Quantum Machine Learning for Identifying Transient Events in X-Ray Light Curves
We investigate whether a novel method of quantum machine learning can identify anomalous events in X-ray light curves as transient events and apply it to detect such events from the XMM-Newton 4XMM-DR14 catalog. The architecture we adopt is a quantum version of long short-term memory (LSTM) where so...
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2025-01-01
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| Series: | The Astrophysical Journal |
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| Online Access: | https://doi.org/10.3847/1538-4357/adda43 |
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| author | Taiki Kawamuro Shinya Yamada Shigehiro Nagataki Shunji Matsuura Yusuke Sakai Satoshi Yamada |
| author_facet | Taiki Kawamuro Shinya Yamada Shigehiro Nagataki Shunji Matsuura Yusuke Sakai Satoshi Yamada |
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| description | We investigate whether a novel method of quantum machine learning can identify anomalous events in X-ray light curves as transient events and apply it to detect such events from the XMM-Newton 4XMM-DR14 catalog. The architecture we adopt is a quantum version of long short-term memory (LSTM) where some fully connected layers are replaced with quantum circuits. LSTM, making predictions based on preceding data, allows for the identification of anomalies by comparing predicted and actual time-series data. The necessary training data are generated by simulating active-galactic-nucleus-like light curves as these events would be a significant population in the XMM-Newton catalog. Additional anomaly data used to assess trained quantum LSTM (QLSTM) models are produced by adding flare-like quasiperiodic eruptions to the training data. Comparing various aspects of the performances of the quantum and classical LSTM (CLSTM) models, we find that QLSTM models incorporating quantum superposition and entanglement slightly outperform the CLSTM model in expressive power, accuracy, and true-positive rate. The highest-performance QLSTM model is then used to identify transient events in 4XMM-DR14. Out of 40,154 light curves in the 0.2–12 keV band, we detect 113 light curves with anomalies, or transient event candidates. This number is ≈1.3 times that of anomalies detectable with the CLSTM model. By utilizing SIMBAD and four wide-field survey catalogs made by ROSAT, SkyMapper, Pan-STARRS, and the Wide-field Infrared Survey Explorer, no possible counterparts are found for 12 detected anomalies. |
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| language | English |
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| series | The Astrophysical Journal |
| spelling | doaj-art-d6c8173298d747adaaad4729e4dcafaf2025-08-20T02:37:34ZengIOP PublishingThe Astrophysical Journal1538-43572025-01-01987210510.3847/1538-4357/adda43Quantum Machine Learning for Identifying Transient Events in X-Ray Light CurvesTaiki Kawamuro0https://orcid.org/0000-0002-6808-2052Shinya Yamada1https://orcid.org/0000-0003-4808-893XShigehiro Nagataki2https://orcid.org/0000-0002-7025-284XShunji Matsuura3Yusuke Sakai4https://orcid.org/0000-0002-5809-3516Satoshi Yamada5https://orcid.org/0000-0002-9754-3081Department of Earth and Space Science, Osaka University , 1-1 Machikaneyama, Toyonaka 560-0043, Osaka, Japan ; kawamuro@ess.sci.osaka-u.ac.jp; RIKEN Cluster for Pioneering Research , 2-1 Hirosawa, Wako, Saitama, Saitama 351-0198, JapanDepartment of Physics, Rikkyo University , 3-34-1 Nishi Ikebukuro, Toshima-ku, Tokyo 171-8501, JapanAstrophysical Big Bang Laboratory (ABBL), RIKEN Pioneering Research Institute (PRI) , 2-1 Hirosawa, Wako, Saitama 351-0198, Japan; RIKEN Center for Interdisciplinary Theoretical & Mathematical Science (iTHEMS), 2-1 Hirosawa, Wako, Saitama 351-0198, Japan; Astrophysical Big Bang Group (ABBG), Okinawa Institute of Science and Technology (OIST) , 1919-1 Tancha, Onna-son, Kunigami-gun, Okinawa 904-0495, JapanRIKEN Center for Interdisciplinary Theoretical & Mathematical Science (iTHEMS), 2-1 Hirosawa, Wako, Saitama 351-0198, Japan; Department of Electrical and Computer Engineering, University of British Columbia , Vancouver, BC V6T 1Z4, Canada; Department of Physics, University of Guelph , ON N1G 1Y2, Canada; Center for Mathematical Science and Advanced Technology, Japan Agency for Marine-Earth Science and Technology , Yokohama 236-0001, JapanDepartment of Physics, Rikkyo University , 3-34-1 Nishi Ikebukuro, Toshima-ku, Tokyo 171-8501, JapanRIKEN Cluster for Pioneering Research , 2-1 Hirosawa, Wako, Saitama, Saitama 351-0198, Japan; Frontier Research Institute for Interdisciplinary Sciences, Tohoku University , Sendai 980-8578, Japan; Astronomical Institute, Graduate School of Science, Tohoku University , Sendai 980-8578, JapanWe investigate whether a novel method of quantum machine learning can identify anomalous events in X-ray light curves as transient events and apply it to detect such events from the XMM-Newton 4XMM-DR14 catalog. The architecture we adopt is a quantum version of long short-term memory (LSTM) where some fully connected layers are replaced with quantum circuits. LSTM, making predictions based on preceding data, allows for the identification of anomalies by comparing predicted and actual time-series data. The necessary training data are generated by simulating active-galactic-nucleus-like light curves as these events would be a significant population in the XMM-Newton catalog. Additional anomaly data used to assess trained quantum LSTM (QLSTM) models are produced by adding flare-like quasiperiodic eruptions to the training data. Comparing various aspects of the performances of the quantum and classical LSTM (CLSTM) models, we find that QLSTM models incorporating quantum superposition and entanglement slightly outperform the CLSTM model in expressive power, accuracy, and true-positive rate. The highest-performance QLSTM model is then used to identify transient events in 4XMM-DR14. Out of 40,154 light curves in the 0.2–12 keV band, we detect 113 light curves with anomalies, or transient event candidates. This number is ≈1.3 times that of anomalies detectable with the CLSTM model. By utilizing SIMBAD and four wide-field survey catalogs made by ROSAT, SkyMapper, Pan-STARRS, and the Wide-field Infrared Survey Explorer, no possible counterparts are found for 12 detected anomalies.https://doi.org/10.3847/1538-4357/adda43Astronomy data analysisX-ray astronomyTime domain astronomyTime series analysisActive galactic nuclei |
| spellingShingle | Taiki Kawamuro Shinya Yamada Shigehiro Nagataki Shunji Matsuura Yusuke Sakai Satoshi Yamada Quantum Machine Learning for Identifying Transient Events in X-Ray Light Curves The Astrophysical Journal Astronomy data analysis X-ray astronomy Time domain astronomy Time series analysis Active galactic nuclei |
| title | Quantum Machine Learning for Identifying Transient Events in X-Ray Light Curves |
| title_full | Quantum Machine Learning for Identifying Transient Events in X-Ray Light Curves |
| title_fullStr | Quantum Machine Learning for Identifying Transient Events in X-Ray Light Curves |
| title_full_unstemmed | Quantum Machine Learning for Identifying Transient Events in X-Ray Light Curves |
| title_short | Quantum Machine Learning for Identifying Transient Events in X-Ray Light Curves |
| title_sort | quantum machine learning for identifying transient events in x ray light curves |
| topic | Astronomy data analysis X-ray astronomy Time domain astronomy Time series analysis Active galactic nuclei |
| url | https://doi.org/10.3847/1538-4357/adda43 |
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