Related papers: Exploring Magnetic Fields in Molecular Clouds thro…
The magnetic field strength in interstellar clouds can be estimated indirectly by using the spread of dust polarization angles ($\delta \theta$). The method developed by Davis 1951 and by Chandrasekhar and Fermi 1953 (DCF) assumes that…
Polarimeric maps have been used on the characterization of the magnetic field in molecular clouds. However, it is difficult to determine the 3-dimensional properties of these regions from the projected maps. In that case, numerical…
We introduce two new ways of obtaining the strength of plane-of-sky (POS) magnetic field by simultaneous use of spectroscopic Doppler-shifted lines and the information on magnetic field direction. The latter can be obtained either through…
Denoising diffusion probabilistic models (DDPM) are a class of generative models which have recently been shown to produce excellent samples. We show that with a few simple modifications, DDPMs can also achieve competitive log-likelihoods…
Advances in microscopy imaging enable researchers to visualize structures at the nanoscale level thereby unraveling intricate details of biological organization. However, challenges such as image noise, photobleaching of fluorophores, and…
We observe the magnetic field morphology towards a nearby star-forming filamentary cloud, G202.3+2.5, by the JCMT/POL-2 850 {\mu}m thermal dust polarization observation with an angular resolution of 14.4" (~0.053 pc). The average magnetic…
Analysis of galaxy--galaxy strong lensing systems is strongly dependent on any prior assumptions made about the appearance of the source. Here we present a method of imposing a data-driven prior / regularisation for source galaxies based on…
We discuss an extension to the Chandrasekhar-Fermi method for the evaluation of the mean magnetic field strength in molecular clouds to cases where the spatial orientation of the field is known. We apply the results to M17, using previously…
The dynamical state of star-forming molecular clouds cannot be understood without determining the structure and strength of their magnetic fields. Measurements of polarized far-infrared radiation from thermally aligned dust grains are used…
Far-infrared (FIR) dust polarimetry enables the study of interstellar magnetic fields via tracing of the polarized emission from dust grains that are partially aligned with the direction of the field. The advent of high quality polarimetric…
Denoising diffusion probabilistic models (DDPMs) (Ho et al. 2020) have shown impressive results on image and waveform generation in continuous state spaces. Here, we introduce Discrete Denoising Diffusion Probabilistic Models (D3PMs),…
Linearly polarized emission from dust grains and molecular spectroscopy is an effective probe of the magnetic field topology in the interstellar medium and molecular clouds. The longstanding Davis-Chandrasekhar-Fermi (DCF) method and the…
3D Galactic magnetic fields are critical for understanding the interstellar medium, Galactic foreground polarization, and the propagation of ultra-high-energy cosmic rays. Leveraging recent theoretical insights into anisotropic…
Diffusion models have emerged as a popular family of deep generative models (DGMs). In the literature, it has been claimed that one class of diffusion models -- denoising diffusion probabilistic models (DDPMs) -- demonstrate superior image…
Out-of-distribution detection is crucial to the safe deployment of machine learning systems. Currently, unsupervised out-of-distribution detection is dominated by generative-based approaches that make use of estimates of the likelihood or…
We expand on the dispersion analysis of polarimetry maps toward applications to interferometry data. We show how the filtering of low-spatial frequencies can be accounted for within the idealized Gaussian turbulence model, initially…
Recent studies of the diffuse parts of molecular clouds have revealed the presence of parallel, ordered low-density filaments termed striations. Flows along magnetic field lines, Kelvin-Helmholtz instabilities and hydromagnetic waves are…
The currently most viable methods to estimate magnetic field strengths in molecular cloud cores are Zeeman measurements and the Chandrasekhar-Fermi (CF) method. The CF-method estimates magnetic field strengths from polarimetry and relies on…
Methods for out-of-distribution (OOD) detection that scale to 3D data are crucial components of any real-world clinical deep learning system. Classic denoising diffusion probabilistic models (DDPMs) have been recently proposed as a robust…
This Letter introduces an approach for precisely designing surface friction properties using a conditional generative machine learning model, specifically a diffusion denoising probabilistic model (DDPM). We created a dataset of synthetic…