Upload
lytu
View
215
Download
2
Embed Size (px)
Citation preview
69 Transiting planets
UCF 2010, G. Campanella
• Transit depth
• Total Transit Duration
Impact Parameter
• Ingress Duration
• We can calculate r, a, i.
• RV follow up observations
the sin i indetermination for
the mass disappears
2 2
1 cosT
PR r at i
a R R
cosa
b iR
21T
rt t b
R
2
off on
off
F F rF
F R
3Seager & Mallén-Ornelas 2002
Transit hunters
COROT (CNES/ESA)
launch Dec. 2006
Kepler (NASA)
launch Mar. 20094UCF 2010, G. Campanella
PLANET Transit depth (%)
HD 209458b 1.5
Earth-like 0.01
• A smaller, natural satellite that orbits an extrasolarplanet.
• There are no known exomoons, but their existence is theorized around many exoplanets.
Motivation Detection Methods Detectability with KCP
UCF 2010, G. Campanella
1. A novel detection and proof of principle.
2. Exomoons are likely to be < MEARTH and rocky.
3. Complex life may not form on exoplanets without large moons.
Lathe 2005
Waltham 2004Ward & Brownlee 2000Laskar et al. 1993
Motivation Detection Methods Detectability with KCP
UCF 2010, G. Campanella
1. A novel detection and proof of principle.
2. Exomoons are likely to be < MEARTH and rocky.
3. Complex life may not form on exoplanets without large moons.
4. There may be more habitable exomoons than exoplanets.
5. Implications for planetary formation theory.
7
Motivation Detection Methods Detectability with KCP
UCF 2010, G. Campanella
• Highly challenging!
• Transit timing variations (Sartoretti & Schneider 1999)
• Microlensing (Han et al. 2002)
• Planet-moon eclipses (Cabrera & Schneider 2007)
• Lightcurve distortions (Simon et al. 2007)
• Pulsar timing (Lewis et al. 2008)
• Transit duration variations (Kipping 2009)
• Rossiter-McClaughlin effect distortions (Simon et al. 2009, Szabo et al. 2009)
Motivation Detection Methods Detectability with KCP
UCF 2010, G. Campanella
9
Motivation Detection Methods Detectability with KCP
UCF 2010, G. Campanella
10
Motivation Detection Methods Detectability with KCP
UCF 2010, G. Campanella
11
Motivation Detection Methods Detectability with KCP
UCF 2010, G. Campanella
12
• Caused by the apparent motion of the planet along the projected diameter of its orbit around the barycentre of the planet-satellite system.
TTV is a positional effect, "astrometry"-like detection
Sartoretti & Schneider 1999
Motivation Detection Methods Detectability with KCP
• TTV can also be caused by:
- gravitational influence of other planets,
- general relativistic precession of the orbit,
- tidal deformations of both the star and the planet.
• The period of an exomoon must always be much smaller than the period of the host exoplanet
TTV frequency > Nyquist frequency => harmonics
• TTV MMoon aMoon
• 1 measureable, 2 unknowns => Can’t solve!13
Motivation Detection Methods Detectability with KCP
UCF 2010, G. Campanella
14
Kipping 2009
• TDV is a velocity effect (velocity vector of the planet and of the barycentreon opposite direction, the transit will last longer)
"radial velocity"-like detection
Motivation Detection Methods Detectability with KCP
• TTV MMoon aMoon
• TTV and TDV are 90°out-of-phase.
• TTV and TDV provide the mass and orbital radius of the exomoon uniquely.
• TTV TDV 1s -150 seconds.
1 2
Moon MoonTDV M a
15
Motivation Detection Methods Detectability with KCP
16
• Kepler is the most precise photometer
currently available: 20ppm/hour
• But the ground is catching
up fast!
