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Trifid Nebula in 3D: Fascinating Complexity of Star Birth

The Trifid Nebula is one of the most beautiful star-forming regions in the sky. Each star-forming regions is different and the overall formation and evolution of these regions is fairly well understood. However, they still hide from us one of the big riddles of astrophysics.

The formation of individual or groups of stars is still not fully understood, especially how diffuse interstellar gas turns into dense, collapsing clouds that form stars. While gravity is a key driver, the roles of turbulence, magnetic fields, and radiation in either aiding or resisting collapse are still debated. It's also unclear why star formation is so inefficient—why so little of the available gas in galaxies becomes stars.

Another puzzle is how stars end up with their final masses. In crowded, chaotic regions where stars form, like in the Trifid Nebula, it's not well known what controls whether a star grows into a small red dwarf or a massive giant. Feedback from young stars—like radiation and stellar winds—can disrupt the process, but how these forces balance with ongoing growth remains an open question.

Apart from their scientific importance, star-forming regions are very beautiful colored regions in our Milky Way and easily observed in nearby galaxies. With it’s complex mixture of luminous gas and dust, the Trifid Nebula was a big challenge to model in 3D.

Based on a wide range of scientific literature, with physical lighting simulations we were able to derive a realistic 3D-structure for the nebula. With ilumbra’s optimized nesting environment (.ONE) we were able to provide a very high resolution for volumetric models in small regions of interest, such as the stellar jets from the young stellar objects in the southern region of the nebula.

Modelling the Trifid in 3D Key information for the model of the Trifid Nebula comes from infrared observations from the Spitzer and Herschel space telescopes, which show that the dark dust lanes in front of the nebula are most of the dust and there isn't much high-density dust on the other side. This is consistent with it being a "Champaign flow" away from us, similar to the Orion Nebula but seen from the opposite side.

Such a Champaign flow is reminiscent of a volcanic eruption, leaving behind a cavity. The escaping gas in this cavity is heated to 10.000 degrees by the ultraviolet radiation from the hot newborn stars. The increased pressure due to the hundred fold temperature rise is the cause for the gas outflow.

A key feature and challenge for the visualization was the high-resolution region around the "Unicorn" with the two small YSO jets. Here the constraints for the structure and the position were hard to get and the optically thick illumination was useful. So we did a lot of illumination simulations. Volumetric visualization of this region at adequate resolution was achieved using several nesting levels of resolution in our Optimized Nesting Environment (.ONE).

Note also that the images that we used for the "look" are highly variable among them because of the different processing methods, which we can, of course, not apply to our data in the same way. This is mostly due to the different dynamic range of the brightness of the different parts in the photographs and simulated object.

The scientific research for this model
A fundamental ingredient of all models made by ilumbra is their astrophysical background. We incorporate as much of the astronomical research information in the form of observations and theory as possible.

For our model of the Trifid Nebula we have incorporated information from original research papers on the following key topics:

1. Hubble Space Telescope and high-quality amateur images.

2. Velocity information for the “unicorn” jet.

3. Theory of star forming regions, including “Champaign-flows”.

4. Scattering and illumination simulations.

5. Infrared space observations from Spitzer and Herschel

The main sources of this information comes from the following research papers:

Baes, M., et al., 2022, A&A 659, A149

Kalari, et al., 2021, ApJ 921 81

Kuhn, et al., 2019, ApJ 870 32

Kuhn, et al., 2021, A&A 651, L10

Kuhn, et al.,2022, ApJ 937 46