Search for solar axions using a 7-GridPix IAXO prototype detector at CAST
Search for solar axions using a 7-GridPix IAXO prototype detector at CAST
View/Type of Scholarly Publication
DissertationDate of Exam
25.04.2024Date of Publication
17.06.2024Advisor
Desch, KlausCo-Referee
García Irastorza, IgorMetadata
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Abstract
The Standard Model of particle physics describes three out of four known fundamental forces of nature very successfully. However, open questions remain. One of these, the 'strong CP problem', asks why the neutron has no electric dipole moment. This 'mathematical' problem can be neatly solved by the introduction of a new hypothetical particle, the axion. This particle can feebly interact with photons and leptons. Axions can be produced in the core of the Sun at very high
rates. Once produced they escape the Sun. On Earth we can try to detect them using strong magnetic fields. The latest such experiment is the CERN Axion Solar Telescope (CAST). Axions carry the energy of the photons which produced them, placing them in the soft X-ray energy range.
In this thesis, a gaseous detector containing 7-GridPixes was deployed at the CAST experiment in 2017/18 to follow up on measurements with a single GridPix detector in 2014/15. 3150 h of background and 160 h of solar tracking data was taken. Such data is dominated by cosmic radiation, radioactive background and X-ray fluorescence. Methods to suppress this background and extract the few possible axion signals are needed. A software library 'TimepixAnalysis' was developed during this thesis to reconstruct and analyze the CAST data.
Classification of events into background- or signal-like (X-ray) data, uses a machine learning approach. A multilayer perceptron (type of artificial neural network) was trained on synthetic X-rays and background data of 6 of the 7 chips (without the axion sensitive chip). Significant improvements to signal efficiency at comparable background rates are achieved, compared to the method used for the old detector.
As the solar tracking data showed no signal excess, a limit calculation method was developed. It builds on the unbinned Bayesian likelihood method used in the 2017 CAST Nature paper. It is extended to allow the inclusion of systematic uncertainties as nuisance parameters. Due to the expensive evaluation of such a likelihood function, a Markov Chain Monte Carlo approach is used.
The limit calculation requires the 'axion image' produced by the Lawrence Livermore National Laboratory telescope at CAST. To characterize this, a raytracing simulation taking into account the axion production rates in the Sun and reflection through the X-ray optic was developed. It was verified against PANTER measurements of the telescope.
With the software and detector advances, world best limits could be set on the axion-electron and the chameleon coupling (another hypothetical particle). The previous best limit on the axion-electron coupling is improved from gae·gaγ ≲ 8.1·10-23 GeV-1 to gae·gaγ ≲ 7.35·10-23 GeV-1 and the chameleon coupling from βγ < 5.74·1010 to βγ ≲ 3.1·1010.
Finally, the software developed during the course of this thesis is ready for data analysis for a future GridPix3 based detector.
In this thesis, a gaseous detector containing 7-GridPixes was deployed at the CAST experiment in 2017/18 to follow up on measurements with a single GridPix detector in 2014/15. 3150 h of background and 160 h of solar tracking data was taken. Such data is dominated by cosmic radiation, radioactive background and X-ray fluorescence. Methods to suppress this background and extract the few possible axion signals are needed. A software library 'TimepixAnalysis' was developed during this thesis to reconstruct and analyze the CAST data.
Classification of events into background- or signal-like (X-ray) data, uses a machine learning approach. A multilayer perceptron (type of artificial neural network) was trained on synthetic X-rays and background data of 6 of the 7 chips (without the axion sensitive chip). Significant improvements to signal efficiency at comparable background rates are achieved, compared to the method used for the old detector.
As the solar tracking data showed no signal excess, a limit calculation method was developed. It builds on the unbinned Bayesian likelihood method used in the 2017 CAST Nature paper. It is extended to allow the inclusion of systematic uncertainties as nuisance parameters. Due to the expensive evaluation of such a likelihood function, a Markov Chain Monte Carlo approach is used.
The limit calculation requires the 'axion image' produced by the Lawrence Livermore National Laboratory telescope at CAST. To characterize this, a raytracing simulation taking into account the axion production rates in the Sun and reflection through the X-ray optic was developed. It was verified against PANTER measurements of the telescope.
With the software and detector advances, world best limits could be set on the axion-electron and the chameleon coupling (another hypothetical particle). The previous best limit on the axion-electron coupling is improved from gae·gaγ ≲ 8.1·10-23 GeV-1 to gae·gaγ ≲ 7.35·10-23 GeV-1 and the chameleon coupling from βγ < 5.74·1010 to βγ ≲ 3.1·1010.
Finally, the software developed during the course of this thesis is ready for data analysis for a future GridPix3 based detector.
Subjects
Axionen, CAST, IAXO, GridPix, Raytracing, Monte Carlo, axions, solar axions, ingrid, gaseous detectors, ALPs, chameleons
Classification (DDC)
520 Astronomie, Kartografie 530 PhysikRelated Publications
https://phd.vindaar.deSupplementary Research Data
https://zenodo.org/records/10521887https://github.com/Vindaar/TimepixAnalysis
Schmidt, Sebastian Michael: Search for solar axions using a 7-GridPix IAXO prototype detector at CAST. - Bonn, 2024. - Dissertation, Rheinische Friedrich-Wilhelms-Universität Bonn.
