IRAS 18357-0604 – an analogue of the galactic yellow hypergiant IRC +10420?

Context. Yellow hypergiants represent a short-lived evolutionary episode experienced by massive stars as they transit to and from a red supergiant phase. As such, their properties provide a critical test of stellar evolutionary theory, while recent observations unexpectedly suggest that a subset may explode as Type II supernovae. Aims. The galactic yellow hypergiant IRC +10420 is a cornerstone system for understanding this phase since it is the strongest post-RSG candidate known, has demonstrated real-time evolution across the Hertzsprung-Russell diagram and been subject to extensive mass loss. In this paper we report on the discovery of a twin of IRC +10420 - IRAS 18357-0604. Methods. Optical and near-IR spectroscopy are used to investigate the physical properties of IRAS 18357-0604 and also provide an estimate of its systemic velocity, while near- to mid-IR photometry probes the nature of its circumstellar environment. Results. These observations reveal pronounced spectral similarities between IRAS 18357-0604 and IRC +10420, suggesting comparable temperatures and wind geometries. IR photometric data reveals a similarly dusty circumstellar environment, although historical mass loss appears to have been heavier in IRC +10420. The systemic velocity implies a distance compatible with the red supergiant-dominated complex at the base of the Scutum Crux arm; the resultant luminosity determination is consistent with a physical association but suggests a lower initial mass than inferred for IRC +10420 (≲20 M⊙ versus ~40 M⊙). Evolutionary predictions for the physical properties of supernova progenitors derived from ~18–20 M⊙ stars – or ~12–15 M⊙ stars that have experienced enhanced mass loss as red supergiants – compare favourably with those of IRAS 18357-0604, which in turn appears to be similar to the the progenitor of SN2011dh; it may therefore provide an important insight into the nature of the apparently H-depleted yellow hypergiant progenitors of some Type IIb SNe.

3D Maps Representation Using GNG

Current RGB-D sensors provide a big amount of valuable information for mobile robotics tasks like 3D map reconstruction, but the storage and processing of the incremental data provided by the different sensors through time quickly become unmanageable. In this work, we focus on 3D maps representation and propose the use of the Growing Neural Gas (GNG) network as a model to represent 3D input data. GNG method is able to represent the input data with a desired amount of neurons or resolution while preserving the topology of the input space. Experiments show how GNG method yields a better input space adaptation than other state-of-the-art 3D map representation methods.

Coverage and invariability in fuzzy systems

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Invariability, orbits and fuzzy attractors

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