biases. In order to estimate how strongly the cluster observed properties depend on the direction of observation, we use a constrained hydrodynamical simulation of Virgo cluster that replicates the actual cluster of galaxies. In this case study, we analyse Virgo properties when they are projected along different directions including along the Milky Way-Virgo axis which mimics our observation direction. We compare the hydrostatic mass and the hydrostatic mass bias from the projection along the different observation directions to that derived from the 3D simulation. We show that projection effects impact the determination of Virgo mass. We particularly demonstrate that the mass and pressure along the line of sight correlates with the 2D and 3D-deprojected electron density and pressure profiles intensity and thus impacts the derived hydrostatic mass. We also show that the deviations to the hydrostatic equilibrium induced by pressure discontinuities within the cluster are emphasized by the deprojection process and thus makes the hydrostatic mass estimation invalid at these radii., Comment: Published in A&A, proofs version, 18 pages, 10 figures and 3 tables

We have studied the evolution of HuBi 1-like planetary nebulae, considering several stages of mass injection. We have carried out numerical ionization+1D hydrodynamics+atomic/ionic rate models with our code Coral 1D to reproduce planetary nebulae that present multiple shells produced by different ejection events around the ionizing source. Furthermore, we are interested in comparing numerical simulations with H$\alpha$ and [NII]$\lambda$6584 emission structures and the position-velocity diagrams observed in HuBi 1. This object also has a phase where it has drastically decreased the injection of ionized photons ejected from the source. The result of these different stages of ejection is a nebula with intense [NII] line emission in the inner part of the planetary nebula and an extended HII recombination line emission around the central zone. The model for HuBi 1 shows the capability of our code to explain the hydrodynamical and photoionization evolution in ionization nebulae. This is our first step with a 1D code to study these two physical phenomena at the same time., Comment: accepted

The forces acting on bridge structural elements caused by live loads are computed by live load models defined in design codes. In most cases, such live load models are defined by studies performed on girder bridges, where extreme values of shear forces and bending moment are intended to be predicted. This paper shows that when code live load models are applied to truss bridges, the estimated forces in some structural elements may not be representative of those caused by actual traffic. Three WIM (weigh-in-motion) databases, which are recorded on roads in Mexico, are used as real traffic data. The results suggest that current code live load models are not entirely adequate to estimate forces in structural elements of truss bridges.

The strength of electrostatic interactions (EI) between electrons and holes within semiconductor nanocrystals profoundly impact the performance of their optoelectronic systems, and different optoelectronic devices demand distinct EI strength of the active medium. However, achieving a broad range, fine-tuning of the EI strength for specific optoelectronic applications is a daunting challenge, especially in quasi 2-dimensional core-shell semiconductor nanoplatelets (NPLs), as the epitaxial growth of the inorganic shell along the direction of the thickness that solely contributes to the quantum confined effect significantly undermines the strength of the EI. Herein we propose and demonstrate a novel doubly-gradient (DG) core-shell architecture of semiconductor NPLs for on-demand tailoring of the EI strength by controlling the localized exciton concentration via in-plane architectural modulation, demonstrated by a wide tuning of radiative recombination rate and exciton binding energy. Moreover, these exciton-concentration-engineered DG NPLs also exhibit a near-unity quantum yield, remarkable thermal and photo stability, as well as considerably suppressed self-absorption. As proof-of-concept demonstrations, highly efficient color converters and high-performance light-emitting diodes (external quantum efficiency: 16.9%, maximum luminance: 43,000 cd/m2) have been achieved based on the DG NPLs. This work thus opens up new avenues for developing high-performance colloidal optoelectronic device applications.

It is well known that Sobolev embeddings can be improved in the presence of symmetries. In this article, we considere the situation in which given a domain $\Omega=\Omega_1 \times \Omega_2$ in $\mathbb{R}^N$ with a cylindrical symmetry, and acting a group $G$ in $\Omega_1$, for this situation it is shown that the critical Sobolev exponent increases in the case of embeddings into weighted spaces $L^{q}_{h}(\Omega)$. In this paper, we will enunciate several results based from theorems by Wang, helping us with results by Hebey-Vaugon related to compact embeddings of a Sobolev space with radially symmetric functions into some weighted space $L^{q}$, with $q$ higher than the usual critical exponent., Comment: 17 pages

