How Mergers and Flybys Shape Azimuthal Age Patterns in Spiral Galaxies

TL;DR

This study addresses how spiral-arm azimuthal age patterns arise and evolve under environmental perturbations. Using five Milky Way-mass discs from the Auriga L3 simulations, the authors identify spiral-arm ridgelines with a ridgeline-walking algorithm and quantify azimuthal age variations through metrics $Δτ$ and $f_{\mathrm{non-overlap}}$, tracking mergers and fly-bys via merger trees. They find that most snapshots exhibit a younger leading edge consistent with density wave theory inside the co-rotation radius, but gas-rich interactions can erase or modify these patterns, with recovery on roughly $600$ Myr timescales and occasional cases where the age distributions differ primarily in width (Case iii). The results illuminate the transient influence of environment on spiral-arm age structure and provide a framework for interpreting observations in the context of spiral-arm theories and galactic interactions.

Abstract

Spiral structures are one of the most common features in galaxies, yet their origins and evolution remain debated. Stellar age distributions offer crucial insights into galaxy evolution and star formation, though environmental effects can obscure the intrinsic age patterns. Using the Auriga cosmological gravo-magnetohydrodynamical zoom-in simulations, we investigate the azimuthal age distribution of young stars (<2 Gyr) in a sample of five Milky Way-mass spiral galaxies over the past 5 Gyr. We quantify the age gradients across spiral arms using the mean age offset ($Δτ$) and the non-overlap fraction ($f_{non-overlap}$). We further analyse the impact of mergers and fly-by events on the age gradients. Our results show that Auriga spiral galaxies generally feature younger stars in their leading edges compared to the trailing edges, with a typical $Δτ$ between 30 and 80 Myr. However, gas-rich interactions can disrupt this age offset, resulting in similar age distributions on each side of the spiral arms. In three snapshots, we observe similar mean ages on both sides of spiral arms but differing age distribution broadness, coinciding with satellite interactions crossing the host galaxy's disc plane. Our simulation data suggest that the typical azimuthal age variation recovers within ~600 Myr after galaxy interactions. This work highlights the transient role of environmental interactions in shaping spiral arm age patterns.

Figures (19)

• Figure 1: Young star (age $<2$ Gyr) mass map of Halo 23 at $z = 0$. The spiral arms identified by the algorithm (Sec. \ref{['sec:arm_def']}) are presented as purple pixels in the right panel.
• Figure 2: Phase diagram of Halo 23 at $z = 0$, colour-code by the young star ($<2$ Gyr) mass after subtracting the radial gradient. We use the moving average of each 3-pixel bin to represent the radial gradient. The x-axis is the azimuth while the y-axis shows the logarithm of the radial distance (in units of kpc). An ideal spiral arm following Eq. \ref{['eq:arm1']} is a straight line in the phase diagram. We use an automatic algorithm, ridgeline walking, to identify pixels on the spiral arms, shown as green lines.
• Figure 3: This schematic illustrates the four-step process of the ridgeline walking algorithm used to automatically identify spiral arms. The algorithm locates spiral arms on an azimuth $-$ radial distance diagram, where colour represents the young star mass after subtracting the radial gradient. The starting anchor, marked by a black star symbol, is set at the brightest pixel in the middle radius (10kpc $\pm$ 1kpc). From this anchor, the algorithm searches for the next localized maximum and moves toward increasing azimuth, then returns to the anchor to walk towards decreasing azimuth, outlining the brightest spiral arm pixels. After masking out the brightest spiral arm, the algorithm repeats this process to identify the second brightest spiral arm. Appendix \ref{['sec:all_images']} presents the young star maps for all five halos over the past 5Gyr, with spiral arm definitions overlaid (red lines).
• Figure 4: The parameter $\mathrm{\Delta\mathrm{age}}$ (stellar age with the radial gradient subtracted) varies with the azimuthal distance $\mathrm{\Delta\phi}$ to the spiral arms. Spaxels on the leading (trailing) edge are assigned with negative (positive) $\mathrm{\Delta\phi}$. The region $-25^\circ <$$\mathrm{\Delta\phi}$$< 25^\circ$ is highlighted, where the influence of the spiral arms on the age pattern is most significant. Only the coloured pixels within this range are used to quantify azimuthal variations at each snapshot (Fig. \ref{['fig:age_dist']}). The solid white line represents the median value within each moving 20$^\circ$ bin.
• Figure 5: Left: Three cases of $\mathrm{\Delta\mathrm{age}}$ distributions at the leading and trailing edges. Case i: The $f_{\mathrm{non-overlap}}$ is large with a significant $\mathrm{\Delta\tau}$, indicating an evident age gradient across the spiral arms. Case ii: Both $f_{\mathrm{non-overlap}}$ and $\mathrm{\Delta\tau}$ are small, suggesting little to no azimuthal age variation. Case iii: The $f_{\mathrm{non-overlap}}$ is large, but the $\mathrm{\Delta\tau}$ is near zero, indicating distinct age distributions on each side of the spiral arms, with the difference mainly laying in the tail of the distribution. Right: Radial profiles of $\Delta$age (age residual after removing radial medians) for three cases, colour-coded by the location relative to the spiral arms. This panel illustrates how $f_{\mathrm{non-overlap}}$ and $\mathrm{\Delta\tau}$ statistically represent the azimuthal variation in stellar ages. In Case i, the leading edge (purple) is consistently older at most radii; in Case ii, both sides exhibit similar ages; and in Case iii, while the median ages are comparable, the trailing-edge distribution (purple scatters) is more dispersed.