Eucario G. Pérez-Pérez

Neutrino emission from astrophysical sources has long been considered a signature of cosmic-ray acceleration. The IceCube Neutrino Observatory has observed a diffuse flux of TeV–PeV neutrinos, but very few confirmed sources have emerged. With the recent publication of the IceCube Event Catalog (IceCat-1), IceCube has released a list of probable astrophysical neutrinos from 2011 May to 2020 December, with follow-up alerts released via the Astrophysical Multi-messenger Observatory Network (AMON). Using the archival data from the High Altitude Water Cherenkov (HAWC) Gamma-Ray Observatory, we search for gamma-ray outbursts spatially coincident with these neutrino alerts using a Bayesian block algorithm. In this work, we consider 380 alerts, up to 2026 May 19, that are within HAWC’s field of view. We observe approximately a 5% coincident detection rate, which is consistent with expectations from background. Two of these detections contain the active galactic nuclei (AGNs) Mrk 421 and Mrk 501. We discuss the possibility of these AGNs producing the observed neutrino events when considering the HAWC results.

Multi-TeV gamma-ray emission around eHWC J1850+001 (a source from the first HAWC catalog of gamma-ray sources emitting above 56 TeV) is spatially coincident with the pulsar wind nebula (PWN) G32.64+0.53, powered by PSR J1849-0001. The absence of counterparts in radio, optical, and GeV energy ranges, contrasted with clear detections in X-rays and very-high-energy (VHE) gamma rays, is indicative of a nonthermal leptonic origin for the nebula. We apply a systematic analysis pipeline, including a sophisticated model for the Galactic diffuse emission, to 2860 days of data from the HAWC Observatory. Our detailed analysis confirms that the ultra-high-energy (UHE) emission originates from G32.64+0.53, and we measure its spectrum up to 270 TeV with significant emission well beyond 100 TeV. We fit the multiwavelength observations with a time-dependent leptonic model powered by the pulsar’s rotational energy, and the results establish the nebula as a leptonic PeV accelerator, capable of accelerating electrons to a maximum energy of E _cut ≥ 2.9 PeV (95% one-sided lower bound). The model also constrains the nebular magnetic field to 4.28 μ G, supporting a leptonic PWN origin for the observed UHE emission.

Precision agriculture is a key technology for addressing challenges such as increasing food demand, labour shortages, and the environmental impact of intensive agrochemical use. In this context, selective weed management remains a critical issue due to its direct effect on crop productivity and sustainability. This article presents a simulation-based framework for the design and evaluation of an agricultural robotic module for the detection, classification, and selective intervention of weeds. The proposed system integrates convolutional neural networks and the kinematic model of a 2DOF robot manipulator with 5 links for weed classification and treatment. The system is evaluated in a virtual environment, where camera calibration, perception accuracy, and the performance of the kinematic model are analysed. Quantitative results include detection accuracy, localization error, and intervention success rate under simulated field conditions. The results demonstrate selective weed management and the feasibility of simulation for developing weed control systems, while also identifying the main challenges for real-world deployment.

Dwarf spheroidal galaxies (dSphs) are excellent targets for indirect dark matter (DM) searches using gamma-ray telescopes because they are thought to have high DM content and a low astrophysical background. The sensitivity of these searches is improved by combining the observations of dSphs made by different gamma-ray telescopes. We present the results of a combined search by the most sensitive currently operating gamma-ray telescopes, namely: the satellite-borne Fermi -LAT telescope; the ground-based imaging atmospheric Cherenkov telescope arrays H.E.S.S., MAGIC, and VERITAS; and the HAWC water Cherenkov detector. Individual datasets were analyzed using a common statistical approach. Results were subsequently combined via a global joint likelihood analysis. We obtain constraints on the velocity-weighted cross section 〈 σv 〉 for DM self-annihilation as a function of the DM particle mass. This five-instrument combination allows the derivation of up to 2-3 times more constraining upper limits on 〈 σv 〉 than the individual results over a wide mass range spanning from 5 GeV to 100 TeV. Depending on the DM content modeling, the 95% confidence level observed limits reach 1.5×10 ^-24 cm ³ s ^-1 and 3.2×10 ^-25 cm ³ s ^-1 , respectively, in the τ ⁺ τ ^- annihilation channel for a DM mass of 2 TeV.

