IMFIT-MW: Multiwavelength photometric decomposition with IMFIT
Abstract
Photometric decomposition is often used to study galaxy structure by modelling surface brightness distribution in its components. However, most of the relevant codes are designed for single-band analysis. I present IMFIT-MW: a lightweight, open source Python tool that extends IMFIT capabilities to simultaneously model multiwavelength images. IMFIT-MW is able to set shared parameters between filters and to impose smooth variation of them with wavelength. IMFIT-MW achieves this by using IMFIT to produce model brightness distributions for given parameters, whereas hyperparameters defining their wavelength dependence are fitted by an external optimizer. The code is available on GitHub (https://github.com/IVChugunov/IMFIT-MW). I provide a few examples of use, showing that IMFIT-MW matches GALFITM in single-thread performance and can have advantage of multithreading. In a broader sense, I demonstrate the possibility to extend IMFIT features even without modifying its source code.
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BibTeX
@misc{chugunov2026imfitmw,
title = {{IMFIT-MW: Multiwavelength photometric decomposition with IMFIT}},
author = {Ilia V. Chugunov},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.10231},
eprint = {2610.10231},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.10231},
note = {CC BY-NC-SA 4.0},
}
RIS
TY - UNPB
TI - IMFIT-MW: Multiwavelength photometric decomposition with IMFIT
AU - Ilia V. Chugunov
PY - 2026
DA - 2026/10/07
PB - arXiv
DO - 10.48550/arxiv.2610.10231
UR - https://arxiv.org/abs/2610.10231
AB - Photometric decomposition is often used to study galaxy structure by modelling surface brightness distribution in its components. However, most of the relevant codes are designed for single-band analysis. I present IMFIT-MW: a lightweight, open source Python tool that extends IMFIT capabilities to simultaneously model multiwavelength images. IMFIT-MW is able to set shared parameters between filters and to impose smooth variation of them with wavelength. IMFIT-MW achieves this by using IMFIT to produce model brightness distributions for given parameters, whereas hyperparameters defining their wavelength dependence are fitted by an external optimizer. The code is available on GitHub (https://github.com/IVChugunov/IMFIT-MW). I provide a few examples of use, showing that IMFIT-MW matches GALFITM in single-thread performance and can have advantage of multithreading. In a broader sense, I demonstrate the possibility to extend IMFIT features even without modifying its source code.
ER -