We investigate the effect of microlensing on the standardisation of strongly lensed Type Ia supernovae (GLSNe Ia). We present predictions for the amount of scatter induced by microlensing across a range of plausible strong lens macromodels. We find that lensed images in regions of low convergence, shear and stellar density are standardisable, where the microlensing scatter is 0.15 magnitudes, comparable to the intrinsic dispersion of for a typical SN Ia. These standardisable configurations correspond to asymmetric lenses with an image located far outside the Einstein radius of the lens. Symmetric and small Einstein radius lenses ( 0.5 arcsec) are not standardisable. We apply our model to the recently discovered GLSN Ia iPTF16geu and find that the large discrepancy between the observed flux and the macromodel predictions from More et al. (2017) cannot be explained by microlensing alone. Using the mock GLSNe Ia catalogue of , we predict that ∼ 22% of GLSNe Ia discovered by LSST will be standardisable, with a median Einstein radius of 0.9 arcseconds and a median time-delay of 41 days. By breaking the mass-sheet degeneracy the full LSST GLSNe Ia sample will be able to detect systematics in H 0 at the 0.5% level.
We determine the viability of exploiting lensing time delays to observe strongly gravitationally lensed supernovae (gLSNe) from first light. Assuming a plausible discovery strategy, the Legacy Survey of Space and Time (LSST) and the Zwicky Transient Facility (ZTF) will discover ∼110 and ∼1 systems per year before the supernova (SN) explosion in the final image, respectively. Systems will be identified $11.7^{+29.8}_{-9.3}$ d before the final explosion. We then explore the possibility of performing early-time observations for Type IIP and Type Ia SNe in LSST-discovered systems. Using a simulated Type IIP explosion, we predict that the shock breakout in one trailing image per year will peak at ≲24.1 mag (≲23.3) in the B-band (F218W), however evolving over a time-scale of ∼30 min. Using an analytic model of Type Ia companion interaction, we find that in the B-band we should observe at least one shock cooling emission event per year that peaks at ≲26.3 mag (≲29.6) assuming all Type Ia gLSNe have a 1 M⊙ red giant (main sequence) companion. We perform Bayesian analysis to investigate how well deep observations with 1 h exposures on the European Extremely Large Telescope would discriminate between Type Ia progenitor populations. We find that if all Type Ia SNe evolved from the double-degenerate channel, then observations of the lack of early blue flux in 10 (50) trailing images would rule out more than 27 per cent (19 per cent) of the population having 1 M⊙ main sequence companions at 95 per cent confidence.
Throughout the COVID-19 pandemic, valuable datasets have been collected on the effects of the virus SARS-CoV-2. In this study, we combined whole genome sequencing data with clinical data (including clinical outcomes, demographics, comorbidity, treatment information) for 929 patient cases seen at a large UK hospital Trust between March 2020 and May 2021. We identified associations between acute physiological status and three measures of disease severity; admission to the intensive care unit (ICU), requirement for intubation, and mortality. Whilst the maximum National Early Warning Score (NEWS2) was moderately associated with severe COVID-19 (A = 0.48), the admission NEWS2 was only weakly associated (A = 0.17), suggesting it is ineffective as an early predictor of severity. Patient outcome was weakly associated with myriad factors linked to acute physiological status and human genetics, including age, sex and pre-existing conditions. Overall, we found no significant links between viral genomics and severe outcomes, but saw evidence that variant subtype may impact relative risk for certain sub-populations. Specific mutations of SARS-CoV-2 appear to have little impact on overall severity risk in these data, suggesting that emerging SARS-CoV-2 variants do not result in more severe patient outcomes. However, our results show that determining a causal relationship between mutations and severe COVID-19 in the viral genome is challenging. Whilst improved understanding of the evolution of SARS-CoV-2 has been achieved through genomics, few studies on how these evolutionary changes impact on clinical outcomes have been seen due to complexities associated with data linkage. By combining viral genomics with patient records in a large acute UK hospital, this study represents a significant resource for understanding risk factors associated with COVID-19 severity. However, further understanding will likely arise from studies of the role of host genetics on disease progression.
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