The PLATO Input Catalogue of targets (tPIC) for the first Long Pointing Field

arXiv:2604.03369 · astro-ph.EP, astro-ph.IM, astro-ph.SR · Submitted 2026-04-03 · Read on arXiv

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Introduction to the show: ident: Astrophysics Radio. Generated commentary on the latest astrophysics papers.

Vera: Today's paper: "The PLATO Input Catalogue of targets (tPIC) for the first Long Pointing Field".

Jocelyn: The gist The tPIC provides the first public release of the PLATO Input Catalogue of targets (tPIC2.2),

Vera: First, who's behind it and why it matters.

Title and authors: Vera: Okay, so we're diving into the PLATO Input Catalogue of targets (tPIC2.2), which is essentially the first public list of stars that will be observed during PLATO's first Long-duration Observation Phase field in South, or LOPS2 <ref:2604.03369#pg1,the PLATO Input Catalogue of targets (tPIC2.2), which>. It’s the essential starting point for all their science, right?

Jocelyn: Exactly. Think of it as a master checklist of stars that meet all the strict requirements for being picked out for observation in this specific field. They've put together this catalogue to make sure they're focusing on the right kind of stars to find those terrestrial planets.

Subrahmanyan: From a theoretical side, this selection process is key because it sets up a very clean sample. It’s not just throwing a random list at the mission; it’s filtering based on spectral type and luminosity class, which helps them later statistically study planet frequency across different stellar environments.

Vera: That sounds right. And the authors are making sure they handle all the technical constraints of the PLATO mission, especially those telemetry limitations we talked about earlier regarding data downlinks.

Jocelyn: They’ve got these four main stellar samples they define: P1 through P5, and each one has very specific requirements for how many stars there need to be and what their visual magnitude range should be.

Subrahmanyan: That precision in defining the samples is important because it directly ties into the physics of detecting transiting planets; a tighter sample means less uncertainty when you later try to calculate things like a planet's radius from the star’s properties.

Vera: And they aren't just looking at one type of star, either. They are combining data from Gaia and other sources to get these stellar parameters—effective temperature, radius, and mass—for almost every target in this tPIC2 point 2 catalogue <ref:2604.03369#pg1>.

Jocelyn: That parameter derivation is where the methodology gets really detailed; they use an iterative process to estimate interstellar reddening using a three-dimensional extinction map from Lallement et al. two thousand twenty-two and then convert that into the extinction across different photometric bands using MARCS library spectra <ref:2604.03369#pg1>.

Subrahmanyan: It’s a sophisticated way to handle the complexity of how light gets affected by dust in space, which is a big hurdle when trying to characterize the true properties of these distant stars.

Vera: And they use Monte Carlo simulations for quantifying uncertainties on those stellar parameters, which gives us error estimates for things like effective temperature and mass based on simulated perturbations.

Jocelyn: That uncertainty quantification is what really grounds the results; it tells us just how much confidence we can have when we try to estimate a planet's radius later on.

Subrahmanyan: It’s important because knowing those precise stellar parameters allows them to statistically study the frequency of planets as a function of stellar type and metallicity, which feeds directly into our understanding of planet formation models.

Title and authors: Vera: So, when we look at the final catalogue for this paper, we're looking at over two hundred thousand stars in total, including two hundred two thousand FGK dwarfs and subgiants across all the defined samples.

Jocelyn: That’s a massive list; and they also include fifteen thousand M dwarfs and seven hundred eighty-nine known planet host stars that were identified within this specific LOPS2 field.

Subrahmanyan: The median distances they found for these samples are quite different, with five hundred twelve parsecs for the FGK stars and one hundred thirty-three parsecs for the M dwarfs, which shows how this catalogue probes different environments in the sky.

Vera: So what does this mean when we look at "The PLATO Input Catalogue of targets (tPIC) for the first Long Pointing Field"? It means we have a very solid foundation for understanding the first long observation phase of PLATO’s work.

