Analysis of minimum ionising particles and soft protons using XMM-Newton EPIC pn-CCD as a particle detector
Abstract
Context. Spacecrafts with imaging telescopes often carry a charge-coupled device (CCD) in their focal plane to detect electromagnetic radiation. Charged particles such as electrons, protons, and heavy ions can reach the CCD and deposit their energy in the detector material. To counteract this undesirable effect, algorithms are usually implemented to reject them. On the other hand, CCDs can also be seen as particle detectors.
Aims: Even though rejection algorithms are often active to immediately discard undesired radiation, data including charged particles of ESA's XMM-Newton and Gaia were stored over the whole mission lifetime. In this article we primarily analyse and characterise the charged particles that were detected by XMM-Newton. A comparison to data from Gaia's CCDs is also presented.
Methods: To characterise the particle flux in the spacecraft orbits we used all publicly available observations where no rejection algorithm was used in combination with observations where the rejection algorithm was used. The particle flux is analysed over time and space of the XMM-Newton orbit. Comparisons to external data are shown as well.
Results: Our analysis shows that the rate of charged particle events has a modulation of about 11 yr and that particle flux and solar activity are anti-correlated. Moreover, we also show that often more than one charged particle hits the CCD simultaneously.
Conclusions: Rejection algorithms are typically used to remove charged particle detection and preserve the scientific data missions. In this article, using XMM-Newton and Gaia data, we show that by neglecting rejection algorithms, charged particles detected on CCDs can be analysed and characterised over the spacecraft orbit.
- Publication:
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Astronomy and Astrophysics
- Pub Date:
- February 2023
- DOI:
- 10.1051/0004-6361/202244421
- Bibcode:
- 2023A&A...670A..78B
- Keywords:
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- cosmic rays;
- Sun: activity;
- sunspots;
- methods: data analysis