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C A Bailer-Jones

Publications and source records attributed to C A Bailer-Jones.

2 recordsLinked to original sources

Discovery of young, isolated planetary mass objects in the final sigma Orionis star cluster.

We present the discovery by optical and near-infrared imaging of an extremely red, low-luminosity population of isolated objects in the young, nearby stellar cluster around the multiple, massive star final sigma Orionis. The proximity (352 parsecs), youth (1 million to 5 million years), and low internal extinction make this cluster an ideal site to explore the substellar domain from the hydrogen mass limit down to a few Jupiter masses. Optical and near-infrared low-resolution spectroscopy of three of these objects confirms the very cool spectral energy distribution (atmospheric effective temperatures of 1700 to 2200 kelvin) expected for cluster members with masses in the range 5 to 15 times that of Jupiter. Like the planets of the solar system, these objects are unable to sustain stable nuclear burning in their interiors, but in contrast they are not bound to stars. This new kind of isolated giant planet, which apparently forms on time scales of less than a few million years, offers a challenge to our understanding of the formation processes of planetary mass objects.

Journal Article↗

A recurrent neural network for modelling dynamical systems.

We introduce a recurrent network architecture for modelling a general class of dynamical systems. The network is intended for modelling real-world processes in which empirical measurements of the external and state variables are obtained at discrete time points. The model can learn from multiple temporal patterns, which may evolve on different timescales and be sampled at non-uniform time intervals. We demonstrate the application of the model to a synthetic problem in which target data are only provided at the final time step. Despite the sparseness of the training data, the network is able not only to make good predictions at the final time step for temporal processes unseen in training, but also to reproduce the sequence of the state variables at earlier times. Moreover, we show how the network can infer the existence and role of state variables for which no target information is provided. The ability of the model to cope with sparse data is likely to be useful in a number of applications, including, in particular, the modelling of metal forging.

Models, Theoretical↗