arXiv subjects

D. Sierra-Porta

Publications and source records attributed to D. Sierra-Porta.

13 recordsLinked to original sources

arXiv2026

RACiMo: Red Ambiental Ciudadana de Monitoreo: A Student-Centred Citizen Science Network for Environmental Monitoring, Data Literacy, and Climate Awareness in Colombia

This paper traces the evolution of Red Ambiental Ciudadana de Monitoreo (RACiMo), a citizen-science and environmental-education initiative developed by Universidad Industrial de Santander in Colombia. RACiMo has evolved through successive editions from a school-based open-hardware programme into a regional environmental monitoring network that integrates meteorological and air-quality data, open-data practices, and student-centred research. The project's central objective is to train citizens, especially secondary-school students, teachers, and university mentors, to record, curate, analyse, interpret, and communicate climate and air-quality data relevant to their local contexts. RACiMo has involved approximately 515 students from urban, metropolitan, rural, and páramo communities in Santander, Colombia. It has progressed from Arduino-Raspberry Pi stations assembled through a Do-It-With-Others approach to low-maintenance commercial instruments and a multi-municipality network of professional weather and air-quality stations. Its pedagogy has shifted from learning by building sensors to learning by analysing real environmental datasets through Python, Jupyter notebooks, visualisation tools, and student-led research projects. The current RACiMo-Orquídeas implementation expands the network across five municipalities, emphasises local environmental issues such as freight traffic and industrial activity, and provides public access to data through repositories and interactive tools. The paper discusses the trade-offs between openness, educational value, reliability, data quality, sustainability, and scalability. It argues that citizen environmental monitoring can strengthen climate awareness and adaptation when local communities are trained not only to collect data, but also to understand and use it critically.

physics.soc-ph↗

arXiv2026

Rigidity spectra and onset geometry of the two largest Forbush decreases of solar cycle 25 from visibility-graph curvature

We apply a geometric network diagnostic -- the nodal Forman-Ricci (FR) curvature of natural visibility graphs (NVG) -- to the two largest Forbush decreases (FDs) of solar cycle 25: the Gannon storm of 2024 May 10-11 and the 2025 June 1 event. Using 30-min NMDB records from six NM64 stations spanning cutoff rigidities $R_c \simeq 0$-$7\,$GV, we show that nodal FR curvature exhibits a sharp, coherent minimum at FD onset in both events, with onset-to-quiet median curvature ratios of 2.5-5.2 (Gannon) and 3.3-8.2 (June 2025). A placement (block-permutation) test that preserves the full autocorrelation structure confirms the onset signature at $p \lesssim 2 \times 10^{-3}$ (the floor of the test) for all 24 station-phase combinations, with $|z| = 4.7$-$23.7$. The signature is robust to edge weighting of the Forman curvature and to sliding-window (6-24 h) graph construction, and an amplitude-blind (horizontal-visibility) control confirms that it is specific to the amplitude geometry of the decrease. Nodal FR curvature nearly doubles the effect size obtained from the NVG degree sequence alone (mean Cliff's $δ$ of 0.89 vs 0.54), because it encodes two-hop (neighbor-degree) structure. Folding the station amplitudes through the Dorman coupling function yields FD spectral indices $γ= 0.80$ (Gannon) and $0.50$ (June 2025) with $A_{10} = 11.2\%$ and $18.8\%$: the deeper event is also spectrally harder. Across the 12 station-event pairs the curvature extreme scales with FD amplitude as $|\mathcal{F}_{\min}| \propto A^{0.57}$ (Spearman $ρ= 0.75$). These results establish local graph curvature as a compact, rigidity-resolved descriptor of FD morphology.

astro-ph.IM↗

arXiv2026

Morphological Fingerprints of Forbush Decreases and Their Relation to Geomagnetic Storm Severity

