Ignacio De La Calle Perez

Identification and/or association of unidentified γ -ray sources with lower-energy counterparts represents a key challenge in modern astronomy, due to the relatively large positional uncertainty provided by γ -ray detectors. We selected unidentified Fermi objects (UFOs) positionally compatible with hard X-ray sources in the latest Palermo Swift-BAT hard X-ray Catalog and in the SRG/ART-XC all-sky X-ray survey Catalog, to identify lower-energy sources and possibly associate them to the UFOs. We found 17 UFOs with overlapping hard X-ray sources. We then collected soft X-ray data from Swift-XRT, Chandra-ACIS, XMM-Newton-EPIC, and eROSITA, identified 16 soft X-ray counterparts to the hard X-ray sources, and associate 15 with known astronomical objects, classified as: blazars/blazar candidates (two sources), Seyfert galaxies (five sources), X-ray binaries (two sources), generic X-ray sources (one source), cataclysmic variables (CVs; two sources), and variable stars (three sources). Blazars and Seyfert galaxies are likely lower-energy counterparts to the UFOs, since their mid-IR colors and broadband spectral energy distributions suggest significant jetted, nonthermal emission. X-ray binaries can be potential lower-energy counterparts to the UFOs, since this class of sources has been already observed to emit γ -rays. The generic X-ray source has been proposed as a pulsar candidate, and we therefore suggest that it can be the lower-energy counterpart to the UFO. CVs have been suggested as potential γ -ray emitters, so, if confirmed, the two sources classified as CVs would represent the first γ -ray emitting sources of this kind. Finally, we consider the association of the three variable stars with the UFOs unlikely.

Given the increasing frequency, severity, and socioecological impacts of wildfires, there is an urgent need for robust frameworks to better characterize fire behavior and flammability patterns across ecosystems to support early warning, mitigation, and management strategies. However, flammability remains difficult to quantify and scale, as it involves multiple interacting components that are typically measured at the bench scale. This study aimed to establish empirical links between spectral information, plant traits, and flammability metrics, and to scale these relationships to satellite imagery to translate these metrics into a spatial context. We combined laboratory spectroscopy, plant trait measurements including leaf mass per area, carbon, and cellulose, and combustion experiments using a simple and reproducible burning device. In total, 84 samples were collected and analysed, allowing us to characterise how spectral signatures relate to vegetation traits and fire behaviour. Spectral indices were developed to estimate plant traits, which were subsequently used as predictors in flammability models. These models were then transferred to Environmental Mapping and Analysis Program (EnMAP) hyperspectral imagery to derive spatial estimates across eucalypt forests and grasslands of the Australian Capital Territory (ACT). Spectral information distinguished fuel types and captured variability of the plant traits, while these traits showed associations with combustion behaviour. Based on these links, the best-performing model predicted the rate of temperature increase, a combustibility metric, in eucalypt forests (R² = 0.70; Root Mean Square Error = 32.48 °C/s). In contrast, grassland models showed limited predictive performance, likely due to weaker relationships between plant traits and flammability metrics. Overall, this study demonstrates a practical and scalable approach for deriving flammability maps from hyperspectral and in situ data, highlighting the potential of plant-trait-based remote sensing. The resulting maps should not be interpreted as standalone fire risk products, but rather as a characterization of the structural and biochemical drivers of flammability. The main constraint of this work is the limited sample size. Future research should expand spatial and temporal coverage to better capture vegetation variability and enable the inclusion of independent validation datasets. Exploring alternative combustion protocols and testing more advanced spectral modelling approaches for trait estimation would provide additional insights.

Reliable global turbidity monitoring is crucial for water resource management, yet existing satellite-based methods face limitations in accuracy, generalization, and scalability across diverse aquatic environments. This study presents a robust, globally applicable turbidity estimation model using Sentinel-2 imagery and a machine-learning approach, developed based on harmonized global open-source datasets (GLORIA and MAGEST; turbidity range: 0–2200 FNU) encompassing 68 lakes, 2 rivers, 2 estuaries, and 11 coastal oceans across 17 countries. Among the evaluated machine-learning models, gradient boosting regression demonstrated the best performance, achieving a high correlation (r: 0.95) with minimal bias (1.32 FNU) and robust generalization across all water types, outperforming existing turbidity models when evaluated on the same test dataset. Shapley Additive exPlanations-based model interpretability identified the Rrs865/Rrs560 ratio as the dominant predictor, with critical contributions from Rrs783, Rrs665, and Rrs865. The model’s performance is evaluated across various optical water types and aquatic systems in diverse geographical settings, showcasing its robustness in sediment-rich and highly turbid environments that underscores its suitability for reliable turbidity monitoring after severe storms or extreme precipitation. Additionally, innovative automated pipelines integrated within a scientific exploitation platform facilitate scalable and near-real-time operational monitoring. This methodological integration provides a significant advancement in satellite-based turbidity monitoring, enabling informed water quality management under diverse environmental and climatic conditions.

