Dr Payel Das
Dr Payel Das is a researcher in the fields of astrophysics and artificial intelligence. She is currently a UKRI Future Leaders Fellow in the Astrophysics research group at the University of Surrey, working on the GLEAM project. Das is also a Manager and Principal Research Staff Member in AI research at IBM Watson, New York.
Biography
Das has a PhD in Astrophysics from the Max Planck Institute for Extraterrestrial Physics in Germany. She left the field of astrophysics for a few years and worked on designing homes for energy efficiency and comfort at University College London (UCL) before returning to astrophysics as a Postdoctoral Research Associate (PDRA) at the University of Oxford.
Das's research interests include galactic archaeology, using stars as fossil histories to uncover the formation histories of galaxies. She also has an interest in machine learning tools that can interpret big datasets.
Publications
Das has published extensively in the fields of astrophysics and AI. Some notable publications include:
- MADE: a spectroscopic mass, age, and distance estimator for red giant stars with Bayesian machine learning (2019). In this paper, Das presents a new approach (MADE) that estimates the mass, age, and distance of red giant stars using a combination of astrometric, photometric, and spectroscopic data.
- Using NMAGIC to probe the dark matter halo and orbital structure of the X-ray bright, massive elliptical galaxy, NGC 4649 (2011). In this work, Das and her colleagues create dynamical models of the massive elliptical galaxy NGC 4649 using the N-body made-to-measure code, NMAGIC. They explore a range of potentials and find that the properties of the hot gas derived from X-rays in the outer halo have considerable uncertainties.
- The impact of binary stars on the dust and metal evolution of galaxies (2023). This publication presents detailed implementations of binary stellar evolution and dust production and destruction in the cosmological semi-analytic galaxy evolution simulation, L-Galaxies.
- Characterizing stellar halo populations - I. An extended distribution function for halo K giants (2016). In this paper, Das and her co-author fit an extended distribution function (EDF) to K giants in the Sloan Extension for Galactic Understanding and Exploration survey. The EDF depends on [Fe/H] in addition to actions and encodes the entanglement of metallicity with dynamics within the Galaxy's stellar halo.
- Ages and kinematics of chemically selected, accreted Milky Way halo stars (2020). Here, Das and her colleagues exploit the [Mg/Mn]-[Al/Fe] chemical abundance plane to identify nearby halo stars in the 14th data release from the APOGEE survey that have been accreted onto the Milky Way. They find a 'blob' of 856 likely accreted stars and estimate new ages for them.
Other Work
In addition to her research, Das has been involved in various outreach activities and has given tutorials in mechanics, special relativity, and quantum mechanics while at the University of Oxford. She is also a member of the U.S. Artificial Intelligence Safety Institute Consortium.
Dr Payel Das
Overview
Dr Payel Das is a researcher in the fields of astrophysics, galactic archaeology, and artificial intelligence. Her work focuses on understanding galaxy formation and the application of AI in scientific research. Das is currently a UKRI Future Leaders Fellow in the Astrophysics research group at the University of Surrey, working on the GLEAM project. She previously held positions at the University of Oxford, University College London (UCL), and IBM.
Education and Career
Das obtained her PhD in Astrophysics from the Max Planck Institute for Extraterrestrial Physics in Germany. After completing her PhD, she briefly left astrophysics to explore energy-efficient home design at UCL. She then returned to astrophysics as a Postdoctoral Research Associate (PDRA) at the University of Oxford. Das is currently a research fellow at the University of Surrey, where she combines equilibrium dynamical models, machine learning tools, and evolutionary biology methods to study the evolutionary histories and dark matter contents of galaxies.
Research and Publications
Das has published extensively in the fields of astrophysics, machine learning, and building science. Her notable works include:
- MADE: a spectroscopic mass, age, and distance estimator for red giant stars with Bayesian machine learning (2019): In this paper, Das and her colleague Jason L. Sanders propose a new approach, called MADE, for estimating the mass, age, and distance of red giant stars using a combination of astrometric, photometric, and spectroscopic data. The method utilizes a Bayesian artificial neural network (ANN) that learns from and replaces stellar isochrones. The ANN is trained on a sample of red giant stars with mass estimates from asteroseismology. The paper demonstrates that the approach can reduce uncertainties in mass and age estimates, with fractional uncertainties of <10% and 10-25% respectively.
- The impact of binary stars on the dust and metal evolution of galaxies (2023): This paper, co-authored by Das and several colleagues, presents detailed implementations of binary stellar evolution and dust production and destruction in the cosmological semi-analytic galaxy evolution simulation, L-Galaxies. The study finds that binary stars have a negligible impact on stellar and gas masses but can affect carbon and nitrogen enrichment in galaxies. The work highlights the need for enhanced dust production in early simulations to match observed dust masses at higher redshifts.
- Using NMAGIC to probe the dark matter halo and orbital structure of the X-ray bright, massive elliptical galaxy, NGC 4649 (2011): In this paper, Das and her colleagues use the N-body made-to-measure code, NMAGIC, to create dynamical models of the elliptical galaxy NGC 4649. They explore a range of potentials based on previous X-ray observations and globular cluster (GC) velocity data. The study finds that planetary nebula (PN) velocities are sensitive tracers of the mass and prefer a less massive halo than that derived from the GC mass profile. The results suggest that GCs may form a dynamically distinct system.
Dr Payel Das
Overview
Dr Payel Das is a UKRI Future Leaders Fellow in the Astrophysics research group at the University of Surrey, working on the GLEAM project. She is also a Principal Research Staff Member and Manager of Trusted AI at IBM Research.
Research Interests
Das' research interests lie in galactic archaeology, which involves exploiting stars as fossil histories of the formation histories of galaxies. She has a PhD in Astrophysics from the Max Planck Institute for Extraterrestrial Physics in Germany.
Notable Works
- Das has been involved in a number of research projects, including work on the dynamical properties of stars within galaxies, the relationship between stellar and neutral gas properties of field faint dwarf galaxies, and the impact of binary stars on the dust and metal evolution of galaxies.
- Das has also published on the use of machine learning in astronomy, such as her work on a Bayesian artificial neural network that learns from and replaces stellar isochrones to generate mass, age, and distance estimates of red giant stars.
- In addition to her astronomical research, Das has also published on building energy simulation and indoor air quality modelling, with a focus on the impact of housing on health and sustainability.
Employment History
- Research Staff Member and Manager, Trusted AI, IBM Research
- UKRI Future Leaders Fellow, Astrophysics research group, University of Surrey
- PDRA, Astrophysics, University of Oxford
- UCL
- Max Planck Institute for Extraterrestrial Physics, Germany
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