Selected Publications:
1. Li Z., Zhang M., Peng Q.-H., Liu X., “A new model of quasar mass evolution”, ApSS 367 71 (2022)
2. Li Z., Peng Q.-H., Kang M., Liu X., Zhang M., Huang Y.-F., Chou C.-K., “Neutrino rocket jet model: An explanation of high-velocity pulsars and their spin-down Evolution”, ApJ 931 123 (2022)
3. Birrer S., Shajib A., Gilman,D., Galan A., Aalbers J., Millon M., Morgan R., Pagano G., Park J., Teodori L., Tessore N., Ueland M., Van de Vyvere L., Wagner-Carena S., Wempe E., Yang L., Ding X., Schmidt T., Sluse D., Zhang M., Amara A., “lenstronomy II: A gravitational lensing software ecosystem”, JOSS 6 62 3283 (2021)
4. Zhang L., Xu L., Mi L.G., Zhang M., Liu X., Wang F., Li D.Y., Ruan Y.-J., Li D.-Y., “Deconvolution with hybrid parameterizations for radio emission
reconstruction”, RAA, 21 4 101 (2021)
5. Zhang L., Mi L.G., Xu L., Zhang M., Li D.Y., Liu X., , Wang F., Xiao Y.-F., Wu Z.-Z., “Adaptive scale model reconstruction for radio synthesis imaging”, RAA, 21 3 63 (2021)
6. Zhang L., Mi L.G., Zhang M., Liu X., Xu L., Wang F., Ruan Y.J., Li D.Y., “Parameterized Reconstruction with Random Scales for Radio Synthesis Imaging”, A&A 646 A44 (2021).
7. Zhang M., Array Networking between“Quasar”and QVN, Transactions of IAA RAS (Ru) vol. 53/Applied Astronomy no. 1 (2020)
8. Zhang L., Mi L.-G., Zhang M., Liu X., He C.-L., Adaptive-scale wide-field reconstruction for radio synthesis imaging, A&A 640 A80 (2020)
9. Zhang L., Xu L., Zhang M., Parameterized CLEAN Deconvolution in Radio Synthesis Imaging, PASP 132 1010 (2020)
10. Zhang L., Xu L., Zhang M., Wu Z.-Z., An adaptive loop gain selection for CLEAN deconvolution algorithm, RAA, 19, 6 (2019)
11. Ghirlanda G., Salafia O. S., Paragi Z., Giroletti M., Yang J., Marcote B., Blanchard J., Agudo I., An T., Bernardini M. G., Beswick R., Branchesi M., Campana S., Casadio C., Chassande-Mottin E., Colpi M., Covino S., D‘Avanzo P., D'Elia V., Frey S., Gawronski M., Ghisellini G., Gurvits L. I., Jonker P.G., van Langevelde H. J., Melandri A., Moldon J., Nava L., Perego A., Perez-Torres M. A., Reynolds C., Salvaterra R., Tagliaferri G., Venturi T., Vergani S. D., Zhang M., “Compact radio emission indicates a structured jet was produced by a binary neutron star merger”, Science 363 6249 (2019)
12. Zhang L., Long X., Zhang M., Wu Z.-Z., “An adaptive loop gain selection ofr CLEAN deconvolution algorithm”, RAA 19 6 (2019)
13. Zhang M., Cui L., Wang N., Urumqi - A Pivotal VLBI node in Central Asia, URSI Asia Pacific Radio Science Conference (2019)
14. Xu W., Zhang M., Theory of Generative Deep Learning II: Probe Landscape of Empirical Error via Norm Based Capacity Control, 5th IEEE International Conference on Cloud Computing and Intelligence Systems (2018)
15. Xu W., Zhang M., Towards WARSHIP: Combining Components of Brain-Inspired Computing of RSH for Image Super Resolution, 5th IEEE International Conference on Cloud Computing and Intelligence Systems (2018)
16. Zhang M., Coullioud A., Charlot P., “SAND: an automated VLBI imaging and analysing pipeline - I. Stripping component trajectories”, MNRAS 473 450 (2018)
17. Liu F.Y., Zhang M., Multi-Band and Multi-Epoch Morphological Evolution and Computational Physics of PKS 0528+134, JCTN, 14, 2 (2017)
18. Zhang L., Bhatnagar S., Rau U., Zhang M., Efficient implementation of the adaptive scale pixel decomposition algorithm, A&A 592A 128 (2016)
19. Zhang L., Zhang M., Liu X., The adaptive-loop-gain adaptive-scale CLEAN deconvolution of radio interferometric images, Ap&SS 361 153 (2016)
20. Zhang M., “TOW2015@MITHAY”, IVS Newsletter, Issue 42 4-5 (2015)
21. Zhang M., “SAND: Automated VLBI imaging and analyzing pipeline” (2016) [ascl:1605.015] http://ascl.net/code/v/1292