Context. Blazars exhibit strong variability across the entire electromagnetic spectrum, including periods of high-flux states commonly known as flares. The physical mechanisms in blazar jets responsible for flares remain poorly understood to date. Aims. Our aim is to better understand the emission mechanisms during blazar flares using X-ray polarimetry and broadband observations from the archetypical TeV blazar Mrk 421, which can be studied with higher accuracy than other blazars that are dimmer and/or located farther away. Methods. We studied a flaring activity from December 2023 that was characterized from radio to very high-energy (VHE; E > 0.1 TeV) gamma rays with MAGIC, Fermi-LAT, Swift, XMM-Newton, and several optical and radio telescopes. These observations included, for the first time for a gamma-ray flare of a blazar, simultaneous X-ray polarization measurements with IXPE, in addition to optical and radio polarimetry data. We quantify the variability and correlations among the multi-band flux and polarization measurements, and describe the varying broadband emission within a theoretical scenario constrained by the polarization data. Results. We find substantial variability in both X-rays and VHE gamma rays throughout the campaign, with the highest VHE flux above 0.2 TeV occurring during the IXPE observing window, and exceeding twice the flux of the Crab Nebula. However, the VHE and X-ray spectra are on average softer, and the correlation between these two bands is weaker than those reported in the previous flares of Mrk 421. IXPE reveals an X-ray polarization degree significantly higher than that at radio and optical frequencies, similar to previous results for Mrk 421 and other high synchrotron peaked blazars. Differently to past observations, the X-ray polarization angle varies by ∼100° on timescales of days, and the polarization degree changes by more than a factor of 4. The highest X-ray polarization degree, analyzed in 12 h time intervals, reaches 26 ± 2%, around which an X-ray counter-clockwise hysteresis loop is measured with XMM-Newton. It suggests that the X-ray emission comes from particles close to the high-energy cutoff, hence possibly probing an extreme case of the Turbulent Extreme Multi-Zone model for which the chromatic trend in the polarization may be more pronounced than theoretically predicted. We model the broadband emission with a simplified stratified jet model throughout the flare. The polarization measurements imply an electron distribution in the X-ray emitting region with a very high minimum Lorentz factor ($ \gamma\prime_{\mathrm{min}}\gtrsim10^4 $), which is expected in electron-ion plasma, as well as a variation of the emitting region size of up to a factor of 3 during the flaring activity. We find no correlation between the fluxes and the evolution of the model parameters, which indicates a stochastic nature of the underlying physical mechanism that likely explains the lack of a tight X-ray/VHE correlation during this flaring activity. Such behavior would be expected in a highly turbulent electron-ion plasma crossing a shock front.

Monitoring faecal indicator bacteria (FIB) such as Escherichia coli (E. coli) and Enterococcus in water bodies is critical for public health and environmental safety. While satellite data is widely used for water quality monitoring, its use to specifically map and monitor FIB represents a novel and valuable application. This study leverages Sentinel-2 satellite data to develop empirical statistical models to identify, map and monitor E. coli and Enterococcus across 13 beaches in Calvià, Mallorca, Spain. Through the integration of spectral features such as the Shortwave Infrared (SWIR1)/Coastal-Aerosol Ratio (Band11/Band1), SWIR1/SWIR2 Ratio (Band11/Band12), and the Normalized Difference SWIR1-Coastal Index (B11-B1/B11+B1), the models effectively identified and distinguished these bacteria from other key water quality indicators, including chlorophyll-a, turbidity, suspended matter, and coloured dissolved organic matter. The models achieved R² values of 0.79 for E. coli and 0.74 for Enterococcus, with p-values of 5.37e^-13 and 1.17e^-11, respectively, demonstrating high accuracy and specificity. This study offers a new tool for monitoring faecal contamination, thus highlighting the potential of satellite technology to enhance environmental monitoring and ensure the safety of recreational waters.

The Sentinel-2 NDVI time series information content from 2017 to 2023 at a 10 m spatial resolution was evaluated based on the NDVI temporal dependency in five scenarios in central Spain. First, time series were interpolated and then filtered using the Savitzky–Golay, Fast Fourier Transform, Whittaker, and Maximum Value filters. Temporal dependency was assessed using the Q-Ljung-Box and Fisher’s Kappa tests, and similarity between raw and filtered time series was assessed using Correlation Coefficient and Root Mean Square Error. An Interpolating Efficiency Indicator (IEI) was proposed to summarize the number and temporal distribution of low-quality observations. Type of climate, atmospheric disturbances, land cover dynamics, and management were the main sources of variability in five scenarios: (1) rainfed wheat and barley presented high short-term variability due to clouds (lower IEI in winter and spring) during the growing cycle and high interannual variability due to precipitation; (2) maize showed stable summer cycles (high IEI) and low interannual variability due to irrigation; (3) irrigated alfalfa was cut five to six times during summer, resulting in specific intra-annual variability; (4) beech forest showed a strong and stable summer cycle, despite the short-term variability due to clouds (low IEI); and (5) evergreen pine forest had a highly variable growing cycle due to fast responses to temperature and precipitation through the year and medium IEI values. Interpolation after removing non-valid observations resulted in an increase in temporal dependency (Q-test), particularly a short term in areas with low IEI values. The information improvement made it possible to identify hidden periodicities and trends using the Fisher’s Kappa test. The SG filter showed high similarity values and weak influence on dynamics, while the MVF showed an overestimation of the NDVI values.

