9 research outputs found
Supervised Machine Learning for Intercomparison of Model Grids of Brown Dwarfs: Application to GJ 570D and the Epsilon Indi B Binary System
Self-consistent model grids of brown dwarfs involve complex physics and
chemistry, and are often computed using proprietary computer codes, making it
challenging to identify the reasons for discrepancies between model and data as
well as between the models produced by different research groups. In the
current study, we demonstrate a novel method for analyzing brown dwarf spectra,
which combines the use of the Sonora, AMES-Cond and HELIOS model grids with the
supervised machine learning method of the random forest. Besides performing
atmospheric retrieval, the random forest enables information content analysis
of the three model grids as a natural outcome of the method, both individually
on each grid and by comparing the grids against one another, via computing
large suites of mock retrievals. Our analysis reveals that the different
choices made in modelling the alkali line shapes hinder the use of the alkali
lines as gravity indicators. Nevertheless, the spectrum longward of 1.2 micron
encodes enough information on the surface gravity to allow its inference from
retrieval. Temperature may be accurately and precisely inferred independent of
the choice of model grid, but not the surface gravity. We apply random forest
retrieval to three objects: the benchmark T7.5 brown dwarf GJ 570D; and Epsilon
Indi Ba (T1.5 brown dwarf) and Bb (T6 brown dwarf), which are part of a binary
system and have measured dynamical masses. For GJ 570D, the inferred effective
temperature and surface gravity are consistent with previous studies. For
Epsilon Indi Ba and Bb, the inferred surface gravities are broadly consistent
with the values informed by the dynamical masses.Comment: Accepted for publication in The Astronomical Journa
End-to-End Trainable Deep Active Contour Models for Automated Image Segmentation: Delineating Buildings in Aerial Imagery
The automated segmentation of buildings in remote sensing imagery is a
challenging task that requires the accurate delineation of multiple building
instances over typically large image areas. Manual methods are often laborious
and current deep-learning-based approaches fail to delineate all building
instances and do so with adequate accuracy. As a solution, we present Trainable
Deep Active Contours (TDACs), an automatic image segmentation framework that
intimately unites Convolutional Neural Networks (CNNs) and Active Contour
Models (ACMs). The Eulerian energy functional of the ACM component includes
per-pixel parameter maps that are predicted by the backbone CNN, which also
initializes the ACM. Importantly, both the ACM and CNN components are fully
implemented in TensorFlow and the entire TDAC architecture is end-to-end
automatically differentiable and backpropagation trainable without user
intervention. TDAC yields fast, accurate, and fully automatic simultaneous
delineation of arbitrarily many buildings in the image. We validate the model
on two publicly available aerial image datasets for building segmentation, and
our results demonstrate that TDAC establishes a new state-of-the-art
performance.Comment: Accepted to European Conference on Computer Vision (ECCV) 202