We study Diffusion Schrödinger Bridge (DSB) models in the context of dynamical astrophysical systems, specifically tackling observational inverse prediction tasks within Giant Molecular Clouds (GMCs) for star formation. We introduce the AstroDSB model, a variant of DSB with the pairwise domain assumption tailored for astrophysical dynamics.

We present a neural-network emulator for the thermal and chemical evolution in Population III star formation. The emulator accurately reproduces the thermochemical evolution over a wide density range spanning 21 orders of magnitude (10$^{-3}$-10$^{18}$ cm$^{-3}$), tracking six primordial species: H, H$_2$, e$^{-}$, H$^{+}$, H$^{-}$, and H$_2^{+}$. To handle the broad dynamic range, we partition the density range into five subregions and train separate deep operator networks (DeepONets) in each region. When applied to randomly sampled thermochemical states, the emulator achieves relative errors below 10% in over 90% of cases for both temperature and chemical abundances (except for the rare species H$_2^{+}$). The emulator is roughly ten times faster on a CPU and more than 1000 times faster for batched predictions on a GPU, compared with conventional numerical integration. Furthermore, to ensure robust predictions under many iterations, we introduce a novel timescale-based update method, where a short-timestep update of each variable is computed by rescaling the predicted change over a longer timestep equal to its characteristic variation timescale. In one-zone collapse calculations, the results from the timescale-based method agree well with traditional numerical integration even with many iterations at a timestep as short as 10$^{-4}$ of the free-fall time. This proof-of-concept study suggests the potential for neural network-based chemical emulators to accelerate hydrodynamic simulations of star formation.

We study Diffusion Schrödinger Bridge (DSB) models in the context of dynamical astrophysical systems, specifically tackling observational inverse prediction tasks within Giant Molecular Clouds (GMCs) for star formation. We introduce the Astro-DSB model, a variant of DSB with the pairwise domain assumption tailored for astrophysical dynamics. By investigating its learning process and prediction performance in both physically simulated data and in real observations (the Taurus B213 data), we present two main takeaways. First, from the astrophysical perspective, our proposed paired DSB method improves interpretability, learning efficiency, and prediction performance over conventional astrostatistical and other machine learning methods. Second, from the generative modeling perspective, probabilistic generative modeling reveals improvements over discriminative pixel-to-pixel modeling in Out-Of-Distribution (OOD) testing cases of physical simulations with unseen initial conditions and different dominant physical processes. Our study expands research into diffusion models beyond the traditional visual synthesis application and provides evidence of the models' learning abilities beyond pure data statistics, paving a path for future physics-aware generative models which can align dynamics between machine learning and real (astro)physical systems.

ABSTRACT Data-driven astrophysics currently relies on the detection and characterisation of correlations between objects' properties, which are then used to test physical theories that make predictions for them. This process fails to utilise information in the data that forms a crucial part of the theories' predictions, namely which variables are directly correlated (as opposed to accidentally correlated through others), the directions of these determinations, and the presence or absence of confounders that correlate variables in the dataset but are themselves absent from it. We propose to recover this information through causal discovery, a well-developed methodology for inferring the causal structure of datasets that is however almost entirely unknown to astrophysics. INTRODUCTION Understanding the physical processes that shape galaxies is a central goal of astrophysics. Empirical progress has traditionally relied on identifying correlations between observed properties, which can then be interpreted in light of theoretical models for galaxy formation and used to constrain them. The advent of large surveys and powerful machine learning techniques has greatly expanded our ability to find such statistical associations, uncovering intricate patterns across high-dimensional parameter spaces. However, correlation alone cannot determine causal influences among variables: which properties are actually responsible for determining others, in what direction this influence goes, and whether there exist confounding variables that are not included in the dataset but influence those that are.

Galaxies are often modelled as composites of separable components with distinct spectral signatures, implying that different wavelength ranges are only weakly correlated. They are not. We present a data-driven model which exploits subtle correlations between physical processes to accurately predict infrared (IR) WISE photometry from a neural summary of optical SDSS spectra. The model achieves accuracies of $\chi^2_N \approx 1$ for all photometric bands in WISE, as well as good colors. We are also able to tightly constrain typically IR-derived properties, e.g. the bolometric luminosities of AGN and dust parameters such as $\mathrm{q_{PAH}}$. We find that current SED-fitting methods are incapable of making comparable predictions, and that model misspecification often leads to correlated biases in star-formation rates and AGN luminosities. To help improve SED models, we determine what features of the optical spectrum are responsible for our improved predictions, and identify several lines (CaII, SrII, FeI, [OII] and H$\alpha$), which point to the complex chronology of star formation and chemical enrichment being incorrectly modelled.

