Rare strong lens gems emerging from the Space Warps ESA Euclid Data Release 1 search
A few months ago, we started the full-scale search for strong gravitational lenses in the ESA Euclid Data Release 1 (DR1) survey. While we are carefully analysing your classifications, in this blog post we highlight some of our favourite strong lens candidates that you’ve collectively found in this dataset.
Congratulations!! You’ve now passed the milestone of an incredible 5 million classifications since the first ESA Euclid Quick Data Release 1 (Q1) lens search began in 2024, including 2.7 million in the current ESA Euclid DR1 search. We have been completely blown away, and are excitedly analysing all your classifications! While the lens search and our analysis continue, we wanted to highlight some of the stunning strong lens candidates that you have found and let you know what’s happening next with the lens search.
Beyond the fascinating individual systems we highlight in this blog post, thousands of other lens candidates are emerging from your collective classifications – such a large population which will help to revolutionise the study of strong lenses. Prior to Euclid DR1 only a few hundred strong gravitational lenses had been identified in space-based imaging and so the thousands of lens systems which you have found will change how we can study these systems, enabling us to analyse the strong lens population as a whole.
Amongst this treasure trove of lens systems are some scientific gems:
First up is a lensed quasar (ID: 119727051); this has four lensed images forming a ‘quad’ configuration. The light from the quasar (a bright supermassive black-hole at the centre of a distant galaxy) is strong enough to outshine both the light of the deflector or lens galaxy and that of the host galaxy it resides in. Unlike galaxy-galaxy strong lenses, the light from the central deflector can’t be seen in the image. Quasars, in particular quadruply imaged quasars like this one, are particularly useful probes of cosmology. Light emitted from the background quasar will arrive at the Earth at different times depending on which of the four routes (corresponding to the 4 lensed images) that it takes. The arrival time of the emitted light is dependent on the degree of lensing (how much mass there is in the deflector galaxy), and the expansion rate of the universe. From the separation of the lensed images we can estimate the mass of the deflector or lens and therefore, by measuring the time delay of each of the four images, strongly lensed quasars can help to constrain the expansion of the universe!
Next up are two lens systems with bright dust lanes (119696543 and 119705025 ). In both of these systems, at least 3 lensed images are visible (in the left example the counter-image is likely hidden behind the smaller satellite galaxy to the right). These systems highlight the superb resolution of the Euclid telescope, as well as the benefits of colour imaging which helps distinguish the dust lanes from lensed arcs or tidal features.
This is an example of a candidate double-source-plane lens (ID: 119665581) – here the deflector galaxy is lensing two different background galaxies, creating two sets of lensed arcs. These are very rare systems even within strongly lensed systems since they require the close alignment of two source galaxies with the deflector galaxy rather than just one. They are also especially useful for a range of astrophysical and cosmological studies, for example the two Einstein rings allow us to tightly constrain how much mass is present in the deflector galaxy, and where it is most concentrated.
This final system (ID: 121188143) suggests that the lensed background galaxy has interesting structure. In this image you can see four images of the same bright, white blob but around these finer stellar filaments can be seen. This implies that the background galaxy has a central bright component and more extended fainter structure around it. By developing a model of such systems, we can remove the distortion caused by gravitational lensing and reconstruct what the source galaxy really looks like. The extra detail visible in the lensed arcs can help us produce a sharper, higher-resolution image of the background galaxy than would be possible if we were observing the same galaxy without the amplification and magnification by strong lensing.
These systems are just a small snapshot of the lensed systems you have already identified. Behind the scenes, we are analysing all of your classifications both in classify and refine in preparation for the public release of all the Euclid DR1 images in November this year. In the meantime, we are adding the second batch of approximately 50,000 Euclid DR1 images to Space Warps Classify. These systems were flagged as lens candidates by a new machine learning network, and we’re keen to see what you think! This network used multiple bands (colours) in order to classify the lensed images, so it will be really interesting to see if different types of strong lens systems appear in this second batch compared to the first.
