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Ria Ramkumar

Publications and source records attributed to Ria Ramkumar.

2 recordsLinked to original sources

Characterising magnetic fields at the onset of star cluster formation: From giant molecular clouds to infrared dark clumps

The role of magnetic fields in the observed inefficiency of star formation in Galactic molecular clouds is a widely debated topic, with the past decade seeing an explosion of observational characterisation of magnetic fields in star-forming regions. However, few have studied the spatial evolution of magnetic fields from entire molecular clouds down to parsec-size cluster-forming clumps. In this work, the plane-of-sky morphology of the magnetic fields of eight infrared dark clumps and their parent molecular clouds are derived from Planck and JCMT POL-2 polarisation data (including some from the BISTRO survey). We also use this data to test multiple methods of calculating B-field strengths. Our study shows that the morphologies of magnetic fields in clumps and their parent molecular clouds systematically, and significantly, differ, supported by a line-of-sight correction of the cloud-scale magnetic fields using the velocity gradient technique. We find a strong correlation between gas velocity dispersion and the alignment of magnetic field lines with column density gradients from scales of tens of parsecs to a few parsecs. This correlation is clear evidence of a link between the kinematic properties of the gas and the dynamical importance of magnetic fields. Conversely, the higher magnetic field strengths we measure on cloud scale compared to clump scale contradict magnetic flux conservation and thus highlight the unreliability of such measurements. Altogether, our analysis supports a picture in which magnetic fields have little impact on the dynamical evolution of cluster-forming clumps but do play a role in providing support on larger scales.

astro-ph.GA

PySSED: an automated method of collating and fitting stellar spectral energy distributions

Stellar atmosphere modelling predicts the luminosity and temperature of a star, together with parameters such as the effective gravity and the metallicity, by reproducing the observed spectral energy distribution. Most observational data comes from photometric surveys, using a variety of passbands. We herein present the Python Stellar Spectral Energy Distribution (PySSED) routine, designed to combine photometry from disparate catalogues, fit the luminosity and temperature of stars, and determine departures from stellar atmosphere models such as infrared or ultraviolet excess. We detail the routine's operation, and present use cases on both individual stars, stellar populations, and wider regions of the sky. PySSED benefits from fully automated processing, allowing fitting of arbitrarily large datasets at the rate of a few seconds per star.

astro-ph.IM