Transits: planet detection and false signals. Raphaëlle D. Haywood Sagan Fellow, Harvard College Observatory

Transcription

1 Transits: planet detection and false signals Raphaëlle D. Haywood Sagan Fellow, Harvard College Observatory

2 Outline Transit method basics Transit discoveries so far From transit detection to planet confirmation Astrophysical false positives Factors that can affect derived planet parameters: stellar activity stellar parameter estimations

3 What is the transit method? Image credits: NASA Collier Cameron, A., Methods of Detecting Exoplanets Seager & Mallén-Ornelas 2003, ApJ 585, 1038 Winn Exoplanets edited by S. Seager

4 What is the transit method? Transit depth δ Image credits: NASA Collier Cameron, A., Methods of Detecting Exoplanets Seager & Mallén-Ornelas 2003, ApJ 585, 1038 Winn Exoplanets edited by S. Seager

5 What is the transit method? Assuming circular orbit and i = 90 : Jupiter δ = 1% Transit depth δ Image credits: NASA Collier Cameron, A., Methods of Detecting Exoplanets Seager & Mallén-Ornelas 2003, ApJ 585, 1038 Winn Exoplanets edited by S. Seager

6 What is the transit method? Assuming circular orbit and i = 90 : Jupiter δ = 1% Earth δ = 84 ppm Transit depth δ Image credits: NASA Collier Cameron, A., Methods of Detecting Exoplanets Seager & Mallén-Ornelas 2003, ApJ 585, 1038 Winn Exoplanets edited by S. Seager

7 What is the transit method? Assuming circular orbit and i = 90 : Jupiter δ = 1% Earth δ = 84 ppm Transit depth δ It is easier to find small planets around small stars. Image credits: NASA Collier Cameron, A., Methods of Detecting Exoplanets Seager & Mallén-Ornelas 2003, ApJ 585, 1038 Winn Exoplanets edited by S. Seager

8 What is the transit method? Assuming circular orbit and i = 90 : Jupiter δ = 1% Earth δ = 84 ppm Transit duration T ~ Transit depth δ It is easier to find small planets around small stars. Image credits: NASA Collier Cameron, A., Methods of Detecting Exoplanets Seager & Mallén-Ornelas 2003, ApJ 585, 1038 Winn Exoplanets edited by S. Seager

9 What is the transit method? Assuming circular orbit and i = 90 : Jupiter δ = 1% T 30h Earth δ = 84 ppm Transit duration T ~ Transit depth δ It is easier to find small planets around small stars. Image credits: NASA Collier Cameron, A., Methods of Detecting Exoplanets Seager & Mallén-Ornelas 2003, ApJ 585, 1038 Winn Exoplanets edited by S. Seager

10 What is the transit method? Assuming circular orbit and i = 90 : Jupiter δ = 1% T 30h Earth δ = 84 ppm T 3h Transit duration T ~ Transit depth δ It is easier to find small planets around small stars. Image credits: NASA Collier Cameron, A., Methods of Detecting Exoplanets Seager & Mallén-Ornelas 2003, ApJ 585, 1038 Winn Exoplanets edited by S. Seager

11 What is the transit method? Assuming circular orbit and i = 90 : Jupiter δ = 1% T 30h Earth δ = 84 ppm T 3h Hot Jupiter T 2.5h Transit duration T ~ Transit depth δ It is easier to find small planets around small stars. Image credits: NASA Collier Cameron, A., Methods of Detecting Exoplanets Seager & Mallén-Ornelas 2003, ApJ 585, 1038 Winn Exoplanets edited by S. Seager

12 What is the probability of a planet transiting its star? Collier Cameron, A., Methods of Detecting Exoplanets Seager & Mallén-Ornelas 2003, ApJ 585, 1038 Winn Exoplanets edited by S. Seager

13 What is the probability of a planet transiting its star? Celestial sphere i To observer Collier Cameron, A., Methods of Detecting Exoplanets Seager & Mallén-Ornelas 2003, ApJ 585, 1038 Winn Exoplanets edited by S. Seager

14 What is the probability of a planet transiting its star? Celestial sphere i a To observer Collier Cameron, A., Methods of Detecting Exoplanets Seager & Mallén-Ornelas 2003, ApJ 585, 1038 Winn Exoplanets edited by S. Seager

