Optical Follow-up Study of Gravitational-wave Sources : Analysis Software, Observations of Transients, and Constraints on Kilonova Properties = 중력파원 광학 후속 관측 연구: 분석 소프트웨어, 돌발천체 관측, 그리고 킬로노바 특성 제약

국문 초록 (Abstract)

중력파 다중 메신저 천문학(Gravitational-wave Multi-Messenger Astronomy, GW MMA)의 시대는 천체 물리학 연구에서 새로운 전환점을 마련했으며, 여러 관측 채널을 통해 우주의 현상을 연구할 수 있게 되었습니다. 특히, 중력파 사건의 전자기파(EM) 대응체인 킬로노바(KNe)를 식별하려는 전 세계적인 노력이 이루어지고 있습니다. KNe는 이중 중성자별(BNS) 병합 또는 중성자별-블랙홀(NSBH) 병합 사건과 같이 적어도 하나의 중성자별이 포함된 이중성 병합 사건에 의해 발생합니다. 중력파 사건과 관련된 KNe를 식별하면 우주론, 무거운 원소의 생성에 대한 역사, 별의 진화, 중력파원의 환경 연구에 대한 과학적 영향을 확대할 수 있습니다. 불행히도, 지금까지 GW170817 사건을 제외하고는 성공한 사례가 없습니다. 이 논문은 중력파 소스의 광학 후속 관측에 대한 종합적인 연구를 제시하며, 분석 소프트웨어의 개발과 후속 관측 전략, BNS 또는 NSBH 병합 사건의 신호에 대응하는 KNe를 찾기 위한 시도, 그리고 KNe 특성에 대한 제약 조건을 다룹니다. 논문의 첫 번째 부분은 GECKO(GW EM-Counterpart Korean Observatories)와 KMTNet(Korea Microlensing Telescope Network) 시설을 사용하여 GW190425(BNS) 및 S230518h(NSBH)와 같은 중요한 중력파 사건의 후속 관측을 다룹니다. 후속 관측을 제시하는 과정에서 분석 소프트웨어와 관측 전략을 설명합니다. 또한 중력파 후속관측에서 발견한 돌발 천체 (transient) 후보를 제시하고 무거운 방출물질을 선호하지 않는 예상되는 킬로노바의 특성을 제약합니다. 논문의 두 번째 부분은 기계 학습 기법을 사용하여 개발된 단일 시기의 스펙트럼 에너지 분포(SED)를 이용한 분류기를 소개하며, 7차원 망원경(7-Dimensional Telescope; 7DT) 중간 밴드 관측 자료에 적용된 저해상도 스펙트럼을 사용하여 새로운 돌발 천체 분류 방법을 제시합니다. 우리의 연구는 단일 시기의 SED 분류기가 다양한 유형의 초신성을 95\%의 정확도로 구분하고, 그 중에서 AT2017gfo와 같은 KNe를 90\%의 정확도로 구분하는 데 빠르고 효율적임을 보여줍니다. 마지막으로, 논문의 세 번째 부분에서는 Gaia XP 합성 등급(synthetic magnitude)과 분광-광도 표준별(SPSS)을 사용한 7DT의 엄격한 광도 보정 과정을 제시합니다. 우리는 400 nm에서 875 nm 및 광대역($u, g, r, i$, 및 $z$)에서 모든 7DT 중간 밴드에 대해 50 mmag 이내의 불확실성이 달성될 수 있음을 보여줍니다. 이 계획은 7DT 광도 보정 파이프라인에 적용되고 있습니다. 이 논문의 발견은 GW MMA 분야에서 달성된 중요한 발전을 강조합니다. GECKO 시설을 사용한 중요한 중력파 사건의 체계적인 후속 관측부터 효과적인 분석 도구 및 관측 전략의 개발에 이르기까지, 이 연구는 미래의 탐사를 위한 강력한 초석을 마련했습니다. 또한 7DT를 사용한 돌발 천체 분류 방법의 구현과 엄격한 광도 보정 과정은 이 분야에서 정밀성과 효율성의 중요성을 강조합니다. 이러한 결합된 노력은 KNe를 감지하고 분석하는 우리의 능력을 향상시킬 뿐만 아니라 획기적인 발견을 위한 무대를 마련합니다. 이 작업은 궁극적으로 허블 갈등(Hubble tension)와 같은 중요한 우주론적 난제를 해결하는 데 기여하여 우주에 대한 더 깊은 이해를 제공할 것입니다. 우리의 방법론을 계속해서 개선하고 관측 능력을 확장함에 따라, 향상된 전략과 도구는 성공적인 GW MMA에 귀중한 자산이 될 것입니다.

