Strongly lensed SNe Ia in the era of LSST: observing cadence for lens discoveries and time-delay measurements

The upcoming Large Synoptic Survey Telescope (LSST) will detect many strongly lensed Type Ia supernovae (LSNe Ia) for time-delay cosmography. This will provide an independent and direct way for measuring the Hubble constant $H_0$, which is necessary to address the current $4.4 \sigma$ tension in $H_0$ between the local distance ladder and the early Universe measurements. We present a detailed analysis of different observing strategies for the LSST, and quantify their impact on time-delay measurement between multiple images of LSNe Ia. For this, we produced microlensed mock-LSST light curves for which we estimated the time delay between different images. We find that using only LSST data for time-delay cosmography is not ideal. Instead, we advocate using LSST as a discovery machine for LSNe Ia, enabling time delay measurements from follow-up observations from other instruments in order to increase the number of systems by a factor of 2 to 16 depending on the observing strategy. Furthermore, we find that LSST observing strategies, which provide a good sampling frequency (the mean inter-night gap is around two days) and high cumulative season length (ten seasons with a season length of around 170 days per season), are favored. Rolling cadences subdivide the survey and focus on different parts in different years; these observing strategies trade the number of seasons for better sampling frequency. In our investigation, this leads to half the number of systems in comparison to the best observing strategy. Therefore rolling cadences are disfavored because the gain from the increased sampling frequency cannot compensate for the shortened cumulative season length. We anticipate that the sample of lensed SNe Ia from our preferred LSST cadence strategies with rapid follow-up observations would yield an independent percent-level constraint on $H_0$.Comment: 25 pages, 22 figures; accepted for publication in A&

Automated cross-identifying radio to infrared surveys using the LRPY algorithm: A case study

Cross-identifying complex radio sources with optical or infra red (IR) counterparts in surveys such as the Australia Telescope Large Area Survey (ATLAS) has traditionally been performed manually. However, with new surveys from the Australian Square Kilometre Array Pathfinder detecting many tens of millions of radio sources, such an approach is no longer feasible. This paper presents new software (LRPY - Likelihood Ratio in PYTHON) to automate the process of cross-identifying radio sources with catalogues at other wavelengths. LRPY implements the likelihood ratio (LR) technique with a modification to account for two galaxies contributing to a sole measured radio component. We demonstrate LRPY by applying it to ATLAS DR3 and a Spitzer-based multiwavelength fusion catalogue, identifying 3848 matched sources via our LR-based selection criteria. A subset of 1987 sources have flux density values for all IRAC bands which allow us to use criteria to distinguish between active galactic nuclei (AGNs) and star-forming galaxies (SFG). We find that 936 radio sources (˜47 per cent) meet both of the Lacy and Stern AGN selection criteria. Of the matched sources, 295 have spectroscopic redshifts and we examine the radio to IR flux ratio versus redshift, proposing an AGN selection criterion below the Elvis radio-loud AGN limit for this dataset. Taking the union of all three AGNs selection criteria we identify 956 as AGNs (˜48 per cent). From this dataset, we find a decreasing fraction of AGNs with lower radio flux densities consistent with other results in the literature

Developing a model for decision-making around antibiotic prescribing for patients with COVID-19 pneumonia in acute NHS hospitals during the first wave of the COVID-19 pandemic: Qualitative results from the Procalcitonin Evaluation of Antibiotic use in COVID-19 Hospitalised patients (PEACH Study)

