English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Prediction and Understanding of Soft-proton Contamination in XMM-Newton: A Machine Learning Approach

Kronberg, E. A., Gastaldello, F., Haaland, S., Smirnov, A., Berrendorf, M., Ghizzardi, S., et al. (2020). Prediction and Understanding of Soft-proton Contamination in XMM-Newton: A Machine Learning Approach. The Astrophysical Journal, 903(2): 89. doi:10.3847/1538-4357/abbb8f.

Item is

Basic

show hide
Item Permalink: http://hdl.handle.net/21.11116/0000-0007-6527-C Version Permalink: http://hdl.handle.net/21.11116/0000-0007-6528-B
Genre: Journal Article

Files

show Files

Locators

show

Creators

show
hide
 Creators:
Kronberg, Elenea A., Author
Gastaldello, Fabio, Author
Haaland, Stein1, Author              
Smirnov, Artem, Author
Berrendorf, Max, Author
Ghizzardi, Simona, Author
Kuntz, K. D., Author
Sivadas, Nithin, Author
Allen, Robert C., Author
Tiengo, Andrea, Author
Ilie, Raluca, Author
Huang, Yu, Author
Kistler, Lynn, Author
Affiliations:
1Department Planets and Comets, Max Planck Institute for Solar System Research, Max Planck Society, ou_1832288              

Content

show
hide
Free keywords: X-ray telescopes ; X-ray detectors ; X-ray observatories ; Space plasmas ; Astronomy data modeling ; Astronomy data analysis
 Abstract: One of the major and unfortunately unforeseen sources of background for the current generation of X-ray telescopes are few tens to hundreds of keV (soft) protons concentrated by the mirrors. One such telescope is the European Space Agency's (ESA) X-ray Multi-Mirror Mission (XMM-Newton). Its observing time lost due to background contamination is about 40%. This loss of observing time affects all the major broad science goals of this observatory, ranging from cosmology to astrophysics of neutron stars and black holes. The soft-proton background could dramatically impact future large X-ray missions such as the ESA planned Athena mission (http://www.the-athena-x-ray-observatory.eu/). Physical processes that trigger this background are still poorly understood. We use a machine learning (ML) approach to delineate related important parameters and to develop a model to predict the background contamination using 12 yr of XMM-Newton observations. As predictors we use the location of the satellite and solar and geomagnetic activity parameters. We revealed that the contamination is most strongly related to the distance in the southern direction, Z (XMM-Newton observations were in the southern hemisphere), the solar wind radial velocity, and the location on the magnetospheric magnetic field lines. We derived simple empirical models for the first two individual predictors and an ML model that utilizes an ensemble of the predictors (Extra-Trees Regressor) and gives better performance. Based on our analysis, future missions should minimize observations during times associated with high solar wind speed and avoid closed magnetic field lines, especially at the dusk flank region in the southern hemisphere.

Details

show
hide
Language(s): eng - English
 Dates: 2020
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.3847/1538-4357/abbb8f
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: The Astrophysical Journal
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: Bristol; Vienna : IOP Publishing; IAEA
Pages: - Volume / Issue: 903 (2) Sequence Number: 89 Start / End Page: - Identifier: ISSN: 0004-637X
CoNE: https://pure.mpg.de/cone/journals/resource/954922828215_3