UCF 2010, G. Campanella
For the purposes of TTV and TDV measurements we need equations for:
• with photon collection rate
• Uncertainty on transit depth
• Uncertainty on transit duration
• Uncertainty on mid-transit time
17
Exomoons detection
Carter et al. 2008
1 1d
ph
W dT
1 2T W T
1 2ct
W T
phW d T 8 1 0.4( 12)6.3 10 10 m
ph hr
In order to account for red noise, we modify W to W′:
18
The total noise
phW d T 2 21 1 1
' ph
I SW d T
UCF 2010, G. Campanella
Test of the modified formulation:
• Generate limb darkened lightcurves for a Neptune, Saturn and Jupiter-like planet in the habitable zone of a mKep = 12, G2V Sun-like star with i = 90°and e = 0.
• Generate 30,000 noisy lightcurves. The correlated noises and photon noise are randomly generated in all cases.
• Fit the noisy lightcurves.
• Obtain 10,000 estimates of T and tc in each case.
19
Lightcurve simulations
results for the Saturn-case T values
Jupiters vs Saturns vs Neptunes
20
• What is the optimum planet to search for moons around?
• Simulate the detectability of the TTV signal:
Calculate the TTV signal amplitude and mid-transit time uncertainty as a function of planetary orbital period
find the value for each case.
• Low-density planets offer largest SNR.
MS = 0.2M⊕
aS = 0.4895 RH
G2V star, mKep = 12
i = 90°
2
ξ-mKep parameter space
21
• Which exomoons are detectable?
• Consider an exomoon in the condition of a Saturn hosting a single satellite in the habitable zone of the host star.
• Acceptance criteria: 1) TTV confidence is ≥ 3-σ and TDV confidence is ≥ 8-σ
2) TTV confidence is ≥ 8-σ and TDV confidence is ≥ 3-σ
M0V star
MS = 1/3 M⊕
1 2
Moon MoonTDV M a
TTV MMoon aMoon
i = 90°
3
*
2( )
habhab
P S
aP
G M M M
*haba L L AU
1 3
2 PRoche P
S
r R
1 3
*3
PHill P
Mr a
M
Minimum detectable hab exomoons
22
• Assume the probability distribution of exomoons with respect to ξ is flat between ξmin and ξmax.
• Calculate the magnitude limits to detect 25%, 50% or 75% of the exomoons in the given sample, i.e. the quartile values.
• Determine the minimum detectable exomoon mass for various star types.
• With KCP habitable moons down to ~ 0.2 M⊕ are detectable.
• Consider a 0.2 M⊕ habitable moon with an M2V host star detected with Kepler. Expect RS = 0.65 R⊕ (Valencia et al. 2006).
• Collected multiple transit lightcurves of the planet-moon system.
• For each transit event subtract the planetary transit signal and then fold the residuals on the exomoon period.
• This produces a composite exomoon transit lightcurve:
( reducible to 3.2 ppm by observing 28 transits) RS can be estimated
• Transmission spectroscopy with JWST: rms scatter of 7.5 ppm per transit.
• Consider the moon with atmosphere (3 scale heights - 20km each)
transit depth of ∼ 146 ppm
• This difference (4 ppm) is detectable to 3-σ confidence by binning 32 transits together.
23
What Next ?
142 17F ppm
F
Conclusions
• The search for exoplanets has imposed itself like one of the most dynamic research fields: 422 planets discovered in 14 years.• Attention has switched from finding planets to characterizing them.
• Exomoons may be detected through transit timing effects (TTV & TDV). • Our results highlight the promising opportunity of making the first exomoon detection using Kepler.• Saturn-like planets are the ideal host candidates for detection due to their large radius to mass ratio.• Kepler should be sensitive down to 0.2 M⊕ habitable exomoons.
• Transmission spectroscopy with JWST should be able to detect molecular species with 20-80 transit events, in the best cases (Type-M stars).