Thank you again for your classifications and we look forward to discussing further exciting strong lens systems with you!
The Space Warps ESA Euclid Strong Lens team.
Space Warps Refine – Honing in on our best lens candidates
Phil Holloway, Space Warps researcher, has put together a new project on the ESA Euclid first batch of data for you to try – read on to find out more.
From the hard work of the Space Warps volunteers (you!), we’ve now classified over 130,000 images from the ESA Euclid telescope. This was a hugely successful project thanks to all your contributions!
We’re very excited to launch the next stage; Space Warps Refine!! You might recall a similar project using CFHT-LS data (our very first Space Warps project) where we ran a second phase of the inspection aiming to carefully discern high scoring lens candidates; this project is in a similar vein. This time, you’ll be asked to look at the much smaller sample your crowd classifications generated and categorise the lens candidates into four grades: definite lens (A-grade), probable lens (B-grade), possible lens (C-grade) and not a lens (X-grade). This is a little different to the original Refine, where we asked you to clean the smaller sample with a yes or no. In this Refine inspection, we want to know your opinion on the likelihood of the lens.
This grading scheme is the same as the one researchers use to refine the larger sample of promising lens candidates into those that are most likely lenses. The highest grade candidates (the definite and probable lenses) are typically published as the lens candidates emerging from a survey.
You’ll notice the grades don’t have precise definitions or boundaries. In fact the final lens grading can be very subjective, and we are asking you to reflect on the system and let us know how likely you think a candidate is to be a strong lens system. For example, for some systems, it may be hard to decide between the ‘probable’ and ‘possible’ lens categories since the lensing signatures can be varied and not all images visible. Even if you are unsure we are asking you to select the grade that you think is best. There’s no absolute right or wrong answer and indeed researchers in strong lensing often disagree! As with the ‘classify’ stage, and as we do with the group of researchers, we will combine your grades to arrive at the final grade for any given candidate. The crowd grade will be the best impression we have for the likelihood of something being a likely or unlikely lens candidate.
To help guide you through the process, we’ve included some training images with detailed feedback. These feedback messages explain which characteristics to look out for, and why a given system might be given a high or low grade. As usual, you can also check out the Tutorial if you’re unsure of what to do. There are no wrong answers – we are really interested in which systems you think are the most likely lens candidates.
The feedback also includes info on how a small group of researchers graded the lens candidate. In some cases you’ll notice strong agreement between the researchers, in others a much wider spread. We want to know your thoughts, even if they are diverse, this information from your crowd grading can also tell us about the kind of systems that are unclear versus those that aren’t.
This project is a proof-of-concept study, preparing us for much larger datasets of strong lenses which we’ll find with the ESA Euclid telescope. From our models, we’re expecting to find roughly 100,000 strong lens systems in the ESA Euclid data nestled within samples that are factors of a few to ten times larger, this is far more than the researchers can handle without your help! We’re aiming to get a purer sample of strong lenses by separating out the systems which show clear lensing features from those which might be non-lenses (false positives).
For the initial launch, we’re including around 10,000 images that your crowd inspection identified from the ‘classify’ work flow for refinement. These include lenses which received high scores from Space Warps volunteers in our initial ESA Euclid lens search, as well as some simulated lenses. You should therefore expect a higher proportion of interesting lens candidates in this grading workflow than the classification stream.
I have recorded a short talk on the results from our Euclid lens search, as well as more details on this `Refine’ stage. Do take a look and ask any questions which come up on our dedicated talk forum here.
Thanks again for all your contributions – we can’t wait to see what you find. Happy refining!
Space Warps helps to find 497 spectacular lenses in Euclid data
by Phil Holloway
Thanks to your incredible efforts, we are delighted to have found 497 strong lens candidates in Euclid Q1 data. Over the course of the project we had more than 800,000 classifications from over 1000 wonderful volunteers, and the results are a testament to your hard work. As part of the lens search, we developed the Strong Lens Discovery Engine, a pipeline to search for lenses in Euclid data, of which Space Warps was an integral part.