Forbush decreases (FDs) are transient depressions in the galactic cosmic-ray flux observed by global neutron-monitor networks and are commonly associated with interplanetary disturbances driven by coronal mass ejections and related shocks. Despite extensive observational work, quantitatively comparing FD morphology across events and linking it to storm severity remains challenging due to heterogeneous station responses, coverage gaps, and the multivariate nature of the network. This work introduces a graph-based event representation in which each FD is mapped to an event network constructed from pairwise dissimilarities between station response time series. A controlled sparse backbone is obtained via the minimum spanning tree, enabling comparable event graphs across cases. From each graph, a compact set of geometric/topological fingerprints is computed, including global integration measures, spectral summaries, mesoscopic structure, centrality aggregates, and complexity descriptors. Predictive skill is assessed using strict leave-one-event-out validation over a pre-defined grid of distance metrics and distance-domain transformations, with selection criteria fixed \emph{a priori}. The proposed fingerprints exhibit measurable signal for three tasks: (i) multi-class classification of geomagnetic storm intensity (G3/G4/G5) with moderate but consistent performance and errors dominated by adjacent categories; (ii) stronger binary severity screening ($\ge$G4 vs. G3) with high sensitivity to severe events; and (iii) drop regression with partial least squares achieving positive explained variance relative to a fold-wise mean baseline.

astro-ph.IM↗

arXiv2026

Graph-Based Light-Curve Features for Robust Transient Classification

We investigate graph-based representations of astronomical light curves for transient classification on a quality-controlled, class-balanced subset of the MANTRA benchmark (minimum coverage N_min=100 epochs; N=1705 objects after filtering and Non-Tr. subsampling). Each series is mapped to three visibility-graph views -- horizontal (HVG), directed (DHVG), and weighted (W-HVG) -- from which we extract compact, length-aware network descriptors (degree/strength moments, clustering and motifs, assortativity, path/efficiency, and spectral summaries). Using object-level stratified five-fold validation and tree-based learners, the best configuration (LightGBM with HVG+DHVG+W-HVG features) attains a macro-F1 of 0.622 +/- 0.010 and accuracy of 0.661 +/- 0.010 on this subset. For context, the published MANTRA baseline reports F1_macro=0.528 on the full dataset; because class priors differ after quality control, this reference is not a like-for-like comparison. Ablations show that weighted contrasts and directed asymmetry contribute complementary gains to undirected topology. Per-class analysis highlights strong performance for CV, HPM, and Non-Tr., with residual confusions concentrated in the AGN-Blazar-SN block. These results indicate that visibility graphs offer a simple, survey-agnostic bridge between irregular photometric time series and standard classifiers, yielding competitive multiclass performance without bespoke deep architectures. We release code and feature definitions, together with the list of object IDs used in the evaluation subset, to facilitate reproducibility and future extensions.

astro-ph.IM↗

arXiv2026

Vision-Based CNN Prediction of Sunspot Numbers from SDO/HMI Images

Sunspot numbers provide the longest continuous record of solar activity and remain a key index for heliophysical research and space-weather applications. Standard sunspot determination relies on visual inspection and algorithmic feature-detection pipelines, both of which involve methodological choices and can be sensitive to image quality and implementation details. Convolutional neural networks (CNNs) offer an alternative by learning an end-to-end mapping from solar images to a scalar index, reducing reliance on explicit, handcrafted feature design. Here we present a supervised vision-based regression framework to estimate the daily sunspot number from full-disk continuum images acquired by the Helioseismic and Magnetic Imager (HMI) onboard NASA Solar Dynamics Observatory (SDO). We pair daily images from 2011-2024 with the SILSO Version 2.0 daily sunspot number and train a CNN to infer the scalar value at the observation time of each image. On an independent test split, the model achieves R2=0.964, RMSE=9.75, and MAE=6.74, indicating close agreement with SILSO across a wide activity range. Interpretability analyses using Grad-CAM and Integrated Gradients show that the network attributions concentrate on sunspot-bearing regions, supporting the physical plausibility of the learned representations. These results demonstrate the feasibility of direct image-to-index estimation for scalable solar monitoring. Future work will explore multimodal fusion with complementary observables (e.g., magnetograms) and standardized cross-cycle benchmarks to assess robustness under changing solar conditions.

astro-ph.SR↗

arXiv2026

Derivative-Aligned Anticipation of Forbush Decreases from Entropy and Fractal Markers