15 What is the probability of a planet transiting its star? Celestial sphere i a To observer For transits we need: acosi R * R p Collier Cameron, A., Methods of Detecting Exoplanets Seager & Mallén-Ornelas 2003, ApJ 585, 1038 Winn Exoplanets edited by S. Seager

16 What is the probability of a planet transiting its star? Celestial sphere i a To observer For transits we need: acosi R * R p The probability is R * a Collier Cameron, A., Methods of Detecting Exoplanets Seager & Mallén-Ornelas 2003, ApJ 585, 1038 Winn Exoplanets edited by S. Seager

17 What is the probability of a planet transiting its star? Celestial sphere i a To observer For transits we need: acosi R * R p The probability is R * a 10% 0.5% 0.1% 0.05AU 1AU 5AU Collier Cameron, A., Methods of Detecting Exoplanets Seager & Mallén-Ornelas 2003, ApJ 585, 1038 Winn Exoplanets edited by S. Seager

18 What is the probability of a planet transiting its star? Celestial sphere i a To observer For transits we need: acosi R * R p The probability is R * a 10% 0.5% 0.1% 0.05AU 1AU 5AU Transit surveys must monitor thousands of stars at a time. They find planets in small orbits around large parent stars. Collier Cameron, A., Methods of Detecting Exoplanets Seager & Mallén-Ornelas 2003, ApJ 585, 1038 Winn Exoplanets edited by S. Seager

19 >2920 transiting planets confirmed since HD Cnc e CoRoT-7b 0.03 Kepler-37b

20 >2920 transiting planets confirmed since HD sub-neptunes Cnc e CoRoT-7b 0.03 Kepler-37b

21 >2920 transiting planets confirmed since HD sub-neptunes 0.1 super-earths 55 Cnc e CoRoT-7b 0.03 Kepler-37b

22 Radius distribution of small planets Fulton et al. (2017) See also Zeng et al. (2017), Van Eylen et al. (2017), Schlichting (2018)

23 Radius distribution of small planets Fulton et al. (2017) See also Zeng et al. (2017), Van Eylen et al. (2017), Schlichting (2018)

24 Radius distribution of small planets Fulton et al. (2017) See also Zeng et al. (2017), Van Eylen et al. (2017), Schlichting (2018)

25 Radius distribution of small planets Fulton et al. (2017) sub-neptunes super-earths See also Zeng et al. (2017), Van Eylen et al. (2017), Schlichting (2018)

26 Radius distribution of small planets Fulton et al. (2017) evaporation valley Owen & Wu (2017) Jin & Mordasini (2017) sub-neptunes super-earths See also Zeng et al. (2017), Van Eylen et al. (2017), Schlichting (2018)

27 Radius distribution of small planets Fulton et al. (2017) evaporation valley evaporation desert Sanchez-Ojeda et al. (2014) Lundkvist et al. (2016) Lopez (2016) Owen & Wu (2017) Jin & Mordasini (2017) sub-neptunes super-earths See also Zeng et al. (2017), Van Eylen et al. (2017), Schlichting (2018)

28 From initial detection of a transit to confirmation or validation of a planet

29 Photometric monitoring Data processing pipeline Pipeline identifies planet candidates (TCE) Robo vetting (KOI) Confirmed planet From initial detection of a transit to confirmation or validation of a planet Human vetting Statistical validation Follow-up observations 1I: Radial-velocity monitoring for independent confirmation Follow-up observations 1: Reconnaissance spectroscopy, ground-based photometry, speckle interferometry, lucky imaging, AO imaging (astrophysical) false positive Validated planet

30 Photometric monitoring Data processing pipeline Pipeline identifies planet candidates (TCE) Robo vetting (KOI) Confirmed planet From initial detection of a transit to confirmation or validation of a planet Human vetting Statistical validation See Tim Morton and Andrew Vanderburg s talks after lunch Follow-up observations 1I: Radial-velocity monitoring for independent confirmation Follow-up observations 1: Reconnaissance spectroscopy, ground-based photometry, speckle interferometry, lucky imaging, AO imaging (astrophysical) false positive Validated planet

31 Photometric monitoring Data processing pipeline Pipeline identifies planet candidates (TCE) Robo vetting (KOI) Confirmed planet From initial detection of a transit to confirmation or validation of a planet Human vetting Statistical validation See Tim Morton and Andrew Vanderburg s talks after lunch Follow-up observations 1I: Radial-velocity monitoring for independent confirmation Follow-up observations 1: Reconnaissance spectroscopy, ground-based photometry, speckle interferometry, lucky imaging, AO imaging (astrophysical) false positive Validated planet See tomorrow s talks!