Neutrino emission from astrophysical sources has long been considered a signature of cosmic-ray acceleration. The IceCube neutrino observatory has observed a diffuse flux of TeV-PeV neutrinos, but very few confirmed sources have emerged. With the recent publication of IceCube Event Catalog (IceCat-1), IceCube has released a list of the most promising astrophysical neutrino events since May 2011. Using the archival data from the High Altitude Water Cherenkov (HAWC) $γ$-ray observatory, we perform a coincidence search for gamma rays and neutrinos using a Bayesian Block algorithm with the public IceCube alerts from IceCat-1, along with additional alerts issued later. In this work, we consider 368 alerts, up to July 8, 2025, that are within HAWC's field of view. We observe approximately a 5\% coincident detection rate, which is consistent with expectations from background. Two of these detections contain the Active Galactic Nuclei (AGN) Markarian 421 and Markarian 501. We discuss the likelihood that the neutrino/$γ$-ray coincidences are false positives and a brief overview of the results.

The classification of breast cancer using machine learning techniques has become a critical tool in modern medical diagnostics. This study analyzes the performance of hybrid models that combine traditional machine learning algorithms (TMLAs) with a convolutional neural network (CNN)-based VGG16 model for feature extraction to improve accuracy for classifying eight breast cancer subtypes (BCS). The methodology consists of three steps. First, image preprocessing is performed on the BreakHis dataset at 400× magnification, which contains 1820 histopathological images classified into eight BCS. Second, the CNN VGG16 is modified to function as a feature extractor that converts images into representative vectors. These vectors constitute the training set for TMLAs, such as Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Naive Bayes (NB), leveraging VGG16’s ability to capture relevant features. Third, k-fold cross-validation is applied to evaluate the model’s performance by averaging the metrics obtained across all folds. The results reveal that hybrid models leveraging a CNN-based VGG16 model for feature extraction, followed by TMLAs, achieve accuracy outstanding experimental accuracy. The KNN-based hybrid model stood out with a precision of 0.97, accuracy of 0.96, sensitivity of 0.96, specificity of 0.99, F1-score of 0.96, and ROC-AUC of 0.97. These findings suggest that, with an appropriate methodology, hybrid models based on TMLA have strong potential in classification tasks, offering a balance between performance and predictive capability.

The results of a survey of middle-aged pulsars suggest that a feature previously seen around a handful of pulsars might be ubiquitous.

Very-high-energy (0.1–100 TeV) gamma-ray emissions were observed in High-Altitude Water Cherenkov (HAWC) data from the lobes of the microquasar SS 433, making them the first set of astrophysical jets that were resolved at TeV energies. In this work, we update the analysis of SS 433 using 2565 days of data from the HAWC observatory. Our analysis reports the detection of a point-like source in the east lobe at a significance of 6.6σ and in the west lobe at a significance of 8.2σ. For each jet lobe, we localize the gamma-ray emission and identify a best-fit position. The locations are close to the X-ray emission sites “e1” and “w1” for the east and west lobes, respectively. We analyze the spectral energy distributions and find that the energy spectra of the lobes are consistent with a simple power law d N/d E ∝ E ^α with α = − 2.44 − 0.12 − 0.04 + 0.13 + 0.04 and α = − 2.35 − 0.11 − 0.03 + 0.12 + 0.03 for the east and west lobes, respectively. The maximum energy of photons from the east and west lobes reaches 56 TeV and 123 TeV, respectively. We compare our observations to various models and conclude that the very-high-energy gamma-ray emission can be produced by a population of electrons that were efficiently accelerated.

The origin of high-energy galactic cosmic rays is yet to be understood, but some galactic cosmic-ray accelerators can accelerate cosmic rays up to PeV energies. The high-energy cosmic rays are expected to interact with the surrounding material or radiation, resulting in the production of gamma-rays and neutrinos. To optimize for the detection of such associated production of gamma-rays and neutrinos for a given source morphology and spectrum, a multimessenger analysis that combines gamma-rays and neutrinos is required. In this study, we use the Multi-Mission Maximum Likelihood framework with IceCube Maximum Likelihood Analysis software and HAWC Accelerated Likelihood to search for a correlation between 22 known gamma-ray sources from the third HAWC gamma-ray catalog and 14 yr of IceCube track-like data. No significant neutrino emission from the direction of the HAWC sources was found. We report the best-fit gamma-ray model and 90% CL neutrino flux limit from the 22 sources. From the neutrino flux limit, we conclude that, for five of the sources, the gamma-ray emission observed by HAWC cannot be produced purely from hadronic interactions. We report the limit for the fraction of gamma-rays produced by hadronic interactions for these five sources.