Modeling strong gravitational lenses is computationally expensive for the complex data from modern and next-generation cosmic surveys. Deep learning has emerged as a promising approach for finding lenses and predicting lensing parameters, such as the Einstein radius. Mean-variance Estimators (MVEs) are a common approach for obtaining aleatoric (data) uncertainties from a neural network prediction. However, neural networks have not been demonstrated to perform well on out-of-domain target data successfully -- e.g., when trained on simulated data and applied to real, observational data. In this work, we perform the first study of the efficacy of MVEs in combination with unsupervised domain adaptation (UDA) on strong lensing data. The source domain data is noiseless, and the target domain data has noise mimicking modern cosmology surveys. We find that adding UDA to MVE increases the accuracy on the target data by a factor of about two over an MVE model without UDA. Including UDA also permits much more well-calibrated aleatoric uncertainty predictions. Advancements in this approach may enable future applications of MVE models to real observational data.

In astrophysics, a gravitational lens is a matter distribution (e.g., a black hole) able to bend the trajectory of transiting light, similar to an optical lens. Such apparent distortion is caused by the curvature of the geometry of space-time around the massive body acting as a lens, a phenomenon that forces the light to travel along the geodesics (i.e., the shortest paths in the curved space-time). Strong and weak gravitational lensing focus on the effects produced by particularly massive bodies (e.g., galaxies and black holes), while microlensing addresses the consequences produced by lighter entities (e.g., stars). This research proposes an approach to automatically classify strong gravitational lenses with respect to the lensed objects and to their evolution through time. Automatically finding and classifying gravitational lenses is a major challenge in astrophysics. As [103, 91, 39, 44] show, gravitational lensing systems can be complex, ubiquitous and hard to detect without computer-aided data processing. The volumes of data gathered by contemporary instruments make manual inspection unfeasible. As an example, the Vera C. Rubin Observatory is expected to collect petabytes of data [108]. Moreover, strong lensing is involved in major astrophysical problems: studying massive bodies that are too faint to be analyzed with current instrumentation; characterizing the geometry, content and kinematics of the universe; and investigating mass distribution in the galaxy formation process [103].

Advanced artificial intelligence has identified thousands of possible "gravitational lenses" -- warps in space-time predicted by Albert Einstein -- promising to enhance our understanding of dark matter and the evolution of galaxies. Einstein realized that mass warps space, and massive galaxies and galaxy clusters can distort space around them to such a degree that they form a cosmic lens, bending and magnifying the path of light from more distant galaxies through that warped space. Gravitational lenses are important tools for cosmologists. They can magnify the light of distant galaxies that are too faint to be otherwise seen in detail, or reveal where invisible dark matter is warping space. However, astronomers had only about a hundred good gravitational lenses to use.

We present a novel machine learning based approach for detecting galaxy-scale gravitational lenses from interferometric data, specifically those taken with the International LOFAR Telescope (ILT), which is observing the northern radio sky at a frequency of 150 MHz, an angular resolution of 350 mas and a sensitivity of 90 uJy beam-1 (1 sigma). We develop and test several Convolutional Neural Networks to determine the probability and uncertainty of a given sample being classified as a lensed or non-lensed event. By training and testing on a simulated interferometric imaging data set that includes realistic lensed and non-lensed radio sources, we find that it is possible to recover 95.3 per cent of the lensed samples (true positive rate), with a contamination of just 0.008 per cent from non-lensed samples (false positive rate). Taking the expected lensing probability into account results in a predicted sample purity for lensed events of 92.2 per cent. We find that the network structure is most robust when the maximum image separation between the lensed images is greater than 3 times the synthesized beam size, and the lensed images have a total flux density that is equivalent to at least a 20 sigma (point-source) detection. For the ILT, this corresponds to a lens sample with Einstein radii greater than 0.5 arcsec and a radio source population with 150 MHz flux densities more than 2 mJy. By applying these criteria and our lens detection algorithm we expect to discover the vast majority of galaxy-scale gravitational lens systems contained within the LOFAR Two Metre Sky Survey.

A new class of extremely large telescopes has evolved to collect vast amounts of data; The volume of data collected from an entire survey ten years ago can now be collected in one night. One example of these new generation telescopes is fifth Sloan Digital Sky Survey (SDSS-V), launched in 2020 and slated to collect optical and infrared spectra for more than six million objects during its five-year lifetime [1]. Human inspection is wholly inadequate for dealing with millions of pieces of fuzzy and distorted images. Even traditional, centralized data processing systems can't keep up with the petabytes of astronomical data emerging from new surveys. The solution is a new field called Astro information, which astronomy with machine learning and AI to search for habitable exoplanets, estimate red shifts, and classify galaxies and supernovas [2].

Machine learning key to discovery of over 1200 gravitational lenses. Data from the DESI (Dark Energy Spectroscopic Instrument) Legacy Imaging Surveys have revealed over 1200 new gravitational lenses, approximately doubling the number of known lenses. Discovered using machine learning trained on rea

A research team of scientists from the Berkeley lab has used Artificial Intelligence(AI) to discover about 1200 possible gravitational lenses. According to phys.org if this count is accurate, this could double the number of existing gravitational lenses. Read more to find out what gravitational lenses are. So what exactly are these gravitational lenses? When light emitted by stars from distant galaxies pass massive objects in the universe, like a cluster of star systems or a bunch of galaxies, the light gets bent or distorted due to the incredibly powerful gravitational force.

Brian Nord wants to build a self-driving telescope. The Fermilab astrophysicist envisions an instrument that, when presented with a hypothesis about the nature of the Universe, figures out the best observations to make on its own. He anticipates that it could take up to thirty years to understand and put together the project's nuts and bolts. One known component is artificial intelligence (AI)--algorithms similar to those that underpin facial recognition software and nascent self-driving car technology. Building toward his telescope dream, Nord has begun applying AI to problems in astronomy, such as identifying unusual astronomical objects known as gravitational lenses.

In our efforts to understand the Universe, we're getting greedy, making more observations than we know what to do with. Satellites beam down hundreds of terabytes of information each year, and one telescope under construction in Chile will produce 15 terabytes of pictures of space every night. It's impossible for humans to sift through it all. As astronomer Carlo Enrico Petrillo told The Verge: "Looking at images of galaxies is the most romantic part of our job. The problem is staying focused."

Kevin Schawinski had a problem. In 2007 he was an astrophysicist at Oxford University and hard at work reviewing seven years' worth of photographs from the Sloan Digital Sky Survey--images of more than 900,000 galaxies. He spent his days looking at image after image, noting whether a galaxy looked spiral or elliptical, or logging which way it seemed to be spinning. Technological advancements had sped up scientists' ability to collect information, but scientists were still processing information at the same rate. After working on the task full time and barely making a dent, Schawinski and colleague Chris Lintott decided there had to be a better way to do this.