Related papers: Implementation of the Random Forest Method for the…
Despite widespread interest and practical use, the theoretical properties of random forests are still not well understood. In this paper we contribute to this understanding in two ways. We present a new theoretically tractable variant of…
The optimization of the observation schedule of gamma-ray emitters by the new generation of Cherenkov Telescopes to extract cosmological parameters from the measurement of the Gamma Ray Horizon at different redshifts is discussed. It is…
Aerial imagery is increasingly used in Earth science and natural resource management as a complement to labor-intensive ground-based surveys. Aerial systems can collect overlapping images that provide multiple views of each location from…
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…
Telescopes based on the imaging atmospheric Cherenkov technique (IACTs) detect images of the atmospheric showers generated by gamma rays and cosmic rays as they are absorbed by the atmosphere. The much more frequent cosmic-ray events form…
Random forests are a powerful method for non-parametric regression, but are limited in their ability to fit smooth signals, and can show poor predictive performance in the presence of strong, smooth effects. Taking the perspective of random…
Aims: $\gamma$ rays can be used as a tracer in the search of sources of Galactic cosmic rays (CRs). We present deep observations of the Galactic Centre (GC) region with the MAGIC telescopes, which we use for inferring the underlying CR…
Machine Learning (ML) algorithms, like Convolutional Neural Networks (CNN), Support Vector Machines (SVM), etc. have become widespread and can achieve high statistical performance. However their accuracy decreases significantly in…
The Robinson-Foulds (RF) distance is by far the most widely used measure of dissimilarity between trees. Although the distribution of these distances has been investigated for twenty years, an algorithm that is explicitly polynomial time…
The monitoring of the atmosphere is very relevant for Imaging Atmospheric Cherenkov Telescopes. Adverse weather conditions (strong wind, high humidity, etc.) may damage the telescopes and must therefore be monitored continuously to…
Telescopes, designed with semi-conductor based photo sensors, have the potential to detect Cherenkov or fluorescence light emitted by cosmic-rays in the atmosphere. Such telescopes promise a high duty cycle and efficiency in remote harsh…
By-tree information gathering is an essential task in precision agriculture achieved by ground mobile sensors, but it can be time- and labor-intensive. In this paper we present an algorithmic framework to perform real-time and on-the-go…
The identification of $\gamma$-rays from the predominant hadronic-background is a key aspect in their ground-based detection using Imaging Atmospheric Cherenkov Telescopes (IACTs). While current methods are limited in their ability to…
Random Forest (RF) is a widely used machine learning algorithm known for its flexibility, user-friendliness, and high predictive performance across various domains. However, it is non-interpretable. This can limit its usefulness in applied…
As machine learning models are increasingly deployed in sensitive application areas, the demand for interpretable and trustworthy decision-making has increased. Random Forests (RF), despite their widespread use and strong performance on…
This paper explores the application of machine learning methods for classifying astronomical sources using photometric data, including normal and emission line galaxies (ELGs; starforming, starburst, AGN, broad line), quasars, and stars. We…
Imaging atmospheric Cherenkov telescope (IACT) arrays record images from air showers initiated by gamma rays entering the atmosphere, allowing astrophysical sources to be observed at very high energies. To maximize IACT sensitivity,…
The Major Atmospheric Gamma-ray Imaging Cherenkov (MAGIC) Telescope collaboration is constructing a large Cherenkov telescope (17 m diameter) for the exploration of the gamma-ray energy regime above 10 GeV with high sensitivity. One of the…
We propose generalized random forests, a method for non-parametric statistical estimation based on random forests (Breiman, 2001) that can be used to fit any quantity of interest identified as the solution to a set of local moment…
There is a rising interest in mapping trees using satellite or aerial imagery, but there is no standardized evaluation protocol for comparing and enhancing methods. In dense canopy areas, the high variability of tree sizes and their spatial…