Jocelyn: Right, and because they’ve built this entire selection process around these four specific stellar samples—P1 through P5—we know exactly what kind of stars we're focusing on when they go into observation.

Subrahmanyan: It really shows how the observational constraints of a mission like PLATO force you to be incredibly precise about which targets you even bother looking at, connecting the theoretical goals to an actionable plan.

Vera: Exactly, and I think the way they handled the stellar parameters—deriving temperature, radius, and mass using those specific photometric relationships—is what makes this catalogue so useful for actual planet characterization later on.

Jocelyn: I mean, when you look at all those numbers they’re throwing out about uncertainty from Monte Carlo simulations, it tells you just how much confidence you can actually have in the resulting planetary radius estimates.

Subrahmanyan: And that precision matters because it lets us statistically study the frequency of planets based on stellar type and environment later, which is how we understand planet formation.

Vera: So, to wrap up this discussion on "The PLATO Input Catalogue of targets (tPIC) for the first Long Pointing Field," this paper delivers a highly constrained target list for LOPS2 that’s ready for deep observation.

Jocelyn: It gives us a fantastic baseline for what the mission will be looking at in its first real data collection period, and it sets a benchmark we can use going forward.

Subrahmanyan: I just want to say that getting this catalogue right is really important because it sets the stage for all the actual science we’re going to do with PLATO.

Vera: It really does, and I think we’re excited to see what kind of signals these initial observations bring back when they come in.

Jocelyn: Definitely, and next up, we'll be looking at how that environment changes things—we’ve got a paper coming up on collision environments in Low Earth Orbit.

The paper's summary: Vera: So, we’re moving past just listing stars and actually looking at how this tPIC catalogue improves everything before PLATO even starts looking at its main science data in LOPS2.

Jocelyn: Basically, the paper outlines a whole process for creating this pre-selected list of targets. They aren't just picking random stars; they’re filtering them based on very specific physical requirements to make sure the final sample is exactly what the PLATO mission needs for its long observation phase.

Subrahmanyan: The core idea here is that by doing this rigorous selection upfront, they are setting up a much cleaner environment for the mission. It tackles the telemetry limitations head-on; if you can’t constantly stream high-resolution data, you have to be incredibly smart about what you choose to observe when it finally gets your attention.

Vera: And they show how this catalogue isn't just a list of names; it’s packed with technical details about the stellar parameters they derive. They use Gaia and other data to figure out things like effective temperature and mass, even dealing with the complexity of interstellar dust extinction using three dee maps.

Jocelyn: That detailed characterization is what matters because it means when PLATO actually finds a planet, we don't just have a star; we have a star with its radius and mass known to a very high degree of confidence.

Subrahmanyan: Exactly. That precision feeds directly into the science goals—it lets us statistically study how common planets are based on the type of star you’re looking at, which is crucial for understanding planet formation pathways in different stellar neighborhoods.

Vera: They also use Monte Carlo simulations to quantify the uncertainties on those parameters, so they tell us exactly how much error we should expect when we try to model a planet's size later on. That kind of quantified uncertainty is what makes the whole process reliable.

Jocelyn: So, for someone listening just for the big picture, this means that when PLATO starts collecting data in LOPS2, it’s not just looking at a random bunch of stars; it’s looking at a carefully chosen set where every star has been thoroughly vetted by a detailed methodology.

Subrahmanyan: It shows how the mission planners took the big theoretical goals and translated them into an actionable observational plan that respects the real-world constraints of space.

Vera: And they even compare their findings to other catalogs, like asPIC1 point one and TICv8 point two showing that while there are slight differences in temperature estimates for very hot stars, the fundamental stellar physics they are using is solid across the board <ref:2604.03369#pg3>.

Jocelyn: That comparison helps us see where their new method fits in compared to what we already have established out there, which validates their approach to target selection.

Subrahmanyan: It’s a necessary step in turning the theoretical goals of PLATO into a practical observation plan, providing that crucial link between what we want to study and what’s actually possible with current technology.

Vera: So, this paper shows how you build a reliable observational foundation for the first long-duration phase of PLATO’s work.