We found a whole range of lens candidates – a collage of our favourites is below, with a whole range of lens configurations. We also found 4 double-source-plane lenses! These are incredibly rare systems where the lensing galaxy deflects light from two different background galaxies, forming double rings/arcs.
We have temporarily removed these images while the Euclid Refine project is live
Credit: ESA/Euclid/Euclid Consortium/NASA, M. Walmsley, T. Li, N. Lines, and Euclid SL SWG
We have temporarily removed these images while the Euclid Refine project is live
Credit: Euclid Collaboration: Walmsley et al (2025)
As part of the Strong Lens Discovery Engine we used multiple machine learning algorithms (including ‘Zoobot’ trained using zooniverse classifications in Galaxy Zoo) to do an initial sift of the data which the Space Warps volunteers inspected to find the most likely lens candidates. This machine + volunteer partnership will be crucial with the much larger data releases coming soon from the Euclid survey. We also used Euclid’s incredible resolution to produce precise models of all the lens candidates and will continue to analyse these fascinating lenses for many months and years to come!
You can read the full results in the 5 science papers released today:
B: Lens search around massive galaxies,
C: Finding lenses with machine learning,
D: Double-source-plane lenses,
E: Lens classification combining machine learning and Space Warps.
Thank you again for your incredible hard work in finding these amazing lenses – we couldn’t have done it without you! Keep an eye out for future Space Warps projects – this initial data release was only 0.4% of the sky area of the full survey, so there will be many many more exciting lenses to find soon!
Stay tuned!
Phil and the Space Warps Team
Phil Holloway is a final year PhD student in the Department of Physics at Oxford and has done amazing work through his time with us including on Space Warps! We’re so thankful to Phil and to you all for making these results possible. Phil, Anu & Aprajita (Space Warps co-leads).
Space Warps finds new lenses in the Dark Energy Survey
A while ago, we ran a project lead by Jimena Gonzalez Lozano ran the FIRST machine learning+citizen inspection system to find strong gravitational lenses searching all of galaxies in the Dark Energy Survey.
The machine learning model makes use of the transformer encoder, which is based on the attention mechanism. Transformers were originally designed for natural language processing tasks. However, they can also be employed in image-processing tasks — like facial recognition in photographs — that in our case score images on their likelihood of being a gravitational lens. All of you then helped to sift through these likely candidates producing amazing results! A few words from Jimena…
Thank You for Helping Us Discover Hundreds of Strong Lenses!
Thanks to the incredible efforts of hundreds of volunteers who classified over 20,000 images, we have identified hundreds of strong gravitational lenses! After carefully reviewing the highest-scored images, we classified the final candidates into three categories based on confidence:
• 149 “definite” lenses
• 516 “probable” lenses
• 663 “could-be” lenses
You can find the full results in our publication available on arXiv, where Figures 12–15 showcase examples of candidates from each confidence category. Below is an image highlighting some of the high-confidence strong lenses that had not been identified before!
This project holds the record for finding the most strong lenses in the Dark Energy Survey. Additionally, we found that our machine learning methodology produces significantly fewer false positives (incorrect lens classifications) than previous techniques. This makes it a powerful tool for the next generation of astronomical surveys, where we will be dealing with massive amounts of data.
Importantly, even the images classified as not being lenses are valuable! They can be used to train future machine learning models, helping refine and improve their accuracy.
Finally, here is a collage showcasing the incredible diversity of strong lenses discovered in this project—featuring a variety of shapes, sizes, and colors.
A collage of strong lenses that you helped to identify! This image was the winning entry in the UW-Madison 2023 Cool Science Image Contest.
Thank you once again for your time and dedication. Your contributions have made a real impact on the search for these rare cosmic phenomena!
Jimena and the Space Warps Team
Space Warps 