Impact of Stellar Variability and Inhomogeneity on Rocky Planet Characterization

@benrackham Rocky Exoplanets in the Era of JWST Impact of
Stellar Variability and Inhomogeneity Impact of Stellar Variability and Inhomogeneity on   Rocky Planet Characterization Benjamin V. Rackham

Stellar Variability and Inhomogeneity Outline 1.Stellar Spottedness 2.Impact of Stellar Spots 3.Four Avenues for Constraints

Stellar Variability and Inhomogeneity The Transit Opportunity transits eclipses phase curves

Stellar Variability and Inhomogeneity transits eclipses phase curves All observations include stellar and planetary signals. The Transit Opportunity

Stellar Variability and Inhomogeneity Credit: NASA GSFC SVC/SDO

Stellar Variability and Inhomogeneity Credit: NASA GSFC SVC/SDO How spotted are   other stars?

Stellar Variability and Inhomogeneity Extended Data Figure 5 | Photometric variability of TRAPPIST-1. a, Global light curve of the star as measured by TRAPPIST. The photometric measurements are shown unbinned (cyan dots) and binned per night (black dots with error bars (±s.e.m.)). This light curve is compared with that of the comparison star 2MASS J23063445 − 0507511, shifted along the y axis for clarity. b, The same light curve for TRAPPIST-1, folded on the period P = 1.40 days and binned by 10-minute intervals (error bars indicate ±s.e.m.). For clarity, two consecutive periods are shown. Rotational variability provides a clue TRAPPIST-1 (M8V) Gillon et al. (2016)

Stellar Variability and Inhomogeneity Extended Data Figure 5 | Photometric variability of TRAPPIST-1. a, Global light curve of the star as measured by TRAPPIST. The photometric measurements are shown unbinned (cyan dots) and binned per night (black dots with error bars (±s.e.m.)). This light curve is compared with that of the comparison star 2MASS J23063445 − 0507511, shifted along the y axis for clarity. b, The same light curve for TRAPPIST-1, folded on the period P = 1.40 days and binned by 10-minute intervals (error bars indicate ±s.e.m.). For clarity, two consecutive periods are shown. Rotational variability provides a clue TRAPPIST-1 (M8V) Gillon et al. (2016) >10x  active sun { Harder et al. (2009)

Stellar Variability and Inhomogeneity McQuillan et al. (2014), Rackham et al. (2019) Newton et al. (2016) F to K dwarfs M dwarfs Rotational variability increases for later spectral types

Stellar Variability and Inhomogeneity Converting variability to spot coverage is tricky Giant spots (R=7º) Sun-like spots (R=2º) Rackham et al. (2018)

Stellar Variability and Inhomogeneity Converting variability to spot coverage is tricky Giant spots (R=7º) Sun-like spots (R=2º) Rackham et al. (2018) Amplitude depends on  spot sizes, contrasts, latitudes

Stellar Variability and Inhomogeneity Rackham et al. (2018) Variability traces nonaxisymmetric distribution Amplitude depends on  spot sizes, contrasts, latitudes Giant spots (R=7º) Sun-like spots (R=2º) Converting variability to spot coverage is tricky

Stellar Variability and Inhomogeneity Rackham et al. (2018) Variability traces nonaxisymmetric distribution Amplitude depends on  spot sizes, contrasts, latitudes Even at their brightest,   stars can still be spotted Giant spots (R=7º) Sun-like spots (R=2º) Converting variability to spot coverage is tricky

Stellar Variability and Inhomogeneity Rackham et al. (2019) Rackham et al. (2018) f spot = 0.9+ 1.3 −0.4 % f spot = 12+ 23 −6 % low: high: F to K dwarfs M dwarfs Rotational variability enables spot coverage estimates

Stellar Variability and Inhomogeneity Rackham et al. (2019) Rackham et al. (2018) f spot = 0.9+ 1.3 −0.4 % f spot = 12+ 23 −6 % low: high: F to K dwarfs M dwarfs Rotational variability enables spot coverage estimates Wide range depends on typical   spot size, contrast, distributions

Stellar Variability and Inhomogeneity We’re jazzed about rocky exoplanets with JWST, especially via transits. Feasible rocky planet hosts will generally be more active than the Sun. Typical spot coverages are   ~1% for K dwarfs, ~10% for M dwarfs. Minisummary 1.Stellar Spottedness

Stellar Variability and Inhomogeneity Credit: ESO/L. Calçada Impact of Stellar Spottedness Problem 1: Variability Unocculted spots Problem 2: Problem 3: Spot crossings

Stellar Variability and Inhomogeneity Morris et al. (2017) Problem 1: Spot crossings

Stellar Variability and Inhomogeneity Morris et al. (2017) If the transit chord is dimmer,   the planet blocks less light. Problem 1: Spot crossings

Stellar Variability and Inhomogeneity Morris et al. (2017) If the transit chord is dimmer,   the planet blocks less light. Problem 1: Spot crossings If hidden in the noise,   these events bias depths. ? ?

