Related papers: Random Forests applied to High Precision Photometr…

The Infrared Array Camera (IRAC) on the Spitzer Space Telescope currently offers the greatest potential for high-precision astrometry of faint mid-IR sources across arcminute-scale fields, which would be especially valuable for measuring…

We examine the repeatability, reliability, and accuracy of differential exoplanet eclipse depth measurements made using the InfraRed Array Camera (IRAC) on the Spitzer Space Telescope during the post-cryogenic mission. We have re-analyzed…

With the coming data deluge from synoptic surveys, there is a growing need for frameworks that can quickly and automatically produce calibrated classification probabilities for newly-observed variables based on a small number of time-series…

We present 20 band photometry from the far-IR to X-ray in the Spitzer IRAC dark field. The bias for the near-IR camera on Spitzer is calibrated by observing a ~20 arcminute diameter "dark" field near the north ecliptic pole roughly every…

We present several corrections for point source photometry to be applied to data from the Infrared Array Camera (IRAC) on the Spitzer Space Telescope. These corrections are necessary because of characteristics of the IRAC arrays and optics…

In recent years, dynamically growing data and incrementally growing number of classes pose new challenges to large-scale data classification research. Most traditional methods struggle to balance the precision and computational burden when…

Accurate estimation of photometric redshifts (photo-$z$) is crucial in studies of both galaxy evolution and cosmology using current and future large sky surveys. In this study, we employ Random Forest (RF), a machine learning algorithm, to…

We observed Proxima Centauri with the Spitzer Space Telescope InfraRed Array Camera (IRAC) five times in 2016 and 2017 to search for transits of Proxima Centauri b. Following standard analysis procedures, we found three asymmetric,…

Combining machine learning with econometric analysis is becoming increasingly prevalent in both research and practice. A common empirical strategy involves the application of predictive modeling techniques to 'mine' variables of interest…

The Random Forest (RF) classifier is often claimed to be relatively well calibrated when compared with other machine learning methods. Moreover, the existing literature suggests that traditional calibration methods, such as isotonic…

The research of effective and reliable detrending methods for Spitzer data is of paramount importance for the characterization of exoplanetary atmospheres. To date, the totality of exoplanetary observations in the mid- and far-infrared, at…

Access to below-canopy volumetric vegetation data is crucial for understanding ecosystem dynamics. We address the long-standing limitation of remote sensing to penetrate deep into dense canopy layers. LiDAR and radar are currently…

Aims. Construction of a new quasar candidate catalog from the Red-Sequence Cluster Survey 2 (RCS-2), identified solely from photometric information using an automated algorithm suitable for large surveys. The algorithm performance is tested…

The Infrared Array Camera (IRAC) on the Spitzer Space Telescope is absolutely calibrated by comparing photometry on a set of A stars near the north ecliptic pole to predictions based on ground-based observations and a stellar atmosphere…

The paper describes an application of the tree classification method Random Forest (RF), as used in the analysis of data from the ground-based gamma telescope MAGIC. In such telescopes, cosmic gamma-rays are observed and have to be…

Tree height estimation serves as an important proxy for biomass estimation in ecological and forestry applications. While traditional methods such as photogrammetry and Light Detection and Ranging (LiDAR) offer accurate height measurements,…

Context: Given the current big data era in Astronomy, machine learning based methods have being applied over the last years to identify or classify objects like quasars, galaxies and stars from full sky photometric surveys. Aims: Here we…

Metric learning makes it plausible to learn distances for complex distributions of data from labeled data. However, to date, most metric learning methods are based on a single Mahalanobis metric, which cannot handle heterogeneous data well.…

Random forest (RF) stands out as a highly favored machine learning approach for classification problems. The effectiveness of RF hinges on two key factors: the accuracy of individual trees and the diversity among them. In this study, we…

We measure the 4.5 $\mu$m thermal emission of five transiting hot Jupiters, WASP-13b, WASP-15b, WASP-16b, WASP-62b and HAT-P-22b using channel 2 of the Infrared Array Camera (IRAC) on the {\sl Spitzer Space Telescope}. Significant…