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SN 2022riv in RX J2129: Discovery, Spectroscopic Classification, and Microlensing of a Strongly Lensed Type Ia Supernova from JWST and HST Observations

This paper reports the discovery and spectroscopic classification of SN 2022riv, a strongly lensed Type Ia supernova in RX J2129, and presents a cosmology-independent measurement of its magnification alongside an analysis of how well various cluster lens models predict this value in the presence of expected microlensing effects.

Original authors: Birendra Dhanasingham, Patrick L. Kelly, Wenlei Chen, Justin Pierel, Masamune Oguri, Derek Perera, Jose M. Diego, Adi Zitrin, Ashish K. Meena, Mathilde Jauzac, Guillaume Mahler, Elias Mamuzic, Liliya

Published 2026-04-15

📖 5 min read🧠 Deep dive

CC BY 4.0

Original authors: Birendra Dhanasingham, Patrick L. Kelly, Wenlei Chen, Justin Pierel, Masamune Oguri, Derek Perera, Jose M. Diego, Adi Zitrin, Ashish K. Meena, Mathilde Jauzac, Guillaume Mahler, Elias Mamuzic, Liliya L. R. Williams, Yoon Chan Taak, Anton M. Koekemoer, Thomas J. Broadhurst, Lukas J. Furtak, David Lagattuta, Hayley Williams, Kyle Dalrymple, Alexei V. Filippenko, Christa Gall, Daniel Gilman, Jens Hjorth, Saurabh W. Jha, Conor Larison, Chien-Hsiu Lee, Paolo A. Mazzali, Keren Sharon, Sherry H. Suyu

Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine the universe as a giant, cosmic funhouse mirror. Sometimes, massive objects like galaxy clusters act as these mirrors, bending the light from distant stars and galaxies behind them. This phenomenon is called gravitational lensing. It can make faint, distant objects appear much brighter and larger, allowing us to see things we otherwise couldn't.

This paper is the story of SN 2022riv, a specific stellar explosion (a supernova) that was caught in the middle of this cosmic funhouse. Here is the story of its discovery, its identity, and what it taught us about the invisible stuff in the universe.

1. The Discovery: A Ghost in the Machine

Astronomers were using the Hubble Space Telescope to scan a galaxy cluster named RX J2129. They were looking for "highly magnified stars"—basically, looking for the universe's most powerful natural telescopes.

Instead of a single star, they found a ghostly flash of light that appeared out of nowhere. This was SN 2022riv. It wasn't just one image; because of the lensing effect, the light from this explosion was split into multiple copies, like a reflection in a funhouse mirror. The astronomers found the "last-to-arrive" copy of the light (labeled S3), which had taken the longest, most winding path to reach us.

2. The Detective Work: What Kind of Explosion Was It?

To understand what they were looking at, the team needed to know what exploded. Was it a massive star collapsing? Or was it a white dwarf star blowing up?

They used the James Webb Space Telescope (JWST), the most powerful telescope ever built, to take a "fingerprint" (spectrum) of the light.

The Analogy: Imagine finding a broken toy in a forest. You can't see the toy clearly, but you find a specific piece of plastic and a unique scratch pattern. By matching these clues to a catalog, you know exactly what toy it was.
The Result: The "fingerprint" showed a perfect match for a Type Ia Supernova. This is a very specific type of explosion that happens when a white dwarf star hits a critical weight limit and detonates. Crucially, Type Ia supernovae are "standard candles." This means we know exactly how bright they should be intrinsically. If they look brighter than they should, it's because something magnified them.

3. The Magnification Puzzle: How Much Did the Mirror Boost the Light?

Because we know how bright a Type Ia supernova should be, the astronomers could calculate how much the galaxy cluster magnified the light.

The Calculation: They found that the galaxy cluster acted like a magnifying glass, boosting the brightness of the supernova by about 5.35 times.
The Twist: The supernova was located very close to the center of the galaxy cluster's brightest galaxy (the "Big Boss" galaxy). This is a crowded neighborhood filled with billions of stars.

4. The "Microlensing" Effect: The Cosmic Pinball

Here is where it gets tricky. The galaxy cluster isn't just a smooth, giant lens. It's filled with individual stars, planets, and dark matter clumps.

