FRB 20190128C , the SIMBAD biblio

FRB 20190128C , the SIMBAD biblio (10 results) C.D.S. - SIMBAD4 rel 1.8 - 2026.10.15CEST18:47:13

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Bibcode/DOI Score in Title|Abstract|
Keywords 		in a table in teXt, Caption, ... Nb occurence Nb objects in ref Citations
(from ADS) 		Title First 3 Authors
2021ApJS..257...59C viz 17       D               1 494 238 The first CHIME/FRB Fast Radio Burst catalog. CHIME/FRB COLLABORATION, AMIRI M., ANDERSEN B.C., et al.
2022MNRAS.509.1227C viz 17       D               1 486 19 Uncloaking hidden repeating fast radio bursts with unsupervised machine learning. CHEN B.H., HASHIMOTO T., GOTO T., et al.
2022MNRAS.511.1961H viz 17       D               1 498 21 Energy functions of fast radio bursts derived from the first CHIME/FRB catalogue. HASHIMOTO T., GOTO T., CHEN B.H., et al.
2023MNRAS.518.1629L 17       D               2 35 7 Machine learning classification of CHIME fast radio bursts - I. Supervised methods. LUO J.-W., ZHU-GE J.-M. and ZHANG B.
2024MNRAS.533.3283S 18       D               1 71 ~ Positive and unlabelled machine learning reveals new fast radio burst repeater candidates. SHARMA A. and RAJPAUL V.M.
2024ApJ...977..273G 18       D               1 33 ~ Separating Repeating Fast Radio Bursts Using the Minimum Spanning Tree as an Unsupervised Methodology. GARCIA C.R., TORRES D.F., ZHU-GE J.-M., et al.
2025ApJ...980..185S 19       D               1 147 ~ Exploring the Key Features of Repeating Fast Radio Bursts with Machine Learning. SUN W.-P., ZHANG J.-G., LI Y., et al.
2025ApJ...982...16Q 19       D               1 273 ~ Unsupervised Machine Learning for Classifying CHIME Fast Radio Bursts and Investigating Empirical Relations. QIANG D.-C., ZHENG J., YOU Z.-Q., et al.
2025A&A...698A..18Z viz 19       D               1 378 ~ Revisiting the energy distribution and formation rate of CHIME fast radio bursts. ZHANG K.J., ZHANG Z.B., RODIN A.E., et al.
2026ApJ...998..339S 20       D               1 135 ~ A Practical Framework for Estimating the Repetition Likelihood of Fast Radio Bursts from Spectral Morphology. SUN W.-P., ZHANG Y.-K., ZHANG J.-G., et al.

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