32 Photometric monitoring Data processing pipeline Pipeline identifies planet candidates (TCE) Robo vetting (KOI) Confirmed planet From initial detection of a transit to confirmation or validation of a planet Human vetting Statistical validation See Tim Morton and Andrew Vanderburg s talks after lunch Follow-up observations 1I: Radial-velocity monitoring for independent confirmation Follow-up observations 1: Reconnaissance spectroscopy, ground-based photometry, speckle interferometry, lucky imaging, AO imaging (astrophysical) false positive Validated planet See tomorrow s talks!

33 Detecting transits: the Box-fitting Least Squares technique (BLS) Kovács, Zucker & Mazeh (2002) See also Aigrain & Irwin (2004), Collier Cameron et al. (2006) and others

34 Detecting transits: the Box-fitting Least Squares technique (BLS) 10 parameters: P, t₀, ω, e, i,, a/r, Rp/R F₀, u1, u2 Kovács, Zucker & Mazeh (2002) Flux Time See also Aigrain & Irwin (2004), Collier Cameron et al. (2006) and others

35 Detecting transits: the Box-fitting Least Squares technique (BLS) 10 parameters: P, t₀, ω, e, i,, a/r, Rp/R F₀, u1, u2 Kovács, Zucker & Mazeh (2002) 3 non-linear parameters: P, t₀, width and 2 linear parameters: F₀, depth Flux Time See also Aigrain & Irwin (2004), Collier Cameron et al. (2006) and others

36 Detecting transits: the Box-fitting Least Squares technique (BLS) 10 parameters: P, t₀, ω, e, i,, a/r, Rp/R F₀, u1, u2 Kovács, Zucker & Mazeh (2002) 3 non-linear parameters: P, t₀, width and 2 linear parameters: F₀, depth Flux Time Step through a grid of P, t₀, width. See also Aigrain & Irwin (2004), Collier Cameron et al. (2006) and others

37 Detecting transits: the Box-fitting Least Squares technique (BLS) 10 parameters: P, t₀, ω, e, i,, a/r, Rp/R F₀, u1, u2 Kovács, Zucker & Mazeh (2002) 3 non-linear parameters: P, t₀, width and 2 linear parameters: F₀, depth Flux Time Step through a grid of P, t₀, width. At each step, fit for F₀, depth and optimise χ² See also Aigrain & Irwin (2004), Collier Cameron et al. (2006) and others

38 Detecting transits: the Box-fitting Least Squares technique (BLS) 10 parameters: P, t₀, ω, e, i,, a/r, Rp/R F₀, u1, u2 Kovács, Zucker & Mazeh (2002) 3 non-linear parameters: P, t₀, width and 2 linear parameters: F₀, depth Flux Time Step through a grid of P, t₀, width. At each step, fit for F₀, depth and optimise χ² χ² P See also Aigrain & Irwin (2004), Collier Cameron et al. (2006) and others

39 Example of a BLS: SuperWASP candidate Figure credit: A. Collier Cameron

40 Example of a BLS: SuperWASP candidate BLS works on sparse and unevenly sampled data. Figure credit: A. Collier Cameron

41 Example of a BLS: SuperWASP candidate BLS works on sparse and unevenly sampled data. Beware of window-function effects often the true signal is the n-th strongest peak. Figure credit: A. Collier Cameron

42 Example of a BLS: SuperWASP candidate BLS works on sparse and unevenly sampled data. Beware of window-function effects often the true signal is the n-th strongest peak. Does not account for correlated noise. Figure credit: A. Collier Cameron

43 Example of a BLS: SuperWASP candidate BLS works on sparse and unevenly sampled data. Beware of window-function effects often the true signal is the n-th strongest peak. Does not account for correlated noise. There are other techniques for transit detection, eg. the Transiting Planet Search (TPS, Jenkins 2002), Trend Filtering Analysis (TFA, Kovàcs et al. 2005), Schwarzenberg-Czerny (1989, 1996), etc. Figure credit: A. Collier Cameron