The Analogy: Imagine the galaxy cluster is a giant, smooth sheet of glass (the main lens) sitting on top of a table. But scattered on that glass are thousands of tiny pebbles (individual stars). When light passes through the smooth glass, it gets magnified. But when it hits a pebble, the light gets jiggled, distorted, or dimmed slightly. This is called microlensing.
The Prediction: Because SN 2022riv was so close to the "Big Boss" galaxy, the team expected the light to be heavily affected by these tiny pebbles (stars). They predicted the magnification could fluctuate by 20% to 50% just because of these stars.

5. The Great Model Showdown

Six different teams of scientists built computer models to predict how the galaxy cluster should bend light. They didn't know the answer beforehand (a "double-blind" test).

The Results: Five of the six teams predicted a magnification of roughly 4 to 7 times. This matched the astronomers' measurement almost perfectly!
The Outlier: One team (using a model called HoliGRALE) predicted a magnification of 15 times. This was way off.
The Resolution: When the astronomers added the "microlensing" effect (the jiggling from the stars) into the HoliGRALE model, the prediction dropped from 15 down to about 6. Suddenly, it matched the real data!

6. Why This Matters

This paper is a huge success for two reasons:

Testing the Maps: It proved that our computer maps of how galaxy clusters bend light are incredibly accurate. We can now trust these maps to find even fainter, more distant galaxies in the future.
Weighing the Invisible: By seeing how much the "pebbles" (stars) messed with the light, the astronomers confirmed that there is a lot of stellar mass right where the supernova was. It's like using a floating leaf to measure the current of a river; the leaf's movement tells us about the water we can't see.

Summary

In short, astronomers found a distant exploding star that was magnified by a galaxy cluster. They used the known brightness of the explosion to measure exactly how much the cluster magnified it. They then compared this real-world measurement against six different computer models. The models mostly agreed, but one was way off until they accounted for the "jiggling" caused by individual stars (microlensing). This confirmed that our understanding of how gravity bends light—and how stars hide within galaxy clusters—is spot on.

1. Problem and Scientific Context

Gravitational lensing by galaxy clusters allows astronomers to observe high-redshift objects that would otherwise be too faint. However, accurately modeling the mass distribution of these clusters (lens models) is challenging. While strongly lensed quasars are used for time-delay cosmography, they suffer from host-galaxy contamination and chromatic microlensing effects.

Strongly lensed Type Ia Supernovae (SNe Ia) offer a complementary tool. Because SNe fade over time, the underlying host galaxy can be modeled more accurately, and their light curves allow for precise time-delay measurements. Furthermore, SNe Ia have a known intrinsic luminosity, allowing for a direct, cosmology-independent measurement of the magnification ( $\mu$ ) imposed by the lens.

The specific problem addressed in this paper is the characterization of SN 2022riv, a strongly lensed SN Ia in the galaxy cluster RX J2129.7+0005 ( $z_{lens} = 0.234$ ). The supernova was detected in the "last-to-arrive" image (S3) of a multiply imaged host galaxy at $z_{source} = 1.522$ . The location of Image S3 is exceptionally close to the Brightest Cluster Galaxy (BCG), resulting in a high stellar mass density. This environment makes the image highly susceptible to microlensing (magnification fluctuations caused by individual stars) and millilensing (caused by dark matter substructure), complicating the comparison between observed magnification and theoretical lens model predictions.

2. Methodology

The study employs a multi-faceted approach combining deep imaging, spectroscopy, and independent lens modeling:

Observations:
- Discovery: Detected via Hubble Space Telescope (HST) SNAP program (GO-16729) by subtracting archival CLASH images from 2022 data.
- Follow-up: Extensive observations using JWST (NIRCam imaging and NIRSpec spectroscopy) and HST (WFC3/IR monitoring).
- Spectroscopy: JWST NIRSpec G140M and PRISM modes provided spectra covering rest-frame 2700–21,000 Å, crucial for classification at high redshift.
Classification:
- Spectroscopic: Used the SNID code to cross-correlate spectra with template libraries, identifying key features like Si II $\lambda$ 6355 absorption.
- Photometric: Used sncosmo and StarDust2 to fit light curves against Bayesian models for various SN types (Ia, Ib/c, II).
- Magnification Inference: Used SALT3-NIR and other SALT variants to fit the light curve and derive the distance modulus. By comparing this to a control sample of unlensed SNe Ia at similar redshifts, the authors derived a cosmology-independent magnification ( $\mu$ ).
Lens Modeling:
- Six independent teams (GLAFIC, Chen2020, HoliGRALE, WSLAP+, Zitrin-Analytic, LENSTOOL) constructed mass models of RX J2129 using updated astrometry from JWST NIRCam.
- Models were constrained by 11 spectroscopically confirmed multiple-image systems.
Microlensing/Millilensing Simulations:
- Millilensing: Simulated dark matter subhalo effects using pyHalo and lenstronomy, assuming substructure mass fractions of 1–10%.
- Microlensing: Simulated stellar microlensing using GERLUMPH and ray-tracing. Stellar mass densities were estimated using Prospector on SEDs of the BCG and intracluster light (ICL). Simulations considered four SN Ia explosion models (W7, N100, Sub-Chandrasekhar, Double CO WD Merger) and two Initial Mass Functions (Chabrier and Salpeter).

