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The asteroseismic analysis of stellar power density spectra is often computationally expensive. The models used in the analysis may use several dozen parameters to accurately describe features in the spectra caused by oscillation modes and surface granulation. Many parameters are often highly correlated, making the parameter space difficult to quickly and accurately sample. They are, however, all dependent on a smaller set of parameters, namely the fundamental stellar properties. We aim to leverage this to simplify the process of sampling the model parameter space for the asteroseismic analysis of solar-like oscillators, with an emphasis on mode identification. Using a large set of previous observations, we applied principal component analysis to the sample covariance matrix to select a new basis on which to sample the model parameters. Selecting the subset of basis vectors that explains the majority of the sample variance, we redefine the model parameter prior probability density distributions in terms of a smaller set of latent parameters. We are able to reduce the dimensionality of the sampled parameter space by a factor of two to three. The number of latent parameters needed to accurately model the stellar oscillation spectra cannot be determined exactly but is likely only between four and six. Using two latent parameters, the method is able to describe the bulk features of the oscillation spectrum, while including more latent parameters allows for a frequency precision better than $\approx10\%$ of the small frequency separation for a given target. We find that sampling a lower-rank latent parameter space still allows for accurate mode identification and parameter estimation on solar-like oscillators over a wide range of evolutionary stages. This allows for the potential to increase the complexity of spectrum models without a corresponding increase in computational expense., Comment: Accepted for publication in Astronomy & Astrophysics. 11 pages. 10 figures

Exoplanet searches through space-based photometric time series have shown to be very efficient in recent years. However, follow-up efforts on the detected planet candidates have been demonstrated to be critical to uncover the true nature of the transiting objects. In this paper we show a detailed analysis of one of those false positives hidden as planetary signals. In this case, the candidate KOI-3886.01 showed clear evidence of a planetary nature from various techniques. Indeed, the properties of the fake planet set it among the most interesting and promising for the study of planetary evolution as the star leaves the main sequence. To unveil the true nature of this system, we present a complete set of observational techniques including high-spatial resolution imaging, high-precision photometric time series (showing eclipses, phase curve variations and asteroseismology signals), high-resolution spectroscopy and derived radial velocities, to unveil the true nature of this planet candidate. We find that KOI-3886.01 is an interesting false positive case: a hierarchical triple system composed by a $\sim$K2III giant star (KOI-3886A) accompanied by a close-in eclipsing binary formed by a subgiant $\sim$G4IV star (KOI-3886B) and a brown dwarf (KOI-3886C). In particular, KOI-3886C is one of the most irradiated brown dwarfs known to date, showing the largest radius in this substellar regime. It is also the first eclipsing brown dwarf known around an evolved star. In this paper we highlight the relevance of complete sets of follow-up observations to extrasolar planets detected by the transit technique using large-pixel photometers such as Kepler and TESS, and in the future, PLATO. In particular, multi-color high-spatial resolution imaging was the first hint toward ruling out the planet scenario in this system., Comment: Accepted for publication in A&A, 21 pages, 18 figures, 5 tables (language corrected version)

Asteroseismology is an exceptional tool for studying stars by using the properties of observed modes of oscillation. So far the process of performing an asteroseismic analysis of a star has remained somewhat esoteric and inaccessible to non-experts. In this software paper we describe PBjam, an open-source Python package for analyzing the frequency spectra of solar-like oscillators in a simple but principled and automated way. The aim of PBjam is to provide a set of easy-to-use tools to extract information about the radial and quadrupole oscillations in stars that oscillate like the Sun, which may then be used to infer bulk properties such as stellar mass, radius and age or even structure. Asteroseismology and its data analysis methods are becoming increasingly important as space-based photometric observatories are producing a wealth of new data, allowing asteroseismology to be applied in a wide range of contexts such as exoplanet, stellar structure and evolution, and Galactic population studies., Comment: 12 Pages, 4 figures. Accepted for publication in AJ. Associated software available at https://doi.org/10.5281/zenodo.4300079