Monday, Aug 5: 8:30 AM - 10:20 AM

1825

Topic-Contributed Paper Session

1825

Topic-Contributed Paper Session

Oregon Convention Center

Room: CC-B110

In this session, the five finalists in the ASA Astrostatistics Interest Group 2024 Student Paper Competition will present their topic-contributed papers! The winner will be announced at the end of the presentations.

Yes

Astrostatistics Interest Group

Section on Bayesian Statistical Science

SSC (Statistical Society of Canada)

Galaxy stellar mass is known to be monotonically related to the size of the galaxy's globular cluster (GC) population, but the relation becomes ambiguous for dwarf galaxies. Smaller dwarfs are increasingly likely to have no GCs, and these zeros cannot be easily incorporated into linear models. We introduce the hierarchical errors-in variables Bayesian lognormal hurdle (HERBAL) model to represent the relationship between dwarf galaxies and their GC populations. Our model thoroughly accounts for all uncertainties, including measurement uncertainty, uncertainty in luminosity to stellar mass conversions, and intrinsic scatter. The hierarchical nature of our Bayesian model also allows us to estimate galaxy masses and individual mass-to-light ratios from luminosity data. We find that 50% of galaxies are expected to host globular cluster populations at a stellar mass of log10(M∗) = 6.996, and that the expected mass of GC populations remains linear down to the smallest galaxies. Our hierarchical model recovers an accurate estimate of the Milky Way stellar mass. Under our assumed error model, we find a non-zero intrinsic scatter of 0.59 that should be accounted for in future models.

We present GausSN, a Bayesian semi-parametric Gaussian Process (GP) model for time-delay estimation with resolved systems of gravitationally lensed supernovae (glSNe). GausSN models the underlying light curve non-parametrically using a GP. Without assuming a template light curve for each SN type, GausSN fits for the time delays of all images using data in any number of wavelength filters simultaneously. We also introduce a novel time-varying magnification model to capture the effects of microlensing alongside time-delay estimation. In this analysis, we model the time-varying relative magnification as a sigmoid function, as well as a constant for comparison to existing time-delay estimation approaches. We demonstrate that GausSN provides robust time-delay estimates for simulations of glSNe from the Nancy Grace Roman Space Telescope and the Vera C. Rubin Observatory's Legacy Survey of Space and Time (Rubin-LSST). We find that up to 43.6% of time-delay estimates from Roman and 52.9% from Rubin-LSST have fractional errors of less than 5%. We then apply GausSN to SN Refsdal and find the time delay for the fifth image is consistent with the

Discrepancies between cosmological parameter estimates from cosmic shear surveys and from recent Planck cosmic microwave background measurements challenge the ability of the highly successful ΛCDM model to describe the nature of the universe. To rule out systematic biases in cosmic shear survey analyses, accurate redshift calibration within tomographic bins is key. We improve photometric redshift (photo-z) calibration via Bayesian hierarchical modeling of full galaxy photo-z conditional densities, by employing StratLearn, a recently developed statistical methodology, which accounts for systematic differences in the distribution of the spectroscopic source set and the photometric target set. Using realistic simulations that were designed to resemble the KiDS+VIKING-450 dataset, we show that StratLearn-estimated conditional densities improve the galaxy tomographic bin assignment, and that our StratLearn-Bayesian framework leads to nearly unbiased estimates of the target population means, with a factor of ∼2 improvement upon the previously best photo-z calibration method.

We introduce a new method for constructing a smooth probability density model of stellar streams using all of the available astrometric and photometric data in a joint model. Modeling the stream and background with neural network driven mixture models, our method enables a flexible and statistically sound determination of stream membership probabilities. By using neural networks our models capture the variations in the stream's path and density in a model-free way not possible with traditional mixture models. The background is similarly model-free and is flexibly applicable to any observational field. Our method requires no assumptions about the gravitational potential. Moreover, it is capable of handling data with incomplete phase-space observations, making the method applicable to the growing census of Milky Way stellar streams. As demonstration the method is applied to the streams GD-1 and Palomar 5. When applied to a population of streams, the resulting homogeneous potential-model-free catalog of membership probabilities from our model form the required input to map the Milky Way's and even external galaxies' matter and dark matter distribution on scales large and small.

In the Milky Way, the distribution of stars in the chemistry vs. age planes holds essential information about the formation history of its disk. I investigate these planes by applying novel statistical methods called copulas and elicitable maps to the ages and chemical compositions of stars in the APOGEE survey. I find that the two populations of stars in the disk, the low- and high-α sequences, have a clean separation in copula space. Using this space, I provide an automated separation of the α sequences using a purely statistical approach. This separation supports that the high- and low-α sequences formed sequentially as opposed to having a continuous star formation history. I then combine copulas with elicitable maps to precisely obtain the correlation between stellar age and metallicity (iron composition) conditional on radius and height in the disk. The resulting trends in the age-metallicity correlation with radius, height, and [α/Fe] are consistent with the effects of strong spiral-driven migration of stars across radii in the disk.

In this talk, I will discuss my statistical methods, their application, and the implications of my results for understanding the Milky Way.

In this talk, I will discuss my statistical methods, their application, and the implications of my results for understanding the Milky Way.