Published April 30, 2026 | Version CC-BY-NC-ND 4.0

Multiscale Modelling of Galaxy Collisions with Integrated Resistive MHD and Stellar Feedback

  • 1. Independent Researcher, Department of Physics, Qazvin, Iran.

Description

Abstract: Predicting the relative roles of gravitational collapse and stellar feedback in star formation within extreme, low-density environments—such as the tidal tails produced by galaxy mergers—remains a fundamental challenge. These environments provide unique natural laboratories for testing star formation theories under conditions analogous to the early universe. However, existing models often fail to reconcile large-scale gravitational dynamics with localized feedback processes in such diffuse media. To bridge this gap, a reproducible, open-sciencebased theoretical framework is presented that integrates public, multi-wavelength observational datasets with high-resolution **resistive magnetohydrodynamic (MHD)** simulations. Our methodology is built on archival data from three flagship observatories: the James Webb Space Telescope (JWST), which is used to study young stellar populations and newly formed clusters. This telescope provides high-resolution infrared imaging and spectroscopy, enabling precise measurements of stellar ages, masses, and dust extinction. - Atacama Large Millimetre/submillimetre Array (ALMA): used to trace cold molecular gas and analyze kinematic structures. These public datasets are used as quantitative constraints in resistive magnetohydrodynamic (MHD) simulations that incorporate magnetic fields, radiative cooling, sub-grid star formation, and stellar feedback, ensuring that the simulation results remain consistent with observational reality. Using the open-source code **PLUTO**, we model the formation of tidal structures while resolving key plasma physics, including **localized resistivity** to capture magnetic reconnection effects. “Synthetic observations” are directly generated from simulation outputs using radiative transfer post-processing, enabling point-by-point comparison with real data. To rigorously quantify agreement between model and observation, we implement a **Bayesian inference framework** that propagates observational uncertainties and yields posterior constraints on key parameters (e.g., magnetic field strength, feedback coupling efficiency). Through this integrated pipeline, the aim is to determine whether star formation efficiency in lowdensity tails is regulated by gravitational confinement from tidal compression or by localized feedback. Expected outcomes include quantitative estimates of virial stability parameters for observed gas complexes, spatial correlation analyses to gauge feedback coupling efficiency, and statistically robust constraints on uncertain model parameters. This framework is fully reproducible: all data are public, simulation codes are opensource, and analysis scripts will be archived with a DOI upon acceptance. By transparently linking theory and observation, this approach provides a methodological blueprint for studying star formation in interacting systems, with direct implications for galaxy evolution models and future observational strategies.

Files

A107306010426.pdf

Files (587.4 kB)

Name Size Download all
md5:b3b78b6a02048d2f3eb3b2085232cdff
587.4 kB Preview Download

Additional details

Identifiers

DOI
10.54105/ijap.A1073.06010426
EISSN
2582-8983

Dates

Accepted
2026-04-15
Manuscript received on 15 November 2025 | First Revised Manuscript received on 14 December 2025 | Second Revised Manuscript received on 18 March 2026 | Manuscript Accepted on 15 April 2026 | Manuscript published on 30 April 2026.

