A machine learning approach for computing solar flare locations in X-rays on-board Solar Orbiter/STIX
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866908032548470784 |
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| author | Massa, Paolo Felix, Simon Etesi, László István Dickson, Ewan C. M. Xiao, Hualin Ramunno, Francesco P. Selcuk-Simsek, Merve Panos, Brandon Csillaghy, André Krucker, Säm |
| author_facet | Massa, Paolo Felix, Simon Etesi, László István Dickson, Ewan C. M. Xiao, Hualin Ramunno, Francesco P. Selcuk-Simsek, Merve Panos, Brandon Csillaghy, André Krucker, Säm |
| contents | The Spectrometer/Telescope for Imaging X-rays (STIX) on-board the ESA Solar Orbiter mission retrieves the coordinates of solar flare locations by means of a specific sub-collimator, named the Coarse Flare Locator (CFL). When a solar flare occurs on the Sun, the emitted X-ray radiation casts the shadow of a peculiar "H-shaped" tungsten grid over the CFL X-ray detector. From measurements of the areas of the detector that are illuminated by the X-ray radiation, it is possible to retrieve the $(x,y)$ coordinates of the flare location on the solar disk.
In this paper, we train a neural network on a dataset of real CFL observations to estimate the coordinates of solar flare locations. Further, we apply a post-training quantization technique specifically tailored to the adopted model architecture. This technique allows all computations to be in integer arithmetic at inference time, making the model compatible with the STIX computational requirements. We show that our model outperforms the currently adopted algorithm for estimating the flare locations from CFL data regarding prediction accuracy while requiring fewer parameters. We finally discuss possible future applications of the proposed model on-board STIX. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_16642 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A machine learning approach for computing solar flare locations in X-rays on-board Solar Orbiter/STIX Massa, Paolo Felix, Simon Etesi, László István Dickson, Ewan C. M. Xiao, Hualin Ramunno, Francesco P. Selcuk-Simsek, Merve Panos, Brandon Csillaghy, André Krucker, Säm Solar and Stellar Astrophysics Instrumentation and Methods for Astrophysics The Spectrometer/Telescope for Imaging X-rays (STIX) on-board the ESA Solar Orbiter mission retrieves the coordinates of solar flare locations by means of a specific sub-collimator, named the Coarse Flare Locator (CFL). When a solar flare occurs on the Sun, the emitted X-ray radiation casts the shadow of a peculiar "H-shaped" tungsten grid over the CFL X-ray detector. From measurements of the areas of the detector that are illuminated by the X-ray radiation, it is possible to retrieve the $(x,y)$ coordinates of the flare location on the solar disk. In this paper, we train a neural network on a dataset of real CFL observations to estimate the coordinates of solar flare locations. Further, we apply a post-training quantization technique specifically tailored to the adopted model architecture. This technique allows all computations to be in integer arithmetic at inference time, making the model compatible with the STIX computational requirements. We show that our model outperforms the currently adopted algorithm for estimating the flare locations from CFL data regarding prediction accuracy while requiring fewer parameters. We finally discuss possible future applications of the proposed model on-board STIX. |
| title | A machine learning approach for computing solar flare locations in X-rays on-board Solar Orbiter/STIX |
| topic | Solar and Stellar Astrophysics Instrumentation and Methods for Astrophysics |
| url | https://arxiv.org/abs/2408.16642 |