44 Single transit searches

45 Single transit searches Figure from Wang et al. (2015) PlanetHunters (Kepler) Wang et al. (2015), MEarth Dittmann et al. (2017), TRAPPIST Gillon et al. (2017) See also Yee & Gaudi (2008), Uehara et al. (2016), Foreman-Mackey et al. (2016), Schmitt et al. (2017)

46 Single transit searches Figure from Wang et al. (2015) Detected by human eyeballing (eg. PlanetHunters), machine learning (see Dittmann et al. 2017, Foreman-Mackey et al. 2016) PlanetHunters (Kepler) Wang et al. (2015), MEarth Dittmann et al. (2017), TRAPPIST Gillon et al. (2017) See also Yee & Gaudi (2008), Uehara et al. (2016), Foreman-Mackey et al. (2016), Schmitt et al. (2017)

47 Single transit searches Figure from Wang et al. (2015) Detected by human eyeballing (eg. PlanetHunters), machine learning (see Dittmann et al. 2017, Foreman-Mackey et al. 2016) Find the period with RVs or more photometry PlanetHunters (Kepler) Wang et al. (2015), MEarth Dittmann et al. (2017), TRAPPIST Gillon et al. (2017) See also Yee & Gaudi (2008), Uehara et al. (2016), Foreman-Mackey et al. (2016), Schmitt et al. (2017)

48 Single transit searches Figure from Wang et al. (2015) Detected by human eyeballing (eg. PlanetHunters), machine learning (see Dittmann et al. 2017, Foreman-Mackey et al. 2016) Find the period with RVs or more photometry PlanetHunters (Kepler) Wang et al. (2015), MEarth Dittmann et al. (2017), TRAPPIST Gillon et al. (2017) See also Yee & Gaudi (2008), Uehara et al. (2016), Foreman-Mackey et al. (2016), Schmitt et al. (2017)

49 Single transit searches Figure from Wang et al. (2015) Detected by human eyeballing (eg. PlanetHunters), machine learning (see Dittmann et al. 2017, Foreman-Mackey et al. 2016) Find the period with RVs or more photometry the number of planets detected by TESS [ ] with P > 25 days will be doubled [ ] with P > 250 days will be increased by an order or magnitude Villanueva, Dragomir & Gaudi (submitted to AAS Journals) PlanetHunters (Kepler) Wang et al. (2015), MEarth Dittmann et al. (2017), TRAPPIST Gillon et al. (2017) See also Yee & Gaudi (2008), Uehara et al. (2016), Foreman-Mackey et al. (2016), Schmitt et al. (2017)

50 Photometric monitoring Data processing pipeline Pipeline identifies planet candidates (TCE) Robo vetting (KOI) Confirmed planet From initial detection of a transit to confirmation or validation of a planet Human vetting Statistical validation Follow-up observations 1I: Radial-velocity monitoring for independent confirmation Follow-up observations 1: Reconnaissance spectroscopy, ground-based photometry, speckle interferometry, lucky imaging, AO imaging (astrophysical) false positive Validated planet

51 Photometric monitoring Data processing pipeline Pipeline identifies planet candidates (TCE) Robo vetting (KOI) Confirmed planet From initial detection of a transit to confirmation or validation of a planet Human vetting Statistical validation Follow-up observations 1I: Radial-velocity monitoring for independent confirmation Follow-up observations 1: Reconnaissance spectroscopy, ground-based photometry, speckle interferometry, lucky imaging, AO imaging (astrophysical) false positive Validated planet

52 Astrophysical scenarios that create transits Transiting planets Morton et al. 2011, 2012; Santerne et al., 2012,2013; Latham et al. 2009; Collier Cameron et al. 2007; Pont et al and others

53 Astrophysical scenarios that create transits (astrophysical false positives) Grazing stellar binaries Background transiting planet Transiting planets Blended stellar binaries (background eclipsing binaries) Transiting red/brown dwarfs Morton et al. 2011, 2012; Santerne et al., 2012,2013; Latham et al. 2009; Collier Cameron et al. 2007; Pont et al and others

