Broad balmer wings in BA Hyper/Supergiants distorted by diffuse interstellar bands: five examples in the 30 Doradus Region from the VLT-Flames Tarantula Survey

Extremely broad emission wings at Hβ and Hα have been found in VLT-FLAMES Tarantula Survey data for five very luminous BA supergiants in or near 30 Doradus in the Large Magellanic Cloud. The profiles of both lines are extremely asymmetrical, which we have found to be caused by very broad diffuse interstellar bands (DIBs) in the longward wing of Hβ and the shortward wing of Hα. These DIBs are well known to interstellar but not to many stellar specialists, so that the asymmetries may be mistaken for intrinsic features. The broad emission wings are generally ascribed to electron scattering, although we note difficulties for that interpretation in some objects. Such profiles are known in some Galactic hyper/supergiants and are also seen in both active and quiescent Luminous Blue Variables (LBVs). No prior or current LBV activity is known in these 30 Dor stars, although a generic relationship to LBVs is not excluded; subject to further observational and theoretical investigation, it is possible that these very luminous supergiants are approaching the LBV stage for the first time. Their locations in the HRD and presumed evolutionary tracks are consistent with that possibility. The available evidence for spectroscopic variations of these objects is reviewed, while recent photometric monitoring does not reveal variability. A search for circumstellar nebulae has been conducted, with an indeterminate result for one of them.

Physical model parameter optimisation for calibrated emulation of the Dallas Rangemaster Treble Booster guitar pedal

In this work we explore optimising parameters of a physical circuit model relative to input/output measurements, using the Dallas Rangemaster Treble Booster as a case study. A hybrid metaheuristic/gradient descent algorithm is implemented, where the initial parameter sets for the optimisation are informed by nominal values from schematics and datasheets. Sensitivity analysis is used to screen parameters, which informs a study of the optimisation algorithm against model complexity by fixing parameters. The results of the optimisation show a significant increase in the accuracy of model behaviour, but also highlight several key issues regarding the recovery of parameters.