We develop a feature-based framework to anticipate Forbush decreases in one-minute neutron-monitor records by tracking sliding-window invariants from information theory, scaling, and geometry. For each station we compute marker time series, including Shannon, spectral, approximate and sample entropy; Lempel-Ziv complexity; correlation dimension; and Higuchi and Katz fractal dimensions. Markers are smoothed with an exponentially weighted moving average and analyzed through within-station standardized first differences. Timing is referenced to an operational alignment time defined as the minimum of the smoothed count first difference, and marker leads are reported in minutes (negative values indicate anticipation). Station-level detectability is evaluated on a pre-alignment window using a robust z-score detector with bilateral threshold and persistence, without cross-correlation or hypothesis testing. We apply the pipeline to two FD episodes with broad station coverage (2023-04-23 and 2024-05-10; 28 stations each). Across events, a compact CORE panel shows consistently high detection rates and predominantly anticipatory lead distributions, with typical median leads on the order of several hours depending on the invariant and event. Lead dispersion across stations is substantial, with interquartile ranges commonly spanning a few hours, highlighting the need for station-wise criteria and distributional summaries rather than single-station inference. Representative marker trajectories confirm that early flagging corresponds to sustained pre-alignment excursions in marker differences, not tabulation artifacts. The approach is reproducible from open code, operates on native station units without cross-station homogenization, and remains qualitatively stable under sensitivity sweeps of windowing, smoothing, and detector parameters.

astro-ph.IM↗

arXiv2023

The DESI Survey Validation: Results from Visual Inspection of Bright Galaxies, Luminous Red Galaxies, and Emission Line Galaxies

The Dark Energy Spectroscopic Instrument (DESI) Survey has obtained a set of spectroscopic measurements of galaxies to validate the final survey design and target selections. To assist in these tasks, we visually inspect (VI) DESI spectra of approximately 2,500 bright galaxies, 3,500 luminous red galaxies (LRGs), and 10,000 emission line galaxies (ELGs), to obtain robust redshift identifications. We then utilize the VI redshift information to characterize the performance of the DESI operation. Based on the VI catalogs, our results show that the final survey design yields samples of bright galaxies, LRGs, and ELGs with purity greater than $99\%$. Moreover, we demonstrate that the precision of the redshift measurements is approximately 10 km/s for bright galaxies and ELGs and approximately 40 km/s for LRGs. The average redshift accuracy is within 10 km/s for the three types of galaxies. The VI process also helps improve the quality of the DESI data by identifying spurious spectral features introduced by the pipeline. Finally, we show examples of unexpected real astronomical objects, such as Ly$α$ emitters and strong lensing candidates, identified by VI. These results demonstrate the importance and utility of visually inspecting data from incoming and upcoming surveys, especially during their early operation phases.

astro-ph.CO↗

arXiv2022

On the Convergence of the Milky Way and M31 Kinematics from Cosmological Simulations

The kinematics of the Milky Way (MW) and M31, the dominant galaxies in the Local Group (LG), can be used to estimate the LG total mass. New results on the M31 proper motion have recently been used to improve that estimate. Those results are based on kinematic priors that are sometimes guided and evaluated using cosmological N-body simulations. However, the kinematic properties of simulated LG analogues could be biased due to the effective power spectrum truncation induced by the small size of the parent simulation. Here we explore the dependence of LG kinematics on the simulation box size to argue that cosmological simulations need a box size on the order of 1 Gpc in order to claim convergence on the LG kinematic properties. Using a large enough simulation, we find M31 tangential and radial velocities relative to the MW to be in the range $v_{\mathrm {tan}}=105^{+94}_{-59} $ km/s and $v_{\mathrm {rad}}=-108^{+68}_{-81}$ km/s, respectively. This study highlights that LG kinematics derived from N-body simulations have to be carefully interpreted taking into account the size of the parent simulation.

astro-ph.CO↗

arXiv2022

Muography in Colombia: simulation framework, instrumentation and data analysis

We present the Colombo-Argentinian Muography Program for studying inland Latin-American volcanoes. It describes the implementation of a simulation framework covering various factors with different spatial and time scales: the geomagnetic effects at a particular geographic point, the development of extensive air showers in the atmosphere, the propagation through the scanned structure and the detector response. Next, we sketch the criteria adopted for designing, building, and commissioning MuTe: a hybrid Muon Telescope based on a composite detection technique. It combines a hodoscope for particle tracking and a water Cherenkov detector to enhance the muon-to-background-signal separation due to extensive air showers' soft and multiple-particle components. MuTe also discriminates inverse-trajectory and low-momentum muons by using a picosecond Time-of-Flight system. We also characterise the instrument's structural (mechanical and thermal) behaviour, discussing preliminary results from the background composition and the telescope-health monitoring variables. Finally, we discuss the implementations of an optimisation algorithm to improve the volcano internal density distribution estimation and machine learning techniques for background rejection.

