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GOPREAUX I: Open-source Code and Data to Model Multi-wavelength Emission of Extragalactic Transients using Gaussian Processes

This paper introduces GOPREAUX, an open-source Python package that utilizes Gaussian Processes to model and interpolate multi-wavelength emission of extragalactic transients across phase and wavelength, leveraging a curated dataset of nearly 1,300 Swift, ZTF, and archival observations to enable photometric classification and physical parameter inference for high-redshift events.

Original authors: C. Pellegrino, T. A. Pritchard, M. Modjaz, A. Crawford, S. Khakpash, F. Bianco

Published 2026-04-07

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CC BY 4.0

Original authors: C. Pellegrino, T. A. Pritchard, M. Modjaz, A. Crawford, S. Khakpash, F. Bianco

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 night sky as a massive, bustling city where stars are born, live, and die. Sometimes, a star explodes in a spectacular firework show called a transient. For decades, astronomers have been trying to understand these explosions, but they face a huge problem: there are too many of them, and they are too far away to study in detail.

Think of it like trying to understand the behavior of a million different fireworks by only catching a few blurry snapshots of them from a great distance. You can see the flash, but you can't tell if it's a "Type II" firework or a "Type Ib" firework just by looking at the color.

This paper introduces a new tool called GOPREAUX (a fancy acronym that sounds like a robot's name, but stands for Gaussian process Optimized Photometric Regression of Extragalactic Archival Ultraviolet-infrared eXplosions).

Here is how GOPREAUX works, explained simply:

1. The Problem: The "Blurry Photo" Dilemma

Astronomers have two main ways to study these explosions:

Spectroscopy: Taking a detailed "rainbow" picture that tells you exactly what the explosion is made of. This is like reading the recipe of a cake. But it takes a lot of time and powerful telescopes. We can only do this for a few fireworks.
Photometry: Taking simple "black and white" or "color" snapshots through different colored filters (like taking a photo through red, blue, or green sunglasses). This is fast and easy, but it's like looking at a cake through a foggy window. You know it's a cake, but you don't know the flavor.

With new telescopes coming online (like the Rubin Observatory), we are going to see millions of these explosions. We won't have time to take the detailed "rainbow" pictures for all of them. We need a way to guess what kind of explosion it is just by looking at the blurry snapshots.

2. The Solution: The "Smart Time Machine"

GOPREAUX is a piece of open-source computer code (a "smart time machine" for light) that uses a mathematical trick called Gaussian Process Regression.

Instead of trying to guess the physics of the explosion (like "how much radioactive nickel is inside?"), GOPREAUX acts like a super-smart artist who has seen thousands of fireworks.

The Analogy: Imagine you have a giant box of thousands of photos of different types of fireworks. You want to predict what a new firework will look like at a specific moment in time and through a specific color filter.
How it works: GOPREAUX doesn't just look at one photo. It looks at the entire box of photos simultaneously. It learns the patterns: "Oh, Type II explosions usually look bright in blue light 5 days after the boom, but dim in red light."
The Magic: It connects the dots between time (when the explosion happened) and color (what wavelength of light we see). It creates a smooth, 3D "surface" of light that fills in the gaps between the blurry snapshots.

3. The Data: The "Giant Library"

To teach this computer brain, the authors built a massive library. They gathered data on 1,300 different explosions from the past, including:

Supernovae: Exploding stars.
Tidal Disruption Events: When a black hole eats a star.
Fast Blue Optical Transients: Weird, fast-burning explosions.

They collected over 146,000 individual measurements of light, ranging from ultraviolet (invisible to our eyes) to infrared (heat). They cleaned this data, fixed errors, and organized it so the computer could learn from it.

