Detecting White Dwarf Exoplanets in the Roman Era

Input catalogs and simulated detection grids from [PAPER CITATION — to be updated with the arXiv/journal reference], a study of how many transiting and non-transiting planets orbiting white dwarfs (WDs) the Nancy Grace Roman Space Telescope's Galactic Bulge Time Domain Survey (GBTDS) could detect in its first three high-cadence seasons. See the paper for the simulation setup; this description covers only what each file contains and how to use it. File list - WD_SED_database.zip — white dwarf model spectra, 4000–80000 K (26 files)- WD_population_besancon.txt — Besançon synthetic WD catalog toward the GBTDS fields- detection_grids.zip — six detection-probability grids (transit, phase curve, secondary eclipse)- sightline_background_flux.csv — background flux per sight line (source blending)- sightline_dust_extinction.csv — interstellar extinction A_V per sight line 1. WD_SED_database.zip Twenty-six white dwarf model spectra, one per effective temperature between 4000 and 80000 K. Each is a two-column ASCII table with a one-line header giving Wavelength (nm) and Flux (W/m2/nm). The warm and hot spectra (T_eff ≥ 5000 K) are DA model atmospheres from Koester (2010); the coolest spectra are the cool-WD models used by Kozakis et al. (2018, 2020) and were computed as described in Saumon et al. (2014). Only the file format has been changed; the models themselves are unaltered. 2. WD_population_besancon.txt A synthetic white dwarf catalog toward the GBTDS fields, generated with the Besançon Galactic model (Robin et al. 2003; version m1612, queried through https://model.obs-besancon.fr). Whitespace-delimited, with a one-line header (beginning with #) naming the 39 columns. The quantities most likely to be useful are J (Vega magnitude), Teff (K), Mass (solar masses), Dist (kpc), Av (mag), and the sky positions longitude / latitude (Galactic, deg) and RAJ2000 / DECJ2000 (deg). This is the full cone of 53,669 white dwarfs, not the 16,340 that fall inside the Roman WFI detectors and enter the yield calculation. To select the in-field subset, build the footprint with pysiaf (https://github.com/spacetelescope/pysiaf): load pysiaf.Siaf('Roman'), take the WFI_CEN reference point and the 18 detector apertures WFI01_FULL through WFI18_FULL, and rotate them to each of the six GBTDS pointing centers, in Galactic (l, b): (-0.417948, -1.200) (-0.008974, -1.200) (0.400000, -1.200) ( 0.808974, -1.200) ( 1.217948, -1.200) (0.000000, -0.125) at position angle 90 degrees. A star is in-field if it falls inside any of the 108 resulting detector polygons (6 pointings × 18 detectors). Approximating each pointing by its 0.41 × 0.82 degree bounding rectangle is not equivalent: the rectangles fill in the gaps between detectors and select 20,328 stars instead of 16,340. 3. detection_grids.zip Six HDF5 tables, one per survey configuration. Unzip the archive, then read any of them with: import pandas as pd df = pd.read_hdf('detection_grid_transit_base.h5', key='sim_results') Files inside the archive: - detection_grid_transit_base.h5 — transit grid, central transits (b = 0)- detection_grid_transit_impact_parameter.h5 — transit grid, b = 0.3, 0.6, 0.9- detection_grid_transit_thin_envelope.h5 — transit grid, thin hydrogen envelope- detection_grid_transit_hot_faint.h5 — transit grid, hot and faint WD corners- detection_grid_phase_curve.h5 — phase-curve (non-transiting) grid- detection_grid_secondary_eclipse.h5 — secondary-eclipse grid Every row is one grid point — a white dwarf plus planet configuration — with the resulting signal-to-noise and detection probability. The grid axes are the leading columns (AB_Mag, Period_min, Eccentricity, Omega, Rp_Earth, Teff, WD_Mass, Envelope, and either Impact_Param or, for the phase-curve grid, Inclination_deg); their sampled values can be read off directly with df[col].unique(). The four transit grids are disjoint and intended to be concatenated. Result columns, present in all six grids: - Avg_Exp_SNR — mean expected matched-filter SNR across trials (noise-free optimum)- Avg_Meas_SNR — mean measured SNR, one Gaussian noise realization per trial- Det_Prob — fraction of trials with measured SNR ≥ 7- Std_Exp_SNR, Std_Meas_SNR — standard deviation of the above across trials- Std_Det_Prob — binomial standard error on Det_Prob, sqrt(p(1-p)/N) Transit grids only: DetProb_k columns. The four transit grids carry 64 extra columns, DetProb_k00 through DetProb_k63, saved so the detection probability could be re-evaluated at other thresholds after the fact; the phase-curve and eclipse grids do not have them. Column k holds the fraction of trials with measured SNR ≥ 7 / D_k — that is, the detection probability when the signal is diluted by a factor D_k, as unresolved blended sources do (see file 4): import numpy as np D_GRID = np.logspace(0.0, -3.0, 64) # 1.0 down to 0.001, log-spaced # column DetProb_k00 ... DetProb_k63 → dilution factor D_GRID[k] DetProb_k00 (D = 1) is identical to Det_Prob. For an arbitrary D, interpolate a row's 64 values against log10(D_GRID) at log10(D); treat D < 0.001 as zero. 4 and 5. sightline_background_flux.csv and sightline_dust_extinction.csv Two sight-line tables covering the GBTDS fields, both derived from Besançon model queries (Robin et al. 2003) on a 0.1-degree grid. They contain the same 1,472 sight lines in the same row order, so they can be joined positionally or on (l, b). Each file carries a comment header defining every column and its units. sightline_background_flux.csv gives F_bg, the combined flux of all simulated stars inside a 0.33-arcsecond aperture, on a J_Vega = 0 zero point. The source-blending factor for a white dwarf of magnitude J_Vega is D = f_WD / (f_WD + F_bg), with f_WD = 10^(-0.4 J_Vega); this D is the dilution factor used with the DetProb_k columns above. sightline_dust_extinction.csv gives visual extinction A_V statistics per sight line (median, mean, 16th/84th percentiles, scatter), plus distance-sliced medians at 1, 2 and 6 kpc and a thin shell at 2 kpc. The extinction originates in the Marshall et al. (2006) three-dimensional dust model as implemented in the Besançon model. For J-band extinction, A_J = 0.188 A_V (Nishiyama et al. 2008). References Data products in this record derive from: - Koester, D. (2010) — DA white dwarf model atmospheres- Kozakis, T., et al. (2018, 2020); Saumon, D., et al. (2014) — cool white dwarf spectra- Marshall, D. J., et al. (2006) — three-dimensional Galactic dust model- Nishiyama, S., et al. (2008) — bulge near-infrared extinction law- Robin, A. C., et al. (2003) — Besançon Galactic model- pysiaf — STScI, https://github.com/spacetelescope/pysiaf Please also cite the paper listed at the top of this description. License and reuse The simulation outputs (detection grids and sight-line tables) may be reused with attribution to the paper above. The white dwarf spectra and the Besançon catalog derive from third-party models; please credit their original authors and observe those models' terms of use.