3. Key Contributions

Discovery and Classification: First robust spectroscopic and photometric classification of SN 2022riv as a Type Ia supernova at $z=1.522$ using JWST data.
Cosmology-Independent Magnification: Provided a measurement of the magnification for Image S3 ( $\mu \approx 5.35 \pm 1.01$ ) without assuming a specific cosmological model, serving as a ground-truth benchmark for lens models.
High-Density Microlensing Environment: Identified that Image S3 resides in a region with a stellar mass density $\sim 1-2$ orders of magnitude higher than previous lensed SNe (e.g., SN Refsdal), making it a unique laboratory for studying microlensing in high-mass-density environments.
Double-Blind Lens Model Comparison: Conducted a rigorous comparison between six independent lens models and the observed magnification, explicitly quantifying the impact of microlensing on model accuracy.

4. Key Results

Magnification Measurement: The SALT3-NIR light-curve fit yielded a magnification of $\mu = 5.35 \pm 1.01$ . Other SALT models yielded consistent results ( $\mu \approx 5.4 - 6.0$ ).
Lens Model Performance (Pre-Microlensing):
- Five of the six models (GLAFIC, Chen2020, WSLAP+, Zitrin-Analytic, LENSTOOL) predicted magnifications between 4 and 7, consistent with the measurement within $1\sigma$ (tensions ranging from $0.03\sigma$ to $1.0\sigma$ ).
- The HoliGRALE model predicted a significantly higher magnification ( $\mu = 15.39 \pm 0.85$ ), showing a $7.6\sigma$ tension with the observation.
Microlensing Effects:
- Millilensing: Dark matter substructure had a negligible effect ( $\sim 0.1\% - 2\%$ ) on the magnification, much smaller than seen in SN Refsdal.
- Microlensing: Stellar microlensing introduced a significant modulation of 20–50%. The simulations predicted demagnification (reduction in flux) for Image S3, consistent with it being a saddle-point image in a high stellar density region.
Resolution of Discrepancies:
- When microlensing corrections were applied, the HoliGRALE model's tension dropped from $7.6\sigma$ to $0.7\sigma$ (Chabrier IMF) or $0.9\sigma$ (Salpeter IMF). This suggests the initial discrepancy was primarily due to unmodeled microlensing effects.
- The WSLAP+ model (free-form) showed the best agreement ( $0.03\sigma$ ) even without microlensing corrections, though adding corrections slightly increased the tension to $0.3\sigma$ .
- Four other models showed excellent agreement ( $p < 0.05$ ) even without microlensing, suggesting their macromodels are robust, though the high $p$ -values for HoliGRALE indicate microlensing is the dominant source of error for that specific model.

5. Significance

Validation of Lens Models: The study demonstrates that modern lens models can predict magnifications of strongly lensed SNe with high precision ( $<1\sigma$ ) even in complex, high-stellar-density environments, provided microlensing is accounted for.
Microlensing Physics: It confirms theoretical expectations that saddle-point images near the BCG are subject to strong microlensing demagnification. The results highlight the necessity of including stellar microlensing in lens model comparisons for SNe located near cluster cores.
Future Cosmography: The success of these measurements reinforces the utility of strongly lensed SNe Ia as precision tools for measuring the Hubble constant ( $H_0$ ) and testing dark matter models. The ability to disentangle macrolensing from microlensing in such dense environments is a critical step forward for time-delay cosmography.
JWST Capabilities: The paper showcases the critical role of JWST (spectroscopy and high-resolution imaging) in classifying high-redshift transients and refining lens models, which were previously impossible with HST alone.

In conclusion, SN 2022riv serves as a critical test case, validating that while microlensing introduces significant noise (20–50%), the underlying macrolens models remain robust, and the inclusion of microlensing simulations resolves major discrepancies between theory and observation.