Machine learning methods for delay estimation in gravitationally lensed signals

Strongly lensed variable quasars can serve as precise cosmological probes, provided that time delays between the image fluxes can be accurately measured. This thesis, explores in detail a new approach based on kernel regression estimates, which is able to estimate a single time delay given several data sets for the same quasar. We develop realistic artificial data sets in order to carry out controlled experiments to test the performance of this new approach. We also test our method on real data from strongly lensed quasar Q0957+561 and compare our estimates against existing results. Furthermore, we attempt to resolve the problem for smaller delays in gravitationally lensed photon streams. We test whether a more principled treatment of delay estimation in lensed photon streams, compared with the standard kernel estimation method, can have benefits of more accurate (less biased) and/or more stable (less variance) estimation. To that end, we propose a delay estimation method in which a single latent nonhomogeneous Poisson process underlying the lensed photon streams is imposed. The rate function model is formulated as a linear combination of nonlinear basis functions. Such a unifying rate function is then used in delay estimation based on the corresponding Innovation Process

Kernel regression estimates of time delays between gravitationally lensed fluxes

Strongly lensed variable quasars can serve as precise cosmological probes, provided that time delays between the image fluxes can be accurately measured. A number of methods have been proposed to address this problem. In this paper, we explore in detail a new approach based on kernel regression estimates, which is able to estimate a single time delay given several datasets for the same quasar. We develop realistic artificial data sets in order to carry out controlled experiments to test of performance of this new approach. We also test our method on real data from strongly lensed quasar Q0957+561 and compare our estimates against existing results.Comment: Updated to match published versio

Pregnancy Complications in Pandemics: Is Pregnancy-Related Anxiety a Possible Physiological Risk Factor?

Background: Birth and pregnancy complications increased by 10.2% during the 2019 coronavirus (COVID-19) pandemic. Pregnant women are at high risk for anxiety, which might trigger physio-logical stress, leading to pregnancy complications. Aim: This study aimed to investigate factors leading to antenatal anxiety during the COVID-19 pandemic. We also aimed to discuss our find-ings with regard to the current literature about pregnancy complications. Methods: This cross-sectional study interviewed 377 pregnant women and assessed anxiety using a validated 7-item general anxiety disorder (GAD-7) scale. Anxiety was related to physiological and demo-graphic parameters. Anxiety was subdivided into pandemic- and pregnancy-related anxiety to minimize results bias. Results: Our results showed that 75.3% of pregnant women were anxious. The mean GAD-7 score was 8.28 ± 5. Linear regression analysis showed that for every increase in the number of previous pregnancies, there was a 1.3 increase in anxiety level (p < 0.001). Women with no previous miscarriages were more anxious (p < 0.001). Surprisingly, pregnant women who were previously infected with COVID-19 were 6% less stressed. Pregnant women with comorbid-ities were more stressed (p < 0.001). Low income (p < 0.001) and age (p < 0.05) were the demo-graphic factors most significantly related to increased anxiety. Conclusions: The prevalence of pregnancy-related anxiety increased threefold in Saudi Arabia due to the COVID-19 pandemic. Healthcare support should be available remotely during pandemics; pregnant women (especially those with comorbidities) should be educated about the risks of infection and complications to prevent anxiety-related complications during pregnancy