Astrostatistics Interest Group: Student Paper Award

This decade has seen the first measurements of extrasolar planetary obliquities, characterizing how an exoplanet's spin axis is oriented relative to its orbital axis. These measurements are enabled by combining projected rotational velocities, planetary rotation periods, and astrometric orbits for directly-imaged super-Jupiters. To test whether these super-Jupiters form more like scaled-up planets or scaled-down stars, we develop a Bayesian hierarchical framework to infer their population-level obliquity distribution. Using a single-parameter Fisher distribution, we compare two models: a planet-like formation scenario (κ = 5) predicting moderate alignment, versus a brown dwarf-like formation scenario (κ = 0) predicting isotropic obliquities. Based on a sample of four young super-Jupiter systems, we find early evidence favoring the isotropic case with a Bayes factor of 15, consistent with turbulent fragmentation.
 

Detecting low-amplitude radial velocity (RV) signals is challenging because stellar variability can mimic or obscure the Doppler shifts caused by low-mass planets. Changes in the shapes of spectral lines provide valuable information for disentangling stellar variability from true Doppler shifts. In this work we introduce a novel framework for analyzing spectroscopic time-series data. Our model jointly estimates a shared temporal RV component and line-specific regression coefficients linking RV deviations to multiple shape descriptors extracted from hundreds of spectral lines. Though the proposed statistical model is classical, the novelty lies in the exploitation of estimated line-by-line shape information from the spectra, producing significant reductions in RV root-mean-square errors. Applied to a large spectroscopic dataset with known ground-truth RV signals, the model reduces the root-mean-square error by approximately 76% relative to uncorrected line-by-line Doppler shifts. 

Lyman Alpha Emitters (LAEs) are valuable high-redshift cosmological probes traditionally identified using specialized narrow-band photometric surveys. In ground-based spectroscopy, it can be difficult to distinguish the sharp LAE peak from residual sky emission lines using automated methods, leading to misclassified redshifts. We present a Bayesian spectral component separation technique to automatically determine spectroscopic redshifts for LAEs while marginalizing over sky residuals. We use visually inspected spectra of LAEs obtained using the Dark Energy Spectroscopic Instrument (DESI) to create a data-driven prior and can determine redshift by jointly inferring sky residual, LAE, and residual components for each individual spectrum. We demonstrate this method on 881 spectroscopically observed z = 2-4 DESI LAE candidate spectra and determine their redshifts with >90% accuracy when validated against visually inspected redshifts. Using the Δχ² value from our pipeline as a proxy for detection confidence, we then explore potential survey design choices and implications for targeting LAEs with medium-band photometry. This method allows for scalability and accuracy in determining redshifts from DESI spectra, and the results provide recommendations for LAE targeting in anticipation of future high-redshift spectroscopic surveys. 

Using type Ia supernovae (SNae Ia) as cosmological probes requires empirical corrections, which correlate with their host environment. We present a unified Bayesian hierarchical model designed to infer, from purely photometric observations, the intrinsic dependence of SN Ia brightness on progenitor properties (metallicity & age), the delay-time distribution (DTD) that governs their rate as a function of age, and cosmology, as well as the redshifts of all hosts. The model incorporates physics-based prescriptions for star formation and chemical evolution from Prospector-beta, dust extinction of both galaxy and SN light, and observational selection effects. We show with simulations that intrinsic dependences on metallicity and age have distinct observational signatures, with metallicity mimicking the well-known step of SN Ia magnitudes across a host stellar mass of ~10¹⁰ solar masses. We then demonstrate neural simulation-based inference of all model parameters from mock observations of ≈16000 SNae Ia and their hosts up to redshift 0.9. Our joint physics-based approach delivers robust and precise photometric redshifts (≈0.01 median scatter) and improves cosmological constraints by a factor of ~4 over analyses of the small fraction of objects with spectroscopic follow-up. This unlocks the full power of photometric data and paves the way for an end-to-end simulation-based analysis pipeline in the LSST era. 

We present a method for accurately and precisely inferring photometric dust reddening towards stars at mid-to-high Galactic latitudes (|b| > 20 deg), using probabilistic machine learning to model the colour–magnitude distribution of zero-extinction stars in these regions. Photometric dust maps rely on a robust method for inferring stellar reddening. At high Galactic latitudes, where extinction is low, such inferences are particularly susceptible to contamination from modelling errors and prior assumptions, potentially introducing artificial structure into dust maps. In this work, we demonstrate the use of normalising flows to learn the conditional probability distribution of the photometric colour–magnitude relations of zero-extinction stars, conditioned on Galactic cylindrical coordinates for stars within 2.5 kpc at mid-to-high Galactic latitudes. By using the normalising flow to model the colour–magnitude diagram, we infer the posterior distribution of dust extinction towards stars along different lines of sight by marginalising over the colour-magnitude flow. We validate our method using data from Gaia, Pan-STARRS, and 2MASS, showing that we can recover unbiased posteriors and successfully detect dust along the line-of-sight in two calibration regions at mid-Galactic latitude that have been extensively studied in the context of polarisation surveys.