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55 Full PDC lightcurve

56 Phased full-orbit flux Full PDC lightcurve

57 Full PDC lightcurve Phased full-orbit flux Phase-folded transit

58 Full PDC lightcurve Phased full-orbit flux Secondary eclipse Phase-folded transit

59 Full PDC lightcurve Phased full-orbit flux Secondary eclipse Image from Winn 2008 Phase-folded transit

60 Full PDC lightcurve Phased full-orbit flux Secondary eclipse Phase-folded transit

61 Full PDC lightcurve Phased full-orbit flux Secondary eclipse Phase-folded transit Odd/even transits

62 Full PDC lightcurve Phased full-orbit flux Secondary eclipse Odd/even transits Phase-folded transit Whitened phase-folded transit

63 Full PDC lightcurve Phased full-orbit flux Secondary eclipse Odd/even transits Phase-folded transit Whitened phase-folded transit Centroid offset plot

64 Full PDC lightcurve Phased full-orbit flux Secondary eclipse Odd/even transits Phase-folded transit Whitened phase-folded transit Centroid offset plot

65 Full PDC lightcurve Phased full-orbit flux Secondary eclipse Odd/even transits Phase-folded transit Whitened phase-folded transit Centroid offset plot

66 Full PDC lightcurve Phased full-orbit flux Secondary eclipse Odd/even transits Phase-folded transit Whitened phase-folded transit Centroid offset plot Fit parameters

67 Full PDC lightcurve Phased full-orbit flux It s a planet candidate! Secondary eclipse Odd/even transits Phase-folded transit Whitened phase-folded transit Centroid offset plot Fit parameters

68

69 It could be a background eclipsing binary

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71 It is a binary candidate!

72

73 It is a planet candidate in a multi-planet system

74 It turned out to be Kepler-452b

75 Photometric monitoring Data processing pipeline Pipeline identifies planet candidates (TCE) Robo vetting (KOI) Confirmed planet From initial detection of a transit to confirmation or validation of a planet Human vetting Statistical validation Follow-up observations 1I: Radial-velocity monitoring for independent confirmation Follow-up observations 1: Reconnaissance spectroscopy, ground-based photometry, speckle interferometry, lucky imaging, AO imaging (astrophysical) false positive Validated planet

76 Photometric monitoring Data processing pipeline Pipeline identifies planet candidates (TCE) Robo vetting (KOI) Confirmed planet From initial detection of a transit to confirmation or validation of a planet Human vetting Statistical validation Follow-up observations 1I: Radial-velocity monitoring for independent confirmation Follow-up observations 1: Reconnaissance spectroscopy, ground-based photometry, speckle interferometry, lucky imaging, AO imaging (astrophysical) false positive Validated planet

77 From transit detection to planet characterisation

78 From transit detection to planet characterisation Step 1: Find the global χ² minimum using BLS, TPS, TFA, etc. χ² P

79 From transit detection to planet characterisation Step 1: Find the global χ² minimum using BLS, TPS, TFA, etc. χ² P Step 2: Find the local χ² minimum using Ameoba method or equivalent

80 From transit detection to planet characterisation Step 1: Find the global χ² minimum using BLS, TPS, TFA, etc. χ² See Jason Eastman s talk on EXOFAST this afternoon P Step 2: Find the local χ² minimum using Ameoba method or equivalent

81 From transit detection to planet characterisation Step 1: Find the global χ² minimum using BLS, TPS, TFA, etc. χ² See Jason Eastman s talk on EXOFAST this afternoon P Step 3: Estimate transit parameters and determine errors bars using an MCMC Step 2: Find the local χ² minimum using Ameoba method or equivalent

82 Stellar activity can affect transit-measured planet parameters Stars vary on timescales from minutes to years: Full Sun: SDO/HMI continuum Swedish Telescope, V. Henriques See Schrijver & Zwaan (2000), Hall (2008), Reiners (2012), Haywood (2015, Chap. 1) and others

83 Stellar activity can affect transit-measured planet parameters Stars vary on timescales from minutes to years: Granulation Oscillations and granulation min-hours Full Sun: SDO/HMI continuum Swedish Telescope, V. Henriques See Schrijver & Zwaan (2000), Hall (2008), Reiners (2012), Haywood (2015, Chap. 1) and others