Recent work has pointed out the potential existence of a tight relation between the cosmological parameter $\Omega_{\rm m}$, at fixed $\Omega_{\rm b}$, and the properties of individual galaxies in state-of-the-art cosmological hydrodynamic simulations. In this paper, we investigate whether such a relation also holds for galaxies from simulations run with a different code that made use of a distinct subgrid physics: Astrid. We find that also in this case, neural networks are able to infer the value of $\Omega_{\rm m}$ with a $\sim10\%$ precision from the properties of individual galaxies while accounting for astrophysics uncertainties as modeled in CAMELS. This tight relationship is present at all considered redshifts, $z\leq3$, and the stellar mass, the stellar metallicity, and the maximum circular velocity are among the most important galaxy properties behind the relation. In order to use this method with real galaxies, one needs to quantify its robustness: the accuracy of the model when tested on galaxies generated by codes different from the one used for training. We quantify the robustness of the models by testing them on galaxies from four different codes: IllustrisTNG, SIMBA, Astrid, and Magneticum. We show that the models perform well on a large fraction of the galaxies, but fail dramatically on a small fraction of them. Removing these outliers significantly improves the accuracy of the models across simulation codes., Comment: 16 pages, 12 figures

We discover analytic equations that can infer the value of $\Omega_{\rm m}$ from the positions and velocity moduli of halo and galaxy catalogues. The equations are derived by combining a tailored graph neural network (GNN) architecture with symbolic regression. We first train the GNN on dark matter halos from Gadget N-body simulations to perform field-level likelihood-free inference, and show that our model can infer $\Omega_{\rm m}$ with $\sim6\%$ accuracy from halo catalogues of thousands of N-body simulations run with six different codes: Abacus, CUBEP$^3$M, Gadget, Enzo, PKDGrav3, and Ramses. By applying symbolic regression to the different parts comprising the GNN, we derive equations that can predict $\Omega_{\rm m}$ from halo catalogues of simulations run with all of the above codes with accuracies similar to those of the GNN. We show that by tuning a single free parameter, our equations can also infer the value of $\Omega_{\rm m}$ from galaxy catalogues of thousands of state-of-the-art hydrodynamic simulations of the CAMELS project, each with a different astrophysics model, run with five distinct codes that employ different subgrid physics: IllustrisTNG, SIMBA, Astrid, Magneticum, SWIFT-EAGLE. Furthermore, the equations also perform well when tested on galaxy catalogues from simulations covering a vast region in parameter space that samples variations in 5 cosmological and 23 astrophysical parameters. We speculate that the equations may reflect the existence of a fundamental physics relation between the phase-space distribution of generic tracers and $\Omega_{\rm m}$, one that is not affected by galaxy formation physics down to scales as small as $10~h^{-1}{\rm kpc}$., Comment: 32 pages, 13 figures, summary video: https://youtu.be/STZHvDHkVgo

We train graph neural networks to perform field-level likelihood-free inference using galaxy catalogs from state-of-the-art hydrodynamic simulations of the CAMELS project. Our models are rotational, translational, and permutation invariant and do not impose any cut on scale. From galaxy catalogs that only contain $3$D positions and radial velocities of $\sim 1, 000$ galaxies in tiny $(25~h^{-1}{\rm Mpc})^3$ volumes our models can infer the value of $\Omega_{\rm m}$ with approximately $12$ % precision. More importantly, by testing the models on galaxy catalogs from thousands of hydrodynamic simulations, each having a different efficiency of supernova and AGN feedback, run with five different codes and subgrid models - IllustrisTNG, SIMBA, Astrid, Magneticum, SWIFT-EAGLE -, we find that our models are robust to changes in astrophysics, subgrid physics, and subhalo/galaxy finder. Furthermore, we test our models on $1,024$ simulations that cover a vast region in parameter space - variations in $5$ cosmological and $23$ astrophysical parameters - finding that the model extrapolates really well. Our results indicate that the key to building a robust model is the use of both galaxy positions and velocities, suggesting that the network have likely learned an underlying physical relation that does not depend on galaxy formation and is valid on scales larger than $\sim10~h^{-1}{\rm kpc}$., Comment: 34 pages, 12 figures. For a video summarizing the results, see https://youtu.be/b59ep7cyPOs