Online-Ausgabe in bonndoc: https://nbn-resolving.org/urn:nbn:de:hbz:5-76127
Online-Ausgabe in bonndoc: https://nbn-resolving.org/urn:nbn:de:hbz:5-76127
@phdthesis{handle:20.500.11811/11604,
urn: https://nbn-resolving.org/urn:nbn:de:hbz:5-76127,
doi: https://doi.org/10.48565/bonndoc-303,
author = {{Sebastian Michael Schmidt}},
title = {Search for solar axions using a 7-GridPix IAXO prototype detector at CAST},
school = {Rheinische Friedrich-Wilhelms-Universität Bonn},
year = 2024,
month = jun,
note = {The Standard Model of particle physics describes three out of four known fundamental forces of nature very successfully. However, open questions remain. One of these, the 'strong CP problem', asks why the neutron has no electric dipole moment. This 'mathematical' problem can be neatly solved by the introduction of a new hypothetical particle, the axion. This particle can feebly interact with photons and leptons. Axions can be produced in the core of the Sun at very high rates. Once produced they escape the Sun. On Earth we can try to detect them using strong magnetic fields. The latest such experiment is the CERN Axion Solar Telescope (CAST). Axions carry the energy of the photons which produced them, placing them in the soft X-ray energy range.
In this thesis, a gaseous detector containing 7-GridPixes was deployed at the CAST experiment in 2017/18 to follow up on measurements with a single GridPix detector in 2014/15. 3150 h of background and 160 h of solar tracking data was taken. Such data is dominated by cosmic radiation, radioactive background and X-ray fluorescence. Methods to suppress this background and extract the few possible axion signals are needed. A software library 'TimepixAnalysis' was developed during this thesis to reconstruct and analyze the CAST data.
Classification of events into background- or signal-like (X-ray) data, uses a machine learning approach. A multilayer perceptron (type of artificial neural network) was trained on synthetic X-rays and background data of 6 of the 7 chips (without the axion sensitive chip). Significant improvements to signal efficiency at comparable background rates are achieved, compared to the method used for the old detector.
As the solar tracking data showed no signal excess, a limit calculation method was developed. It builds on the unbinned Bayesian likelihood method used in the 2017 CAST Nature paper. It is extended to allow the inclusion of systematic uncertainties as nuisance parameters. Due to the expensive evaluation of such a likelihood function, a Markov Chain Monte Carlo approach is used.
The limit calculation requires the 'axion image' produced by the Lawrence Livermore National Laboratory telescope at CAST. To characterize this, a raytracing simulation taking into account the axion production rates in the Sun and reflection through the X-ray optic was developed. It was verified against PANTER measurements of the telescope.
With the software and detector advances, world best limits could be set on the axion-electron and the chameleon coupling (another hypothetical particle). The previous best limit on the axion-electron coupling is improved from gae·gaγ ≲ 8.1·10-23 GeV-1 to gae·gaγ ≲ 7.35·10-23 GeV-1 and the chameleon coupling from βγ < 5.74·1010 to βγ ≲ 3.1·1010.
Finally, the software developed during the course of this thesis is ready for data analysis for a future GridPix3 based detector.},
url = {https://hdl.handle.net/20.500.11811/11604}
}
urn: https://nbn-resolving.org/urn:nbn:de:hbz:5-76127,
doi: https://doi.org/10.48565/bonndoc-303,
author = {{Sebastian Michael Schmidt}},
title = {Search for solar axions using a 7-GridPix IAXO prototype detector at CAST},
school = {Rheinische Friedrich-Wilhelms-Universität Bonn},
year = 2024,
month = jun,
note = {The Standard Model of particle physics describes three out of four known fundamental forces of nature very successfully. However, open questions remain. One of these, the 'strong CP problem', asks why the neutron has no electric dipole moment. This 'mathematical' problem can be neatly solved by the introduction of a new hypothetical particle, the axion. This particle can feebly interact with photons and leptons. Axions can be produced in the core of the Sun at very high rates. Once produced they escape the Sun. On Earth we can try to detect them using strong magnetic fields. The latest such experiment is the CERN Axion Solar Telescope (CAST). Axions carry the energy of the photons which produced them, placing them in the soft X-ray energy range.
In this thesis, a gaseous detector containing 7-GridPixes was deployed at the CAST experiment in 2017/18 to follow up on measurements with a single GridPix detector in 2014/15. 3150 h of background and 160 h of solar tracking data was taken. Such data is dominated by cosmic radiation, radioactive background and X-ray fluorescence. Methods to suppress this background and extract the few possible axion signals are needed. A software library 'TimepixAnalysis' was developed during this thesis to reconstruct and analyze the CAST data.
Classification of events into background- or signal-like (X-ray) data, uses a machine learning approach. A multilayer perceptron (type of artificial neural network) was trained on synthetic X-rays and background data of 6 of the 7 chips (without the axion sensitive chip). Significant improvements to signal efficiency at comparable background rates are achieved, compared to the method used for the old detector.
As the solar tracking data showed no signal excess, a limit calculation method was developed. It builds on the unbinned Bayesian likelihood method used in the 2017 CAST Nature paper. It is extended to allow the inclusion of systematic uncertainties as nuisance parameters. Due to the expensive evaluation of such a likelihood function, a Markov Chain Monte Carlo approach is used.
The limit calculation requires the 'axion image' produced by the Lawrence Livermore National Laboratory telescope at CAST. To characterize this, a raytracing simulation taking into account the axion production rates in the Sun and reflection through the X-ray optic was developed. It was verified against PANTER measurements of the telescope.
With the software and detector advances, world best limits could be set on the axion-electron and the chameleon coupling (another hypothetical particle). The previous best limit on the axion-electron coupling is improved from gae·gaγ ≲ 8.1·10-23 GeV-1 to gae·gaγ ≲ 7.35·10-23 GeV-1 and the chameleon coupling from βγ < 5.74·1010 to βγ ≲ 3.1·1010.
Finally, the software developed during the course of this thesis is ready for data analysis for a future GridPix3 based detector.},
url = {https://hdl.handle.net/20.500.11811/11604}
}
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