TS-Stellar: Unified Stellar Analysis

• TS-Stellar is a comprehensive framework integrating databases, methodologies, and processing pipelines to measure and model stellar time-series, magnetic activity, and contamination effects in exoplanet studies.
• It consolidates long-baseline activity records, cross-matched survey catalogs, and advanced photometric pipelines to enable robust variability analyses and empirical investigations of stellar evolution.
• It employs Bayesian retrieval and dynamical modeling techniques to correct stellar contamination in transit spectra and to calibrate dynamo theories using asteroseismic data.

TS-Stellar is a collective term encompassing databases, methodologies, and frameworks for the measurement, analysis, and modeling of stellar time-series and properties, particularly as they pertain to magnetic activity, stellar evolution, observational characterization, and contamination effects in exoplanet studies. TS-Stellar solutions aggregate published time-series observations, provide cross-matched catalogs for survey missions, enable robust processing pipelines, and facilitate quantitative modeling and retrieval in exoplanet transmission spectroscopy and dynamical studies.

1. Time Series Databases: Sunstardb and Solar–Stellar Connection

The Sunstardb database is designed to standardize the aggregation and analysis of stellar magnetic activity records, which are essential for solar–stellar connection research. Decades-long time-series of magnetic proxies such as the Mount Wilson S-index, $R'_{HK}$, X-ray flux, and rotational/cycle periods are scattered across literature and disparate databases. Sunstardb consolidates these into a relational PostgreSQL data model keyed by star names and aliases, ensuring unambiguous star-datatype-provenance mapping, fine-grained provenance, and support for both scalar and time-series measurements. Specialized data tables for each property (dat_S_mean, dat_Rprime_HK, dat_P_rot) and for time-series (dat_S_ts, dat_Rprime_HK_ts, etc.) provide consistent storage of values, uncertainties, time, duration, and per-datum metadata via JSONB columns.

Sunstardb exposes these records via fast Python and SQL interfaces, supporting queries for long-baseline activity series, filtering by time coverage, and integration with cycle-search algorithms (e.g., Lomb–Scargle for $P_{\text{cyc}}$ estimation). These design principles eliminate ad-hoc catalog cross-matching, lower the barrier to assembling large homogeneous cycle-analysis datasets, and directly support the empirical and theoretical paper of stellar magnetic dynamos (Egeland, 2018).

2. Survey Property Catalogs: K2–TESS Database

The K2–TESS Stellar Properties Catalog is a cross-matched repository offering homogeneous stellar parameters for Kepler/K2 targets, synthesized via positional tolerance matching ($1''$) between the TESS Target Catalog (primarily 2MASS $J<13$) and the Kepler EPIC catalog. Following further augmentation with NOMAD, Tycho-2, Hipparcos, APASS, UCAC4, and dedicated M-dwarf lists, the catalog provides 117,521 vetted targets (plus a faint-extended subset) with robust identifiers, coordinates, multi-band photometry ($JHK, V$, Kepler-band), proper motions, and luminosity-class flags (dwarf/giant/subgiant via reduced proper motion, RPM).

Key methodologies include empirical color–$T_{\text{eff}}$ relations for dwarfs, subgiants, and giants (using $(V-K_S)$ and, where available, [Fe/H]), with fully propagated uncertainties. Spectroscopic cross-matches with APOGEE, RAVE, and LAMOST supplement the data, providing independently measured $T_{\text{eff}}$, $\log g$, and metallicity for select subsets. The catalog’s interactive portal supports map and tabular views and is optimized for bulk downloads required for population studies, luminosity-function modeling, and selection in exoplanet surveys (Stassun et al., 2014).

3. Photometric Time-Series Processing: STRESS Survey Pipeline

The STRESS (STEREO TRansiting Exoplanet and Stellar Survey) pipeline is oriented toward the reduction and analysis of wide-field, high-cadence stellar photometry from the STEREO/HI-1 and HI-2 imagers. The pipeline encompasses bias/dark subtraction, flat-fielding, pixel-specific corrections for "shutterless" CCD readouts, robust F-corona background subtraction, catalog-based source extraction (NOMAD, $R<12$), confusion filtering for crowded fields, PSF/centroiding, and rigorous aperture photometry with fully accounted error budgets (photon, sky, read noise).

Detrending and variability analysis utilize 3 hr and 1 d boxcar smoothing, polynomial fits, and outlier rejection (interquartile range diagnostics). Transit and variability searches deploy Lomb–Scargle, phase-dispersion minimization, and box-fitting least squares, with ongoing exploration of advanced trend-filtering (SysRem, TFA). The pipeline achieves $\lesssim1\%$ relative precision for bright stars ($R<8$), delivers multi-month light curves for millions of targets, and has demonstrated the recovery and discovery of numerous variable/eclipsing systems, complementing Kepler/TESS capabilities (Sangaralingam et al., 2011).

