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Understanding the evolutionary history of the Magellanic Clouds requires an in-depth exploration and characterization of the stellar content in their outer regions, which ultimately are key to tracing the epochs and nature of past interactions. We present new deep images of a shell-like over-density of stars in the outskirts of the Small Magellanic Cloud (SMC). The shell, also detected in photographic plates dating back to the fifties, is located at ~1.9 degr from the center of the SMC in the north-east direction.The structure and stellar content of this feature were studied with multi-band, optical data from the Survey of the MAgellanic Stellar History (SMASH) carried out with the Dark Energy Camera on the Blanco Telescope at Cerro Tololo Inter-American Observatory. We also investigate the kinematic of the stars in the shell using the Gaia Data Release 2. The shell is composed of a young population with an age ~ 150 Myr, with no contribution from an old population. Thus, it is hard to explain its origin as the remnant of a tidally disrupted stellar system. The spatial distribution of the young main-sequence stars shows a rich sub-structure, with a spiral arm-like feature emanating from the main shell and a separated small arc of young stars close to the globular cluster NGC 362. We find that the absolute g-band magnitude of the shell is M_{g,shell} = -10.78+/- 0.02, with a surface brightness of mu_{g,shell} = 25.81+/- 0.01 mag/arcsec^{2}. We have not found any evidence that this feature is of tidal origin or a bright part of a spiral arm-like structure. Instead, we suggest that the shell formed in a recent star formation event, likely triggered by an interaction with the Large Magellanic Cloud and/or the Milky Way, ~150 Myr ago., Comment: 16 pages, 14 figures, submitted to Astronomy & Astrophysics

KIC 8462852 stood out among more than 100,000 stars in the Kepler catalogue because of the strange features of its light curve: a wide, asymmetric dimming taking up to 15 per cent of the light at D793 and a period of multiple, narrow dimmings happening approximately 700 days later. Several models have been proposed to account for this abnormal behaviour, most of which require either unlikely causes or a finely-tuned timing. We aim at offering a relatively natural solution, invoking only phenomena that have been previously observed, although perhaps in larger or more massive versions. We model the system using a large, ringed body whose transit produces the first dimming and a swarm of Trojan objects sharing its orbit that causes the second period of multiple dimmings. The resulting orbital period is $T\approx12$ years, with a semi-major axis $a\approx6$ au. Our model allows us to make two straightforward predictions: we expect the passage of a new swarm of Trojans in front of the star starting during the early months of 2021, and a new transit of the main object during the first half of 2023., Comment: 5 pages, 2 figures. v3: Accepted for publication in MNRAS Letters

As the number of detected extrasolar planets increases, exoplanet databases become a valuable resource, confirming some details about planetary formation, but also challenging our theories with new unexpected properties., Comment: 7 pages and 1 figure

The origin and shape of metabolic scaling has been controversial since Kleiber found that basal metabolic rate of animals seemed to vary as a power law of their body mass with exponent 3/4, instead of 2/3, as a surface-to-volume argument predicts. The universality of exponent 3/4 -claimed in terms of the fractal properties of the nutrient network- has recently been challenged according to empirical evidence that observed a wealth of robust exponents deviating from 3/4. Here we present a conceptually simple thermodynamic framework, where the dependence of metabolic rate with body mass emerges from a trade-off between the energy dissipated as heat and the energy efficiently used by the organism to maintain its metabolism. This balance tunes the shape of an additive model from which different effective scalings can be recovered as particular cases, thereby reconciling previously inconsistent empirical evidence in mammals, birds, insects and even plants under a unified framework. This model is biologically motivated, fits remarkably well the data, and also explains additional features such as the relation between energy lost as heat and mass, the role and influence of different climatic environments or the difference found between endotherms and ectotherms., Comment: 5 figures. Accepted for publication in Scientific Reports

A novel class of graphs, here named quasiperiodic, are constructed via application of the Horizontal Visibility algorithm to the time series generated along the quasiperiodic route to chaos. We show how the hierarchy of mode-locked regions represented by the Farey tree is inherited by their associated graphs. We are able to establish, via Renormalization Group (RG) theory, the architecture of the quasiperiodic graphs produced by irrational winding numbers with pure periodic continued fraction. And finally, we demonstrate that the RG fixed-point degree distributions are recovered via optimization of a suitably defined graph entropy.

