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ABSTRACT The dark matter (DM) distribution in dwarf galaxies provides crucial insights into both structure formation and the particle nature of DM. GraphNPE (Graph Neural Posterior Estimator), first introduced in Nguyen et al. (2023), is a novel simulation-based inference framework that combines graph neural networks and normalizing flows to infer the DM density profile from line-of-sight stellar velocities. Here, we apply GraphNPE to satellite dwarf galaxies in the FIRE-2 Latte simulation suite of Milky Way-mass haloes, testing it against both Cold and Self-Interacting DM scenarios. Our method demonstrates superior precision compared to conventional Jeans-based approaches, recovering DM density profiles to within the 95 per cent confidence level even in systems with as few as 30 tracers. Moreover, we present the first evaluation of mass modelling methods in constraining two key parameters from realistic simulations: the peak circular velocity, $$V_\mathrm{max}$$, and the peak virial mass, $$M_\mathrm{200m}^\mathrm{peak}$$. Using only line-of-sight velocities, GraphNPE can reliably recover both $$V_\mathrm{max}$$ and $$M_\mathrm{200m}^\mathrm{peak}$$ within our quoted uncertainties, including those experiencing tidal effects ($$\gtrsim 63~{{\rm per\ cent}}$$ of systems are recovered within our 68 per cent confidence intervals and $$\gtrsim 92~{{\rm per\ cent}}$$ within our 95 per cent confidence intervals). The method achieves $$10-20~{{\rm per\ cent}}$$ accuracy in $$V_\mathrm{max}$$ recovery, while $$M_\mathrm{200m}^\mathrm{peak}$$ is recovered to $$0.1-0.4 \, \mathrm{dex}$$ accuracy. This work establishes GraphNPE as a robust tool for inferring DM density profiles in dwarf galaxies, offering promising avenues for constraining DM models. The framework’s potential extends beyond this study, as it can be adapted to non-spherical and disequilibrium models, showcasing the broader utility of simulation-based inference and graph-based learning in astrophysics. 

Many data analytics and scientific applications rely on data transformation tasks, such as encoding, decoding, parsing of structured and unstructured data, and conversions between data formats and layouts. Previous work has shown that data transformation can represent a performance bottleneck for data analytics workloads. The transducers computational abstraction can be used to express a wide range of data transformations, and recent efforts have proposed configurable engines implementing various transducer models (from finite state transducers, to pushdown transducers, to extended models). This line of research, however, is still at an early stage. Notably, expressing data transformation using transducers requires a paradigm shift, impacting programmability. To address this problem, we propose a programming framework to map data transformation tasks onto a variety of transducer models. Our framework includes: (1) a platform agnostic programming language (xPTLang) to code transducer programs using intuitive programming constructs, and (2) a compiler that, given an xPTLang program, generates efficient transducer processing engines for CPU and GPU. Our compiler includes a set of optimizations to improve code efficiency. We demonstrate our framework on a diverse set of data transformation tasks on an Intel CPU and an Nvidia GPU. 

Scientific simulations running on HPC facilities generate massive amount of data, putting significant pressure onto supercomputers’ storage capacity and network bandwidth. To alleviate this problem, there has been a rich body of work on reducing data volumes via error-controlled lossy compression. However, fixed-ratio compression is not very well-supported, not allowing users to appropriately allocate memory/storage space or know the data transfer time over the network in advance. To address this problem, recent ratio-controlled frameworks, such as FXRZ, have incorporated methods to predict required error bound settings to reach a user-specified compression ratio. However, these approaches fail to achieve fixed-ratio compression in an accurate, efficient and scalable fashion on diverse datasets and compression algorithms. This work proposes an efficient, scalable, ratio-controlled lossy compression framework (CAROL). At the core of CAROL are four optimization strategies that allow for improving the prediction accuracy and runtime efficiency over state-of-the-art solutions. First, CAROL uses surrogate-based compression ratio estimation to generate training data. Second, it includes a novel calibration method to improve prediction accuracy across a variety of compressors. Third, it leverages Bayesian optimization to allow for efficient training and incremental model refinement. Forth, it uses GPU acceleration to speed up prediction. We evaluate CAROL on four compression algorithms and six scientific datasets. On average, when compared to the state-of-the-art FXRZ framework, CAROL achieves 4 × speedup in setup time and 36 × speedup in inference time, while maintaining less than 1% difference in estimation accuracy. 

