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Connecting the rich, complex outputs of galaxy formation simulations with real-world astronomical observations is a fundamental challenge in modern astrophysics. In our new paper, led by Christopher C. Lovell, we introduce Synthesizer (2508.03888), a powerful, open-source software package designed to bridge this gap by rapidly generating synthetic astronomical observables. This work, co-authored by William J. Roper, Aswin P. Vijayan, Stephen M. Wilkins, Sophie Newman, and Louise Seeyave, provides the community with a versatile tool for both forward and inverse modeling of astrophysical phenomena.

Synthesizer is built on a core philosophy that prioritizes speed and adaptability, distinguishing it from more computationally intensive, high-fidelity radiative transfer codes. Its design is guided by four key principles:

  • Fast: To enable the rapid exploration of large parameter spaces and the generation of extensive training sets for machine learning applications.
  • Flexible: To allow users to easily modify and test the impact of various physical assumptions, such as different stellar population synthesis (SPS) models, dust attenuation laws, or photoionization parameters.
  • Modular: To provide reusable components that can be combined to construct complex modeling pipelines tailored to specific scientific questions.
  • Extensible: To create a framework that can be easily expanded with new functionality by the user community.

Core Architecture

The power of Synthesizer lies in its modular architecture, which combines three main components to produce a wide range of synthetic data products:

  1. Galaxy Objects: These are the foundational containers for the physical properties of galaxies. They can be particle-based, directly using outputs from cosmological hydrodynamic simulations like Illustris-TNG (10.1093/mnras/stx3112), or parametric, describing star formation and metal enrichment histories through analytical functions. This dual approach allows for broad applicability, from detailed simulation post-processing to more idealized theoretical studies.

  2. Grids: These are pre-computed, multi-dimensional libraries of emission spectra. Synthesizer comes packaged with grids from various well-established SPS models (e.g., BPASS, BC03, FSPS) and can also handle grids for Active Galactic Nuclei (AGN) emission. These grids can be further processed through photoionization codes like Cloudy to self-consistently include nebular emission.

  3. EmissionModels: This is the heart of Synthesizer’s flexibility. An EmissionModel is a user-defined recipe that dictates how Galaxy properties are mapped onto Grids to generate emission. It allows for the construction of complex scenarios, such as combining stellar and AGN light, or implementing sophisticated dust models like the two-component screen for birth clouds and the diffuse ISM, first popularized by Charlot & Fall (10.1086/309250).

From Physics to Observables

By combining these components, Synthesizer can generate a suite of realistic observables, enabling direct, apples-to-apples comparisons with observational data. The outputs include:

  • High-resolution spectra and integrated photometry.
  • Emission line luminosities and diagnostic diagrams (e.g., BPT).
  • Mock multi-band imaging, including support for Point Spread Function (PSF) convolution and noise modeling.
  • Spatially resolved data cubes, mimicking Integral Field Unit (IFU) observations.

This comprehensive suite of tools empowers researchers to robustly test the predictions of their theoretical models and quantify the impact of different physical assumptions on observable properties.

A New Era of Simulation-Based Inference

Perhaps one of the most significant applications of Synthesizer is in the burgeoning field of Simulation-Based Inference (SBI). As detailed in foundational reviews (10.1073/pnas.1912789117), SBI methods leverage machine learning to perform robust parameter inference in scenarios where the likelihood is intractable, a common problem in astrophysics. These techniques require vast training sets of synthetic data, a task for which Synthesizer is perfectly suited. Its speed allows for the efficient generation of millions of mock observations, paving the way for advanced inference on complex cosmological and galaxy formation models, as has already been demonstrated with the CAMELS simulation suite (2411.13960).

Synthesizer is a free and open-source platform, and we encourage the community to use, test, and contribute to its development. We hope it will become an indispensable tool for interpreting the wealth of data from current and future astronomical surveys.

Christopher C. Lovell

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