GLADIUS · Gravitational Lensing Analysis for Data Intensive Upcoming Surveys

Gravitational Lensing Analysis for Data Intensive Upcoming Surveys

Gravitational lensing is one of the major tools to address long-standing problems in astrophysics and cosmology, such as dark energy, dark matter, and black hole physics. Specifically: I) Analysis of lensed quasars through time-delay cosmography provides an independent probe for the value of the Hubble constant, a crucial cosmological parameter for which different methods provide inconsistent results creating a tension in modern cosmology. II) Dark matter properties within and around galaxies are still poorly understood. Gravitational lensing and microlensing constitute a unique way to measure them and test different dark matter models. III) Quasars are crucial for understanding galaxy evolution and supermassive black hole growth. However, very little is known about the heart of the quasar: a supermassive black hole surrounded by a disc of accreting material. Rare microlensing events provide a unique resolving power, orders of magnitude higher than any current and foreseeable telescope, that can be used to measure the geometry and kinematics of quasar central regions. Progress has so far been limited by the small number of known lenses and the intricate nature of the phenomenon, requiring painstaking handcrafted modelling approaches, hard-to-procure high-quality data, and lengthy computations. Upcoming all-sky surveys will revolutionize the field by discovering orders-of-magnitude more lenses and providing adequate information to understand them in depth. Mining these massive troves of data for the new astrophysical discoveries they hold requires a paradigm shift in our analysis approaches. GLADIUS undertook this task and produced innovative and flexible new methods merging traditional approaches and machine learning for the joint analysis of imaging and time-domain data. GLADIUS addressed the following three ambitious objectives: 1. Quantifying and mitigating the effect of microlensing on measuring the Hubble constant through time delay cosmography. 2. Creating a self-consistent lens modelling approach that fuses strong and microlensing models through a combination of traditional and machine learning methods. 3. Creating and deploying machine learning predictors using monitoring data from LSST, in order to provide alerts and optimal observation strategies for imminent microlensing high magnification events. GLADIUS has achieved most of its objectives and milestones for the period, with relatively minor deviations. I have completed developing the method to quantify the effect of microlensing on measuring the Hubble constant, created two lens modelling methods that go beyond the state-of-the-art, and produced microlensing event predictors that are in the stage of fine-tuning before deployment within the LSST data processing pipeline.

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