PLATO Input Catalogue (PIC)

• The PLATO Input Catalogue (PIC) is a curated stellar target list for ESA’s PLATO mission, selecting bright, low-mass dwarfs and subgiants for precise transit photometry and asteroseismology.
• It integrates data from Gaia DR2/DR3, 3D interstellar extinction maps, and 2MASS using dereddened CMD constraints to filter candidates and control contamination.
• PIC underpins mission planning by optimizing field selection and follow-up verification, directly impacting exoplanet yield and accurate stellar parameter determination.

The PLATO Input Catalogue (PIC) is the curated stellar target master list for ESA’s PLATO (PLAnetary Transits and Oscillations of stars) mission, designed to enable the detection and characterization of terrestrial exoplanets and the asteroseismic analysis of their host stars. The PIC defines the pool of stars suitable for high-precision transit photometry under strict mission telemetry, photometric, and noise-floor requirements, targeting bright, low-mass dwarfs and subgiants across the entire sky (Montalto et al., 2021, Prisinzano et al., 16 Dec 2025, &&&2&&&).

1. Scientific Goals and Functional Requirements

PLATO’s principal science objectives are to detect and analyze transiting terrestrial planets in the habitable zones of solar-type stars and to precisely determine stellar ages and radii through asteroseismology. Due to telemetry bandwidth constraints, only selected pixels (“imagettes”) or pre-computed light curves for a small set of pre-specified targets may be downlinked. The PIC serves as the foundational tool to select optimal observing fields, target samples, and to support planning for follow-up verification (Montalto et al., 2021, Prisinzano et al., 16 Dec 2025).

PIC target samples are defined in the mission Science Requirement Document (SRD) and include:

• P1/P2/P3: FGK-type dwarfs or subgiants with $V \leq 11/8$, for asteroseismology and planetary transits.
• P4: M-dwarfs, specifically $T_{\mathrm{eff}} < 3\,850$ K, $V \leq 16$, to ensure comprehensive coverage of late-type hosts.

The catalogue is fundamental for optimizing field selection (long-duration fields, LOPs), maximizing exoplanet yield, and controlling contamination, completeness, and parameter precision (Montalto et al., 2021, Prisinzano et al., 16 Dec 2025).

2. Catalogue Construction: Data Sources and Selection Workflow

2.1 Data Integration

The foundational catalogue (asPIC1.1) combined the following resources (Montalto et al., 2021, Prisinzano et al., 16 Dec 2025):

• Gaia Data Release 2/3 (astrometry, photometry)
• 3D interstellar extinction maps (Lallement et al. 2018, 2022)
• Bailer-Jones et al. (2018) distance estimates
• 2MASS Near-IR photometry for validation and parameter estimation

Earlier efforts (UCAC4-RPM) utilized UCAC4 proper motions, APASS DR6 $UBV$ photometry, Tycho-2 for bright stars, and the RAVE DR4 calibration sample for surface gravity and temperature (Nascimbeni et al., 2016).

2.2 Target Selection Logic

Stars are filtered according to dereddened color–absolute-magnitude diagram (CMD) constraints designed to distinguish dwarfs/subgiants from giants and early-type stars, controlling both completeness and contamination:

• FGK sample (P5-like):
  • $0.56 \leq (G_\mathrm{BP}-G_\mathrm{RP})_0 < 1.84$
  • $$M_{G,0} \leq 4.1\,(G_\mathrm{BP} - G_\mathrm{RP})_0 + 5.0$$
  • $$M_{G,0} \geq 4.1\,(G_\mathrm{BP} - G_\mathrm{RP})_0 - 2.2$$
  • $V \leq 13$
• M-dwarf sample (P4):
  • $(G_\mathrm{BP}-G_\mathrm{RP})_0 \geq 1.84$
  • $$M_{G,0} > 2.334\,(G_\mathrm{BP} - G_\mathrm{RP})_0 + 2.259$$
  • $V \leq 16$, $d < 600$ pc

Extinction corrections are applied per target using 3D maps and temperature-dependent extinction coefficients; distances are adopted from Bailer-Jones et al. to mitigate parallax inversion biases and negative parallaxes (Montalto et al., 2021, Prisinzano et al., 16 Dec 2025).

3. Parameter Estimation and Validation

Fundamental astrophysical parameters are homogeneously computed using empirical photometric calibrations and iterative de-reddening.

3.1 Effective Temperature

• $T_{\mathrm{eff}}$ for FGKM stars: fifth or sixth-order polynomial in $(G_\mathrm{BP}-G_\mathrm{RP})_0$, with iterated extinction updates. For example:

$$T_{\mathrm{eff}} = 9453.14 - 6859.40\,x + 3542.16\,x^2 - 1053.09\,x^3 + 165.635\,x^4 - 10.5672\,x^5$$

Where $x = (G_{BP}-G_{RP})_0$, valid for $0.5 < x < 5$ with rms $\sim$65 K (Montalto et al., 2021).

• For the P4 M-dwarf sample in PIC 2.1.0.1:

$$T_{\mathrm{eff}} = \sum_{i=0}^{5} c_i (G_{\mathrm{BP}}-G_{\mathrm{RP}})_0^i$$

Coefficients yield $\sigma \approx 55$ K (Prisinzano et al., 16 Dec 2025).