Jocelyn: It gives us a fantastic baseline for what we can expect to see in those initial observations as they start collecting data.

Subrahmanyan: Getting this catalogue right is really important because it sets the stage for all the actual science we’re going to do with PLATO later on.

Vera: It really does, and I think we’re excited to see what kind of signals these initial observations bring back when they come in.

Jocelyn: Definitely, and next up, we'll be looking at how that environment changes things—we’ve got a paper coming up on collision environments in Low Earth Orbit.

The paper's improvements: Vera: So we're talking now about the specific technical improvements they suggest in this tPIC catalogue, which goes beyond just making a bigger list of stars for PLATO’s first long observation phase in LOPS2.

Jocelyn: The main thing is how they handle the interstellar dust and stellar properties. They use an iterative method to figure out the extinction across different light bands by starting with a three dimensional map and then using specific library spectra for those dwarf stars.

Subrahmanyan: That’s important because it tackles how much dust is actually obscuring the light from these distant stars, which directly affects how accurately we measure their true brightness and physical size.

Vera: Then they use this multi-band color calibration to get a really accurate intrinsic color for each star. This is vital because those colors are what help us sort them by spectral type when we look at the data later.

Jocelyn: So, it’s not just one number; it’s a whole system for translating what the telescope sees into the actual physical properties of the star before you even start hunting for planets.

Subrahmanyan: And they use Monte Carlo simulations to check those fundamental stellar parameters—temperature, radius, and mass—so they give us a statistical measure of how reliable those numbers are before we do any planet hunting.

Vera: It means that when we later try to calculate the size of a transiting planet, the uncertainty in that radius is directly tied to the uncertainty in our star's radius, and they’ve made sure those uncertainties are well-mapped.

Jocelyn: That level of detail is what allows them to provide a truly homogeneous sample for PLATO. It’s not just a big list; it’s a set where every single parameter is as accurate as possible for the mission's science goals.

Subrahmanyan: This helps us statistically study the planet frequency across different stellar types later on, because you can trust that those differences in planet counts aren't just noise from bad measurements.

Vera: Basically, they are taking all this complex data and making sure it translates into a clean observational plan for LOPS2.

Jocelyn: That’s all we have for looking at the specific technical improvements of "The PLATO Input Catalogue of targets (tPIC) for the first Long Pointing Field". We’ll see what those initial observations reveal next time.

Conclusion: Vera: So we just talked through "The PLATO Input Catalogue of targets (tPIC) for the first Long Pointing Field," which sets up this really detailed list of stars for PLATO's first major observation phase in LOPS2.

Jocelyn: Right, and it’s huge because they’ve built this entire selection process around these four specific stellar samples—P1 through P5—so we know exactly what kind of stars we're focusing on.

Subrahmanyan: It really shows how the observational constraints of a mission like PLATO force you to be incredibly precise about which targets you even bother looking at.

Vera: Exactly, and I think the way they handled the stellar parameters—deriving temperature, radius, and mass using those specific photometric relationships—is what makes this catalogue so useful for actual planet characterization later on.

Jocelyn: I mean, when you look at all those numbers they’re throwing out about uncertainty from Monte Carlo simulations, it tells you just how much confidence you can actually have in the resulting planetary radius estimates.

Subrahmanyan: And that precision matters because it lets us statistically study the frequency of planets based on stellar type and environment later, which is how we understand planet formation.

Vera: So, to sum up, this paper delivers a highly constrained target list for LOPS2 that’s ready for deep observation.

Jocelyn: It gives us a fantastic baseline for what the mission will be looking at in its first real data collection period.

Subrahmanyan: I just want to say that getting this catalogue right is really important because it sets the stage for all the actual science we’re going to do with PLATO.

Vera: It really does, and I think we’re excited to see what kind of signals these initial observations bring back when they come in.

Jocelyn: Definitely, and next up, we'll be looking at how that environment changes things—we’ve got a paper coming up on collision environments in Low Earth Orbit.

ESA

astro-ph.EP, astro-ph.IM, astro-ph.SR

Submitted: 2026-04-03

Updated: 2026-10-09

Comments: 21 pages, 16 tables, 13 figures. Accepted for publication in A&A

Code: https://github.com/Exo-MerCat/Exo-MerCat

Project page: https://www.cosmos.esa.int/plato

License: http://creativecommons.org/licenses/by/4.0/

Importance score: 82/100

The gist: The gist The tPIC provides the first public release of the PLATO Input Catalogue of targets (tPIC2.2), which lists stars to be observed during PLATO’s first Long-duration Observation Phase field at

Key concepts

PLATO Mission
The PLATO mission is designed to find terrestrial planets in the habitable zones of solar-type stars. It aims to detect small dips in stellar brightness caused by planetary transits, searching for Earth-like planets under conditions potentially favorable for life.
tPIC2.2
This is the first public release of the PLATO Input Catalogue of targets. It is a curated list of 217,741 stars that will be observed during PLATO's initial Long-duration Observation Phase field at South (LOPS2).
Stellar Parameter Determination
This process uses astrometric and photometric data to calculate key properties like effective temperature, radius, and mass for stars. It involves estimating interstellar reddening using 3D maps and then iteratively determining temperature through calibration relations with stellar spectra.
LOPS2 Field
LOPS2 refers to the specific field of view where PLATO will conduct its first Long-duration Observation Phase. The tPIC lists stars that fall within this designated area for the mission's core science program.

Terminology

Summary

The gist The tPIC provides the first public release of the PLATO Input Catalogue of targets (tPIC2.2), which lists stars to be observed during PLATO’s first Long-duration Observation Phase field at South (LOPS2) as part of its core science program The tPIC fulfills all the science requirements of the PLATO mission and includes a list of stars that host known exoplanets located within the LOPS2 field.

Context and Aims

The PLATO mission is designed to detect terrestrial planets in the habitable zones of solar-type stars The primary objectives are the detection and characterization of terrestrial exoplanets orbiting up to the habitable zone of bright solar-type stars, together with a detailed investigation of their host stars. PLATO is optimized to detect terrestrial planets on long orbital periods, including those located within the habitable zones of their parent star, with the ultimate goal of identifying Earth analogues under potentially favorable conditions for the emergence of life. To achieve these goals, PLATO will search for small decreases in stellar brightness caused by planetary transits across the disks of their host star.

Methodology and Target Selection

The tPIC was constructed using astrometric and photometric data from Gaia Data Release 3, together with three-dimensional maps of the local interstellar medium, to identify stars belonging to the PLATO stellar samples as from mission requirements. The target selection strategy adopted follows the methodology originally described by Montalto et al. (2021) for the Gaia DR2-based all-sky catalog (asPIC 1.1.0). The construction of the tPIC requires identifying stars meeting required spectral type and luminosity class criteria, taking into account photometric noise constraints to estimate planetary transit capability.

The PLATO targets are divided into four main stellar samples:

** P1 (F5-K7 dwarfs and subgiants) is defined by requirements such as Stars ≥15 000 dwarf and subgiant stars of spectral types from F5 to K7, with a goal of 20 000. The dynamic range for P1 shall be V ≤ 11. The random noise level for stellar sample P1 shall be below 50 ppm in 1 h. It must be observed during two LOPs. **

** P2 (F5-K7 dwarfs and subgiants) requires at least 1 000 dwarf and subgiant stars of spectral types from F5 to K7, with a dynamic range of V ≤ 8.5. It must be observed during two LOPs. **

** P4 (M dwarfs) requires at least 5 000 cool late-type dwarfs (M dwarfs) monitored during a Long-duration Observation Phase, with a dynamic range of V ≤ 16. **

** P5 (F5-late K dwarf and subgiant stars) requires at least 245 000 dwarf and subgiant stars of spectral types from F5 to late K, with a dynamic range of V ≤ 13. It must be observed during two LOPs. **