References

  • Kennicutt, R. C., & Evans, N. J. (2012). Star formation in the Milky Way and nearby galaxies. Annual Review of Astronomy and Astrophysics, 50, 531–594. DOI: https://doi.org/10.1146/annurev-astro-081811-125610
  • Renaud, F., Bournaud, F., & Duc, P.-A. (2015). Tidal dwarf galaxies and missing baryons. Monthly Notices of the Royal Astronomical Society, 446(2), 2038–2048. DOI: https://doi.org/10.1093/mnras/stu2208
  • Komarov, I., Kravtsov, A., & Gnedin, N. (2018). Tidal dwarf galaxies in cosmological simulations. Monthly Notices of the Royal Astronomical Society, 478(4), 431–447. DOI: https://doi.org/10.1093/mnras/sty1015
  • Whitmore, B. C., & Schweizer, F. (1995). Hubble Space Telescope observations of young star clusters in NGC 4038/4039, 'the Antennae' galaxies. The Astronomical Journal, 109(3), 960–980. DOI: https://doi.org/10.1086/117337
  • Whitmore, B. C., Zhang, Q., Leitherer, C., Fall, S. M., Schweizer, F., & Miller, B. W. (1999). The luminosity function of young star clusters in "the Antennae" galaxies (NGC 4038/4039). The Astronomical Journal, 118(4), 1551–1576. DOI: https://doi.org/10.1086/301071
  • Whitmore, B. C., Chandar, R., & Fall, S. M. (2007). Star cluster demographics. The Astronomical Journal, 133(3), 1067–1084. https://arxiv.org/abs/astro-ph/0612695
  • Piatti, A. E. (2023). The dual nature of the tidal tails of NGC 5904 (M5). Monthly Notices of the Royal Astronomical Society: Letters, 525(1), L72–L75. DOI: https://doi.org/10.1093/mnrasl/slad098
  • Braine, J., Combes, F., van Driel, W., & Casoli, F. (2001). CO observations of molecular gas in tidal dwarf galaxies. Astronomy & Astrophysics, 378, 443–453. DOI: https://doi.org/10.1051/0004-6361:20011244
  • Delos, M. S., & Schmidt, F. (2022). Time-varying substructure properties. Monthly Notices of the Royal Astronomical Society, 513(3), 3682–3708. DOI: https://doi.org/10.1093/mnras/stac1022
  • Renaud, F., Bournaud, F., & Duc, P.-A. (2021). The virial state of molecular clouds in tidal debris: A comparison with simulations. Monthly Notices of the Royal Astronomical Society, 508(3), 3528– 3542. DOI: https://doi.org/10.1093/mnras/stab2619
  • Toomre, A., & Toomre, J. (1972). Galactic bridges and tails. The Astrophysical Journal, 178, 623–666. DOI: https://doi.org/10.1086/151823
  • Barnes, J. E., & Hernquist, L. (1996). Transformations of galaxies. II. Gasdynamics in merging disk galaxies. The Astrophysical Journal, 471, 115–142. DOI: https://doi.org/10.1086/177957
  • Pillepich, A., Nelson, D., Hernquist, L., Springel, V., Pakmor, R., Torrey, P., Weinberger, R., Vogelsberger, M., & Marinacci, F. (2018). First results from the IllustrisTNG simulations: The stellar mass content of groups and clusters of galaxies. Monthly Notices of the Royal Astronomical Society, 475(1), 648–675. DOI: https://doi.org/10.1093/mnras/stx3112
  • Geng, A., Kotarba, H., Bürzle, F., Dolag, K., Stasyszyn, F., Beck, A., & Nielaba, P. (2012). Magnetic field amplification and X-ray emission in galaxy minor mergers. Monthly Notices of the Royal Astronomical Society, 419(4), 3571–3589. DOI: https://doi.org/10.1111/j.1365-2966.2011.20001.x
  • Mignone, A., Bodo, G., Massaglia, S., Matsakos, T., Tesileanu, O., Zanni, C., & Ferrari, A. (2007). PLUTO: A numerical code for computational astrophysics. The Astrophysical Journal Supplement Series, 170(1), 228–242. DOI: https://doi.org/10.1086/513316
  • Villaescusa-Navarro, F., Anglés-Alcázar, D., Genel, S., Spergel, D. N., Somerville, R. S., Dave, R., Pillepich, A., Hernquist, L., Nelson, D., & Torrey, P. (2021). The CAMELS project: Cosmology and astrophysics with machine-learning simulations. The Astrophysical Journal, 915(1), 71. DOI: https://doi.org/10.3847/1538-4357/abf7ba
  • Agertz, O., Kravtsov, A. V., Leitner, S. N., & Gnedin, N. Y. (2013). Toward a complete accounting of energy and momentum from stellar feedback in galaxy formation simulations. The Astrophysical Journal, 770(1), 25. DOI: https://doi.org/10.1088/0004-637X/770/1/25
  • Wright, A. J., & Hawke, I. (2019). A resistive extension for ideal magnetohydrodynamics. *Monthly Notices of the Royal Astronomical Society*, 491(4), 5510–5523. DOI: https://doi.org/10.1093/mnras/stz2779
  • Schure, K. M., Kosenko, D., Kaastra, J. S., Keppens, R., & Vink, J. (2009). A new radiative cooling curve based on an up-to-date plasma emission code. Astronomy & Astrophysics, 508(2), 751–758. DOI: https://doi.org/10.1051/0004-6361/200912495
  • McMullin, J. P., Waters, B., Schiebel, D., Young, W., & Golap, K. (2007). CASA architecture and applications. In R. A. Shaw, F. Hill, & D. J. Bell (Eds.), Astronomical Data Analysis Software and Systems XVI (Vol. 376, p. 127). Astronomical Society of the Pacific. https://ui.adsabs.harvard.edu/abs/2007ASPC..376..127M/abstract
  • Astropy Collaboration, Robitaille, T. P., Tollerud, E. J., Greenfield, P., Droettboom, M., Bray, E., Aldcroft, T., Davis, M., Ginsburg, A., PriceWhelan, A. M., Kerzendorf, W. E., Conley, A., Crighton, N., Barbary, K., Muna, D., Ferguson, H., Grollier, F., Parikh, M. M., Nair, P. H., … Streicher, O. (2013). Astropy: A community Python package for astronomy. Astronomy & Astrophysics, 558, A33. DOI: https://doi.org/10.1051/0004-6361/201322068
  • Turk, M. J., Smith, B. D., Oishi, J. S., Skory, S., Skillman, S. W., Abel, T., & Norman, M. L. (2011). yt: A Multi-code Analysis Toolkit for Astrophysical Simulation Data. The Astrophysical Journal Supplement Series, 192(1), 9. DOI: https://doi.org/10.1088/0067-0049/192/1/9
  • Dullemond, C. P. (2019). RADMC 3D (Version 2.0) [Software]. GitHub. https://github.com/dullemond/radmc3d-2.0
  • Speagle, J. S. (2020). dynesty: A dynamic nested sampling package for estimating Bayesian posteriors and evidences. Monthly Notices of the Royal Astronomical Society, 493(3), 3132–3158. DOI: https://doi.org/10.1093/mnras/staa278