X-Ray Source Classification Using Machine Learning: A Study with EP-WXT Pathfinder LEIA Article Swipe
X. Zuo
,
Yihan Tao
,
Yuan Liu
,
Yunfei Xu
,
Wenda Zhang
,
Haiwu Pan
,
Hui Sun
,
Zhen Zhang
,
Chenzhou Cui
,
Weimin Yuan
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1088/1674-4527/ad634f
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1088/1674-4527/ad634f
X-ray observations play a crucial role in time-domain astronomy. The Einstein Probe (EP), a recently launched X-ray astronomical satellite, emerges as a forefront player in the field of time-domain astronomy and high-energy astrophysics. With a focus on systematic surveys in the soft X-ray band, EP aims to discover high-energy transients and monitor variable sources in the universe. To achieve these objectives, a quick and reliable classification of observed sources is essential. In this study, we developed a machine learning classifier for autonomous source classification using data from the EP-WXT Pathfinder—Lobster Eye Imager for Astronomy (LEIA) and EP-WXT simulations. The proposed Random Forest classifier, built on selected features derived from light curves, energy spectra, and location information, achieves an accuracy of approximately 95% on EP simulation data and 98% on LEIA observational data. The classifier is integrated into the LEIA data processing pipeline, serving as a tool for manual validation and rapid classification during observations. This paper presents an efficient method for the classification of X-ray sources based on single observations, along with implications of most effective features for the task. This work facilitates rapid source classification for the EP mission and also provides valuable insights into feature selection and classification techniques for enhancing the efficiency and accuracy of X-ray source classification that can be adapted to other X-ray telescope data.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/1674-4527/ad634f
- OA Status
- green
- Cited By
- 1
- References
- 26
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4400641294Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1088/1674-4527/ad634fDigital Object Identifier
- Title
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X-Ray Source Classification Using Machine Learning: A Study with EP-WXT Pathfinder LEIAWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-07-15Full publication date if available
- Authors
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X. Zuo, Yihan Tao, Yuan Liu, Yunfei Xu, Wenda Zhang, Haiwu Pan, Hui Sun, Zhen Zhang, Chenzhou Cui, Weimin YuanList of authors in order
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https://doi.org/10.1088/1674-4527/ad634fPublisher landing page
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2407.11462Direct OA link when available
- Concepts
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Pathfinder, Classifier (UML), Physics, Random forest, Artificial intelligence, Machine learning, Astrophysics, Computer science, Library scienceTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.feature | 197 |
| abstract_inverted_index.machine | 78 |
| abstract_inverted_index.mission | 190 |
| abstract_inverted_index.monitor | 52 |
| abstract_inverted_index.serving | 143 |
| abstract_inverted_index.sources | 54, 69, 166 |
| abstract_inverted_index.surveys | 39 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Einstein | 11 |
| abstract_inverted_index.accuracy | 119, 207 |
| abstract_inverted_index.achieves | 117 |
| abstract_inverted_index.discover | 48 |
| abstract_inverted_index.features | 107, 177 |
| abstract_inverted_index.insights | 195 |
| abstract_inverted_index.launched | 16 |
| abstract_inverted_index.learning | 79 |
| abstract_inverted_index.location | 115 |
| abstract_inverted_index.observed | 68 |
| abstract_inverted_index.presents | 157 |
| abstract_inverted_index.proposed | 100 |
| abstract_inverted_index.provides | 193 |
| abstract_inverted_index.recently | 15 |
| abstract_inverted_index.reliable | 65 |
| abstract_inverted_index.selected | 106 |
| abstract_inverted_index.spectra, | 113 |
| abstract_inverted_index.valuable | 194 |
| abstract_inverted_index.variable | 53 |
| abstract_inverted_index.Astronomy | 94 |
| abstract_inverted_index.astronomy | 30 |
| abstract_inverted_index.developed | 76 |
| abstract_inverted_index.effective | 176 |
| abstract_inverted_index.efficient | 159 |
| abstract_inverted_index.enhancing | 203 |
| abstract_inverted_index.forefront | 23 |
| abstract_inverted_index.pipeline, | 142 |
| abstract_inverted_index.selection | 198 |
| abstract_inverted_index.telescope | 219 |
| abstract_inverted_index.universe. | 57 |
| abstract_inverted_index.astronomy. | 9 |
| abstract_inverted_index.autonomous | 82 |
| abstract_inverted_index.classifier | 80, 134 |
| abstract_inverted_index.efficiency | 205 |
| abstract_inverted_index.essential. | 71 |
| abstract_inverted_index.integrated | 136 |
| abstract_inverted_index.processing | 141 |
| abstract_inverted_index.satellite, | 19 |
| abstract_inverted_index.simulation | 125 |
| abstract_inverted_index.systematic | 38 |
| abstract_inverted_index.techniques | 201 |
| abstract_inverted_index.transients | 50 |
| abstract_inverted_index.validation | 149 |
| abstract_inverted_index.classifier, | 103 |
| abstract_inverted_index.facilitates | 183 |
| abstract_inverted_index.high-energy | 32, 49 |
| abstract_inverted_index.objectives, | 61 |
| abstract_inverted_index.time-domain | 8, 29 |
| abstract_inverted_index.astronomical | 18 |
| abstract_inverted_index.implications | 173 |
| abstract_inverted_index.information, | 116 |
| abstract_inverted_index.observations | 2 |
| abstract_inverted_index.simulations. | 98 |
| abstract_inverted_index.approximately | 121 |
| abstract_inverted_index.astrophysics. | 33 |
| abstract_inverted_index.observational | 131 |
| abstract_inverted_index.observations, | 170 |
| abstract_inverted_index.observations. | 154 |
| abstract_inverted_index.classification | 66, 84, 152, 163, 186, 200, 211 |
| abstract_inverted_index.Pathfinder—Lobster | 90 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 10 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.47999998927116394 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile.value | 0.8657305 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |