Strong Lensing Studies Towards Next Generation Surveys

Strong gravitational lensing, an effect predicted by general relativity, occurs when the light from a distant source is deflected by the gravity of an intervening object (i.e. the lens), resulting in multiple lensed images. This phenomenon, with its remarkable sensitivity to gravity and cosmology, serves as a powerful tool for studying the three primary components of the Universe: baryons, dark matter, and dark energy. However, strong lensing events are inherently rare due to the close alignment required among the observer, the lens, and the source. It took sixty years to discover the first strong-lens system after the initial detection of light deflection in 1919. Thanks to modern large-scale imaging and spectroscopic surveys, the number of confirmed strong-lens systems has now reached several hundred, leading to significant breakthroughs in our understanding of galaxy evolution, dark matter properties, and cosmology.

In the coming decade, we will see the advent of even more extensive surveys including CSST, Euclid, 4MOST, DESI, Rubin LSST, WFST, SKA, SVOM, and LISA. These efforts will substantially expand the strong lensing landscape in both size and diversity. In particular, we anticipate discovering entirely new types of strong lensing events, such as those involving gravitational waves, fast radio bursts, and gamma-ray bursts. Additionally, the rapid advancement of artificial intelligence is transforming the research paradigm in numerous fields, including astronomy. The combination of next-generation surveys and AI techniques holds immense potential to revolutionize our understanding of the Universe.

With these great opportunities also come challenges, primarily due to the unprecedented volumes of data generated by upcoming surveys. The timing is ideal as DESI and Euclid have recently released their early observations, and several other surveys are expected to begin full operations within a couple of years. “How to get best prepared” is hence the main focus of this Research Topic. Quantifying the expected yields of various kinds of strongly lensed events from upcoming surveys is a priority, as well as developing and testing effective and efficient methods for discovering and modeling strong lenses. Additionally, ideas on how to fully leverage the power of large samples of strong lenses need to be explored.

This Research Topic therefore invites contributions broadly related to the following topics:

1. Strong-lens simulations and forecasts
2. Strong-lens searches and modelling
3. Strong-lens applications (i.e. dark matter, dark energy, cosmology, galaxy formation and evolution, etc.)
4. Development/integration of AI-driven methods and models.

Original research papers and review papers are particularly welcome.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review
• Study Protocol
• Technology and Code

Keywords: Strong Gravitational Lensing, Galaxy Formation and Evolution, Cosmology, Dark Matter, Surveys, Machine Learning, Statistics

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.