arXiv subjects

Diego Bueso

Publications and source records attributed to Diego Bueso.

5 recordsLinked to original sources

arXiv2026

GeV emission in the region of Vela: a new view of the supernova remnant

The Vela supernova remnant (SNR), G263.9-3.3, and its pulsar wind nebula (PWN), Vela X, is one of the closest such systems, and it has been studied using observations across the electromagnetic spectrum. SNRs are known sources of gamma rays with energies from GeV to the TeV range. In the GeV band, a cluster of cataloged unidentified Fermi-LAT point sources are found across the large angular extension of the Vela SNR. We aim to search for a high-energy signature associated to the SNR. We applied two independent machine learning algorithms to classify unidentified point sources in the Vela region by comparing their properties to those of known populations of Fermi pulsars and active galactic nuclei. We analyzed LAT data and modeled the spectrum of any emission attributable to Vela using leptonic and hadronic processes typical of SNRs. We find that most of the "point sources" cataloged within the extent of Vela do not share characteristics with those of the two most common Fermi point-like source populations and that even after the emission attributed to these "point sources" is subtracted, considerable residual emission is seen throughout Vela. Morphologically, most of the GeV emission is found within the shell of the SNR. We conclude that the majority of the cataloged point sources are likely spurious, and the GeV gamma rays come from an extended source, which we argue is the counterpart of the Vela SNR. Adopting a simple morphology given by a uniform disk for the emission the resulting extension is 6.5 deg. The northeastern portion of G263.9-3.3, where the ambient density is thought to be higher, is brighter in gamma rays than the south and west. The spectrum of the emission is best fit with a hadronic model. These facts make the hadronic origin for the gamma rays more likely.

astro-ph.HE↗

arXiv2020

Nonlinear Complex PCA for spatio-temporal analysis of global soil moisture

Soil moisture (SM) is a key state variable of the hydrological cycle, needed to monitor the effects of a changing climate on natural resources. Soil moisture is highly variable in space and time, presenting seasonalities, anomalies and long-term trends, but also, and important nonlinear behaviours. Here, we introduce a novel fast and nonlinear complex PCA method to analyze the spatio-temporal patterns of the Earth's surface SM. We use global SM estimates acquired during the period 2010-2017 by ESA's SMOS mission. Our approach unveils both time and space modes, trends and periodicities unlike standard PCA decompositions. Results show the distribution of the total SM variance among its different components, and indicate the dominant modes of temporal variability in surface soil moisture for different regions. The relationship of the derived SM spatio-temporal patterns with El Ni{ñ}o Southern Oscillation (ENSO) conditions is also explored.

physics.ao-ph↗

arXiv2020

Understanding Climate Impacts on Vegetation with Gaussian Processes in Granger Causality

Global warming is leading to unprecedented changes in our planet, with great societal, economical and environmental implications, especially with the growing demand of biofuels and food. Assessing the impact of climate on vegetation is of pressing need. We approached the attribution problem with a novel nonlinear Granger causal (GC) methodology and used a large data archive of remote sensing satellite products, environmental and climatic variables spatio-temporally gridded over more than 30 years. We generalize kernel Granger causality by considering the variables cross-relations explicitly in Hilbert spaces, and use the covariance in Gaussian processes. The method generalizes the linear and kernel GC methods, and comes with tighter bounds of performance based on Rademacher complexity. Spatially-explicit global Granger footprints of precipitation and soil moisture on vegetation greenness are identified more sharply than previous GC methods.

physics.ao-ph↗

arXiv2020

Explicit Granger causality in kernel Hilbert spaces

Granger causality (GC) is undoubtedly the most widely used method to infer cause-effect relations from observational time series. Several nonlinear alternatives to GC have been proposed based on kernel methods. We generalize kernel Granger causality by considering the variables cross-relations explicitly in Hilbert spaces. The framework is shown to generalize the linear and kernel GC methods, and comes with tighter bounds of performance based on Rademacher complexity. We successfully evaluate its performance in standard dynamical systems, as well as to identify the arrow of time in coupled Rössler systems, and is exploited to disclose the El Niño-Southern Oscillation (ENSO) phenomenon footprints on soil moisture globally.

nlin.CD↗

arXiv2020

Nonlinear PCA for Spatio-Temporal Analysis of Earth Observation Data

Remote sensing observations, products and simulations are fundamental sources of information to monitor our planet and its climate variability. Uncovering the main modes of spatial and temporal variability in Earth data is essential to analyze and understand the underlying physical dynamics and processes driving the Earth System. Dimensionality reduction methods can work with spatio-temporal datasets and decompose the information efficiently. Principal Component Analysis (PCA), also known as Empirical Orthogonal Functions (EOF) in geophysics, has been traditionally used to analyze climatic data. However, when nonlinear feature relations are present, PCA/EOF fails. In this work, we propose a nonlinear PCA method to deal with spatio-temporal Earth System data. The proposed method, called Rotated Complex Kernel PCA (ROCK-PCA for short), works in reproducing kernel Hilbert spaces to account for nonlinear processes, operates in the complex kernel domain to account for both space and time features, and adds an extra rotation for improved flexibility. The result is an explicitly resolved spatio-temporal decomposition of the Earth data cube. The method is unsupervised and computationally very efficient.We illustrate its ability to uncover spatio-temporal patterns using synthetic experiments and real data. Results of the decomposition of three essential climate variables are shown: satellite-based global Gross Primary Productivity (GPP) and Soil Moisture (SM), and reanalysis Sea Surface Temperature (SST) data. The ROCK-PCA method allows identifying their annual and seasonal oscillations, as well as their non-seasonal trends and spatial variability patterns.

physics.ao-ph↗