Stellar Parameter Determination

For almost all targets, interstellar reddening is estimated using the 3D interstellar extinction map of Lallement et al. (2022), and fundamental stellar parameters—effective temperature, radius, and mass—are derived based on astrometric and photometric observables. The determination of extinction involves an iterative process where monochromatic extinction A550 is calculated from the 3D interstellar extinction map. This monochromatic extinction is then converted into the extinction in other photometric bands using a set of 31 solar metallicity dwarf stellar spectra from the MARCS library. The effective temperature and intrinsic color are determined simultaneously using an iterative procedure involving calibration relations between intrinsic color and effective temperature, such as Equation 9.

Magnitude Calculation and Uncertainty

The visible magnitude is taken equal to the Johnson V magnitude if provided, otherwise it is calculated from Gaia photometry or Hipparcos photometry. The PLATO magnitude for the targets of the tPIC has been derived using a procedure that first computes the PLATO response function and then interpolates a polynomial to relate (P − G)0 vs (GBP − GRP)0 colors. The apparent PLATO magnitude is obtained from P = P0 + AP, where AP can be calculated from the monochromatic extinction and the effective temperature. Uncertainties on stellar parameters are determined by using Monte Carlo simulations, perturbing all observed quantities accordingly to their associated uncertainties.

Scientific Ranking and Flags

The targets in tPIC are scientifically ranked according to a metric M = 80 ppm NSR R R⊙ −2 Teff 5778 K −1. The catalog includes several flags to associate targets with their respective sub-samples, such as the main source flag (PICmainSourceFlagBOL) and the source flag (tPICsourceFlagNCAM BOL). Furthermore, a planet flag (tPICplanetFlag) indicates the presence of at least a confirmed planet and/or a candidate planet according to the discovery method. The final catalogue includes 217 741 stars, including 202 315 FGK dwarfs and subgiants, 15 037 M dwarfs and 789 known planet host stars.

Comparisons with Other Catalogs

Comparisons with other catalogs show that stellar parameters and extinctions between tPIC and asPIC1.1 are compatible considering their uncertainties. TICv8.2 tends to list higher extinction and effective temperature with respect to tPIC, but stars with temperatures beyond ∼ 6 500 K are part of the sample of stars hosting planets which was added to tPIC. The final catalogue includes all known confirmed and candidate exoplanet host stars located within the LOPS2 field.

Conclusion

The resulting catalogue fulfills all the requirements established for its definition and provides a homogeneous and well-characterised target sample for PLATO observations in LOPS2. The final catalogue includes 217 741 stars, including 202 315 FGK dwarfs and subgiants, 15 037 M dwarfs and 789 known planet host stars. The two samples probe different distance regimes, with median distances of 512 pc for FGK stars and 133 pc for M dwarfs. The final catalogue includes all known confirmed and candidate exoplanet host stars located within the LOPS2 field.

Acknowledgements

This work presents results from the European Space Agency (ESA) space mission PLATO. The PLATO payload, the PLATO Ground Segment and PLATO data processing are joint developments of ESA and the PLATO Mission Consortium (PMC). Funding for the PMC is provided at national levels, in particular by countries participating in the PLATO Multilateral Agreement. We thank the teams working for PLATO for all their work. The work has made use of data from the European Space Agency (ESA) mission Gaia (Gaia Collaboration et al. 2023), processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www.cosmos.esa.int/web/gaia/dpac/consortium). The GALAH survey is based on observations made at the Australian Astronomical Observatory, under programmes A/2013B/13, A/2014A/25, A/2015A/19, A/2017A/18.

References

The references listed are: Alei et al. 2025 Astronomy and Computing 51, 100936; Alvarez & Plez 1998 A&A 330, 1109; Asplund et al. 2009 ARA&A 47, 481; Bailer-Jones et al. 2021 AJ, 161, 147; Bessell & Murphy S. & Murphy S. PASP, 124, 140; Bohlin R. C. in Astronomical Society of the Pacific Conference Series Vol. 364, The Future of Photometric Spectrophotometric and Polarimetric Standardization ed. C. Sterken, 315; Börner et al. 2024 Experimental Astronomy, 58, 1; Borucki W. J., Koch D., Basri G., et al. Science, 327, 977; Bressan A., Marigo P., Girardi L., et al. MNRAS, 427, 127; Brown T. M., Latham D. W., Everett M. E., & Esquerdo G. A.