84 Stellar activity can affect transit-measured planet parameters Stars vary on timescales from minutes to years: Granulation Oscillations and granulation min-hours Flares/coronal mass ejections min-hours (esp. M dwarfs) Full Sun: SDO/HMI continuum Swedish Telescope, V. Henriques Flare Spitzer lightcurve, TRAPPIST-1 Gillon et al. (2017) See Schrijver & Zwaan (2000), Hall (2008), Reiners (2012), Haywood (2015, Chap. 1) and others

85 Stellar activity can affect transit-measured planet parameters Stars vary on timescales from minutes to years: Granulation Sunspots Oscillations and granulation min-hours Flares/coronal mass ejections min-hours (esp. M dwarfs) Magnetic surface features (spots, faculae) stellar rotation period Flare Full Sun: SDO/HMI continuum Spitzer lightcurve, TRAPPIST-1 Gillon et al. (2017) Swedish Telescope, V. Henriques See Schrijver & Zwaan (2000), Hall (2008), Reiners (2012), Haywood (2015, Chap. 1) and others

86 Stellar activity can affect transit-measured planet parameters Stars vary on timescales from minutes to years: Granulation Sunspots Oscillations and granulation min-hours Flares/coronal mass ejections min-hours (esp. M dwarfs) Magnetic surface features (spots, faculae) stellar rotation period Magnetic cycles years Flare Full Sun: SDO/HMI continuum Spitzer lightcurve, TRAPPIST-1 Gillon et al. (2017) Swedish Telescope, V. Henriques SORCE lightcurve of the Sun over a magnetic cycle See Schrijver & Zwaan (2000), Hall (2008), Reiners (2012), Haywood (2015, Chap. 1) and others

87 Example: starspots (occulted AND unocculted) can affect estimate of planet radius Kepler photometry of HAT-P-11 Sanchis-Ojeda et al. (2011) See also Oshagh et al. (2014), Llama & Shkolnik (2015), Rackham et al. (2017a,b, 2018), Mallonn et al. (2018), and Apai et al. (2018) and references therein.

88 Example: starspots (occulted AND unocculted) can affect estimate of planet radius Kepler photometry of HAT-P-11 Sanchis-Ojeda et al. (2011) Individual transits of HAT-P-11b Sanchis-Ojeda et al. (2011) See also Oshagh et al. (2014), Llama & Shkolnik (2015), Rackham et al. (2017a,b, 2018), Mallonn et al. (2018), and Apai et al. (2018) and references therein.

89 Example: starspots (occulted AND unocculted) can affect estimate of planet radius Kepler photometry of HAT-P-11 Sanchis-Ojeda et al. (2011) Individual transits of HAT-P-11b Sanchis-Ojeda et al. (2011) Occulted spots can make transits appear shallower See also Oshagh et al. (2014), Llama & Shkolnik (2015), Rackham et al. (2017a,b, 2018), Mallonn et al. (2018), and Apai et al. (2018) and references therein.

90 Example: starspots (occulted AND unocculted) can affect estimate of planet radius Kepler photometry of HAT-P-11 Sanchis-Ojeda et al. (2011) Individual transits of HAT-P-11b Sanchis-Ojeda et al. (2011) Occulted spots can make transits appear shallower Pont et al. (2008), Pont et al. (2013) See also Oshagh et al. (2014), Llama & Shkolnik (2015), Rackham et al. (2017a,b, 2018), Mallonn et al. (2018), and Apai et al. (2018) and references therein.

91 Example: starspots (occulted AND unocculted) can affect estimate of planet radius Kepler photometry of HAT-P-11 Sanchis-Ojeda et al. (2011) Individual transits of HAT-P-11b Sanchis-Ojeda et al. (2011) Occulted spots can make transits appear shallower Unocculted spots make transits appear deeper Pont et al. (2008), Pont et al. (2013) See also Oshagh et al. (2014), Llama & Shkolnik (2015), Rackham et al. (2017a,b, 2018), Mallonn et al. (2018), and Apai et al. (2018) and references therein.

