Transits: Planet Detection and False Signals

Transits: planet detection and false signals

Raphaëlle D. Haywood Sagan Fellow, Harvard College Observatory 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 What is the transit method?

Collier Cameron, A., 2016. Methods of Detecting Exoplanets Seager & Mallén-Ornelas 2003, ApJ 585, 1038 Image credits: NASA Winn 2010. Exoplanets edited by S. Seager What is the transit method?

Transit depth δ ≈

Collier Cameron, A., 2016. Methods of Detecting Exoplanets Seager & Mallén-Ornelas 2003, ApJ 585, 1038 Image credits: NASA Winn 2010. Exoplanets edited by S. Seager What is the transit method?

Assuming circular orbit and i = 90°:

Jupiter δ = 1%

Transit depth δ ≈

Collier Cameron, A., 2016. Methods of Detecting Exoplanets Seager & Mallén-Ornelas 2003, ApJ 585, 1038 Image credits: NASA Winn 2010. Exoplanets edited by S. Seager What is the transit method?

Assuming circular orbit and i = 90°:

Jupiter Earth δ = 1% δ = 84 ppm

Transit depth δ ≈

Collier Cameron, A., 2016. Methods of Detecting Exoplanets Seager & Mallén-Ornelas 2003, ApJ 585, 1038 Image credits: NASA Winn 2010. Exoplanets edited by S. Seager What is the transit method?

Assuming circular orbit and i = 90°:

Jupiter Earth δ = 1% δ = 84 ppm

Transit depth δ ≈

⇒ It is easier to ﬁnd small planets around small stars.

Collier Cameron, A., 2016. Methods of Detecting Exoplanets Seager & Mallén-Ornelas 2003, ApJ 585, 1038 Image credits: NASA Winn 2010. Exoplanets edited by S. Seager What is the transit method?

Assuming circular orbit and i = 90°:

Jupiter Earth δ = 1% δ = 84 ppm

Transit duration T ~ Transit depth δ ≈

⇒ It is easier to ﬁnd small planets around small stars.

Collier Cameron, A., 2016. Methods of Detecting Exoplanets Seager & Mallén-Ornelas 2003, ApJ 585, 1038 Image credits: NASA Winn 2010. Exoplanets edited by S. Seager What is the transit method?

Assuming circular orbit and i = 90°:

Jupiter Earth δ = 1% δ = 84 ppm T ≈ 30h

Transit duration T ~ Transit depth δ ≈

⇒ It is easier to ﬁnd small planets around small stars.

Collier Cameron, A., 2016. Methods of Detecting Exoplanets Seager & Mallén-Ornelas 2003, ApJ 585, 1038 Image credits: NASA Winn 2010. Exoplanets edited by S. Seager What is the transit method?

Assuming circular orbit and i = 90°:

Jupiter Earth δ = 1% δ = 84 ppm T ≈ 30h T ≈ 3h

Transit duration T ~ Transit depth δ ≈

⇒ It is easier to ﬁnd small planets around small stars.

Collier Cameron, A., 2016. Methods of Detecting Exoplanets Seager & Mallén-Ornelas 2003, ApJ 585, 1038 Image credits: NASA Winn 2010. Exoplanets edited by S. Seager What is the transit method?

Assuming circular orbit and i = 90°:

Jupiter Earth δ = 1% δ = 84 ppm T ≈ 30h T ≈ 3h

Hot Jupiter T ≈ 2.5h

Transit duration T ~ Transit depth δ ≈

⇒ It is easier to ﬁnd small planets around small stars.

Collier Cameron, A., 2016. Methods of Detecting Exoplanets Seager & Mallén-Ornelas 2003, ApJ 585, 1038 Image credits: NASA Winn 2010. Exoplanets edited by S. Seager What is the probability of a planet transiting its star?

Collier Cameron, A., 2016. Methods of Detecting Exoplanets Seager & Mallén-Ornelas 2003, ApJ 585, 1038 Winn 2010. Exoplanets edited by S. Seager What is the probability of a planet transiting its star?

sphere i Celestial

To observer

sphere a i Celestial

To observer

sphere a i Celestial

To observer For transits we need:

acosi ≤ R* − Rp

sphere a i Celestial

To observer For transits we need:

acosi ≤ R* − Rp

" R* + Rp % R* + Rp R Prob$ cos i < The probability' = is ≈ * # a & a a

€

sphere a i Celestial

To observer For transits we need:

acosi ≤ R* − Rp

" R* + Rp % R* + Rp R Prob$ cos i < The probability' = is ≈ * # a & a a

10% 0.5% 0.1%

0.05AU € 1AU 5AU

sphere a i Celestial

To observer For transits we need:

acosi ≤ R* − Rp

" R* + Rp % R* + Rp R Prob$ cos i < The probability' = is ≈ * # a & a 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., 2016. Methods of Detecting Exoplanets Seager & Mallén-Ornelas 2003, ApJ 585, 1038 Winn 2010. Exoplanets edited by S. Seager >2920 transiting planets conﬁrmed since 1999