\ua9 Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.Objective To explore and model factors affecting antibiotic prescribing decision-making early in the pandemic. Design Semistructured qualitative interview study. Setting National Health Service (NHS) trusts/health boards in England and Wales. Participants Clinicians from NHS trusts/health boards in England and Wales. Method Individual semistructured interviews were conducted with clinicians in six NHS trusts/health boards in England and Wales as part of the Procalcitonin Evaluation of Antibiotic use in COVID-19 Hospitalised patients study, a wider study that included statistical analysis of procalcitonin (PCT) use in hospitals during the first wave of the pandemic. Thematic analysis was used to identify key factors influencing antibiotic prescribing decisions for patients with COVID-19 pneumonia during the first wave of the pandemic (March to May 2020), including how much influence PCT test results had on these decisions. Results During the first wave of the pandemic, recommendations to prescribe antibiotics for patients with COVID-19 pneumonia were based on concerns about secondary bacterial infections. However, as clinicians gained more experience with COVID-19, they reported increasing confidence in their ability to distinguish between symptoms and signs caused by SARS-CoV-2 viral infection alone, and secondary bacterial infections. Antibiotic prescribing decisions were influenced by factors such as clinician experience, confidence, senior support, situational factors and organisational influences. A decision-making model was developed. Conclusion This study provides insight into the decision-making process around antibiotic prescribing for patients with COVID-19 pneumonia during the first wave of the pandemic. The importance of clinician experience and of senior review of decisions as factors in optimising antibiotic stewardship is highlighted. In addition, situational and organisational factors were identified that could be optimised. The model presented in the study can be used as a tool to aid understanding of the complexity of the decision-making process around antibiotic prescribing and planning antimicrobial stewardship support in the context of a pandemic. Trial registration number ISRCTN66682918

Determining the Repertoire of Immunodominant Proteins via Whole-Genome Amplification of Intracellular Pathogens

Culturing many obligate intracellular bacteria is difficult or impossible. However, these organisms have numerous adaptations allowing for infection persistence and immune system evasion, making them some of the most interesting to study. Recent advancements in genome sequencing, pyrosequencing and Phi29 amplification, have allowed for examination of whole-genome sequences of intracellular bacteria without culture. We have applied both techniques to the model obligate intracellular pathogen Anaplasma marginale and the human pathogen Anaplasma phagocytophilum, in order to examine the ability of phi29 amplification to determine the sequence of genes allowing for immune system evasion and long-term persistence in the host. When compared to traditional pyrosequencing, phi29-mediated genome amplification had similar genome coverage, with no additional gaps in coverage. Additionally, all msp2 functional pseudogenes from two strains of A. marginale were detected and extracted from the phi29-amplified genomes, highlighting its utility in determining the full complement of genes involved in immune evasion

Measuring neutrino masses with a future galaxy survey

We perform a detailed forecast on how well a Euclid-like photometric galaxy and cosmic shear survey will be able to constrain the absolute neutrino mass scale. Adopting conservative assumptions about the survey specifications and assuming complete ignorance of the galaxy bias, we estimate that the minimum mass sum of sum m_nu ~ 0.06 eV in the normal hierarchy can be detected at 1.5 sigma to 2.5 sigma significance, depending on the model complexity, using a combination of galaxy and cosmic shear power spectrum measurements in conjunction with CMB temperature and polarisation observations from Planck. With better knowledge of the galaxy bias, the significance of the detection could potentially reach 5.4 sigma. Interestingly, neither Planck+shear nor Planck+galaxy alone can achieve this level of sensitivity; it is the combined effect of galaxy and cosmic shear power spectrum measurements that breaks the persistent degeneracies between the neutrino mass, the physical matter density, and the Hubble parameter. Notwithstanding this remarkable sensitivity to sum m_nu, Euclid-like shear and galaxy data will not be sensitive to the exact mass spectrum of the neutrino sector; no significant bias (< 1 sigma) in the parameter estimation is induced by fitting inaccurate models of the neutrino mass splittings to the mock data, nor does the goodness-of-fit of these models suffer any significant degradation relative to the true one (Delta chi_eff ^2< 1).Comment: v1: 29 pages, 10 figures. v2: 33 pages, 12 figures; added sections on shape evolution and constraints in more complex models, accepted for publication in JCA

Constraining Scale-Dependent Non-Gaussianity with Future Large-Scale Structure and the CMB

We forecast combined future constraints from the cosmic microwave background and large-scale structure on the models of primordial non-Gaussianity. We study the generalized local model of non-Gaussianity, where the parameter f_NL is promoted to a function of scale, and present the principal component analysis applicable to an arbitrary form of f_NL(k). We emphasize the complementarity between the CMB and LSS by using Planck, DES and BigBOSS surveys as examples, forecast constraints on the power-law f_NL(k) model, and introduce the figure of merit for measurements of scale-dependent non-Gaussianity.Comment: 28 pages, 8 figures, 2 tables; v2: references update