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Auto-identification of unphysical source reconstructions in strong gravitational lens modelling (2021)
Journal Article
Maresca, J., Dye, S., & Li, N. (2021). Auto-identification of unphysical source reconstructions in strong gravitational lens modelling. Monthly Notices of the Royal Astronomical Society, 503(2), 2229–2241. https://doi.org/10.1093/mnras/stab387

With the advent of next-generation surveys and the expectation of discovering huge numbers of strong gravitational lens systems, much effort is being invested into developing automated procedures for handling the data. The several orders of magnitude... Read More about Auto-identification of unphysical source reconstructions in strong gravitational lens modelling.

Optimizing automatic morphological classification of galaxies with machine learning and deep learning using Dark Energy Survey imaging (2020)
Journal Article
Cheng, T., Conselice, C. J., Aragón-Salamanca, A., Li, N., Bluck, A. F., Hartley, W. G., …Tarle, G. (2020). Optimizing automatic morphological classification of galaxies with machine learning and deep learning using Dark Energy Survey imaging. Monthly Notices of the Royal Astronomical Society, 493(3), 4209-4228. https://doi.org/10.1093/mnras/staa501

There are several supervised machine learning methods used for the application of automated morphological classification of galaxies; however, there has not yet been a clear comparison of these different methods using imaging data, or a investigation... Read More about Optimizing automatic morphological classification of galaxies with machine learning and deep learning using Dark Energy Survey imaging.

The use of convolutional neural networks for modelling large optically-selected strong galaxy-lens samples (2019)
Journal Article
Pearson, J., Li, N., & Dye, S. (2019). The use of convolutional neural networks for modelling large optically-selected strong galaxy-lens samples. Monthly Notices of the Royal Astronomical Society, 488(1), 991-1004. https://doi.org/10.1093/mnras/stz1750

We explore the effectiveness of deep learning convolutional neural networks (CNNs) for estimating strong gravitational lens mass model parameters. We have investigated a number of practicalities faced when modelling real image data, such as how netwo... Read More about The use of convolutional neural networks for modelling large optically-selected strong galaxy-lens samples.

The importance of secondary halos for strong lensing in massive galaxy clusters across redshift (2019)
Journal Article
Li, N., Gladders, M. D., Heitmann, K., Rangel, E. M., Child, H. L., Florian, M. K., …Finkel, H. J. (2019). The importance of secondary halos for strong lensing in massive galaxy clusters across redshift. Astrophysical Journal, 878(2), Article 122. https://doi.org/10.3847/1538-4357/ab1f74

Cosmological cluster-scale strong gravitational lensing probes the mass distribution of the dense cores of massive dark matter halos and the structures along the line of sight from background sources to the observer. It is frequently assumed that the... Read More about The importance of secondary halos for strong lensing in massive galaxy clusters across redshift.

Detection of high-speed railway subsidence and geometry irregularity using terrestrial laser scanning (2014)
Journal Article
Liu, C., Li, N., Wu, H., & Meng, X. (2014). Detection of high-speed railway subsidence and geometry irregularity using terrestrial laser scanning. Journal of Surveying Engineering, 140(3), Article 04014009. https://doi.org/10.1061/%28ASCE%29SU.1943-5428.0000131

Subsidence and geometry deformation monitoring are essential for safe transportation on a high-speed railway. Terrestrial laser scanning (TLS) is able to collect dense three-dimensional point data from the survey scene and achieve highly accurate mea... Read More about Detection of high-speed railway subsidence and geometry irregularity using terrestrial laser scanning.

Precise determination of mini railway track with ground based laser scanning (2013)
Journal Article
Meng, X., Liu, C., Li, N., & Joe, R. (2014). Precise determination of mini railway track with ground based laser scanning. Survey Review, 46(336), https://doi.org/10.1179/1752270613Y.0000000072

In order to determine the relative or absolute railway track and foundation deformation, ground-based laser scanning technology is utilised in this study to attain a precise 3D track reference. Located at the University of Nottingham’s Innovation Par... Read More about Precise determination of mini railway track with ground based laser scanning.