arXiv subjects

Sven Põder

Publications and source records attributed to Sven Põder.

5 recordsLinked to original sources

arXiv2026

Portrait of a Galaxy on FIRE: Is the $α$-bimodality a natural consequence of inside-out disc growth in a hierarchical formation scenario?

The chemical dichotomy in the [$α$/Fe]-[Fe/H] plane is a consequence of the complex processes underlying the formation and evolution of disc galaxies such as observed in the stellar Milky Way disc. We determine what can drive an $α$-bimodality of the disc in a zoom-in hydrodynamical simulated galaxy which has had no major mergers and negligible radial migration. Using a Milky Way-mass galaxy from the FIRE-2 suite of simulations, we analyse gas flows in the disc together with its star formation and merger history, as well as the chemical evolution of the hot corona, to investigate their connection to transitions in the chemo-dynamical structure of the stellar disc and its radial distribution. The simulated galaxy exhibits high and low-$α$ sequences without having experienced major mergers nor significant radial migration. A high-$α$ thick disc forms during the early chaotic clustering phase. Afterwards, as the star formation rate declines, a dip in the stellar number density appears, coinciding with the dilution of the galactic corona by a minor merger, which subsequently halts the rise of [Fe/H] in the disc. Later, accreted gas onto the disc from minor mergers, mildly enhances the star formation rate and generates the low-$α$ sequence in the outer disc, with radial inward flows of this material feeding the low-$α$ inner disc. Furthermore, we find that even at fixed radii, newly formed stars retain a sizable spread in their chemical abundances, reflecting chemical differences between the in-situ and the infalling gas from which they formed, further indicating that instantaneous gas mixing is invalid. Understanding the chemical evolution of stellar discs requires accounting for their accretion merger history and interaction with the surrounding hot corona, as well as the vertical and radial gas flows that redistribute metals within the disc.

astro-ph.GA↗

arXiv2024

On the detection of stellar wakes in the Milky Way: a deep learning approach

Due to poor observational constraints on the low-mass end of the subhalo mass function, the detection of dark matter (DM) subhalos on sub-galactic scales would provide valuable information about the nature of DM. Stellar wakes, induced by passing DM subhalos, encode information about the mass of the inducing perturber and thus serve as an indirect probe for the DM substructure within the Milky Way (MW). Our aim is to assess the viability and performance of deep learning searches for stellar wakes in the Galactic stellar halo caused by DM subhalos of varying mass. We simulate massive objects (subhalos) moving through a homogeneous medium of DM and star particles, with phase-space parameters tailored to replicate the conditions of the Galaxy at a specific distance from the Galactic center. The simulation data is used to train deep neural networks with the purpose of inferring both the presence and mass of the moving perturber, and assess subhalo detectability in varying conditions of the Galactic stellar and DM halos. We find that our binary classifier is able to infer the presence of subhalos, showing non-trivial performance down to a subhalo mass of $5 \times 10^7 \rm \, M_\odot$. We also find that our binary classifier is generalisable to datasets describing subhalo orbits at different Galactocentric distances. In a multiple-hypothesis case, we are able to discern between samples containing subhalos of different masses. Out of the phase-space observables available to us, we conclude that overdensity and velocity divergence are the most important features for subhalo detection performance.

astro-ph.GA↗

arXiv2024

Dark Matter Halo Parameters from Overheated Exoplanets via Bayesian Hierarchical Inference

Dark Matter (DM) can become captured, deposit annihilation energy, and hence increase the heat flow in exoplanets and brown dwarfs. Detecting such a DM-induced heating in a population of exoplanets in the inner kpc of the Milky Way thus provides potential sensitivity to the galactic DM halo parameters. We develop a Bayesian Hierarchical Model to investigate the feasibility of DM discovery with exoplanets and examine future prospects to recover the spatial distribution of DM in the Milky Way. We reconstruct from mock exoplanet datasets observable parameters such as exoplanet age, temperature, mass, and location, together with DM halo parameters, for representative choices of measurement uncertainty and the number of exoplanets detected. We find that detection of $\mathcal{O}(100)$ exoplanets in the inner Galaxy can yield quantitative information on the galactic DM density profile, under the assumption of 10% measurement uncertainty. Even as few as $\mathcal{O}(10)$ exoplanets can deliver meaningful sensitivities if the DM density and inner slope are sufficiently large.