3.2 Radius and Mass

• Radius: Derived using empirical relations calibrated on absolute $K_S$ magnitude (e.g., Mann et al. 2015):

$R_{*} = a + b X + c X^2,\quad X = M_{K_S,0}$

• Mass: Similar multi-term polynomials in $M_{K_S,0}$.
• Surface gravity:

$$\log g = \log_{10}(M_{*}/M_{\odot}) - 2 \log_{10}(R_{*}/R_{\odot}) + 4.438$$

• Metallicity: Not directly calibrated in PIC; inferred indirectly for population-level analysis.

3.3 Validation and External Comparison

Parameters are validated by comparison with independent literature (interferometric radii, SED fitting, Gaia DR3 FLAME). PIC radii match Mann et al. (2015) to within $-0.8\% \pm 5.3\%$, and other literature values at the $\lesssim 5\%$ level. Gaia DR3 FLAME radii are inflated by $\sim$11\% for M dwarfs below 4 200 K (Prisinzano et al., 16 Dec 2025).

3.4 Uncertainty Characterization

Central estimates are propagated via Monte Carlo, incorporating photometric, parallax, reddening, and bolometric correction uncertainties. For asPIC1.1, combined internal+external uncertainties are $230$ K (4%) for $T_{\rm eff}$, $0.13\,R_{\odot}$ (9%) for radius, and $0.13\,M_{\odot}$ (11%) for mass (Montalto et al., 2021).

4. Catalogue Content, Distribution, and Completeness

asPIC1.1 (publicly released) comprises:

Subsample	Number of Stars	Median Distance	Completeness (V)	TPR (%)	FPR (%)
FGK dwarfs/subgiants (V≤13)	2,378,177	428 pc	≤13	100	12
M dwarfs (V≤16)	297,362	146 pc	≤16	88	0
Total	2,675,539

• Volume completeness for the P4 M-dwarfs in LOPS2 is maintained to $\sim$37 pc for the overall sample, dropping to $\sim$26 pc for the latest M types. Photometric completeness is $>$99% for $V<16$ as inherited from Gaia DR3 (Prisinzano et al., 16 Dec 2025, Montalto et al., 2021).
• Spatial density is modulated by PLATO’s avoidance of the Galactic plane and is used to guide field selection. Two provisional LOPs exceed mission science requirements (≥245,000 FGK with $V \leq 13$; ≥5,000 M with $V \leq 16$ per field) (Montalto et al., 2021).

5. Practical Application, Special Lists, and Field Optimization

The PIC is used to:

• Define all-sky or field-specific target lists meeting photometric noise and cadence requirements for the flight model.
• Select known exoplanet hosts via continuous Virtual Observatory cross-match (Exo-MerCat with Exoplanet.eu, NASA Exoplanet Archive, Exoplanets.org, OEC), ensuring prioritization for high-cadence and follow-up (Montalto et al., 2021).
• Flag binaries, multiples, and variables via catalogue bitmasks; quantify photometric contamination using Gaia flux aggregates within 30–60″ radii around each target, guiding ground-based vetting (Montalto et al., 2021).

Field tiling and crowding/cadence optimization leverage sky density maps generated from the asPIC1.1 or PIC2.x catalogues, balancing photon noise and neighbour contamination versus spacecraft operational constraints (Montalto et al., 2021).

6. Kinematic Population Analysis and CMD Characterization

Galactic velocity components $(U, V, W)$ are computed for all stars with sufficient data. Population classification is performed using probability ratios for thin disk, thick disk, and halo membership based on a Gaussian velocity ellipsoid model (Bensby et al. 2003, 2005):

• In the P4 LOPS2 sample ($V<16$): 81.9% are thin disk, 2.6% thick disk, 15.4% intermediate, and 0.13% halo. Known examples include Kapteyn’s star (halo) (Prisinzano et al., 16 Dec 2025).

The observed spread in the colour–magnitude diagram (CMD) for M dwarfs is explained by:

• Metallicity variations—a direct consequence of thin/thick/halo population mixing.
• Magnetic activity and starspots—impacting observed $T_{\rm eff}$ and luminosity via reduced $T_{\rm eff}$ (Franci osini et al. 2022).
• Unresolved binaries—producing CMD outliers above the single-star locus.
• Stellar age distributions—blurring the main sequence.

Kolmogorov–Smirnov and Anderson–Darling tests corroborate statistically significant differences in $T_{\rm eff}$ distributions among population bins ($p \ll 0.001$) (Prisinzano et al., 16 Dec 2025).

7. Future Developments and Updates

PIC catalogues will continually evolve by:

• Adopting Gaia EDR3/DR3 for astrometry/photometry.
• Incorporating improved 3D extinction maps and metallicities from large-scale spectroscopic surveys.
• Refining M-star parameters with higher-precision empirical calibrations.
• Advancing completeness, contamination, and astrophysical parameter accuracy for optimal PLATO target selection and follow-up strategy (Montalto et al., 2021).