Aims. We have performed the first broadband study of Mrk 421 from radio to TeV gamma rays with simultaneous measurements of the X-ray polarization from IXPE. Methods. The data were collected as part of an extensive multiwavelength campaign carried out between May and June 2022 using MAGIC, Fermi-LAT, NuSTAR, XMM-Newton, Swift, and several optical and radio telescopes to complement IXPE data. Results. During the IXPE exposures, the measured 0.2–1 TeV flux was close to the quiescent state and ranged from 25% to 50% of the Crab Nebula without intra-night variability. Throughout the campaign, the very high-energy (VHE) and X-ray emission are positively correlated at a 4σ significance level. The IXPE measurements reveal an X-ray polarization degree that is a factor of 2–5 higher than in the optical/radio bands; that implies an energy-stratified jet in which the VHE photons are emitted co-spatially with the X-rays, in the vicinity of a shock front. The June 2022 observations exhibit a rotation of the X-ray polarization angle. Despite no simultaneous VHE coverage being available during a large fraction of the swing, the Swift-XRT monitoring reveals an X-ray flux increase with a clear spectral hardening. This suggests that flares in high synchrotron peaked blazars can be accompanied by a polarization angle rotation, as observed in some flat spectrum radio quasars. Finally, during the polarization angle rotation, NuSTAR data reveal two contiguous spectral hysteresis loops in opposite directions (clockwise and counterclockwise), implying important changes in the particle acceleration efficiency on approximately hour timescales.

Aims. We present an XMM–Newton catalogue of BL Lac X-ray, optical, and UV properties based on cross-correlation with the 1151 BL Lacs listed in the fifth edition of the Roma–BZCAT. Methods. We searched for the X-ray counterparts to these objects in the field of view of all pointed observations in the XMM–Newton archive over nearly 20 years of mission. The cross-correlation yields a total of 310 XMM–Newton fields which correspond to 103 different BL Lacs. We homogeneously analysed data from the three EPIC cameras (X-ray) and OM (optical/UV) using the XMM–Newton SAS software, and produced images, light curves, and spectral products for BL Lacs detected in any of the three EPIC cameras. We tested two different phenomenological models, log parabola and power law, with different variations of the absorbing column density and extracted their parameters. We derived time-variability information from the light curves following well-established statistical methods and quantified variability through statistical indicators. OM magnitudes and fluxes were computed wherever possible. Results. We see that the log parabola model is preferred over the power law model for sources showing higher fluxes, which might indicate that curvature is intrinsic to BL Lacs and is only seen when the flux is high. We present the results of our analysis as a catalogue of X-ray spectral properties of the sample in the 0.2–10 keV energy band as well as in the optical/UV band. We complete the catalogue with multi-wavelength information at radio and γ-ray energies.

Context. Better understanding of star formation in clusters with high-mass stars requires rigorous dynamical and spatial analyses of star-forming regions. Aims. We seek to demonstrate that “INDICATE” is a powerful spatial analysis tool which when combined with kinematic data from Gaia DR2 can be used to probe star formation history in a robust way. Methods. We compared the dynamic and spatial distributions of young stellar objects (YSOs) at various evolutionary stages in NGC 2264 using Gaia DR2 proper motion data and INDICATE. Results. The dynamic and spatial behaviours of YSOs at different evolutionary stages are distinct. Dynamically, Class II YSOs predominately have non-random trajectories that are consistent with known substructures, whereas Class III YSOs have random trajectories with no clear expansion or contraction patterns. Spatially, there is a correlation between the evolutionary stage and source concentration: 69.4% of Class 0/I, 27.9% of Class II, and 7.7% of Class III objects are found to be clustered. The proportion of YSOs clustered with objects of the same class also follows this trend. Class 0/I objects are both found to be more tightly clustered with the general populous/objects of the same class than Class IIs and IIIs by a factor of 1.2/4.1 and 1.9/6.6, respectively. An exception to these findings is within 0.05° of S Mon where Class III objects mimic the behaviours of Class II sources across the wider cluster region. Our results suggest (i) current YSOs distributions are a result of dynamical evolution, (ii) prolonged star formation has been occurring sequentially, and (iii) stellar feedback from S Mon is causing YSOs to appear as more evolved sources. Conclusions. Designed to provide a quantitative measure of clustering behaviours, INDICATE is a powerful tool with which to perform rigorous spatial analyses. Our findings are consistent with what is known about NGC 2264, effectively demonstrating that when combined with kinematic data from Gaia DR2 INDICATE can be used to study the star formation history of a cluster in a robust way.