A visual transformer model trained on IRIS inversions predicts chromospheric temperature and density from SDO data with correlations around 0.8 on 80% of test cases.
European Physical Journal Web of Conferences , year = 2023, series =
2 Pith papers cite this work. Polarity classification is still indexing.
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Multi-telescope spectral modeling of HESS J1825-137 using Naima and MCMC shows leptonic dominance for existing GeV-TeV data but lepto-hadronic preference when adding simulated CTAO or LHAASO UHE points.
citing papers explorer
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Predicting the thermodynamics in the chromosphere from the translation of SDO data into the IRIS$^{2}$ inversion results using a visual transformer model
A visual transformer model trained on IRIS inversions predicts chromospheric temperature and density from SDO data with correlations around 0.8 on 80% of test cases.
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Constraining leptonic and hadronic gamma-ray emission from HESS J1825-137 and its environment
Multi-telescope spectral modeling of HESS J1825-137 using Naima and MCMC shows leptonic dominance for existing GeV-TeV data but lepto-hadronic preference when adding simulated CTAO or LHAASO UHE points.