2 HD209458

55 Cnc e CoRoT-7b 0.1

0.03 Kepler-37b

1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 >2920 transiting planets conﬁrmed since 1999

2 HD209458

“sub-Neptunes”

55 Cnc e CoRoT-7b 0.1

0.03 Kepler-37b

1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 >2920 transiting planets conﬁrmed since 1999

2 HD209458

“sub-Neptunes”

55 Cnc e “super-Earths” CoRoT-7b 0.1

0.03 Kepler-37b

1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 Radius distribution of small planets Fulton et al. (2017)

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

evaporation “valley”

Owen & Wu (2017) evaporation Jin & Mordasini (2017) “desert” Sanchez-Ojeda et al. (2014) sub-Neptunes Lundkvist et al. (2016) Lopez (2016)

super-Earths

See also Zeng et al. (2017), Van Eylen et al. (2017), Schlichting (2018) From initial detection of a transit to conﬁrmation or validation of a planet Pipeline identiﬁes planet candidates (TCE) Robo vetting (KOI) Photometric Data processing pipeline monitoring

From initial detection Human Statistical Conﬁrmed of a transit vetting validation planet to conﬁrmation or validation of a planet

Follow-up observations 1: (astrophysical) Validated Follow-up observations 1I: Reconnaissance spectroscopy, false positive ground-based photometry, planet Radial-velocity monitoring for speckle interferometry, lucky independent conﬁrmation imaging, AO imaging Pipeline identiﬁes planet candidates (TCE) Robo vetting (KOI) Photometric Data processing pipeline monitoring

From initial detection Human Statistical Conﬁrmed of a transit vetting validation planet to conﬁrmation or validation of a planet See Tim Morton and Andrew Vanderburg’s talks after lunch

See tomorrow’s talks! Pipeline identiﬁes planet candidates (TCE) Robo vetting (KOI) Photometric Data processing pipeline monitoring

From initial detection Human Statistical Conﬁrmed of a transit vetting validation planet to conﬁrmation or validation of a planet See Tim Morton and Andrew Vanderburg’s talks after lunch

See tomorrow’s talks! 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 Detecting transits: the Box-ﬁtting Least Squares technique (BLS)

Kovács, Zucker & Mazeh (2002)

F₀, u1, u2 ,٭Rp/R ,٭parameters: P, t₀, ω, e, i, a/R 10 Flux

Time

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

Kovács, Zucker & Mazeh (2002)

F₀, u1, u2 ,٭Rp/R ,٭parameters: P, t₀, ω, e, i, a/R 10

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 Detecting transits: the Box-ﬁtting Least Squares technique (BLS)

Kovács, Zucker & Mazeh (2002)

F₀, u1, u2 ,٭Rp/R ,٭parameters: P, t₀, ω, e, i, a/R 10

3 non-linear parameters: P, t₀, width and 2 linear parameters: F₀, depth Flux Step through a Time grid of P, t₀, width.

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

Kovács, Zucker & Mazeh (2002)

F₀, u1, u2 ,٭Rp/R ,٭parameters: P, t₀, ω, e, i, a/R 10

3 non-linear parameters: P, t₀, width and 2 linear parameters: F₀, depth Flux Step through a Time 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 Detecting transits: the Box-ﬁtting Least Squares technique (BLS)

Kovács, Zucker & Mazeh (2002)

F₀, u1, u2 ,٭Rp/R ,٭parameters: P, t₀, ω, e, i, a/R 10

3 non-linear parameters: P, t₀, width and 2 linear parameters: F₀, depth Flux Step through a χ² Time grid of P, t₀, width. At each step, ﬁt for F₀, depth and P optimise χ²

See also Aigrain & Irwin (2004), Collier Cameron et al. (2006) and others Example of a BLS: SuperWASP candidate

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

BLS works on sparse and unevenly sampled data.

Figure credit: A. Collier Cameron 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 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 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 Single transit searches 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) 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)

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

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 […] Figure from Wang et al. (2015) with P > 25 days will be doubled […] Detected by human eyeballing (eg. PlanetHunters), with P > 250 days will be increased by an order or magnitude” machine learning (see Dittmann et al. 2017, Foreman-Mackey et al. 2016) Villanueva, Dragomir & Gaudi (submitted to AAS Journals) ⇒ Find the period with RVs or more photometry

Human Statistical From initial detection vetting validation Conﬁrmed of a transit planet to conﬁrmation or validation of a planet

Transiting planets

Morton et al. 2011, 2012; Santerne et al., 2012,2013; Latham et al. 2009; Collier Cameron et al. 2007; Pont et al. 2005 and others Astrophysical scenarios that create transits (astrophysical false positives)

Grazing stellar binaries Background transiting planet Transiting planets

Blended stellar binaries Transiting red/brown dwarfs (background eclipsing binaries)

Morton et al. 2011, 2012; Santerne et al., 2012,2013; Latham et al. 2009; Collier Cameron et al. 2007; Pont et al. 2005 and others

Full PDC lightcurve Full PDC lightcurve

Phased full-orbit ﬂux Full PDC lightcurve

Phased full-orbit ﬂux

Phase-folded transit Full PDC lightcurve