Context: Several observations of the local Universe (LU) point towards the existence of very prominent structures. The presence of massive galaxy clusters and local super clusters on the one hand, but also large local voids and under-densities on the other hand. However, it is highly non trivial to connect such different observational selected tracers to the underlying dark matter (DM) distribution. Methods (abridged): We used a 500 Mpc/h large constrained simulation of the LU with initial conditions based on peculiar velocities derived from the CosmicFlows-2 catalogue and follow galaxy formation physics directly in the hydro-dynamical simulations to base the comparison on stellar masses of galaxies or X-ray luminosity of clusters. We also used the 2668 Mpc/h large cosmological box from the Magneticum simulations to evaluate the frequency of finding such anomalies in random patches within simulations. Results: We demonstrate that haloes and galaxies in our constrained simulation trace the local DM density field very differently. Thereby, this simulation reproduces the observed 50% under-density of galaxy clusters and groups within the sphere of ~100 Mpc when applying the same mass or X-ray luminosity limit used in the observed cluster sample (CLASSIX), which is consistent with a ~1.5$\sigma$ feature. At the same time, the simulation reproduces the observed over-density of massive galaxy clusters within the same sphere, which on its own also corresponds to a ~1.5$\sigma$ feature. Interestingly, we find that only 44 out of 15635 random realizations (i.e. 0.28%) are matching both anomalies, making the LU to be a ~3$\sigma$ environment. We finally compared a mock galaxy catalogue with the observed distribution of galaxies in the LU, finding also a match to the observed factor of two over-density at ~16 Mpc as well as the observed 15% under-density at ~40 Mpc distance., Comment: 16 pages, 12 figure, accepted for publication in A&A

We present reactive gasdynamic, axisymmetric simulations of dense, high velocity clumps for modelling the CO streamers observed in Orion BN/KL. We have considered 15 chemical species, a cooling function for atomic and molecular gas, and heating through cosmic rays. Our numerical simulations explore different ejection velocities, interstellar medium density configurations, and CO content. Using the CO density and temperature, we have calculated the CO ($J=2\to1$) emissivity, and have built CO maps and spatially resolved line profiles, allowing us to see the CO emitting regions of the streamers and to obtain position velocity diagrams to compare with observations. We find that in order to reproduce the images and line profiles of the BN/KL CO streamers and H$_2$ fingers, we need to have clumps that first travel within a dense cloud core, and then emerge into a lower-density environment., Comment: MNRAS, accepted, 10 figures

We train graph neural networks on halo catalogues from Gadget N-body simulations to perform field-level likelihood-free inference of cosmological parameters. The catalogues contain $\lesssim$5,000 halos with masses $\gtrsim 10^{10}~h^{-1}M_\odot$ in a periodic volume of $(25~h^{-1}{\rm Mpc})^3$; every halo in the catalogue is characterized by several properties such as position, mass, velocity, concentration, and maximum circular velocity. Our models, built to be permutationally, translationally, and rotationally invariant, do not impose a minimum scale on which to extract information and are able to infer the values of $\Omega_{\rm m}$ and $\sigma_8$ with a mean relative error of $\sim6\%$, when using positions plus velocities and positions plus masses, respectively. More importantly, we find that our models are very robust: they can infer the value of $\Omega_{\rm m}$ and $\sigma_8$ when tested using halo catalogues from thousands of N-body simulations run with five different N-body codes: Abacus, CUBEP$^3$M, Enzo, PKDGrav3, and Ramses. Surprisingly, the model trained to infer $\Omega_{\rm m}$ also works when tested on thousands of state-of-the-art CAMELS hydrodynamic simulations run with four different codes and subgrid physics implementations. Using halo properties such as concentration and maximum circular velocity allow our models to extract more information, at the expense of breaking the robustness of the models. This may happen because the different N-body codes are not converged on the relevant scales corresponding to these parameters., Comment: 25 pages, 11 figures, summary video: https://youtu.be/qkw92Z6owJU

The Gauss map of a generic immersion of a smooth, oriented surface into $\mathbb R^4$ is an immersion. But this map takes values on the Grassmanian of oriented 2-planes in $\mathbb R^4$. Since this manifold has a structure of a product of two spheres, the Gauss map has two components that take values on the sphere. We study the singularities of the components of the Gauss map and relate them to the geometric properties of the generic immersion. Moreover, we prove that the singularities are generically stable, and we connect them to the contact type of the surface and $\mathcal J$-holomorphic curves with respect to an orthogonal complex structure $\mathcal J$ on $\mathbb R^4$. Finally, we get some formulas of Gauss-Bonnet type involving the geometry of the singularities of the components with the geometry and topology of the surface., Comment: 27 pages, 1 figures