ABSTRACT The mass assembly history (MAH) of dark matter haloes plays a crucial role in shaping the formation and evolution of galaxies. MAHs are used extensively in semi-analytic and empirical models of galaxy formation, yet current analytic methods to generate them are inaccurate and unable to capture their relationship with the halo internal structure and large-scale environment. This paper introduces florah (FLOw-based Recurrent model for Assembly Histories), a machine-learning framework for generating assembly histories of ensembles of dark matter haloes. We train florah on the assembly histories from the Gadget at Ultra-high Redshift with Extra Fine Time-steps and vsmdplN-body simulations and demonstrate its ability to recover key properties such as the time evolution of mass and concentration. We obtain similar results for the galaxy stellar mass versus halo mass relation and its residuals when we run the Santa Cruz semi-analytic model on florah-generated assembly histories and halo formation histories extracted from an N-body simulation. We further show that florah also reproduces the dependence of clustering on properties other than mass (assembly bias), which is not captured by other analytic methods. By combining multiple networks trained on a suite of simulations with different redshift ranges and mass resolutions, we are able to construct accurate main progenitor branches with a wide dynamic mass range from $z=0$ up to an ultra-high redshift $$z \approx 20$$, currently far beyond that of a single N-body simulation. florah is the first step towards a machine learning-based framework for planting full merger trees; this will enable the exploration of different galaxy formation scenarios with great computational efficiency at unprecedented accuracy. 

Abstract We introduce the DaRk mattEr and Astrophysics with Machine learning and Simulations (DREAMS) project, an innovative approach to understanding the astrophysical implications of alternative dark matter (DM) models and their effects on galaxy formation and evolution. The DREAMS project will ultimately comprise thousands of cosmological hydrodynamic simulations that simultaneously vary over DM physics, astrophysics, and cosmology in modeling a range of systems—from galaxy clusters to ultra-faint satellites. Such extensive simulation suites can provide adequate training sets for machine-learning-based analyses. This paper introduces two new cosmological hydrodynamical suites of warm dark matter (WDM), each comprising 1024 simulations generated using thearepocode. One suite consists of uniform-box simulations covering a ${\left(25h^{-1}\mathrm{Mpc}\right)}^3$volume, while the other consists of Milky Way zoom-ins with sufficient resolution to capture the properties of classical satellites. For each simulation, the WDM particle mass is varied along with the initial density field and several parameters controlling the strength of baryonic feedback within the IllustrisTNG model. We provide two examples, separately utilizing emulators and convolutional neural networks, to demonstrate how such simulation suites can be used to disentangle the effects of DM and baryonic physics on galactic properties. The DREAMS project can be extended further to include different DM models, galaxy formation physics, and astrophysical targets. In this way, it will provide an unparalleled opportunity to characterize uncertainties on predictions for small-scale observables, leading to robust predictions for testing the particle physics nature of DM on these scales. 

We investigate the beneficial effects of rapid thermal annealing on structure and photoluminescence of PbSe thin films on GaAs (001) grown below 150 °C, with a goal of low temperature integration for infrared optoelectronics. Thin films of PbSe deposited on GaAs by molecular beam epitaxy are epitaxial at these reduced growth temperatures, yet the films are highly defective with a mosaic grain structure with low angle and dendritic boundaries following coalescence. Remarkably, we find that rapid thermal annealing for as short as 180 s at temperatures between 300 and 425 °C in nitrogen ambient leads to extensive re-crystallization and transformation of these grain boundaries. The annealing at the same time dramatically improves the band edge luminescence at 3.7 μm from previously undetectable levels to nearly half as intense as our best conventionally grown PbSe films at 300 °C. We show using an analysis of laser pump-power dependent photoluminescence measurements that this dramatic improvement in the photoluminescence intensity is due to a reduction in the trap-assisted recombination. However, we find it much less correlated with improved structural parameters determined by x-ray diffraction rocking curves, thereby pointing to the importance of eliminating point defects over extended defects. Overall, the success of rapid thermal annealing in improving the luminescent properties of low growth temperature PbSe is a step toward the integration of PbSe infrared optoelectronics in low thermal budget, back end of line compatible fabrication processes.