4. What Does It Actually Do?

GOPREAUX does two main things:

Fills in the Blanks: If you have a sparse set of photos of a new explosion (maybe only 3 photos over 10 days), GOPREAUX can predict what the explosion looked like on the days you didn't have photos, and what it would look like through a filter you didn't use.
Predicts the Future: It can guess what an explosion will look like if it were much further away (at a higher redshift). This is crucial because as light travels from far away, it stretches out (redshifts). GOPREAUX can mathematically "stretch" the light curves to see how they will appear to future telescopes.

5. Why Is This Important?

Think of the upcoming Rubin Observatory as a camera that will take a picture of the entire sky every few nights for 10 years. It will find millions of these explosions.

Without GOPREAUX, astronomers would be drowning in data, unable to tell which explosions are interesting and which are boring.
With GOPREAUX, they can instantly compare a new, blurry snapshot against the "Smart Time Machine's" library of templates. It's like having a Shazam for exploding stars. It can say, "That blurry flash looks 90% like a Type II supernova," allowing astronomers to focus their time on the most interesting ones.

Summary

GOPREAUX is a free, open-source tool that uses a massive library of past star explosions to teach a computer how to predict the behavior of future ones. It turns sparse, blurry data into a clear, 3D map of light, helping astronomers classify thousands of cosmic fireworks without needing to take expensive, detailed pictures of every single one. It's the ultimate "pattern recognition" tool for the next generation of sky surveys.

1. Problem Statement

Time-domain astronomy is undergoing a revolution driven by upcoming surveys like the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) and the Nancy Grace Roman Space Telescope. These surveys will detect millions of extragalactic transients, far exceeding the capacity for spectroscopic classification. Consequently, photometric classification and physical parameter inference from sparse, multi-wavelength light curves are becoming critical.

Current challenges include:

Redshift Effects: At higher redshifts ( $z \gtrsim 0.1$ ), rest-frame ultraviolet (UV) and optical emission shifts into the observer-frame optical and infrared. Accurate modeling requires understanding the full Spectral Energy Distribution (SED) evolution across wavelengths.
Data Fragmentation: Existing datasets are often fragmented by instrument or filter, lacking uniform processing across the UV-to-IR spectrum.
Model Limitations: Traditional modeling relies on analytical physics-based models (e.g., radioactive decay, magnetar spin-down) which assume specific functional forms. Many existing machine learning approaches treat filters independently, losing critical inter-filter dependencies and physical correlations.
Lack of Unified Framework: There is no cohesive framework that combines a curated, large-scale archival dataset with non-parametric, data-driven spectrophotometric modeling for diverse transient classes.

2. Methodology

The authors introduce GOPREAUX (Gaussian process Optimized Photometric Regression of Extragalactic Archival Ultraviolet-infrared eXplosions), a Python package utilizing Gaussian Process Regression (GPR) to model transient emission.

A. Data Collection and Reduction

Sample: The dataset comprises 1,297 individual objects across 19 spectroscopic classes, including Type II SNe, Stripped-Envelope SNe (SESN), Superluminous SNe (SLSN), Tidal Disruption Events (TDEs), and Fast Blue Optical Transients (FBOTs).
Sources: Data is aggregated from the Neil Gehrels Swift Observatory (UV/optical backbone), supplemented by ground-based surveys (ZTF, ATLAS, ASAS-SN) and the Open SN Catalog.
Volume: The sample contains over 146,000 photometric detections and 46,000 non-detections.
Reduction Pipeline:
- Swift: Custom UVOT pipeline with automated background and host galaxy subtraction.
- Surveys: Forced photometry from difference images with rigorous outlier rejection (signal-to-noise $\geq 3$ ).
- Corrections: Uniform correction for Milky Way extinction is applied. Host galaxy extinction and $k$ -corrections are intentionally omitted to avoid introducing physical assumptions and biases, treating host extinction as part of the natural population spread.
- Classification: Spectroscopic classes are verified against literature; ambiguous cases are marked as "Transitional" and excluded from template generation to ensure purity.

B. Modeling Framework (GPR)

GOPREAUX employs a 3D Gaussian Process Regression that interpolates emission simultaneously across phase, wavelength, and flux.

