arXiv subjects

Dennis Fremstad

Publications and source records attributed to Dennis Fremstad.

4 recordsLinked to original sources

arXiv2026

Predicting the final states of binary-single scattering with machine learning

Context. Binary-single encounters are particularly frequent in dense stellar environments, where they play a central role in shaping the dynamical evolution of their host systems. However, predicting their final outcomes remains an open question due to the intrinsic chaotic nature of the three-body problem. This challenge motivates the adoption of data-driven machine learning (ML) methods. Aims. We investigate whether ML can predict the final outcomes of binary-single encounters from initial conditions alone. Methods. We generated 5.8 million binary-single scattering simulations using the REBOUND N-body package with the IAS15 integrator. A cascaded binary classification strategy, comprising four sequential XGBoost classifiers, and a single multi-class model were trained on the synthetic dataset and compared. Results. The cascaded strategy outperforms the single multi-class model across all metrics. F1-scores for the cascaded models exceed 0.92, with precision-recall area under the curve (PR-AUC) values reaching 0.99, compared to 0.95 for the multi-class model. Feature importance analysis identifies encounter timescale, binary hardness, and mass ratio as key predictors. Misclassification analysis shows that prediction failures concentrate near chaotic boundaries where the outcome is sensitive to small perturbations. Speed benchmarks demonstrate that the cascaded models are up to 300 times faster than direct N-body integrations. However, all models fail to generalize to new datasets, highlighting a key limitation. Conclusions. This study demonstrates that, for any specific environment and data distribution, the proposed cascaded ML strategy provides a robust and rapid framework for predicting binary-single scattering outcomes.

astro-ph.GA↗

arXiv2026

Separate Universe Super-Resolution Emulator

We present a machine-learning model for generating super-resolution $N$-body simulations with non-vanishing spatial curvature, conditioned on a given low-resolution field, $\Omega_k$, $\Omega_\mathrm{m}$, $\sigma_8$, $h$, and redshift. By upscaling the resolution of $N$-body simulations, such models can drastically reduce the computational cost of producing high-resolution simulations suitable for modelling current and future surveys of large-scale structure. Our model is trained as a generative adversarial network, allowing injected noise to be interpreted as stochastic structure and enabling the generation of an ensemble of plausible high-resolution realisations. We evaluate the model performance by comparing key cosmological summary statistics in the generated simulations to their high-resolution counterparts. We find that the model accurately reproduces large-scale statistics, robustly recovering most of the power that was missing from the low-resolution input, but exhibits a residual suppression of power on small scales of up to $\sim 10\%$ at $k \sim 1\,h\,\mathrm{Mpc}^{-1}$. The abundance of halos around $10^{14}\,M_\odot$ is affected at a similar level, and we find that the profiles of these halos have a lower central density. Although the overall performance is decent, we anticipate that the fidelity of the generative model can be further increased with more and better training data, as well as through improvements in the model architecture and training process. To show a production-scale use case, we apply our model to upscale the resolution of a light cone from a large-volume $N$-body simulation with spatial curvature, producing a first-of-its-kind catalogue that simultaneously captures geometric effects at large scales and accurate nonlinear structure at small scales.

astro-ph.CO↗

arXiv2025

Emulating the Non-Linear Matter Power-Spectrum in Mixed Axion Dark Matter Models

In order to constrain ultra light dark matter models with current and near future weak lensing surveys we need the predictions for the non-linear dark matter power-spectrum. This is commonly extracted from numerical simulations or from using semi-analytical methods. For ultra light dark matter models such numerical simulations are often very expensive due to the need of having a very low force-resolution often limiting them to very small simulation boxes which do not contain very large scales. In this work we take a different approach by relying on fast, approximate $N$-body simulations. In these simulations, axion physics are only included in the initial conditions, allowing us to run a large number of simulations with varying axion and cosmological parameters. From our simulation suite we use machine learning tools to create an emulator for the ratio of the dark matter power-spectrum in mixed axion models - models where dark matter is a combination of CDM and axion - to that of $\Lambda$CDM. The resulting emulator only needs to be combined with existing emulators for $\Lambda$CDM to be able to be used in parameter constraints. We compare the emulator to semi-analytical methods, but a more thorough test to full simulations to verify the true accuracy of this approach is not possible at the present time and is left for future work.

astro-ph.CO↗

arXiv2023

An overview of HMI off-disk flare observations

Context: White-light continuum observations of solar flares often have coronal counterparts, including the classical ``white-light prominence'' (WLP) phenomenon. Aims: Coronal emissions by flares, seen in white-light continuum, have only rarely been reported previously. We seek to use modern data to understand the morphology of WLP events. Methods: We have identified a set of 14 examples of WLP detected by the HMI (Heliospheric and Magnetic Imager) experiment on board SDO (the Solar Dynamics Observatory satellite), using a new on-line catalogue covering 2011-2017. These invariably accompanied white-light flares (WLF) emission from the lower atmosphere by flares near the limb, as identified by hard X-ray images from RHESSI (the Reuven Ramaty High Energy Spectroscopic Imager). HMI provides full Stokes information, and we have used the linear polarisations (Q~and~U) to distinguish Thomson scattering from cool material. Results: The event morphologies fit roughly into three categories: Ejection, Loop, and Spike, but many events show multiple phenomena. Conclusions: The coronal white-light continuum, observed by HMI analogously to the observations made by a coronagraph, detects many examples of coronal emission and dynamics. Using the Stokes linear polarisation, we estimate the masses of hot coronal plasma in 11 of the 14 events and find them to be similar to typical CME masses, but not exceeding 10$^{15}$\,g. We note that the HMI observations do not occult the bright solar disk and were not designed for coronal observations, resulting in relatively low signal-to-noise ratios. We therefore believe that future such observations with better optimisation will be even more fruitful.

astro-ph.SR↗