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Generating LSB-optimised synthetic images for simulated galaxies

Maarten Baes, Peter Camps, Andrea Gebek, Arno Lauwers, Joop Schaye, Paul Vauterin

TL;DR

The paper tackles the challenge of obtaining high signal-to-noise in the low-surface-brightness outskirts of simulated galaxies within Monte Carlo radiative transfer. It introduces an emission-biasing technique in SKIRT where per-particle bias factors scale with the smoothing length $h$ to preferentially boost emission from low-density regions, while conserving energy via photon weight corrections. Application to a Milky-Way-like galaxy from the TNG50 simulation shows that linear or near-linear biasing in $h$ extends the reliable LSB regime, whereas stronger exponents over-boost outskirts and degrade core regions. This simple, computationally inexpensive method enables more accurate and efficient generation of deep synthetic galaxy images for comparison with current and upcoming deep imaging surveys and missions.

Abstract

We introduce an emission-biasing scheme in the SKIRT radiative transfer code that enables efficient generation of synthetic galaxy images optimized for low-surface-brightness (LSB) science. Standard Monte Carlo radiative transfer simulations achieve high signal-to-noise in bright regions but require prohibitively many photon packets to reach reliable depth in galaxy outskirts. By assigning stellar particles bias factors that scale with their smoothing lengths, our method boosts photon emission from low-density regions while conserving energy through weight corrections. Tests on a Milky-Way-like galaxy from the TNG50 cosmological simulation show that bias factors proportional to the smoothing length substantially extend the reliable LSB regime, providing an inexpensive improvement for deep synthetic imaging of simulated galaxies.

Generating LSB-optimised synthetic images for simulated galaxies

TL;DR

The paper tackles the challenge of obtaining high signal-to-noise in the low-surface-brightness outskirts of simulated galaxies within Monte Carlo radiative transfer. It introduces an emission-biasing technique in SKIRT where per-particle bias factors scale with the smoothing length to preferentially boost emission from low-density regions, while conserving energy via photon weight corrections. Application to a Milky-Way-like galaxy from the TNG50 simulation shows that linear or near-linear biasing in extends the reliable LSB regime, whereas stronger exponents over-boost outskirts and degrade core regions. This simple, computationally inexpensive method enables more accurate and efficient generation of deep synthetic galaxy images for comparison with current and upcoming deep imaging surveys and missions.

Abstract

We introduce an emission-biasing scheme in the SKIRT radiative transfer code that enables efficient generation of synthetic galaxy images optimized for low-surface-brightness (LSB) science. Standard Monte Carlo radiative transfer simulations achieve high signal-to-noise in bright regions but require prohibitively many photon packets to reach reliable depth in galaxy outskirts. By assigning stellar particles bias factors that scale with their smoothing lengths, our method boosts photon emission from low-density regions while conserving energy through weight corrections. Tests on a Milky-Way-like galaxy from the TNG50 cosmological simulation show that bias factors proportional to the smoothing length substantially extend the reliable LSB regime, providing an inexpensive improvement for deep synthetic imaging of simulated galaxies.

Paper Structure

This paper contains 4 sections, 3 equations, 1 figure.

Table of Contents

  1. Introduction
  2. The generation of LSB-optimised images
  3. Illustration
  4. Conclusions

Figures (1)

  • Figure 1: $g$-band images and relative error maps for SKIRT simulations of the galaxy TNG000008. Images are shown for face-on and edge-on orientations (rows) and six emission-bias schemes, ranging from no biasing to $b_m \propto h^3$ (columns).