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Time-Delay Cosmography is a technique for measuring $H_0$ with strong gravitational lensing. It requires a correction for line of sight perturbations, and it is necessary to build tools to assess populations of these lines of sight efficiently. We aim demonstrate the techniques necessary to analyze line of sight effects at a population level, and investigate whether strong lenses fall in preferably overdense environments. We analyze a set of 25 galaxy-galaxy lens lines of sight in the Strong Lensing Legacy Survey sample using standard techniques, then perform a hierarchical analysis to constrain the population-level parameters. We introduce a new statistical model for these posteriors that may provide insight into the underlying physics of the system. We find the median value of $\kappa_{\rm{ext}}$ in the population model to be $0.033 \pm 0.010$. The median value of $\kappa_{\rm{ext}}$ for the individual lens posteriors is $0.008 \pm 0.015$. Both approaches demostrate that our systems are drawn from an overdense sample. The different results from these two approaches show the importance of population models that do not multiply the effect of our priors., Comment: 12 pages, 4 figures, 1 table. Accepted for Publication in A&A

Decoherence-free subspaces and subsystems (DFS) preserve quantum information by encoding it into symmetry-protected states unaffected by decoherence. An inherent DFS of a given experimental system may not exist; however, through the use of dynamical decoupling (DD), one can induce symmetries that support DFSs. Here, we provide the first experimental demonstration of DD-generated DFS logical qubits. Utilizing IBM Quantum superconducting processors, we investigate two and three-qubit DFS codes comprising up to six and seven noninteracting logical qubits, respectively. Through a combination of DD and error detection, we show that DFS logical qubits can achieve up to a 23% improvement in state preservation fidelity over physical qubits subject to DD alone. This constitutes a beyond-breakeven fidelity improvement for DFS-encoded qubits. Our results showcase the potential utility of DFS codes as a pathway toward enhanced computational accuracy via logical encoding on quantum processors., Comment: 20 pages, 15 figures

The mass distribution in massive elliptical galaxies encodes their evolutionary history, thus providing an avenue to constrain the baryonic astrophysics in their evolution. The power-law assumption for the radial mass profile in ellipticals has been sufficient to describe several observables to the noise level, including strong lensing and stellar dynamics. In this paper, we quantitatively constrained any deviation, or the lack thereof, from the power-law mass profile in massive ellipticals through joint lensing-dynamics analysis of a large statistical sample with 77 galaxy-galaxy lens systems. We performed an improved and uniform lens modelling of these systems from archival Hubble Space Telescope imaging using the automated lens modelling pipeline dolphin. We combined the lens model posteriors with the stellar dynamics to constrain the deviation from the power law after accounting for the line-of-sight lensing effects, a first for analyses on galaxy-galaxy lenses. We find that the Sloan Lens ACS Survey (SLACS) lens galaxies with a mean redshift of 0.2 are consistent with the power-law profile within 1.1$\sigma$ (2.8$\sigma$) and the Strong Lensing Legacy Survey (SL2S) lens galaxies with a mean redshift of 0.6 are consistent within 0.8$\sigma$ (2.1$\sigma$), for a spatially constant (Osipkov-Merritt) stellar anisotropy profile. We adopted the spatially constant anisotropy profile as our baseline choice based on previous dynamical observables of local ellipticals. However, spatially resolved stellar kinematics of lens galaxies are necessary to differentiate between the two anisotropy models. Future studies will use our lens models to constrain the mass distribution individually in the dark matter and baryonic components., Comment: 31 pages, 20 figures, 9 tables. This version: accepted to MNRAS

We investigate the ability of human 'expert' classifiers to identify strong gravitational lens candidates in Dark Energy Survey like imaging. We recruited a total of 55 people that completed more than 25$\%$ of the project. During the classification task, we present to the participants 1489 images. The sample contains a variety of data including lens simulations, real lenses, non-lens examples, and unlabeled data. We find that experts are extremely good at finding bright, well-resolved Einstein rings, whilst arcs with $g$-band signal-to-noise less than $\sim$25 or Einstein radii less than $\sim$1.2 times the seeing are rarely recovered. Very few non-lenses are scored highly. There is substantial variation in the performance of individual classifiers, but they do not appear to depend on the classifier's experience, confidence or academic position. These variations can be mitigated with a team of 6 or more independent classifiers. Our results give confidence that humans are a reliable pruning step for lens candidates, providing pure and quantifiably complete samples for follow-up studies., Comment: 16 pages, 20 Figures