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Method for calibration sample manufacturing for space instrumentation and regolith characterization
(VIII Planetary Science & Solar System Exploration Congress (CPESS-8), 2025-05) De la Cruz - Karnavas, Leonardo; Fernández Romero, Sergio; Plaza Gallardo, Borja; Poyatos Martinez, David; Marina, Fernández-Ruz
The measurement of the magnetic field by means of magnetometers is the most straightforward method for the characterization of magnetic bodies. The magnetic field generated by a body (in the absence of electrical currents) comprises a remnant and an induced contribution. This last one is due to the magnetization of the body under the effect of a magnetic field. Often, it is important to distinguish between these two contributions. During magnetic surveys, it helps to improve the interpretation of the magnetic carriers that yield the magnetic signature. In general, it is useful to estimate the magnetization changes that a body is expected to have when it is immersed in a magnetic field.
Machine Learning Methods Applied to Broadband Electromagnetic Characterization
(2025-11) Cublier Martínez, Aymar; Frövel de la Torre, Jorge; Sanz, Ruy; Plaza Gallardo, Borja; Poyatos Martinez, David
The introduction of composite materials in the aerospace industry has yielded improved performance and better strength-to-weight ratio in space platforms' design. The ubiquity of these materials in today's space missions involves, among other things, a deep understanding of their electromagnetic (EM) properties. To study the EM properties of dielectric materials, several machine learning algorithms such as Deep Neural Networks (DNN) and ensemble learning methods (Random Forest, Gradient Boosting) are implemented as extraction methods for EM characterization in free space. First, the training and testing datasets for the supervised learning algorithms are generated using EM simulations with given permittivity and loss tangent. Second, an existing high precision test bench is used at INTA's Computational and Applied Electromagnetics Laboratory (CAEM-Lab) for gathering data to feed the prediction models at the validation stage over a broad frequency range of 2.6−40GHz. Finally, results are contrasted with conventional methods for complex permittivity extraction, such as analytical based solutions and optimization related methods. Other features like temperature can be added to the models to study the EM properties of materials at cryogenic temperatures for space applications.
A panchromatic view of N2CLS GOODS-N: The evolution of the dust cosmic density since z ∼ 7
(EDP Sciences, 2025-04-18) Berta, Stefano; Lagache, Guilaine; Beelen, Alexandre; Adam, Rémi; Ade, Peter; Ajeddig, H.; Amarantidis, Stergios; André, P.; Aussel, Hervé; Benoît, A.; Bethermin, M.; Bing, Longji; Bongiovanni, Angel; Bounmy, J.; Bourrion, Olivier; Calvo, M.; Catalano, A.; Cherouvrier, Damien; Ciesla, L.; De Petris, Marco; Désert, François-Xavier; Doyle, S.; Driessen, Eduard; Ejlali, Golshan; Elbaz, D.; Ferragamo, Antonio; Gómez, Alicia; Goupy, J.; Hanser, C.; Katsioli, Stavroula; Kéruzoré, F.; Kramer, Carsten; Ladjelate, B.; Leclercq, S.; Lestrade, Jean-Francois; Macias-Perez, Juan Francisco; Madden, Suzanne; Maury, A.; Mayet, Frederic; Messias, Hugo; Monfardini, Alessandro; Moyer-Anin, Alice; Muñoz Echeverría, Miren; Myserlis, Ioannis; Neri, Roberto; Paliwal, A.; Perotto, Laurence; Pisano, G.; Ponthieu, Nicolas; Revéret, V.; Rigby, Andrew J.; Ritacco, Alessia; Roussel, H.; Ruppin, F.; Sánchez-Portal, Miguel; Savorgnano, Sofia; Schuster, K.; Sievers, A.; Tucker, Carole; Xiao, Mengyuan; Zylka, R.; European Research Council (ERC); European Commission (EC)
To understand early star formation, it is essential to determine the dust mass budget of high-redshift galaxies. Sub-millimeter rest-frame emission, dominated by cold dust, is an unbiased tracer of dust mass. The New IRAM KID Arrays 2 (NIKA2) conducted a deep blank field survey at 1.2 and 2.0 mm in the GOODS-N field as part of the NIKA2 Cosmological Legacy Survey (N2CLS), detecting 65 sources with S/N ≥ 4.2. Thanks to a dedicated interferometric program with NOEMA and other high-angular resolution data, we identified the multi-wavelength counterparts of these sources and resolved them into 71 individual galaxies. We built detailed spectral energy distributions (SEDs) and assigned a redshift to 68 of them over the range 0.6 < z < 7.2. We fit these SEDs using modified blackbody and Draine & Li (2007, ApJ, 657, 810) models and the panchromatic approaches MAGPHYS, CIGALE, and SED3FIT, thus deriving their dust mass (Mdust), infrared luminosity (LIR), and stellar mass (M?). Eight galaxies require an active galactic nucleus torus component, and another six require an unextinguished young stellar population. A significant fraction of our galaxies are classified as starbursts based on their position on the M? versus star formation rate plane or their depletion timescales. We computed the dust mass function in three redshift bins (1.6 < z ≤ 2.4, 2.4 < z ≤ 4.2 and 4.2 < z ≤ 7.2) and determined the Schechter function that best describes it. The dust cosmic density, ρdust, increases by at least an order of magnitude from z ∼ 7 to z ∼ 1.5, as predicted by theoretical works. At lower redshifts, the evolution flattens. Nonetheless, significant differences exist between results obtained with different selections and methods. The superb GOODS-N data set enabled a systematic investigation into the dust properties of distant galaxies. N2CLS holds promise for combining these deep field findings with the wide COSMOS field into a self-consistent analysis of dust in galaxies both near and far.
