CV
The pdf file is a summary of the resume.
Basics
Name | Soumyaranjan Dash |
Label | Solar Physics Postdoctoral Fellow |
dashs@hawaii.edu; dash.soumya922@gmail.com | |
Phone | (808) 743-2675 |
Url | https://sr-dash.github.io |
Summary | A theoretical physicist with a strong background in solar physics, specializing in understanding the origin and dynamics of solar magnetic fields. I have experience in developing and running numerical simulations, analyzing observational data, and writing scientific papers. I am passionate about understanding the fundamental processes that govern the behavior of the Sun and other stars. |
Work
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2023.03 - Present Solar Physics Postdoctoral Fellow
Institute for Astronomy, University of Hawaii
Conducting research on the origin and dynamics of solar magnetic fields using numerical simulations and observational data analysis. Writing scientific papers and presenting results at conferences.
- Solar Physics
- Magnetic Fields
- Numerical Simulations
- Observational Data Analysis
- Scientific Writing
Education
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2016.07 - 2022.11 Kolkata, India
PhD
IISER Kolkata, West Bengal, India
Astrophysics
- PhD Thesis: MODELLING THE EVOLUTION OF SOLAR CORONAL MAGNETIC FIELDS AND ITS HELIOSPHERIC CONSEQUENCES
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2013.07 - 2015.06 Dharamshala, India
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2010.06 - 2013.07 Cuttack, India
Publications
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11/2024 Ensemble Kalman Filter Data Assimilation into the Surface Flux Transport Model to Infer Surface Flows: An Observing System Simulation Experiment
The Astrophysical Journal
We present an observing system simulation experiment to assess the feasibility of assimilating surface magnetic field observations into the surface flux transport model using the ensemble Kalman filter.
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11/2023 Long-term forcing of the Sun's coronal field, open flux, and cosmic ray modulation potential during grand minima, maxima, and regular activity phases by the solar dynamo mechanism
Monthly Notices of the Royal Astronomical Society
We investigate the impact of the Sun's magnetic field field, open flux, and cosmic ray modulation potential during grand minima, maxima, and regular activity phases using solar dynamo model and PFSS extrapolations.
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02/2020 Prediction of the Sun's Coronal Magnetic Field and Forward-modeled Polarization Characteristics for the 2019 July 2 Total Solar Eclipse
The Astrophysical Journal
We present a forward modeling study of the Sun's coronal magnetic field and polarization characteristics for the 2019 July 2 total solar eclipse using the SFT simulations and PFSS model.
Skills
Astrophysics | |
Solar Physics | |
Magnetic Fields | |
Numerical Simulations | |
Observational Data Analysis | |
Polarization | |
Data Assimilation | |
Data Visualization |
Languages
Odia | |
Native speaker |
English | |
Fluent |
Hindi | |
Fluent |
Interests
Numerical Modelling | |
PLUTO | |
BATSRUS/SWMF | |
Ensemble Kalman Filter |
Data Analysis | |
IDL | |
Python | |
Matlab | |
Jupyter Notebook |
Scientific Writing | |
LaTeX | |
Overleaf | |
MS Word |
Data Visualization | |
Matplotlib | |
Seaborn | |
Paraview | |
ViSIt | |
IDL | |
TecPlot |
References
Dr Dibyendu Nandy | |
Proffessor, Indian Institute of Science Education and Research Kolkata, India |
Dr Xudong Sun | |
Associate Proffessor, Department of Astronomy, University of Hawaii, USA |
Dr Mausumi Dikpati | |
Senior Scientist, High Altitude Observatory, National Center for Atmospheric Research, USA |
Projects
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Solar surface magnetic field evolution
Developed a surface flux transport model to simulate the evolution of the Sun's surface magnetic field over multiple solar cycles.
- SFT
- Data Assimilation
- Farside Magnetic fields
- Ensemble Kalman Filter
- Global solar surface flows
-
Solar coronal magnetic field dynamics
Utilizing SFT model generated surface megnatic fields, global coronal magnetic field configuration can be inferred using global coronal magnetic field.
- PFSS
- SFT
- BATSRUS/SWMF
-
Coronal magnetic fields
Observations of solar corona at selective emission lines are used for constraining the coronal magnetic field configuration. Polarization vectors are computed using global MHD model outputs and forward modeled to compare with observations.
- pyCELP
- FORWARD
- PFSS
- SFT
- BATSRUS/SWMF