Superphot+: Realtime Fitting and Classification of Supernova Light Curves

The Big Picture

Imagine trying to identify a million strangers in a crowd, not by talking to them, but purely by watching how they move. That’s roughly the challenge facing astronomers today. Every year, telescopes detect tens of thousands of stellar explosions called supernovae, each one a unique cataclysm that can outshine an entire galaxy.

To truly understand what kind of explosion you’re looking at, you need a chemical fingerprint. Astronomers get this by pointing a spectrograph at each supernova, an instrument that splits light into its component wavelengths, revealing which elements are burning. That process is laborious and can cover only about 10% of detected events.

Things are about to get dramatically worse. The Vera C. Rubin Observatory will flood the astronomical community with roughly 100 times more supernova detections than current surveys. The telescope time and equipment needed for spectroscopic follow-up won’t scale anywhere near that fast. By some estimates, 99.9% of supernovae detected by Rubin’s Legacy Survey of Space and Time (LSST) will never receive a traditional spectroscopic classification.

A team led by Kaylee de Soto at the Center for Astrophysics | Harvard & Smithsonian has built a tool to handle that flood. Their package, Superphot+, classifies supernovae using only the shape and color of their light curves (graphs tracking how a supernova brightens and fades over days and weeks), no spectrum required. It runs fast enough for real time.

Key Insight: Superphot+ achieves 83% overall accuracy classifying five types of supernovae using nothing but the light curve, making it one of the few classifiers that can operate without redshift information at scale.

How It Works

The pipeline has two stages: fit the raw light curve data to a mathematical model, then feed those model parameters into a machine learning classifier.

The fitting stage uses a parametric model originally developed for the earlier Superphot pipeline. Given brightness measurements over time in multiple color bands (ZTF observes in red and green), Superphot+ finds the best parameters describing a light curve’s rise, peak, and decay. It does this with nested sampling, a statistical technique that systematically explores all plausible parameter combinations rather than sampling at random. Fitting each light curve takes only seconds, which matters for a real-time system.

The second stage is where machine learning takes over. Those best-fit parameters (how sharply a supernova rises, how quickly it fades, how its color evolves) become input features for a gradient-boosted machine (GBM), an ensemble method that builds many decision trees sequentially, each correcting the errors of the last. The team trained this classifier on 6,061 ZTF supernovae with confirmed spectroscopic labels, spanning five classes:

• Type Ia: thermonuclear explosions of white dwarf stars, the “standard candles” of cosmology
• Type II: core-collapse explosions of massive stars with hydrogen envelopes
• Type Ib/c: stripped-envelope core-collapse explosions
• Type IIn: explosions interacting with surrounding circumstellar material
• SLSN-I: superluminous supernovae, the brightest explosions known

One important design choice was handling class imbalance. Type Ia supernovae dominate the training set; rarer types like SLSNe are scarce. The team synthetically generated extra training examples of underrepresented classes, a technique called oversampling, so the classifier doesn’t just default to shouting “Type Ia!” at everything.

The headline numbers: without any redshift information (a measure of how fast a galaxy is receding, used to estimate distance), Superphot+ achieves a class-averaged F1-score of 0.61 ± 0.02 and overall accuracy of 0.83 ± 0.01. The F1-score penalizes the classifier for ignoring rare classes, so 0.61 across five unequal categories is a meaningful result.

Add redshift as an extra feature and those numbers jump to 0.71 ± 0.02 and 0.88 ± 0.01. The gap is real but not catastrophic, which is precisely the point. Redshifts are often unavailable for the most exotic, distant, or host-galaxy-free supernovae, so a capable redshift-free classifier fills a genuine gap.

Why It Matters

Superphot+ isn’t a research demo. It’s already running. The pipeline operates in real time through the ANTARES Broker, one of the alert-filtering systems that processes ZTF’s nightly stream of detections. It has already assigned probabilistic class labels to 3,558 ZTF transients that looked supernova-like but lacked spectroscopic confirmation, giving astronomers a starting point for allocating follow-up resources.

When compared to the independent ALeRCE classifier (another redshift-free system), the two agree on 82% of spectroscopically confirmed events and 72% of unconfirmed ones. That level of agreement suggests both pipelines are picking up real physical signal, not noise.

The bigger story is preparation for Rubin. LSST will observe in six photometric bands, more than ZTF’s two, and the team built Superphot+ for straightforward adaptation to that richer dataset. In a world where millions of supernovae go unclassified each year, tools like this become the primary scientific record. The classifications Superphot+ assigns will shape which events get studied in detail, which cosmological samples get assembled, and what we learn about stellar life cycles and the expansion history of the universe.

Bottom Line: Machine learning on light curve shapes alone can classify supernovae with better than 80% accuracy, and Superphot+ is already doing it on live telescope data, ready to scale to the Rubin deluge.