astro-ph.IM↗

arXiv2021

Simulated Annealing for Volcano Muography

Muography or muon radiography is a non-invasive emerging image technology relying on high energy atmospheric muons, which complements other standard geophysical tools to understand the Earth's subsurface. This work discusses a geophysical inversion methodology for volcanic muography, based on the Simulated Annealing algorithm, using a semi-empirical model of the muon flux to reach the volcano topography and a framework for the energy loss of muons in rock. The Metropolis-Simulated-Annealing algorithm starts from an 'observed' muon flux and obtains the best associated inner density distribution function inside a synthetic model of the Cerro Machin Volcano (Tolima-Colombia). The estimated initial density model was obtained with GEOMODELER, adapted to the volcano topography. We improved this model by including rock densities from samples taken from the crater, the dome and the areas associated with fumaroles. In this paper, we determined the minimum muon energy (a function of the arrival direction) needed to cross the volcanic building, the emerging integrated flux of muons, and the density profile inside a model of Cerro Machin. The present inversion correctly reconstructed the density differences inside the Machin, within a 1 percent error concerning our initial simulation model, giving a remarkable density contrast between the volcanic duct, the encasing rock and the fumaroles area.

physics.geo-ph↗

arXiv2020

Design and construction of MuTe: a hybrid Muon Telescope to study Colombian Volcanoes

We present a hybrid Muon Telescope, MuTe, designed and built for imaging active Colombian volcanoes. The MuTe has a resolution of tens of meters, low power consumption, robustness and transportability making it suitable for use in difficult access zones where active volcanoes usually are. The main feature of MuTe is the implementation of a hybrid detection technique combining two scintillation panels for particle tracking and a Water Cherenkov Detector for filtering background sources due to the electromagnetic component of extended air showers and multiple particle events. MuTe incorporates particle-identification techniques for reducing the background noise sources and discrimination of fake events by a picosecond Time-of-Flight system. We also describe the mechanical behavior of the MuTe during typical tremors and wind conditions at the observation place, as well as the frontend electronics design and power consumption.

physics.ins-det↗

arXiv2019

Muon Tomography sites for Colombian volcanoes

By using a very detailed simulation scheme, we have calculated the cosmic ray background flux at 13 active Colombian volcanoes and developed a methodology to identify the most convenient places for a muon telescope to study their inner structure. Our simulation scheme considers three critical factors with different spatial and time scales: the geomagnetic effects, the development of extensive air showers in the atmosphere, and the detector response at ground level. The muon energy dissipation along the path crossing the geological structure is modeled considering the losses due to ionization, and also contributions from radiative Bremstrahlung, nuclear interactions, and pair production. By considering each particular volcano topography and assuming reasonable statistics for different instrument acceptances, we obtained the muon flux crossing each structure and estimated the exposure time for our hybrid muon telescope at several points around each geological edifice. After a detailed study from the topography, we have identified the best volcano to be studied, spotted the finest points to place a muon telescope and estimated its time exposures for a significant statistics of muon flux. We have devised a mix of technical and logistic criteria --the ``rule of thumb'' criteria-- and found that only Cerro Machin, located at the Cordillera Central (4$^{\circ}$29'N 75$^{\circ}$22'W), can be feasibly studied today through muography. Cerro Negro and Chiles could be good candidates shortly

physics.geo-ph↗

arXiv2019

Cross correlation and time-lag between cosmic ray intensity and solar activity during solar cycles 21, 22 and 23

In the present paper a systematic study is carried out to validate the similarity or degree of relationship between daily terrestrial cosmic rays intensity and three characteristics of about evolution of solar corona, like a number of sunspots and flare index observed in the solar corona and to Ap index for regular magnetic field variation caused by regular solar radiation changes. The study is made in a range including three solar cycles starting with the cycle 21 (year 1976) and ending on cycle 23 (year 2008). The technique used in this case will be the use of the cross-correlation technique to establish patterns and dependence on the behavior of both variables. This study focused on the time lag calculation for these variables and found a maximum of negative correlation over $CC_1\approx 0.85$, $CC_2\approx 0.75$ and $CC_3\approx 0.63$ with an estimation of 181, 156 and 2 days of deviation between maximum/minimum of peaks for the intensity of cosmic rays related with sunspot number, flare index and Ap index regression, respectively.

astro-ph.SR↗