Background: To determine the ability of a commercially available deep learning system, RetCAD v.1.3.1 (Thirona, Nijmegen, The Netherlands) for the automatic detection of referable diabetic retinopathy (DR) on a dataset of colour fundus images acquired during routine clinical practice in a tertiary hospital screening program, analyzing the reduction of workload that can be released incorporating this artificial intelligence-based technology. Methods: Evaluation of the software was performed on a dataset of 7195 nonmydriatic fundus images from 6325 eyes of 3189 diabetic patients attending our screening program between February to December of 2019. The software generated a DR severity score for each colour fundus image which was combined into an eye-level score. This score was then compared with a reference standard as set by a human expert using receiver operating characteristic (ROC) curve analysis. Results: The artificial intelligence (AI) software achieved an area under the ROC curve (AUC) value of 0.988 [0.981:0.993] for the detection of referable DR. At the proposed operating point, the sensitivity of the RetCAD software for DR is 90.53% and specificity is 97.13%. A workload reduction of 96% could be achieved at the cost of only 6 false negatives. Conclusions: The AI software correctly identified the vast majority of referable DR cases, with a workload reduction of 96% of the cases that would need to be checked, while missing almost no true cases, so it may therefore be used as an instrument for triage., Comment: 15 pages, 3 figures, 2 tables

We study the problem of a Herbig-Haro jet with a uniformly accelerating ejection velocity, travelling into a uniform environment. For the ejection density we consider two cases: a time-independent density, and a time-independent mass loss rate. For these two cases, we obtain analytic solutions for the motion of the jet head using a ram-pressure balance and a center of mass equation of motion. We also compute axisymmetric numerical simulations of the same flow, and compare the time-dependent positions of the leading working surface shocks with the predictions of the two analytic models. We find that if the jet is over-dense and over-pressured (with respect to the environment) during its evolution, a good agreement is obtained with the analytic models, with the flow initially following the center of mass analytic solution, and (for the constant ejection density case) at later times approaching the ram-pressure balance solution., Comment: 15 pages, 5 figures

High velocity clumps joined to the outflow source by emission with a "Hubble law" ramp of linearly increasing radial velocity vs. distance are observed in some planetary nebulae and in some outflows in star formation regions. We propose a simple model in which a "clump" is ejected from a source over a period $\tau_0$, with a strong axis to edge velocity stratification. This non-top hat cross section results in the production of a highly curved working surface (initially being pushed by the ejected material, and later coasting along due to its inertia). From both analytic models and numerical simulations we find that this working surface has a linear velocity vs. position ramp, and therefore reproduces in a qualitative way the "Hubble law clumps" in planetary nebulae and outflows from young stars., Comment: 10 pages, 11 figures. Accepted in the RMxAA

The analysis of 20 years of spectrophotometric data of the double shell planetary nebula PM\,1-188 is presented, aiming to determine the time evolution of the emission lines and the physical conditions of the nebula, as a consequence of the systematic fading of its [WC\,10] central star whose brightness has declined by about 10 mag in the past 40 years. Our main results include that the [\ion{O}{iii}], [\ion{O}{ii}], [\ion{N}{ii}] line intensities are increasing with time in the inner nebula as a consequence of an increase in electron temperature from 11,000 K in 2005 to more than 14,000 K in 2018, due to shocks. The intensity of the same lines are decreasing in the outer nebula, due to a decrease in temperature, from 13,000 K to 7,000 K, in the same period. The chemical composition of the inner and outer shells was derived and they are similar. Both nebulae present subsolar O, S and Ar abundances, while they are He, N and Ne rich. For the outer nebula the values are 12+log He/H= 11.13$\pm$0.05, 12+log O/H = 8.04$\pm$0.04, 12+log N/H= 7.87$\pm$0.06, 12+log S/H = 7.18$\pm$0.10 and 12+log Ar = 5.33$\pm$0.16. The O, S and Ar abundances are several times lower than the average values found in disc non-Type I PNe, and are reminiscent of some halo PNe. From high resolution spectra, an outflow in the N-S direction was found in the inner zone. Position-velocity diagrams show that the outflow expands at velocities in the $-$150 to 100 km s$^{-1}$ range, and both shells have expansion velocities of about 40 km s$^{-1}$., Comment: 15 pages, 6 figures, acepted in MNRAS