다국어 초록 (Multilingual Abstract)

The era of gravitational wave (GW) multi-messenger astronomy (MMA) has ushered in a new frontier in astrophysical research, enabling the study of cosmic events through multiple observational channels. In particular, there is an ongoing worldwide search to identify kilonovae (KNe), the electromagnetic-wave (EM) counterpart of the GW events that are caused by binary star merger events involving at least one neutron star such as the binary neutron star (BNS) or neutron star-black hole (NSBH) merger events. The identification of KNe associated with GW events would broaden the scientific impact of GW MMA to studies of cosmology, cosmic history of heavy element production, stellar evolution, and the environment of GW source. Unfortunately, only one such attempt has seen success so far, namely for the GW170817 event. This dissertation presents a comprehensive study on the optical follow-up of GW sources, focusing on the development of analysis software and the follow-up observation strategies, our past attempts to find KNe corresponding to the signals from BNS or NSBH merger events, and the constraints on KN properties. The first part of this thesis deals with the follow-up observations of significant GW events such as GW190425 (BNS) and S230518h (NSBH) using the facilities of the GW EM-Counterpart Korean Observatories (GECKO) and the Korean Microlensing Telescope Network (KMTNet). In the course of presenting our follow-up observations, we describe the analysis software and the observation strategies. We also show the discovered transient candidates during the GW follow-up observation and constrain the expected characteristics of the KNe which disfavor to have a massive ejecta. The second part of the thesis introduces a novel transient classification method using low-resolution spectra of transients taken at a specific epoch, namely a single-epoch Spectral Energy Distribution (SED) classifier, developed with machine learning techniques and applied to the simulated 7-Dimensional Telescope (7DT) medium-band observation data. Our study shows that the single-epoch SED classifier is rapid and efficient in distinguishing not only various types of SNe with 95\% accuracy but also AT2017gfo-like KNe from various types of SNe at 90\% accuracy. Finally, in the third part of the thesis, we present, a rigorous photometric calibration process for the 7DT using Gaia XP synthetic magnitudes and spectro-photometric standard stars (SPSS). We show that the uncertainties within 50 mmag can be achieved for all the 7DT medium bands from 400 to 875 nm and broad bands ($u,g,r,i$, and $z$). This scheme is being applied to the 7DT photometry calibration pipeline. The findings from this dissertation highlight the critical advancements achieved in the field of GW MMA. From the systematic follow-up observations of significant GW events using the GECKO facilities to the development of robust analysis tools and observation strategies, this research has laid a strong foundation for future explorations. Additionally, the implementation of advanced transient classification methods using the 7DT and the rigorous photometric calibration process underscore the importance of precision and efficiency in this field. These combined efforts not only enhance our ability to detect and analyze KNe but also set the stage for groundbreaking discoveries. This work paves the way for resolving critical cosmological issues such as the Hubble tension, ultimately contributing to a deeper understanding of the universe. As we continue to refine our methodologies and expand our observational capabilities, the strategies and tools developed will serve as invaluable assets in successful GW MMA.

목차 (Table of Contents)

