Microlensing Constraints on Broad Absorption and Emission Line Flows in the Quasar H1413+117

We present new integral field spectroscopy of the gravitationally lensed broad absorption line (BAL) quasar H1413+117, covering the ultraviolet to visible rest-frame spectral range. We observe strong microlensing signatures in lensed image D, and we use this microlensing to simultaneously constrain both the broad emission and broad absorption line gas. By modeling the lens system over the range of probable lensing galaxy redshifts and using on a new argument based on the wavelength-independence of the broad line lensing magnifications, we determine that there is no significant broad line emission from smaller than ~20 light days. We also perform spectral decomposition to derive the intrinsic broad emission line (BEL) and continuum spectrum, subject to BAL absorption. We also reconstruct the intrinsic BAL absorption profile, whose features allow us to constrain outflow kinematics in the context of a disk-wind model. We find a very sharp, blueshifted onset of absorption of 1,500 km/s in both C IV and N V that may correspond to an inner edge of a disk-wind's radial outflow. The lower ionization Si IV and Al III have higher-velocity absorption onsets, consistent with a decreasing ionization parameter with radius in an accelerating outflow. There is evidence of strong absorption in the BEL component which indicates a high covering factor for absorption over two orders of magnitude in outflow radius.Comment: 29 pages, 8 figure

The Most Powerful Lenses in the Universe: Quasar Microlensing as a Probe of the Lensing Galaxy

Optical and X-ray observations of strongly gravitationally lensed quasars (especially when four separate images of the quasar are produced) determine not only the amount of matter in the lensing galaxy but also how much is in a smooth component and how much is composed of compact masses (e.g., stars, stellar remnants, primordial black holes, CDM sub-halos, and planets). Future optical surveys will discover hundreds to thousands of quadruply lensed quasars, and sensitive X-ray observations will unambiguously determine the ratio of smooth to clumpy matter at specific locations in the lensing galaxies and calibrate the stellar mass fundamental plane, providing a determination of the stellar $M/L$. A modest observing program with a sensitive, sub-arcsecond X-ray imager, combined with the planned optical observations, can make those determinations for a large number (hundreds) of the lensing galaxies, which will span a redshift range of $\sim$$0.25<z<1.5$Comment: Astro2020 Science White Pape

Latent Stochastic Differential Equations for Modeling Quasar Variability and Inferring Black Hole Properties

Active galactic nuclei (AGN) are believed to be powered by the accretion of matter around supermassive black holes at the centers of galaxies. The variability of an AGN's brightness over time can reveal important information about the physical properties of the underlying black hole. The temporal variability is believed to follow a stochastic process, often represented as a damped random walk described by a stochastic differential equation (SDE). With upcoming wide-field surveys set to observe 100 million AGN in multiple bandpass filters, there is a need for efficient and automated modeling techniques that can handle the large volume of data. Latent SDEs are well-suited for modeling AGN time series data, as they can explicitly capture the underlying stochastic dynamics. In this work, we modify latent SDEs to jointly reconstruct the unobserved portions of multivariate AGN light curves and infer their physical properties such as the black hole mass. Our model is trained on a realistic physics-based simulation of ten-year AGN light curves, and we demonstrate its ability to fit AGN light curves even in the presence of long seasonal gaps and irregular sampling across different bands, outperforming a multi-output Gaussian process regression baseline.Comment: 10 pages, 5 figures, accepted at the ICLR 2023 Workshop on Physics for Machine Learnin

The HST Survey of BL Lac Objects: Gravitational Lens Candidates and Other Unusual Sources

We present HST observations of seven unusual objects from the HST ``snapshot survey'' of BL Lac objects, of which four are gravitational lens candidates. In three cases a double point sources is observed: 0033+595, with 1.58 arcsec separation, and 0502+675 and 1440+122, each with $\sim 0.3$ arcsec separation. The last two also show one or more galaxies, which could be either host or lensing galaxies. If any are confirmed as lenses, these BL Lac objects are excellent candidates for measuring H$_0$ via gravitational time delay because of their characteristic rapid, high amplitude variability. An additional advantage is that, like other blazars, they are likely superluminal radio sources, in which case the source plane is mapped out over a period of years, providing strong additional constraints on the lensing mass distribution. The fourth gravitational lens candidate is 1517+656, which is surrounded by three arclets forming an almost perfect ring of radius 2.4 arcsec. If this is indeed an Einstein ring, it is most likely a background source gravitationally lensed by the BL Lac object host galaxy and possibly a surrounding group or cluster. In the extreme case that all four candidates are true lenses, the derived frequency of gravitational lensing in this BL Lac sample would be an order of magnitude higher than in comparable quasar samples. We also report on three other remarkable BL Lac objects: 0138-097, which is surrounded by a large number of close companion galaxies; 0806+524, whose host galaxy contains an uncommon arc-like structure; and 1959+650, which is hosted by a gas rich elliptical galaxy with a prominent dust lane of $\sim 5\times 10^5 M_\odot$.Comment: 29 pages in total, 12 figure

