In this research work, we present our open-source Geant4-based Monte-Carlo simulation application, called RadField3D, for generating threedimensional radiation field datasets for dosimetry. Accompanying, we introduce a fast, machine-interpretable data format with a Python API for easy integration into neural network research, that we call RadFiled3D. Both developments are intended to be used to research alternative radiation simulation methods using deep learning. All data used for our validation (measured and simulated), along with our source codes, are published in separate repositories.

Stereotactic body radiation therapy (SBRT) refers to focusing high-energy rays in three-dimensional space on the tumor lesion area, reducing the dose received by surrounding normal tissues, which can effectively improve the local control rate of the tumor and reduce the probability of complications. With the comprehensive development of medical imaging, radiation biology and other disciplines, this less-fractional, high-dose radiotherapy method has been increasingly developed and applied in clinical practice. The background, radio-biological basis, key technologies and main equipment of SBRT are discussed, and its future development direction is prospected.

Galaxy formation and evolution critically depend on understanding the complex photo-chemical processes that govern the evolution and thermodynamics of the InterStellar Medium (ISM). Computationally, solving chemistry is among the most heavy tasks in cosmological and astrophysical simulations. The evolution of such non-equilibrium photo-chemical network relies on implicit, precise, computationally costly, ordinary differential equations (ODE) solvers. Here, we aim at substituting such procedural solvers with fast, pre-trained, emulators based on neural operators. We emulate a non-equilibrium chemical network up to H$_2$ formation (9 species, 52 reactions) by adopting the DeepONet formalism, i.e. by splitting the ODE solver operator that maps the initial conditions and time evolution into a tensor product of two neural networks. We use $\texttt{KROME}$ to generate a training set spanning $-2\leq \log(n/\mathrm{cm}^{-3}) \leq 3.5$, $\log(20) \leq\log(T/\mathrm{K}) \leq 5.5$, $-6 \leq \log(n_i/n) < 0$, and by adopting an incident radiation field $\textbf{F}$ sampled in 10 energy bins with a continuity prior. We separately train the solver for $T$ and each $n_i$ for $\simeq 4.34\,\rm GPUhrs$. Compared with the reference solutions obtained by $\texttt{KROME}$ for single zone models, the typical precision obtained is of order $10^{-2}$, i.e. the $10 \times$ better with a training that is $40 \times$ less costly with respect to previous emulators which however considered only a fixed $\mathbf{F}$. The present model achieves a speed-up of a factor of $128 \times$ with respect to stiff ODE solvers. Our neural emulator represents a significant leap forward in the modeling of ISM chemistry, offering a good balance of precision, versatility, and computational efficiency.

In recent years, researchers have increasingly focused on the NeRF[1] in this regard. NeRF provides an accurate and simple method to represent 3D scenes useing an implicit function based on MLPand, and has achieved satisfactory rendering quality in 3D reconstruction tasks. Current efforts aim to extend the original NeRF to different situations, such as scene synthesis[2, 3], dynamic scenes[4, 5], large scene reconstruction[6, 7] or rapid convergence[8, 9], among others. Since NeRF was published in 2020, the NeRF paper has been cited more than thousands of times in the past three years. In addition, researchers have made numerous improvements to the NeRF technology. Some work have focused on optimizing the rendering speed of NeRF[10, 11], while others have explored different application scenarios[12, 13]. Futhermore, there have been efforts to extended NeRF for scene inpainting[14, 15], texture synthesis[16], handing complex scenes[17], and addressing more challenging problems.

Molecular abundances in protoplanetary disks are highly sensitive to the local physical conditions, including gas temperature, gas density, radiation field, and dust properties. Often multiple factors are intertwined, impacting the abundances of both simple and complex species. We present a new approach to understanding these chemical and physical interdependencies using machine learning. Specifically we explore the case of CO modeled under the conditions of a generic disk and build an explanatory regression model to study the dependence of CO spatial density on the gas density, gas temperature, cosmic ray ionization rate, X-ray ionization rate, and UV flux. Our findings indicate that combinations of parameters play a surprisingly powerful role in regulating CO compared to any singular physical parameter. Moreover, in general, we find the conditions in the disk are destructive toward CO. CO depletion is further enhanced in an increased cosmic ray environment and in disks with higher initial C/O ratios. These dependencies uncovered by our new approach are consistent with previous studies, which are more modeling intensive and computationally expensive. Our work thus shows that machine learning can be a powerful tool not only for creating efficient predictive models, but also for enabling a deeper understanding of complex chemical processes.

Accurately quantifying the impact of radiation feedback in star formation is challenging. To address this complex problem, we employ deep learning techniques, denoising diffusion probabilistic models (DDPMs), to predict the interstellar radiation field (ISRF) strength based on three-band dust emission at 4.5 \um, 24 \um, and 250 \um. We adopt magnetohydrodynamic simulations from the STARFORGE (STAR FORmation in Gaseous Environments) project that model star formation and giant molecular cloud (GMC) evolution. We generate synthetic dust emission maps matching observed spectral energy distributions in the Monoceros R2 (MonR2) GMC. We train DDPMs to estimate the ISRF using synthetic three-band dust emission. The dispersion between the predictions and true values is within a factor of 0.1 for the test set. We extended our assessment of the diffusion model to include new simulations with varying physical parameters. While there is a consistent offset observed in these out-of-distribution simulations, the model effectively constrains the relative intensity to within a factor of 2. Meanwhile, our analysis reveals weak correlation between the ISRF solely derived from dust temperature and the actual ISRF. We apply our trained model to predict the ISRF in MonR2, revealing a correspondence between intense ISRF, bright sources, and high dust emission, confirming the model's ability to capture ISRF variations. Our model robustly predicts radiation feedback distribution, even in complex, poorly constrained ISRF environments like those influenced by nearby star clusters. However, precise ISRF predictions require an accurate training dataset mirroring the target molecular cloud's unique physical conditions.