arXiv subjects

Jose E. Adsuara

Publications and source records attributed to Jose E. Adsuara.

3 recordsLinked to original sources

arXiv2026

Deciphering the Physical Origin of GRB 240825A: A Long GRB Lacking a Bright Supernova

We present a comprehensive multiwavelength analysis of GRB 240825A, a bright gamma-ray burst (GRB) detected by Fermi and Swift, with a prompt duration ($T_{\rm 90}$ $\sim$ 4 sec in 50-300 keV in GBM) near the boundary separating short and long GRBs, prompting a detailed investigation into its classification and progenitor. We use classical prompt metrics (duration, minimum variability timescale (MVT), lag, and spectral hardness) and modern classification techniques (machine-learning (ML) based t-SNE, support vector machine, energy-hardness-duration, and $\varepsilon \equiv E_{γ,\mathrm{iso},52} / E_{p,z,2}^{5/3}$) and find most properties (prompt energetics, placement on the Amati relation, and spectral lag) of GRB 240825A consistent with a collapsar origin. However, extensive late-time optical and NIR follow-up with the 10.4m GTC and 8.4m binocular LBT telescopes reveals no bright supernova (like SN 1998bw) is detected down to stringent limits (e.g., $m_r > 25.0$ mag at 17.59 days), despite a redshift of $z = 0.659$ measured from GTC spectroscopy. Host galaxy SED modeling with Prospector indicates a massive, and star-forming galaxy-typical of collapsar GRB hosts, though with a large offset. We compare these findings with hybrid events like GRB 211211A, GRB 230307A, GRB 200826A, including SNe-GRBs, and conclude that GRB 240825A most likely originated from a massive star collapse, possibly with the associated SN heavily obscured or intrinsically faint. This study emphasizes the need for multiwavelength follow-up and a multi-layered classification to determine GRB progenitors.

astro-ph.HE↗

arXiv2020

Synergistic Integration of Optical and Microwave Satellite Data for Crop Yield Estimation

Developing accurate models of crop stress, phenology and productivity is of paramount importance, given the increasing need of food. Earth observation remote sensing data provides a unique source of information to monitor crops in a temporally resolved and spatially explicit way. In this study, we propose the combination of multisensor (optical and microwave) remote sensing data for crop yield estimation and forecasting using two novel approaches. We first propose the lag between Enhanced Vegetation Index derived from MODIS and Vegetation Optical Depth derived from SMAP as a new joint metric combining the information from the two satellite sensors in a unique feature or descriptor. Our second approach avoids summarizing statistics and uses machine learning to combine full time series of EVI and VOD. This study considers two statistical methods, a regularized linear regression and its nonlinear extension called kernel ridge regression to directly estimate the county-level surveyed total production, as well as individual yields of the major crops grown in the region: corn, soybean and wheat. The study area includes the US Corn Belt, and we use agricultural survey data from the National Agricultural Statistics Service (USDA-NASS) for year 2015 for quantitative assessment.

eess.SP↗

arXiv2020

Nonlinear Distribution Regression for Remote Sensing Applications

In many remote sensing applications one wants to estimate variables or parameters of interest from observations. When the target variable is available at a resolution that matches the remote sensing observations, standard algorithms such as neural networks, random forests or Gaussian processes are readily available to relate the two. However, we often encounter situations where the target variable is only available at the group level, i.e. collectively associated to a number of remotely sensed observations. This problem setting is known in statistics and machine learning as {\em multiple instance learning} or {\em distribution regression}. This paper introduces a nonlinear (kernel-based) method for distribution regression that solves the previous problems without making any assumption on the statistics of the grouped data. The presented formulation considers distribution embeddings in reproducing kernel Hilbert spaces, and performs standard least squares regression with the empirical means therein. A flexible version to deal with multisource data of different dimensionality and sample sizes is also presented and evaluated. It allows working with the native spatial resolution of each sensor, avoiding the need of match-up procedures. Noting the large computational cost of the approach, we introduce an efficient version via random Fourier features to cope with millions of points and groups.

cs.LG↗