Dynamical Masses and Ages of Sirius-like Systems

We measure precise orbits and dynamical masses and derive age constraints for six confirmed and one candidate Sirius-like systems, including the Hyades member HD 27483. Our orbital analysis incorporates radial velocities, relative astrometry, and Hipparcos-Gaia astrometric accelerations. We constrain the main-sequence lifetime of a white dwarf's progenitor from the remnant's dynamical mass and semi-empirical initial-final mass relations and infer the cooling age from mass and effective temperature. We present new relative astrometry of HD 27483 B from Keck/NIRC2 observations and archival HST data, and obtain the first dynamical mass of ${0.798}_{-0.041}^{+0.10}$ $M_{\odot}$, and an age of ${450}_{-180}^{+570}$ Myr, consistent with previous age estimates of Hyades. We also measure precise dynamical masses for HD 114174 B ($0.591 \pm 0.011$ $M_{\odot}$) and HD 169889 B (${0.526}_{-0.037}^{+0.039}$ $M_{\odot}$), but their age precisions are limited by their uncertain temperatures. For HD 27786 B, the unusually small mass of $0.443 \pm 0.012$ $M_{\odot}$ suggests a history of rapid mass loss, possibly due to binary interaction in its progenitor's AGB phase. The orbits of HD 118475 and HD 136138 from our RV fitting are overall in good agreement with Gaia DR3 astrometric two-body solutions, despite moderate differences in the eccentricity and period of HD 136138. The mass of ${0.580}_{-0.039}^{+0.052}$ $M_{\odot}$ for HD 118475 B and a speckle imaging non-detection confirms that the companion is a white dwarf. Our analysis shows examples of a rich number of precise WD dynamical mass measurements enabled by Gaia DR3 and later releases, which will improve empirical calibrations of the white dwarf initial-final mass relation.Comment: 21 pages, 7 figures. Submitted to MNRA

Pathology Steered Stratification Network for Subtype Identification in Alzheimer's Disease

Alzheimer's disease (AD) is a heterogeneous, multifactorial neurodegenerative disorder characterized by beta-amyloid, pathologic tau, and neurodegeneration. There are no effective treatments for Alzheimer's disease at a late stage, urging for early intervention. However, existing statistical inference approaches of AD subtype identification ignore the pathological domain knowledge, which could lead to ill-posed results that are sometimes inconsistent with the essential neurological principles. Integrating systems biology modeling with machine learning, we propose a novel pathology steered stratification network (PSSN) that incorporates established domain knowledge in AD pathology through a reaction-diffusion model, where we consider non-linear interactions between major biomarkers and diffusion along brain structural network. Trained on longitudinal multimodal neuroimaging data, the biological model predicts long-term trajectories that capture individual progression pattern, filling in the gaps between sparse imaging data available. A deep predictive neural network is then built to exploit spatiotemporal dynamics, link neurological examinations with clinical profiles, and generate subtype assignment probability on an individual basis. We further identify an evolutionary disease graph to quantify subtype transition probabilities through extensive simulations. Our stratification achieves superior performance in both inter-cluster heterogeneity and intra-cluster homogeneity of various clinical scores. Applying our approach to enriched samples of aging populations, we identify six subtypes spanning AD spectrum, where each subtype exhibits a distinctive biomarker pattern that is consistent with its clinical outcome. PSSN provides insights into pre-symptomatic diagnosis and practical guidance on clinical treatments, which may be further generalized to other neurodegenerative diseases

Post-processing CHARIS integral field spectrograph data with PyKLIP

We present the pyKLIP-CHARIS post-processing pipeline, a Python library that reduces high contrast imaging data for the CHARIS integral field spectrograph used with the SCExAO project on the Subaru Telescope. The pipeline is a part of the pyKLIP package, a Python library dedicated to the reduction of direct imaging data of exoplanets, brown dwarfs, and discs. For PSF subtraction, the pyKLIP-CHARIS post-processing pipeline relies on the core algorithms implemented in pyKLIP but uses image registration and calibrations that are unique to CHARIS. We describe the pipeline procedures, calibration results, and capabilities in processing imaging data acquired via the angular differential imaging and spectral differential imaging observing techniques. We showcase its performance on extracting spectra of injected synthetic point sources as well as compare the extracted spectra from real data sets on HD 33632 and HR 8799 to results in the literature. The pipeline is a python-based complement to the SCExAO project supported, widely used (and currently IDL-based) CHARIS data post-processing pipeline (CHARIS DPP) and provides an additional approach to reducing CHARIS data and extracting calibrated planet spectra.Comment: 17 pages, 13 figure