
Dr Payel Das
About
Biography
I have a PhD in Astrophysics from the Max Planck Institute for Extraterrestrial Physics in Germany. I then left Astrophysics for a few years and explored how we can optimally design homes for energy efficiency and comfort at UCL before returning to Astrophysics as a PDRA at the University of Oxford. I am currently a UKRI Future Leaders Fellow in the Astrophysics research group at Surrey, working on the GLEAM project.
News
In the media
ResearchResearch interests
My research interests lie in unveiling the evolutionary histories and dark matter contents of nearby galaxies through forensic studies of the chemical and dynamical properties of the stars within them. I use an interdisciplinary approach, combining equilibrium dynamical models that connect stellar motions to dark matter, machine learning tools that interpret big datasets quickly, and methods based on evolutionary biology to decipher high-dimensional datasets.
Research interests
My research interests lie in unveiling the evolutionary histories and dark matter contents of nearby galaxies through forensic studies of the chemical and dynamical properties of the stars within them. I use an interdisciplinary approach, combining equilibrium dynamical models that connect stellar motions to dark matter, machine learning tools that interpret big datasets quickly, and methods based on evolutionary biology to decipher high-dimensional datasets.
Publications
Since chemical abundances are inherited between generations of stars, we use them to trace the evolutionary history of our Galaxy. We present a robust methodology for creating a phylogenetic tree, a biological tool used for centuries to study heritability. Combining our phylogeny with information on stellar ages and dynamical properties, we reconstruct the shared history of 78 stars in the Solar Neighbourhood. The branching pattern in our tree supports a scenario in which the thick disk is an ancestral population of the thin disk. The transition from thick to thin disk shows an anomaly, which we attribute to a star formation burst. Our tree shows a further signature of the variability in stars similar to the Sun, perhaps linked to a minor star formation enhancement creating our Solar System. In this paper, we demonstrate the immense potential of a phylogenetic perspective and interdisciplinary collaboration, where with borrowed techniques from biology we can study key processes that have contributed to the evolution of the Milky Way.
We exploit the [Mg/Mn]-[Al/Fe] chemical abundance plane to help identify nearby halo stars in the 14th data release from the APOGEE survey that have been accreted on to the Milky Way. Applying a Gaussian Mixture Model, we find a ‘blob’ of 856 likely accreted stars, with a low disc contamination rate of ∼7 per cent. Cross-matching the sample with the second data release from Gaia gives us access to parallaxes and apparent magnitudes, which place constraints on distances and intrinsic luminosities. Using a Bayesian isochrone pipeline, this enables us to estimate new ages for the accreted stars, with typical uncertainties of ∼20 per cent. This does not account for systematic uncertainties. Our new catalogue is further supplemented with estimates of orbital parameters. The blob stars span [Fe/H] between −2.5 to −0.5, and [Mg/Fe] between −0.1 to 0.5. They constitute ∼30 per cent of the metal-poor ([Fe/H] < −0.8) halo at [Fe/H] ∼ −1.4. Our new ages mainly range between 8 to 13 Gyr, with the oldest stars the metal-poorest, and with the highest [Mg/Fe] abundance. If the blob stars are assumed to belong to a single progenitor, the ages imply that star formation lasted 5 Gyr after which the system merged with our Milky Way around 8 Gyr ago. Dynamical arguments suggest that such a single progenitor would have had a total mass of $\sim 10^{11}\, \mathrm{M}_{\odot }$, similar to that found by other authors using chemical evolution models and simulations.
We apply four different mass modelling methods to a suite of publicly available mock data for spherical stellar systems. We focus on the recovery of the density and velocity anisotropy as a function of radius, either using line-of-sight velocity data only or adding proper motion data. All methods perform well on isotropic and tangentially anisotropic mock data, recovering the density and velocity anisotropy within their 95 per cent confidence intervals over the radial range 0.25 < R/R1/2 < 4, where R1/2 is the half-light radius. However, radially anisotropic mocks are more challenging. For line-of-sight data alone, only methods that use information about the shape of the velocity distribution function are able to break the degeneracy between the density profile and the velocity anisotropy, β, to obtain an unbiased estimate of both. This shape information can be obtained through directly fitting a global phase-space distribution function, by using higher order ‘virial shape parameters’ or by assuming a Gaussian velocity distribution function locally, but projecting it self-consistently along the line of sight. Including proper motion data yields further improvements, and in this case, all methods give a good recovery of both the radial density and velocity anisotropy profiles.
Selection functions are vital for understanding the observational biases of spectroscopic surveys. With the wide variety of multiobject spectrographs currently in operation and becoming available soon, we require easily generalizable methods for determining the selection functions of these surveys. Previous work, however, has largely been focused on generating individual, tailored selection functions for every data release of each survey. Moreover, no methods for combining these selection functions to be used for joint catalogues have been developed. We have developed a Poisson likelihood estimation method for calculating selection functions in a Bayesian framework, which can be generalized to any multiobject spectrograph. We include a robust treatment of overlapping fields within a survey as well as selection functions for combined samples with overlapping footprints. We also provide a method for transforming the selection function that depends on the sky positions, colour, and apparent magnitude of a star to one that depends on the galactic location, metallicity, mass, and age of a star. This ‘intrinsic’ selection function is invaluable for chemodynamical models of the Milky Way. We demonstrate that our method is successful at recreating synthetic spectroscopic samples selected from a mock galaxy catalogue.
We show how the interplay between feedback and mass-growth histories introduces scatter in the relationship between stellar and neutral gas properties of field faint dwarf galaxies (M-*less than or similar to 10(6) M-circle dot). Across a suite of cosmological, high-resolution zoomed simulations, we find that dwarf galaxies of stellar masses 10(5)