Selection of eligible participants for screening for lung cancer using primary care data

Lung cancer screening is effective if offered to people at increased risk of the disease. Currently, direct contact with potential participants is required for evaluating risk. A way to reduce the number of ineligible people contacted might be to apply risk-prediction models directly to digital primary care data, but model performance in this setting is unknown.Method: The Clinical Practice Research Datalink, a computerised, longitudinal primary care database, was used to evaluate the LLPv2 and PLCOm2012 models. Lung cancer occurrence over 5-6 years was measured in ever-smokers aged 50-80 years and compared with 5-year (LLPv2) and 6-year (PLCOm2012) predicted risk. Results: Over 5 and 6 years, 7,123 and 7,876 lung cancers occurred respectively from a cohort of 842,109 ever smokers. After recalibration, LLPV2 produced a c-statistic of 0.700 (0.694-0.710) but mean predicted risk was over-estimated (predicted: 4.61%, actual: 0.9%). PLCOm2012 showed similar performance (c-statistic: 0.679 (0.673–0.685), predicted risk: 3.76%. Applying risk-thresholds of 1% (LLPv2) and 0.15% (PLCOm2012), would avoid contacting 42.7% and 27.4% of ever-smokers who did not develop lung cancer for screening eligibility assessment, at the cost of missing 15.6% and 11.4% of lung cancers.Conclusion: Risk-prediction models showed only moderate discrimination when applied to routinely collected primary care data, which may be explained by quality and completeness of data. However, they may substantially reduce the number of people for initial evaluation of screening eligibility, at the cost of missing some lung cancers. Further work is needed to establish whether newer models have improved performance in primary care data

Risk of COVID-19 death for people with a pre-existing cancer diagnosis prior to COVID-19-vaccination : A systematic review and meta-analysis

Research Funding National Health and Medical Research Council. Grant Number: APP1194679 World Health Organization Article Funding Open access publishing facilitated by The University of Sydney, as part of the Wiley - The University of Sydney agreement via the Council of Australian University Librarians.Peer reviewedPublisher PD

Risk of COVID-19 death for people with a pre-existing cancer diagnosis prior to COVID-19-vaccination:A systematic review and meta-analysis

While previous reviews found a positive association between pre-existing cancer diagnosis and COVID-19-related death, most early studies did not distinguish long-term cancer survivors from those recently diagnosed/treated, nor adjust for important confounders including age. We aimed to consolidate higher-quality evidence on risk of COVID-19-related death for people with recent/active cancer (compared to people without) in the pre-COVID-19-vaccination period. We searched the WHO COVID-19 Global Research Database (20 December 2021), and Medline and Embase (10 May 2023). We included studies adjusting for age and sex, and providing details of cancer status. Risk-of-bias assessment was based on the Newcastle-Ottawa Scale. Pooled adjusted odds or risk ratios (aORs, aRRs) or hazard ratios (aHRs) and 95% confidence intervals (95% CIs) were calculated using generic inverse-variance random-effects models. Random-effects meta-regressions were used to assess associations between effect estimates and time since cancer diagnosis/treatment. Of 23 773 unique title/abstract records, 39 studies were eligible for inclusion (2 low, 17 moderate, 20 high risk of bias). Risk of COVID-19-related death was higher for people with active or recently diagnosed/treated cancer (general population: aOR = 1.48, 95% CI: 1.36-1.61, I2 = 0; people with COVID-19: aOR = 1.58, 95% CI: 1.41-1.77, I2 = 0.58; inpatients with COVID-19: aOR = 1.66, 95% CI: 1.34-2.06, I2 = 0.98). Risks were more elevated for lung (general population: aOR = 3.4, 95% CI: 2.4-4.7) and hematological cancers (general population: aOR = 2.13, 95% CI: 1.68-2.68, I2 = 0.43), and for metastatic cancers. Meta-regression suggested risk of COVID-19-related death decreased with time since diagnosis/treatment, for example, for any/solid cancers, fitted aOR = 1.55 (95% CI: 1.37-1.75) at 1 year and aOR = 0.98 (95% CI: 0.80-1.20) at 5 years post-cancer diagnosis/treatment. In conclusion, before COVID-19-vaccination, risk of COVID-19-related death was higher for people with recent cancer, with risk depending on cancer type and time since diagnosis/treatment.</p