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  • 1
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    Ionospheric response modeling under eclipse conditions: Evaluation of 14 December 2020, total solar eclipse prediction over the South American sector (2023)
    Frontiers Media S.A.
    Details
    Publication Date: 2023-03-22
    Description: In this work, we evaluate the SUPIM-INPE model prediction of the 14 December 2020, total solar eclipse over the South American continent. We compare the predictions with data from multiple instruments for monitoring the ionosphere and with different obscuration percentages (i.e., Jicamarca, 12.0°S, 76.8°W, 17%; Tucumán 26.9°S, 65.4° W, 49%; Chillán 36.6°S, 72.0°W; and Bahía Blanca, 38.7°S, 62.3°W, reach 95% obscuration) due to the eclipse. The analysis is done under total eclipse conditions and non-total eclipse conditions. Results obtained suggest that the model was able to reproduce with high accuracy both the daily variation and the eclipse impacts of E and F1 layers in the majority of the stations evaluated (except in Jicamarca station). The comparison at the F2 layer indicates small differences (〈7.8%) between the predictions and observations at all stations during the eclipse periods. Additionally, statistical metrics reinforce the conclusion of a good performance of the model. Predicted and calibrated Total Electron Content (TEC, using 3 different techniques) are also compared. Results show that, although none of the selected TEC calibration methods have a good agreement with the SUPIM-INPE prediction, they exhibit similar trends in most of the cases. We also analyze data from the Jicamarca Incoherent Scatter Radar (ISR), and Swarm-A and GOLD missions. The electron temperature changes observed in ISR and Swarm-A are underestimated by the prediction. Also, important changes in the O/N2 ratio due to the eclipse, have been observed with GOLD mission data. Thus, future versions of the SUPIM-INPE model for eclipse conditions should consider effects on thermospheric winds and changes in composition, specifically in the O/N2 ratio.
    Description: Published
    Description: 1021910
    Description: 2A. Fisica dell'alta atmosfera
    Description: JCR Journal
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
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  • 2
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    Particle classification in the LAGO water Cherenkov detectors using clustering algorithms (2024)
    Elsevier
    Details
    Publication Date: 2024-05-16
    Description: The Latin American Giant Observatory (LAGO) is a ground-based observatory studying solar or high-energy astrophysics transient events. LAGO takes advantage of its distributed network of Water Cherenkov Detectors (WCDs) in Latin America as a tool to measure the secondary particle flux reaching the ground. These secondary particles are produced during the interaction between the modulated cosmic rays flux and the atmosphere. The LAGO WCDs are sensitive to secondary charged particles, high energy photons through pair creation and Compton scattering, and even neutrons thanks to, e.g., the deuteration of protons in the water volume. The pulse shape generated by these particles depends on several factors, such as the detector geometry, the water purity, the sensor response, or the reflectivity and diffusivity of the inner coating. Due to the decentralized nature of LAGO, these properties are different for each node. Additionally, the pulse shape depends on the convolution between the response of the central photomultiplier (PMT) to individual photons and the time distribution of the Cherenkov photons reaching the PMT. Typically, a WCD gives pulses with a sharp rise time ( 10 ns) and a longer decay time ( 70 ns). In this work, the WCD data used is acquired using the original LAGO data-acquisition system that digitizes pulses at a sampling rate of 40 MHz and 10 bits resolution on time windows of 400 ns. Here, we apply unsupervised machine learning techniques to find patterns in the WCDs data and subsequently create groups, through clustering, that can be used to provide particle separation. We use data acquired from an individual WCD, showing that density-based clustering algorithms are suitable for automatic particle separation producing good candidate groups. Improved separation would help LAGO to reconstruct in situ the properties of primary cosmic rays flux. These results open the possibility to deploy machine learning-based models in our distributed detection network for onboard data analysis as an operative prototype, allowing detectors to be installed at very remote sites.
    Description: Published
    Description: 168557
    Description: JCR Journal
    Keywords: 05.07. Space and Planetary sciences
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
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