Time series are proficiently converted into graphs via the horizontal visibility (HV) algorithm, which prompts interest in its capability for capturing the nature of different classes of series in a network context. We have recently shown [1] that dynamical systems can be studied from a novel perspective via the use of this method. Specifically, the period-doubling and band-splitting attractor cascades that characterize unimodal maps transform into families of graphs that turn out to be independent of map nonlinearity or other particulars. Here we provide an in depth description of the HV treatment of the Feigenbaum scenario, together with analytical derivations that relate to the degree distributions, mean distances, clustering coefficients, etc., associated to the bifurcation cascades and their accumulation points. We describe how the resultant families of graphs can be framed into a renormalization group scheme in which fixed-point graphs reveal their scaling properties. These fixed points are then re-derived from an entropy optimization process defined for the graph sets, confirming a suggested connection between renormalization group and entropy optimization. Finally, we provide analytical and numerical results for the graph entropy and show that it emulates the Lyapunov exponent of the map independently of its sign., Comment: 19 pages, 11 figures, accepted for publication in Chaos

The recently formulated theory of horizontal visibility graphs transforms time series into graphs and allows the possibility of studying dynamical systems through the characterization of their associated networks. This method leads to a natural graph-theoretical description of nonlinear systems with qualities in the spirit of symbolic dynamics. We support our claim via the case study of the period-doubling and band-splitting attractor cascades that characterize unimodal maps. We provide a universal analytical description of this classic scenario in terms of the horizontal visibility graphs associated with the dynamics within the attractors, that we call Feigenbaum graphs, independent of map nonlinearity or other particulars. We derive exact results for their degree distribution and related quantities, recast them in the context of the renormalization group and find that its fixed points coincide with those of network entropy optimization. Furthermore, we show that the network entropy mimics the Lyapunov exponent of the map independently of its sign, hinting at a Pesin-like relation equally valid out of chaos., Comment: Published in PLoS ONE (Sep 2011)

The visibility algorithm has been recently introduced as a mapping between time series and complex networks. This procedure allows to apply methods of complex network theory for characterizing time series. In this work we present the horizontal visibility algorithm, a geometrically simpler and analytically solvable version of our former algorithm, focusing on the mapping of random series (series of independent identically distributed random variables). After presenting some properties of the algorithm, we present exact results on the topological properties of graphs associated to random series, namely the degree distribution, clustering coefficient, and mean path length. We show that the horizontal visibility algorithm stands as a simple method to discriminate randomness in time series, since any random series maps to a graph with an exponential degree distribution of the shape P(k) = (1/3)(2/3)**(k-2), independently of the probability distribution from which the series was generated. Accordingly, visibility graphs with other P(k) are related to non-random series. Numerical simulations confirm the accuracy of the theorems for finite series. In a second part, we show that the method is able to distinguish chaotic series from i.i.d. theory, studying the following situations: (i) noise-free low-dimensional chaotic series, (ii) low-dimensional noisy chaotic series, even in the presence of large amounts of noise, and (iii) high-dimensional chaotic series (coupled map lattice), without needs for additional techniques such as surrogate data or noise reduction methods. Finally, heuristic arguments are given to explain the topological properties of chaotic series and several sequences which are conjectured to be random are analyzed.

In this work we present a simple and fast computational method, the visibility algorithm, that converts a time series into a graph. The constructed graph inherits several properties of the series in its structure. Thereby, periodic series convert into regular graphs, and random series do so into random graphs. Moreover, fractal series convert into scale-free networks, enhancing the fact that power law degree distributions are related to fractality, something highly discussed recently. Some remarkable examples and analytical tools are outlined in order to test the method's reliability. Many different measures, recently developed in the complex network theory, could by means of this new approach characterize time series from a new point of view.

A study of the galaxy distribution in the field of the elliptical galaxy NGC1600 has been undertaken. Although this galaxy is often classified as a member of a loose group, all the neighbouring galaxies are much fainter and could be taken as satellites of NGC1600. The number density profile of galaxies in the field of this galaxy shows a decline with radius, with evidence of a background at approximately 1.3 Mpc. The density and number density profile are consistent with that found for other isolated early-type galaxies. NGC1600 appears as an extended source in X-rays, and the center of the X-ray emission seems not to coincide with the center of the galaxy. The velocity distribution of neighbouring galaxies has been measured from optical spectroscopic observations and shows that the mean radial velocity is approximately 85 km/s less than that of NGC1600, indicating that the centre of mass could lie outside the galaxy. The velocity dispersion of the `group' is estimated at 429 km/s. The inferred mass of the system is therefore of the order of 10^14 solar masses, a value that corresponds to a large group. NGC1600 therefore shares some similarities, but is not identical to, the `fossil clusters' detected in X-ray surveys. Implications of this result for studies of isolated early-type galaxies are briefly discussed., Comment: 8 pages, 5 figures and 2 table, accepted for publication in the ApJ

Standard Random Boolean Networks display an order-disorder phase transition. We add to the standard Random Boolean Networks a disconnection rule which couples the control and order parameters. By this way, the system is driven to the critical line transition. Under the influence of perturbations the system point out self-organized critical behavior. Several numerical simulations have been done and compared with a proposed analytical treatment., Comment: 8 pages, 13 figures. Accepted in PRE