Understanding how galaxies form and evolve is at the heart of modern astronomy. With the advent of large-scale surveys and simulations, remarkable progress has been made in the last few decades. Despite this, the physical processes behind the phenomena, and particularly their importance, remain far from known, as correlations have primarily been established rather than the underlying causality. We address this challenge by applying the causal inference framework. Specifically, we tackle the fundamental open question of whether galaxy formation and evolution depends more on nature (i.e., internal processes) or nurture (i.e., external processes), by estimating the causal effect of environment on star-formation rate in the IllustrisTNG simulations. To do so, we develop a comprehensive causal model and employ cutting-edge techniques from epidemiology to overcome the long-standing problem of disentangling nature and nurture. We find that the causal effect is negative and substantial, with environment suppressing the SFR by a maximal factor of $\sim100$. While the overall effect at $z=0$ is negative, in the early universe, environment is discovered to have a positive impact, boosting star formation by a factor of $\sim10$ at $z\sim1$ and by even greater amounts at higher redshifts. Furthermore, we show that: (i) nature also plays an important role, as ignoring it underestimates the causal effect in intermediate-density environments by a factor of $\sim2$, (ii) controlling for the stellar mass at a snapshot in time, as is common in the literature, is not only insufficient to disentangle nature and nurture but actually has an adverse effect, though (iii) stellar mass is an adequate proxy of the effects of nature. Finally, this work may prove a useful blueprint for extracting causal insights in other fields that deal with dynamical systems with closed feedback loops, such as the Earth's climate.

Stars are formed by molecular gas and dust coalescing in space. These molecular gases are so dilute and cold that they are invisible to the human eye, but they do emit faint radio waves that can be observed by radio telescopes. Observing from Earth, a lot of matter lies ahead and behind these molecular clouds, and these overlapping features make it difficult to determine their distance and physical properties, such as size and mass. So, even though our galaxy, the Milky Way, is the only galaxy close enough to make detailed observations of molecular clouds in the universe, it has been very difficult to investigate the physical properties of molecular clouds in a cohesive manner from large-scale observations. A research team led by Dr. Shinji Fujita from the Osaka Metropolitan University Graduate School of Science, identified about 140,000 molecular clouds in the Milky Way galaxy, which are areas of star formation, from large-scale data of carbon monoxide molecules, observed in detail by the Nobeyama 45-m radio telescope.

Cosmologist Francisco "Paco" Villaescusa-Navarro has a problem. "We are spending billions of dollars in ground and space telescopes to decipher the mysteries of the universe," he explains, "but we are missing most of the information that the surveys contain." The issue is that in any survey, most of the information is at the very smallest scales. For example, if you look at a picture of a forest, you'll get some information, like a rough idea of how many trees are in there. Once you zoom in a bit, you can see the individual trees and get more information – say, the different species and their heights.

Traditionally, we've learned about nature through observation. Think of Johannes Kepler poring over Tycho Brahe's tables of planetary positions and trying to discern the underlying pattern. Science has also advanced through simulation. An astronomer might model the movement of the Milky Way and its neighboring galaxy, Andromeda, and predict that they'll collide in a few billion years. Both observation and simulation help scientists generate hypotheses that can then be tested with further observations. Generative modeling differs from both of these approaches.

An image of Messier 101, the Pinwheel Galaxy, made with the Hubble Space Telescope. The bright blue clumps in the spiral arms are sites of recent star formation. Black holes with masses equivalent to millions of suns do put a brake on the birth of new stars, say astronomers. Using machine learning and three state-of-the-art simulations to back up results from a large sky survey, the researchers resolve a 20-year long debate on the formation of stars. Joanna Piotrowska, a PhD student at the University of Cambridge, presented the new work on July 20, 2021, at the virtual National Astronomy Meeting (NAM 2021).