Non-Parametric Approach: Unlike analytical models, GPR makes no assumptions about the underlying physics, allowing the data to dictate the shape of the light curve and SED.
Pre-processing Steps:
1. Peak Standardization: Light curves are shifted relative to the time and magnitude of peak brightness in a reference blue filter (rest-frame 4000–5000 Å).
2. Flux Conversion & Warping: Magnitudes are converted to fluxes. An iterative "warping" procedure adjusts effective wavelengths to ensure the convolved synthetic flux matches observed photometry, mitigating issues with filter transmission tails (especially in Swift UV).
3. Residual Calculation: To satisfy GPR's assumption of a zero-mean function, a "template" (polynomial or median fit of the aggregate class) is subtracted from individual light curves. The GPR then models the residuals.
4. Logarithmic Space: Fitting is performed in log-phase and log-wavelength space to mitigate non-stationary behavior (rapid evolution at early times/bluer wavelengths).
Kernel Optimization: Hyperparameters are optimized via marginal likelihood maximization. Final class templates are generated by median-combining the individual GPR fits of all objects in a class.

3. Key Contributions

Open-Source Software (GOPREAUX): A modular Python package that allows users to:
- Ingest raw data from major sources (Swift, ZTF, etc.).
- Perform uniform pre-processing and reduction.
- Generate 3D SED surfaces using GPR.
- Sample probability distributions to generate synthetic photometry.
Curated Dataset: A publicly available, uniformly processed catalog of ~1,300 transients with >146,000 detections spanning UV to Near-Infrared (NIR).
Multi-Wavelength SED Templates: The first set of uniform, non-parametric 3D SED "surfaces" for major transient classes (e.g., SN II, SESN, SLSN-I/II, FBOT), interpolating across phase and wavelength.
Validation: The authors demonstrate robustness through train/test splits, showing residuals generally within $\pm 0.2$ magnitudes, with larger deviations only in data-sparse regions (e.g., Swift UVW2 at late phases).

4. Results

Model Performance: The GPR models successfully track observed photometric evolution within 95% confidence intervals. For example, the SN IIb template accurately captures the double-peaked behavior and early-time diversity.
Uncertainty Quantification: The models provide natural uncertainty estimates. Uncertainties are lowest near peak brightness (~~1 mag) and increase at early times (~~4 mag) due to data scarcity and intrinsic diversity.
Synthetic Data Generation: The models can sample the posterior distribution to generate synthetic light curves and SEDs, essential for simulating survey alerts and testing classification algorithms for LSST and Roman.
Limitations Identified:
- Models are relative to peak brightness, making absolute luminosity comparisons difficult.
- Lack of host extinction correction introduces spread in color/magnitude, particularly in bluer filters.
- The sample is biased toward low-redshift objects (due to Swift requirements), potentially missing redshift-dependent evolution in certain classes (e.g., Type Ia).

5. Significance

GOPREAUX represents a paradigm shift in transient modeling by moving from physics-driven analytical fits to data-driven, non-parametric interpolation. Its significance lies in:

Enabling Future Surveys: It provides the necessary tools to photometrically classify the millions of transients expected from LSST and Roman, particularly those at high redshift where rest-frame UV is redshifted into the optical/IR.
Bridging the Gap: By modeling the full SED evolution, it allows for the prediction of emission in unobserved bands, crucial for multi-messenger astronomy and real-time follow-up.
Community Resource: By releasing both the code and the reduced data, it establishes a "gold standard" for future modeling efforts, allowing researchers to bypass data reduction hurdles and focus on analysis.
Flexibility: The modular design allows the community to easily add new transient classes or specific objects, fostering continuous improvement of the models as new data becomes available.

In conclusion, GOPREAUX provides the essential infrastructure for the next generation of time-domain astronomy, transforming sparse, multi-wavelength observations into robust physical models without relying on restrictive theoretical assumptions.