4. Forward Modeling and Retrieval: STCTM for Stellar Contamination

STCTM (STellar ConTamination Modeling) is a Bayesian retrieval framework that addresses the Transit Light Source Effect (TLSE)—the contamination of exoplanetary transmission spectra by stellar surface heterogeneity. The forward model constructs disk-integrated stellar flux as a mixture of quiescent and heterogeneous spectra (spots, faculae), parameterized by filling factors and temperatures. The effective transit-depth correction,

$$\delta_{\text{obs}}(\lambda) = \left(\frac{R_p}{R_*}\right)^2 \frac{F_{\text{quie}}(\lambda)}{F_{\text{obs}}(\lambda)} ,$$

is solved jointly for stellar and planetary parameters using Markov-chain Monte Carlo (emcee), with arbitrary priors and parallel sampling. The exotune sub-module enables fitting of out-of-transit spectra to derive empirical priors on spot/facula coverings and temperatures, feeding back into TLSE mitigation.

Diagnostic outputs include posterior samples, best-fit spectra, contamination transfer functions, and publication-quality figures. Case studies on TRAPPIST-1, GJ 9827, and GJ 3090 have shown that 1–5% spot covering can dominate apparent transmission signals, necessitating careful stellar contamination modeling for robust exoplanet atmosphere interpretation (Piaulet-Ghorayeb, 25 Aug 2025).

5. Tidal Stream Catalogs and Dynamical Modeling

The Stellar Tidal Stream Survey systematically images external Milky-Way analogs to detect and characterize faint tidal stellar streams—key tracers of hierarchical assembly predicted by $\Lambda$CDM cosmology. Using robotic, privately-owned telescopes, the Survey achieves $3\sigma$ surface-brightness limits of $\mu_g \simeq 28.0$ mag arcsec$^{-2}$, uncovering $\sim$50 streams around 50 hosts with diverse morphologies (great-circle loops, umbrella-shaped plumes, shells, jets, and active tidal tails).

The data pipeline entails meticulous flat-fielding, background modeling, artifact masking, and isophotal analysis to derive surface brightness, physical scales, and luminosities. N-body orbital reconstructions and mass-fitting (e.g., for NGC 5907 and NGC 1097) allow inference of host halo mass profiles and shapes. The empirical frequency (1 stream per galaxy at $\mu_{\text{lim}} \simeq 28$ mag arcsec$^{-2}$) matches simulation forecasts, providing strong validation of $\Lambda$CDM merger rates and halo structure. Planned extensions include deeper imaging, spectroscopic campaigns for velocity/metallicity, and eventual application of 6D phase-space modeling techniques (Martinez-Delgado, 2018).

6. Dynamo Theory and Asteroseismic Calibration: Tayler–Spruit Modeling

TS-Stellar research also encompasses modeling and calibration of angular momentum transport mechanisms in stellar interiors. Eggenberger et al. (2024) present a general formulation for the Tayler–Spruit (TS) dynamo, integrating shellular rotation, meridional circulation, and magnetic instabilities. The effective angular momentum diffusivity is given by

$$\nu_T = C_T^3 r^2 \Omega q^2 \left(\frac{\Omega}{N_{\text{eff}}}\right)^4,$$

where $C_T$ is a dimensionless calibration factor for field damping timescales, $\Omega$ is angular velocity, $q = |\partial\ln\Omega/\partial\ln r|$ quantifies shear, and $N_{\text{eff}}$ is the thermally reduced buoyancy frequency. A crucial empirical finding is that $C_T \simeq 216$—a factor $\sim$200 larger than the original Spruit prescription—is required to reproduce observed slow core-rotation rates from Kepler asteroseismology in subgiant and red giant stars.

This mass-independence of core spin-down is consistent across Geneva and MESA models, robustly explaining the absence of an $M_*$–$\Omega_c$ correlation, and providing a predictive framework for RGB, clump, and subgiant rotational evolution. This calibration reconciles theoretical dynamo efficiency with large Kepler and ground-based datasets, and is straightforward to implement in leading stellar evolution codes (Eggenberger et al., 2023).

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TS-Stellar unifies several key domains: long-baseline magnetic activity databases, cross-matched survey catalogs, advanced photometric pipelines, forward modeling of stellar contamination in exoplanet spectroscopy, statistical catalogs of external tidal streams, and physically calibrated dynamo theory. Collectively, these resources enable high-fidelity empirical and theoretical studies of stellar evolution, magnetic activity cycles, variable stars, exoplanet characterization, and galactic assembly, supporting a broad spectrum of contemporary stellar astrophysics research.