• Abstract i
• List of Figures vii
• List of Tables xviii
• List of Acronyms xix
• 1 Introduction 1
• 1.1 Gravitational-wave Astronomy 1
• 1.1.1 GW Observatories 1
• 1.1.2 Birth and Current Highlights of GW Astronomy 2
• 1.1.3 Promises of GW Astronomy 3
• 1.1.4 Current Limitations of GW Astronomy 3
• 1.2 Electromagnetic Counterparts of GW Sources 4
• 1.2.1 Kilonova 4
• 1.2.2 Short GRB 4
• 1.3 GW170817 6
• 1.3.1 Insights into Kilonova Physics 8
• 1.3.2 Host Galaxy Identification 8
• 1.3.3 Cosmology 9
• 1.3.4 Prospects for MMA 9
• 1.4 Challenges 10
• 1.5 GECKO and 7DT 12
• 1.5.1 Gravitational-wave Electromagnetic Counterpart Korean Observatory (GECKO) 12
• 1.5.2 7-Dimensional Telescope (7DT) 13
• 1.6 Research Purpose and Thesis Outline 14
• 2 Gravitational-wave Electromagnetic Counterpart Korean Observatory (GECKO): GECKO Follow-up Observation of GW190425 17
• 2.1 Introduction 17
• 2.2 GECKO 19
• 2.3 Observing Strategy 22
• 2.3.1 GW Host Galaxy Candidate Selection 22
• 2.3.2 Score 24
• 2.3.3 Application to GW170817 25
• 2.4 Follow-up observation of GW190425 25
• 2.4.1 Observation 26
• 2.4.2 Data Reduction and Photometry 28
• 2.5 Transients from GECKO Observation 30
• 2.6 Nature of Transients and Constraints on KN Property 31
• 2.6.1 GECKO Depths vs KN Models 31
• 2.6.2 GECKO Depths vs KN Models 32
• 2.6.3 Nature of GECKO190427a 34
• 2.7 Discussion 36
• 2.7.1 Outlook for GECKO Observations in O4 36
• 2.7.2 GECKO Prospects for Constraints on KN Models 38
• 2.8 Summary 39
• 3 Gravitational-wave Electromagnetic Counterpart Korean Observatory (GECKO): GECKO Follow-up Observation of S230518h 55
• 3.1 Introduction 55
• 3.2 Follow-up Observation of S230518h 58
• 3.2.1 Observing Facilities 59
• 3.2.2 Observation 59
• 3.3 Data Reduction and Analysis 61
• 3.3.1 gpPy, Data Reduction Pipeline 61
• 3.3.2 KS4 Pipeline 62
• 3.4 Transient Search 62
• 3.4.1 Image Subtraction and Filtering 62
• 3.4.2 Real/Bogus Classifier 64
• 3.4.3 Visual Inspection 65
• 3.4.4 Optical Counterpart Candidates 66
• 3.5 Discussion 69
• 3.5.1 Properties of KN candidates 69
• 3.5.2 Constraints on Kilonova Properties 71
• 3.5.3 Limitations and Challenges of the Follow-up Observation 73
• 3.5.4 Outlook for GECKO Follow-up Observations in O4 75
• 3.6 Summary 76
• 4 7-Dimensional Telescope (7DT): The Low-Resolution Photometric Spectral Energy Distribution Classifier of Transients with LightGBM 88
• 4.1 Introduction 88
• 4.2 Data 92
• 4.2.1 7-Dimensional Telescope 92
• 4.2.2 Simulated 7DT Photometric SED Data 93
• 4.2.3 Synthetic Photometry and SNR Calculation 94
• 4.2.4 Spectra of Various Sources for ML Training 95
• 4.2.5 AT2017gfo Kilonova Spectra 97
• 4.3 Method 98
• 4.3.1 Model Training 98
• 4.3.2 Hyperparameter Tuning 99
• 4.4 Results 100
• 4.5 Discussion 100
• 4.5.1 Model Performance 100
• 4.5.2 Comparison with SNID 102
• 4.5.3 Feature Importance 104
• 4.5.4 Application to the AT2017gfo Kilonova 104
• 4.5.5 Application to the SN 2024diq observed by 7DT 106
• 4.6 Summary 106
• 5 7-Dimensional Telescope (7DT): The Photometric Calibration of Medium-band Filter System with Gaia DR3 XP and Correction 109
• 5.1 Introduction 109
• 5.2 Data 112
• 5.2.1 Observation of Spectro-Photometric Standard Sources with 7DT 112
• 5.2.2 Data Processing Pipeline 114
• 5.2.3 Synthetic Photometric Data from Gaia XP 115
• 5.3 Photometric Calibration Solution of 7DT 116
• 5.3.1 Source Selection 117
• 5.3.2 Zero point Calculation 117
• 5.3.3 Dependence Correction 118
• 5.3.4 Absolute Flux Scale with SPSS spectra 120
• 5.4 Error Budget 122
• 5.5 Discussion 124
• 5.6 Summary 127
• 6 Summary and Conclusion 134
• Bibliography 138
• 한국어 초록 150
• Acknowledgement 152

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