astro-ph.IM↗

arXiv2023

A Bayesian estimation of the Milky Way's circular velocity curve using Gaia DR3

Our goal is to calculate the circular velocity curve of the Milky Way, along with corresponding uncertainties that quantify various sources of systematic uncertainty in a self-consistent manner. The observed rotational velocities are described as circular velocities minus the asymmetric drift. The latter is described by the radial axisymmetric Jeans equation. We thus reconstruct the circular velocity curve between Galactocentric distances from 5 kpc to 14 kpc using a Bayesian inference approach. The estimated error bars quantify uncertainties in the Sun's Galactocentric distance and the spatial-kinematic morphology of the tracer stars. As tracers, we used a sample of roughly 0.6 million stars on the red giant branch stars with six-dimensional phase-space coordinates from Gaia data release 3 (DR3). More than 99% of the sample is confined to a quarter of the stellar disc with mean radial, rotational, and vertical velocity dispersions of $(35\pm 18)\,\rm km/s$, $(25\pm 13)\,\rm km/s$, and $(19\pm 9)\,\rm km/s$, respectively. We find a circular velocity curve with a slope of $0.4\pm 0.6\,\rm km/s/kpc$, which is consistent with a flat curve within the uncertainties. We further estimate a circular velocity at the Sun's position of $v_c(R_0)=233\pm7\, \rm km/s$ and that a region in the Sun's vicinity, characterised by a physical length scale of $\sim 1\,\rm kpc$, moves with a bulk motion of $V_{LSR} =7\pm 7\,\rm km/s$. Finally, we estimate that the dark matter (DM) mass within 14 kpc is $\log_{10}M_{\rm DM}(R<14\, {\rm kpc})/{\rm M_{\odot}}= \left(11.2^{+2.0}_{-2.3}\right)$ and the local spherically averaged DM density is $ρ_{\rm DM}(R_0)=\left(0.41^{+0.10}_{-0.09}\right)\,{\rm GeV/cm^3}=\left(0.011^{+0.003}_{-0.002}\right)\,{\rm M_\odot/pc^3}$. In addition, the effect of biased distance estimates on our results is assessed.

astro-ph.GA↗

arXiv2022

Sensitivity Estimation for Dark Matter Subhalos in Synthetic Gaia DR2 using Deep Learning

The abundance of dark matter (DM) subhalos orbiting a host galaxy is a generic prediction of the cosmological framework, and is a promising way to constrain the nature of DM. In this paper, we investigate the use of machine learning-based tools to quantify the magnitude of phase-space perturbations caused by the passage of DM subhalos. A simple binary classifier and an anomaly detection model are proposed to estimate if stars or star particles close to DM subhalos are statistically detectable in simulations. The simulated datasets are three Milky Way-like galaxies and nine synthetic Gaia DR2 surveys derived from these. Firstly, we find that the anomaly detection algorithm, trained on a simulated galaxy with full 6D kinematic observables and applied on another galaxy, is nontrivially sensitive to the DM subhalo population. On the other hand, the classification-based approach is not sufficiently sensitive due to the extremely low statistics of signal stars for supervised training. Finally, the sensitivity of both algorithms in the Gaia-like surveys is negligible. The enormous size of the Gaia dataset motivates the further development of scalable and accurate data analysis methods that could be used to select potential regions of interest for DM searches to ultimately constrain the Milky Way's subhalo mass function, as well as simulations where to study the sensitivity of such methods under different signal hypotheses.

astro-ph.GA↗