Stellar Variability and Inhomogeneity made with starry (Luger et al. 2019) If the transit chord is brighter,   the planet blocks more light. Problem 2: Unocculted spots

Stellar Variability and Inhomogeneity made with starry (Luger et al. 2019) If the transit chord is brighter,   the planet blocks more light. Transit depth changes  are spectroscopic. Problem 2: Unocculted spots

Stellar Variability and Inhomogeneity Pre-transit Stellar Disk is the Assumed Light Source Actual Light Source is the Chord Defined by the Planet’s Projection The Transit Light Source Effect Spectral Difference due to Different Spot/Faculae Contributions Contaminates Transit Spectrum Observed Transit Spectrum True Planetary Spectrum Rackham et al. (2018) See also: Pont+08, Bean+10, Sing+11, Aigrain+12, Huitson+13, Jordán+13,  Kreidberg+14, McCullough+14, Nikolov+15, Herrero+16, Zellem+17

Stellar Variability and Inhomogeneity Pre-transit Stellar Disk is the Assumed Light Source Actual Light Source is the Chord Defined by the Planet’s Projection The Transit Light Source Effect Spectral Difference due to Different Spot/Faculae Contributions Contaminates Transit Spectrum Observed Transit Spectrum True Planetary Spectrum Rackham et al. (2018) See also: Pont+08, Bean+10, Sing+11, Aigrain+12, Huitson+13, Jordán+13,  Kreidberg+14, McCullough+14, Nikolov+15, Herrero+16, Zellem+17 D obs (λ) = ϵ(λ)D(λ) planet star

Stellar Variability and Inhomogeneity The TLS effect is largest for K and M dwarfs Rackham et al. (2018) Rackham et al. (2019)

Stellar Variability and Inhomogeneity The TLS effect is largest for K and M dwarfs Rackham et al. (2018) Rackham et al. (2019) They can imprint molecular features on transit depths

Stellar Variability and Inhomogeneity The TLS effect is largest for K and M dwarfs Rackham et al. (2018) Rackham et al. (2019) 100 ppm feature 100 ppm feature They can imprint molecular features on transit depths

Stellar Variability and Inhomogeneity The TLS effect is largest for K and M dwarfs Rackham et al. (2018) Rackham et al. (2019) 100 ppm feature 100 ppm feature They can imprint molecular features on transit depths   that are comparable to or much larger than planetary features.

Stellar Variability and Inhomogeneity Problem 3: Variability transits stellar brightness changes

Stellar Variability and Inhomogeneity How spotted is the star here? Problem 3: Variability

Stellar Variability and Inhomogeneity How spotted is the star here? How do we treat data gaps? Problem 3: Variability

Stellar Variability and Inhomogeneity How spotted is the star here? How do we treat data gaps? Are brightness variations driven by spots or faculae? (e.g. Shapiro et al. 2016) Problem 3: Variability

Stellar Variability and Inhomogeneity Spots affect observations, whether they’re inside or outside the transit chord. Unocculted spots are more problematic. Accounting for varying spot coverage when stacking data can be super tricky. Minisummary 2.Impact of Stellar Spots

This is a challenge.

Stellar Variability and Inhomogeneity Atmospheric features This is a challenge.

Stellar Variability and Inhomogeneity Stellar heterogeneity Atmospheric features This is a challenge.

Stellar Variability and Inhomogeneity How can we separate   stellar and planetary signals? Stellar heterogeneity Atmospheric features

Stellar Variability and Inhomogeneity How can we separate   stellar and planetary signals? Stellar heterogeneity Atmospheric features Largest uncertainty

Stellar Variability and Inhomogeneity Constraints We Need:

Stellar Variability and Inhomogeneity Constraints We Need: spectral components

Stellar Variability and Inhomogeneity quiescent photosphere Constraints We Need: spectral components

Stellar Variability and Inhomogeneity quiescent photosphere spots Constraints We Need: spectral components

Stellar Variability and Inhomogeneity quiescent photosphere spots faculae Constraints We Need: spectral components

Stellar Variability and Inhomogeneity quiescent photosphere spots faculae Constraints We Need: spectral components covering fractions

Stellar Variability and Inhomogeneity quiescent photosphere spots faculae Constraints We Need: spectral components covering fractions distributions

Stellar Variability and Inhomogeneity Credit: ESO/L. Calçada Promising Avenues for Constraints Opportunity 1: Stellar and planetary retrievals Spectral monitoring Spot crossings Opportunity 2: Opportunity 3: Opportunity 4: Stellar spectral decomposition

Stellar Variability and Inhomogeneity Credit: NASA/SORCE S 1 S 2 S 3 S disk = F 1 S 1 + F 2 S 2 + F 3 S 3 ~ quiescent photosphere ~ spots ~ faculae Ca II K 3934 Å Opportunity 1: Stellar spectral decomposition

Stellar Variability and Inhomogeneity Figure 9. V-band magnitude in 2014–2015 ﬁlling factor, as measured in the IGRINS sp factor of ∼2 change in visible surface area The Astrophysical Journal, 836:200 Gully-Santiago et al. (2017) Data-driven approach data model hot component cool component O’Neal, Saar, & Neff (1996) active inactive Temperature-sensitive features TiO TiO TiO

Stellar Variability and Inhomogeneity TRAPPIST-1, e.g., shows evidence for three spectral components Zhang, Zhou, Rackham, & Apai (2018) (data from de Wit et al. 2016, 2018)

Stellar Variability and Inhomogeneity TRAPPIST-1, e.g., shows evidence for three spectral components Zhang, Zhou, Rackham, & Apai (2018) (data from de Wit et al. 2016, 2018) Wakeford et al. (2019)

Stellar Variability and Inhomogeneity TRAPPIST-1, e.g., shows evidence for three spectral components Zhang, Zhou, Rackham, & Apai (2018) (data from de Wit et al. 2016, 2018) Wakeford et al. (2019) ~65% 2400 K ~35% 3000 K <3% 5800 K

Impact of Stellar Variability and Inhomogeneity on Rocky Planet Characterization

Impact of Stellar Variability and Inhomogeneity on Rocky Planet Characterization

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