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We present an evaluation of the performance of an automated classification of the Hipparcos periodic variable stars into 26 types. The sub-sample with the most reliable variability types available in the literature is used to train…

Random forests is a state-of-the-art supervised machine learning method which behaves well in high-dimensional settings although some limitations may happen when $p$, the number of predictors, is much larger than the number of observations…

We study the impact of systematic errors on planned weak lensing surveys and compute the requirements on their contributions so that they are not a dominant source of the cosmological parameter error budget. The generic types of error we…

Camera relocalization plays a vital role in many robotics and computer vision tasks, such as global localization, recovery from tracking failure, and loop closure detection. Recent random forests based methods directly predict 3D world…

Spatially Coherent Random Forest (SCRF) extends Random Forest to create spatially coherent labeling. Each split function in SCRF is evaluated based on a traditional information gain measure that is regularized by a spatial coherency term.…

We present new data processing techniques that allow to correct the main instrumental effects that degrade the images obtained by ISOCAM, the camera on board the Infrared Space Observatory (ISO). Our techniques take advantage of the fact…

Random Forests (RF) is a popular machine learning method for classification and regression problems. It involves a bagging application to decision tree models. One of the primary advantages of the Random Forests model is the reduction in…

IRAC excels at detecting distant objects. Due to a combination of the shapes of the spectral energy distributions of galaxies and the low background achieved from space, IRAC reaches greater depth in comparable exposure time at 3.6 and 4.5…

The use of machine learning is becoming ubiquitous in astronomy, but remains rare in the study of the atmospheres of exoplanets. Given the spectrum of an exoplanetary atmosphere, a multi-parameter space is swept through in real time to find…

We identify and correct for small but coherent instrumental drifts in seven years of radial velocity data from the EXtreme PREcision Spectrograph (EXPRES). The systematics are most notable for the six months before and after 2022 January,…

Accurate photometric redshifts are calculated for nearly 200,000 galaxies to a 4.5 micron flux limit of ~13 uJy in the 8.5 deg^2 Spitzer/IRAC Shallow survey. Using a hybrid photometric redshift algorithm incorporating both neural-net and…

This paper proposes a multi-spectral random forest classifier with suitable feature selection and masking for tree cover estimation in urban areas. The key feature of the proposed classifier is filtering out the built-up region using…

Random forests are considered one of the best out-of-the-box classification and regression algorithms due to their high level of predictive performance with relatively little tuning. Pairwise proximities can be computed from a trained…

We present an analysis of Spitzer/IRAC primary transit and secondary eclipse lightcurves measured for HD209458b, using Gaussian process models to marginalise over the intrapixel sensitivity variations in the 3.6 micron and 4.5 micron…

Obtaining accurate photometric redshift estimations is an important aspect of cosmology, remaining a prerequisite of many analyses. In creating novel methods to produce redshift estimations, there has been a shift towards using machine…

The dominant non-instrumental background source for space-based infrared observatories is the zo- diacal light. We present Spitzer Infrared Array Camera (IRAC) measurements of the zodiacal light at 3.6, 4.5, 5.8, and 8.0 {\mu}m, taken as…

We present a new method for performing atmospheric retrieval on ground-based, high-resolution data of exoplanets. Our method combines cross-correlation functions with a random forest, a supervised machine learning technique, to overcome…

Large ground-based telescopes equipped with adaptive optics (AO) systems have ushered in a new era of high-resolution infrared photometry and astrometry. Relative astrometric accuracies of <0.2 mas have already been demonstrated from…

Random Forests (RFs) are widely used Machine Learning models in low-power embedded devices, due to their hardware friendly operation and high accuracy on practically relevant tasks. The accuracy of a RF often increases with the number of…

Synthetic aperture radar tomographic imaging reconstructs the three-dimensional reflectivity of a scene from a set of coherent acquisitions performed in an interferometric configuration. In forest areas, a large number of elements…