相关论文: Random Forests applied to High Precision Photometr…

We present a reanalysis of the VIRMOS-Descart weak lensing data, with a particular focus on different corrections for the variation of the point spread function anisotropy (PSF) across the CCDs. We show that the small scale systematics can…

Intensive reverberation mapping monitoring programs combine ground-based photometric observations from different telescopes, requiring intercalibration of lightcurves to reduce systematic instrumental differences. We present a new iterative…

We present a new procedure rooted in deep learning to construct science images from data cubes collected by astronomical instruments using HxRG detectors in low-flux regimes. It improves on the drawbacks of the conventional algorithms to…

An algorithm to improve performance parameter for unsupervised decision forest clustering and density estimation is presented. Specifically, a dual assignment parameter is introduced as a density estimator by combining Random Forest and…

Using numerical simulations of structure formation, we investigate multiple methods of determining the strength of the proximity effect in the HI Lyalpha forest. We analyze three high resolution (~10kpc) redshift snapshots (z=4,3,2.25) of a…

We observed three regions of the sky with Spitzer in which the Cosmic InfraRed Background (CIRB) has been determined at 3.5 microns using the method of subtracting 2MASS stellar fluxes from zodiacal light subtracted DIRBE maps. For each of…

Magnetic resonance imaging (MRI) enables 3-D imaging of anatomical structures. However, the acquisition of MR volumes with high spatial resolution leads to long scan times. To this end, we propose volumetric super-resolution forests (VSRF)…

This work presents an Artificial Intelligence (AI) system, based on the Faster Region-Based Convolution Neural Network (Faster R-CNN) framework, which detects and counts apples from oblique, aerial drone imagery of giant commercial…

Random Forest (RF) is an ensemble supervised machine learning technique that was developed by Breiman over a decade ago. Compared with other ensemble techniques, it has proved its accuracy and superiority. Many researchers, however, believe…

We characterize the completeness of point source lists from Spitzer Space Telescope surveys in the four Infrared Array Camera (IRAC) bandpasses, emphasizing the Galactic Legacy Infrared Mid-Plane Survey Extraordinaire (GLIMPSE) programs…

When digitizing a print bilingual dictionary, whether via optical character recognition or manual entry, it is inevitable that errors are introduced into the electronic version that is created. We investigate automating the process of…

Quantifying prediction uncertainty when applying object detection models to new, unlabeled datasets is critical in applied machine learning. This study introduces an approach to estimate the performance of deep learning-based object…

We investigate differences in Spitzer/IRAC 3.6 and 4.5micron photometry that depend on observing strategy. Using archival calibration data we perform an in-depth examination of the measured flux densities ("fluxes") of ten calibration…

We propose a random forest (RF) machine learning approach to determine the accreted stellar mass fractions ($f_\mathrm{acc}$) of central galaxies, based on various dark matter halo and galaxy features. The RF is trained and tested using…

We develop a new photometry algorithm that is optimized for $Spitzer$ time series in crowded fields and that is particularly adapted to faint and/or heavily blended targets. We apply this to the 170 targets from the 2015 $Spitzer$…

Wide-field images made by radio interferometers are invariably affected by direction-dependent systematic effects such as the ionosphere or the beam pattern. Calibration along a set of discrete directions in the sky is the default technique…

We apply instance-based machine learning in the form of a k-nearest neighbor algorithm to the task of estimating photometric redshifts for 55,746 objects spectroscopically classified as quasars in the Fifth Data Release of the Sloan Digital…

We report secondary eclipse photometry of the hot Jupiter XO-3b in the 4.5~$\mu$m band taken with the Infrared Array Camera (IRAC) on the Spitzer Space Telescope. We measure individual eclipse depths and center of eclipse times for a total…

The isolation forest algorithm for outlier detection exploits a simple yet effective observation: if taking some multivariate data and making uniformly random cuts across the feature space recursively, it will take fewer such random cuts…

We propose a principled method for autoencoding with random forests. Our strategy builds on foundational results from nonparametric statistics and spectral graph theory to learn a low-dimensional embedding of the model that optimally…