92 Inaccurate stellar radii bias planet radii See Huber et al. (2016, 2014, 2013), Petigura et al. (2017), Bastien et al. (2014), Dressing & Charbonneau (2013) and others

93 Inaccurate stellar radii bias planet radii Revise stellar radius down to lower value The planet gets bigger too See Huber et al. (2016, 2014, 2013), Petigura et al. (2017), Bastien et al. (2014), Dressing & Charbonneau (2013) and others

94 Inaccurate stellar radii bias planet radii Revise stellar radius down to lower value An accurate and precise measure of the stellar parameters is essential The planet gets bigger too See Huber et al. (2016, 2014, 2013), Petigura et al. (2017), Bastien et al. (2014), Dressing & Charbonneau (2013) and others

95 Inaccurate stellar radii bias planet radii See Alessandro Sozzetti s talk about Gaia on Thursday Revise stellar radius down to lower value An accurate and precise measure of the stellar parameters is essential The planet gets bigger too See Huber et al. (2016, 2014, 2013), Petigura et al. (2017), Bastien et al. (2014), Dressing & Charbonneau (2013) and others

96 Conclusions

97 Conclusions The probability of finding a transiting planet in a 1AU orbit is 0.5% (goes as R*/a)

98 Conclusions The probability of finding a transiting planet in a 1AU orbit is 0.5% (goes as R*/a) The transit method is very successful at finding short-period planets

99 Conclusions The probability of finding a transiting planet in a 1AU orbit is 0.5% (goes as R*/a) The transit method is very successful at finding short-period planets For small planets: look around small stars!

100 Conclusions The probability of finding a transiting planet in a 1AU orbit is 0.5% (goes as R*/a) The transit method is very successful at finding short-period planets For small planets: look around small stars! Systematics and astrophysical variability mask transits

101 Conclusions The probability of finding a transiting planet in a 1AU orbit is 0.5% (goes as R*/a) The transit method is very successful at finding short-period planets For small planets: look around small stars! Systematics and astrophysical variability mask transits It takes a lot of human/telescope resources to go from initial transit detection to planet confirmation. Success rates : Ground-based WASP/HAT/KELT surveys (initial transit detection confirmed planets) 1-2% Kepler (TCEs confirmed/validated planets) 15%

102 Conclusions The probability of finding a transiting planet in a 1AU orbit is 0.5% (goes as R*/a) The transit method is very successful at finding short-period planets For small planets: look around small stars! Systematics and astrophysical variability mask transits It takes a lot of human/telescope resources to go from initial transit detection to planet confirmation. Success rates : Ground-based WASP/HAT/KELT surveys (initial transit detection confirmed planets) 1-2% Kepler (TCEs confirmed/validated planets) 15% Stellar activity (eg. spots) can bias derived planet parameters

103 Conclusions The probability of finding a transiting planet in a 1AU orbit is 0.5% (goes as R*/a) The transit method is very successful at finding short-period planets For small planets: look around small stars! Systematics and astrophysical variability mask transits It takes a lot of human/telescope resources to go from initial transit detection to planet confirmation. Success rates : Ground-based WASP/HAT/KELT surveys (initial transit detection confirmed planets) 1-2% Kepler (TCEs confirmed/validated planets) 15% Stellar activity (eg. spots) can bias derived planet parameters For accurate and precise planet parameters, we need accurate and precise stellar parameters!!!

104 References for further reading The basics of exoplanet detection and characterisation: Seager, S. & Mallén-Ornelas, G ApJ 585, Winn Exoplanets edited by S. Seager, University of Arizona Press, Tucson, AZ, p.55-77, ISBN Collier Cameron, A., Methods of Detecting Exoplanets, Astrophysics and Space Science Library 428, Bozza V. et al. (eds), DOI / _2. Haswell, C Transiting Exoplanets,Cambridge University Press, ISBN: Reviews on stellar activity: Haywood, R. D. (2015). Hide and Seek: Radial-Velocity Searches for Planets around Active Stars. Chapter 1, PhD thesis, University of St Andrews. Schrijver, C. J., & Zwaan, C. 2000, Solar and stellar magnetic activity / Carolus J. Schrijver, Cornelius Zwaan. New York : Cambridge University Press, Cambridge astrophysics series; 34. Hall, J. C. 2008, Living Reviews in Solar Physics, 5, 2. Reiners, A. 2012, Living Rev. Solar Phys., 9. (The talk slides contain many more references about specific topics not listed on this slide.)