Understanding the multi-wavelength thermal dust polarisation from the Orion molecular cloud in light of the radiative torque paradigm

Dust grains play a key role in various astrophysical processes and serve as indicators of interstellar medium structures, density, and mass. Understanding their physical properties and chemical composition is a crucial goal in astrophysics. Dust polarisation is a valuable tool for studying these properties. The radiative torque (RAT) paradigm, which includes radiative torque alignment (RAT-A) and radiative torque disruption (RAT-D), is essential to interpreting the dust polarisation data and constraining the fundamental properties of dust grains. However, it has been used primarily to interpret observations at a single wavelength. In this study, we analyse the thermal dust polarisation spectrum obtained from observations with SOFIA/HAWC+ and JCMT/POL-2 in the Orion molecular cloud 1 (OMC-1) region and compare the observational data with our numerical results using the RAT paradigm. In general, we show that the dense gas exhibits a positive spectral slope, whereas the warm regions show a negative one. We demonstrate that a one-layer dust (one-phase) model can only reproduce the observed spectra at certain locations and cannot match those with prominent V-shaped spectra (for which the degree of polarisation initially decreases with wavelength from 54 to ~300µm and then increases at longer wavelengths). To address this, we improved our model by incorporating two dust components (warm and cold) along the line of sight, resulting in a two-phase model. This improved model successfully reproduces the V-shaped spectra. The best model corresponds to a mixture composition of silicate and carbonaceous grains in the cold medium. Finally, by assuming the plausible model of grain alignment, we were able to infer the inclination angle of the magnetic fields in OMC-1. This approach is an important step towards a better understanding the physics of grain alignment and constraining 3D magnetic fields using dust polarisation spectra.