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Recent works have shown that weak lensing magnification must be included in upcoming large-scale structure analyses, such as for the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST), to avoid biasing the cosmological results. In this work, we investigate whether including magnification has a positive impact on the precision of the cosmological constraints, as well as being necessary to avoid bias. We forecast this using an LSST mock catalogue and a halo model to calculate the galaxy power spectra. We find that including magnification has little effect on the precision of the cosmological parameter constraints for an LSST galaxy clustering analysis, where the halo model parameters are additionally constrained by the galaxy luminosity function. In particular, we find that for the LSST gold sample (i < 25.3) including weak lensing magnification only improves the galaxy clustering constraint on Ωm by a factor of 1.03, and when using a very deep LSST mock sample (i < 26.5) by a factor of 1.3. Since magnification predominantly contributes to the clustering measurement and provides similar information to that of cosmic shear, this improvement would be reduced for a combined galaxy clustering and shear analysis. We also confirm that not modelling weak lensing magnification will catastrophically bias the cosmological results from LSST. Magnification must therefore be included in LSST large-scale structure analyses even though it does not significantly enhance the precision of the cosmological constraints.

 

Working towards improved neuromyoelectric control of dexterous prosthetic hands, we explored how differences in training paradigms affect the subsequent online performance of two different motor-decode algorithms. Participants included two intact subjects and one participant who had undergone a recent transradial amputation after complex regional pain syndrome (CRPS) and multi-year disuse of the affected hand. During algorithm training sessions, participants actively mimicked hand movements appearing on a computer monitor. We varied both the duration of the hold-time (0.1 s or 5 s) at the end-point of each of six different digit and wrist movements, and the order in which the training movements were presented (random or sequential). We quantified the impact of these variations on two different motordecode algorithms, both having proportional, six-degree-offreedom (DOF) control: a modified Kalman filter (MKF) previously reported by this group, and a new approach - a convolutional neural network (CNN). Results showed that increasing the hold-time in the training set improved run-time performance. By contrast, presenting training movements in either random or sequential order had a variable and relatively modest effect on performance. The relative performance of the two decode algorithms varied according to the performance metric. This work represents the first-ever amputee use of a CNN for real-time, proportional six-DOF control of a prosthetic hand. Also novel was the testing of implanted high-channelcount devices for neuromyoelectric control shortly after amputation, following CRPS and long-term hand disuse. This work identifies key factors in the training of decode algorithms that improve their subsequent run-time performance. 

We investigate the sensitivity to the effects of lensing magnification on large-scale structure analyses combining photometric cosmic shear and galaxy clustering data (i.e. the now commonly called ‘3 × 2-point’ analysis). Using a Fisher matrix bias formalism, we disentangle the contribution to the bias on cosmological parameters caused by ignoring the effects of magnification in a theory fit from individual elements in the data vector, for Stage-III and Stage-IV surveys. We show that the removal of elements of the data vectors that are dominated by magnification does not guarantee a reduction in the cosmological bias due to the magnification signal, but can instead increase the sensitivity to magnification. We find that the most sensitive elements of the data vector come from the shear-clustering cross-correlations, particularly between the highest redshift shear bin and any lower redshift lens sample, and that the parameters ΩM, $S_8=\sigma _8\sqrt{\Omega _\mathrm{ M}/0.3}$, and w0 show the most significant biases for both survey models. Our forecasts predict that current analyses are not significantly biased by magnification, but this bias will become highly significant with the continued increase of statistical power in the near future. We therefore conclude that future surveys should measure and model the magnification as part of their flagship ‘3 × 2-point’ analysis.