24UCF 2010, G. Campanella
UCF 2010, G. Campanella
422 Extrasolar planets
26
The lightest: Gliese 581 e (~ 1.9 M⊕)
PLANET Transit depth (%)
HD 209458b 1.5
Jupiter 1.01
Earth 0.0084
27UCF 2010, G. Campanella
1. A novel detection and proof of principle.
Simon et al. 2007
Sartoretti & Schneider 1999
Szabo et al. 2006
Kipping 2009
Motivation Detection Methods Detectability with KCP
UCF 2010, G. Campanella
1. A novel detection and proof of principle.
2. Exomoons are likely to be < MEARTH and rocky.
Valencia et al. 2006
Canup & Ward 2007
Belbruno & Gott 2005
Motivation Detection Methods Detectability with KCP
UCF 2010, G. Campanella
1. A novel detection.
2. Exomoons are likely to be < MEARTH.
3. Complex life may not form on exoplanets without large moons.
4. There may be more habitable exomoons than exoplanets.
Scharf 2008 Thommes et al. 2008
Motivation Detection Methods Detectability with KCP
UCF 2010, G. Campanella
• Could we look for the dip in star light due to an exomoon’s shadow?
• Average position of moon results in lightcurves overlapping: indistinguishable.
• => Possible, but somewhat insensitive to low mass objects.
31
Cabrera & Schneider 2007
Motivation Detection Methods Detectability with KCP
Kepler bandpass• A single broad bandpass in order to gather as many photons as
possible.
• Most of the throughput from Johnson V,R,I filters since we are not interested in early-type and active stars.
32
Lightcurve fitting• We fit for , i, the planet-star radii ratio k and tc:
1. The genetic algorithm PIKAIA gets close to a minimum in :
2. The solution is used as a starting point for a -minimisation performed with the AMOEBA routine:
transformations of the simplex (4 vertices) aimed to decrease the function values at its vertices
3. “prayer-bead” Monte Carlo simulation to obtain the parameter uncertainties:
33
*Pa R
2
assigned a fitness
crossovers & mutations
newgeneration
compared to observations
2
the set of residualsfrom the best-fit
solution is shifted by one data point and added to the
best-fit transit model
The new data set is fitted
purely random
set
global solution
Uncertaintiesestimated on
~ 2500 samples
Metcalfe & Charbonneau 2003
5.8 8 3.6
Star flux
At 3.6, 4.5, 5.8 and 8 mm,the planet shows different
transit depths: something is absorbing
in its atmosphere! ~ 0.09%
34
Submitted to MNRAS
G0 V star
P ~ 3.52 days
Mp ~ 0.69MJup
Rp ~ 1.4RJup
UCF 2010, G. Campanella
The Habitable Zone • Habitable Zone (HZ): the region around a star within which an Earth-like
planet can sustain liquid water on its surface.
• Continuously Habitable Zone (CHZ): the region that remains habitable for durations longer than 1 Gyr.
• Planets inside the HZ are not necessarily habitable.
• They can be too small, like Mars,
to maintain active geology and to
limit the gravitational escape of
their atmospheres.
• They can be too massive, like
HD69830d, and have accreted a
thick H2-He envelope below
which water cannot be liquid.
35
Biosignatures• We need to search for features that are specific to biological activities.
• The atmospheres of Mars and Venus are in chemical equilibrium, with an overwhelming abundance of carbon dioxide.
• A biosignature is a molecule which presence alters the chemical equilibrium of a planet atmosphere in a way that this disequilibrium cannot be explained solely by nonbiological processes (Lovelock 1960).
• The mid-IR spectrum of
Earth displays the 9.6 μm O3
band, the 15 μm CO2 band,
the 6.3 μm H2O band and
the H2O rotational band that
extends longward of 12 μm.
• To be able to characterize
terrestrial planet atmospheres
we have to wait for JWST (2014). 36
37UCF 2010, G. Campanella
1 M⊕ Limit
38
• Compare the distance limit for detecting a 1 M⊕ habitable exomoon to the distance limit for a transiting 1 R⊕ habitable exoplanet.
• For moons the volume space is diminished by a factor of ∼4.
• With Kepler around 25,000 stars could be surveyed for habitable-zone exomoons.
UCF 2010, G. Campanella