Improvements for AI systems

  1. Precise stellar parameter derivation for PLATO targets allows for accurate modeling of transiting planets, as The construction of the tPIC requires the identification (within the selected observing field) of stars meeting the required spectral type and luminosity class criteria. This enables the uncertainty on transiting planet’s radius is directly related to the uncertainty on the host star radius, which is crucial for characterizing exoplanets.

  2. Robust magnitude estimation using multiple photometric systems ensures reliable target selection, as The visible magnitude was taken equal to the Johnson V magnitude if provided for a given star; otherwise it was calculated from the Gaia photometry or from the Hipparcos photometry. This systematic approach mitigates errors, as evidenced by Section 5.1 detailing error propagation equations like deltaV = √[deltaV02 + (deltaAV)2].

  3. Homogeneous stellar parameter inference facilitates statistical analysis of planet frequency, as the paper notes that knowing the values of stellar parameters permits the statistical study of the frequency of planets as a function of stellar type, metallicity, environment, etc. The derived parameters include effective temperature, radius, and mass, enabling this detailed study.

  4. Accurate noise assessment for target selection optimizes mission efficiency by using data like Beginning Of Life (BOL) NSR which accounts for noise: The tPIC reports in particular the Beginning Of Life (BOL) NSR for NCAMs considering both random and systematic noise. This helps prioritize targets based on their observability.

  5. Advanced classification flags enhance target management by providing specific identifiers: The main source flag (PICmainSourceFlagBOL) is a bitmask indicating to which subPIC an object belongs and if it is part of the proprietary sample. This allows automated systems to correctly categorize stars into samples like P1 star or fgPIC star.

  6. Multi-band color calibration enables accurate intrinsic property determination, as the system uses relations such as (G − V)0 = ∑ i=6 i=1 bi(GBP − GRP)0, which is used to derive intrinsic colors from observed photometry. This is vital for distinguishing stellar types, as shown in Table C.1 where coefficients are defined for dwarfs or giants.

  7. Comprehensive uncertainty quantification ensures high-confidence results by employing Monte Carlo simulations: The uncertainties on the stellar parameters have been determined by using Monte Carlo simulations, perturbing all observed quantities accordingly to their associated uncertainties, and assuming Gaussian distributions. This yields error estimates for parameters like effective temperature, mass and radius as the half interval between the 16th and the 84th percentile of the cumulative distribution of the simulated values.

Abstract

The ESA PLAnetary Transits and Oscillations of stars (PLATO) mission is designed to detect terrestrial planets in the habitable zones of solar-type stars. Owing to telemetry constraints, the selection of PLATO targets must be performed in advance. We present the first public release of the PLATO Input Catalog of targets (tPIC2.2), which provides the list of stars that will be observed during PLATO's first Long-duration Observation Phase field at South (LOPS2) as part of its core science program. We used astrometric and photometric data from Gaia data release 3 (DR3), together with three-dimensional maps of the local interstellar medium, to identify stars belonging to the PLATO stellar samples in accordance with the mission requirements. The tPIC comprises 217,741 stars, including 202,315 FGK dwarfs and subgiants, 15,037 M dwarfs, and 789 known planet-host stars. The median distances of the samples are 512 pc for FGK stars and 133 pc for M dwarfs. We estimated interstellar reddening for almost all targets and developed an algorithm to infer fundamental stellar parameters (effective temperature, radius, and mass) homogeneously from astrometric and photometric observables. The tPIC fulfills all the science requirements of the PLATO mission. It also includes a list of stars that host known exoplanets, confirmed or still candidate, located within the LOPS2 field.

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