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Andrea Miglio, Karsten Brogaard, Rasmus Handberg
SACSTELLAR ASTROPHYSICS CENTRE
SAC
STELLAR ASTROPHYSICS CENTRE
LogodesignStellar Astrophysics Centre
LOGOVARIANTER
Aarhus University, Denmark
and
LOOKS LIKE A DUCK, MOVES LIKE A DUCK, BUT DOES IT QUACK LIKE A DUCK?
ASTEROSEISMOLOGY OF RED-GIANT STARS IN CLUSTERS
School of Physics and Astronomy, University of Birmingham, UK
4000450050005500600065007000
100
101
102
103
Teff [K]
L/L su
n
Kepler KASC starsKepler Objects of Interest
Chaplin & Miglio, ARAA, 2013
0
1.0e+04
2.0e+04
3.0e+04
4.0e+04
KIC 6949816
0
2.0e+04
4.0e+04
6.0e+04KIC 9269772
0
5.0e+04
1.0e+05
1.5e+05
2.0e+05
KIC 3100193
10 20 30 40 50 60 70 80 90 100 110 0
5.0e+04
1.0e+05
1.5e+05
2.0e+05
KIC 7522297
Frequency [µHz]Po
wer
spe
ctra
l den
sity
[p
pm2 /µ
Hz]
νmax
5
10
15
20
KIC 8006161
20
40
60
KIC 12069424
100
200
300
KIC 6442183
Pow
er s
pect
ral d
ensi
ty
[ppm
2 /µH
z]
200
400
600
KIC 12508433
500 1000 1500 2000 2500 3000 3500 40000
200
400
600
800
KIC 6035199
Frequency [µHz]
νmax
SOLAR-LIKE OSCILLATIONS
Brown et al. 1991Kjeldsen&Bedding 1995
Asteroseismology of old open clusters 2079
0.8 0.9 1 1.1 1.2 1.3 1.4 1.5 1.6
13
14
15
16
17
18
19
20
V
B−V
NGC6791
Stetson et al. photometryRGB stars used in this studyRC stars used in this study
0.4 0.6 0.8 1 1.2 1.4
12
12.5
13
13.5
14
14.5
15
15.5
16
16.5
V
B−V
NGC6819
Hole et al. photometryRGB stars used in this studyRC stars used in this study
Figure 1. CMD of NGC 6791 (left-hand panel) and NGC 6819 (right-hand panel). Photometric data are taken from Stetson, Bruntt & Grundahl (2003) andHole et al. (2009), respectively. RGB stars used in this work are marked by open squares and RC stars by open circles. See Section 3.2 for a description of thetarget selection.
et al. 1991; Kjeldsen & Bedding 1995; Mosser et al. 2010; Belkacemet al. 2011), and therefore
νmax ≃ M/M⊙(R/R⊙)2
!Teff/Teff,⊙
νmax,⊙ , (2)
where νmax,⊙ = 3100 µHz and Teff,⊙ = 5777 K.These scaling relations are widely used to estimate masses and
radii of red giants (see e.g. Stello et al. 2008; Kallinger et al. 2010;Mosser et al. 2010), but they are based on simplifying assumptionsthat remain to be independently verified. Recent advances have beenmade on providing a theoretical basis for the relation between theacoustic cut-off frequency and νmax (Belkacem et al. 2011), andpreliminary investigations with stellar models (Stello et al. 2009)indicate that the scaling relations hold to within ∼3 per cent on themain sequence and RGB (see also White et al. 2011).
Depending on the observational constraints available, we mayderive mass estimates from equations (1) and (2) alone, or via theircombination with other available information from non-seismic ob-servations. When no information on distance/luminosity is avail-able, which is usually the case for field stars, equations (1) and (2)may be solved to derive M and R (see e.g. Kallinger et al. 2010;Mosser et al. 2010):
M
M⊙≃
"νmax
νmax,⊙
#3 ""ν
"ν⊙
#−4 "Teff
Teff,⊙
#3/2
, (3)
R
R⊙≃
"νmax
νmax,⊙
# ""ν
"ν⊙
#−2 "Teff
Teff,⊙
#1/2
. (4)
In the case of clusters, we can use the distance/luminosities esti-mated with independent methods (i.e. via isochrone fitting or eclips-ing binaries) as an additional constraint. Including this informationallows M to be estimated also from equation (1) or equation (2)
alone (see equations 5 and 6, respectively), or in combination lead-ing to a mass estimate with no explicit dependence on Teff (as inequation 7):
M
M⊙≃
""ν
"ν⊙
#2 "L
L⊙
#3/2 "Teff
Teff,⊙
#−6
, (5)
M
M⊙≃
"νmax
νmax,⊙
# "L
L⊙
# "Teff
Teff,⊙
#−7/2
, (6)
M
M⊙≃
"νmax
νmax,⊙
#12/5 ""ν
"ν⊙
#−14/5 "L
L⊙
#3/10
. (7)
In the following sections, we use equations (3)–(7) directly toestimate M (and R) without adding any extra dependence on stellarmodels. As illustrated in detail e.g. by Gai et al. (2011), additional(so-called ‘grid-based’) methods to estimate M and R can be de-signed. These procedures are also based on equations (1) and (2)but, by searching solutions for M and R in grids of evolutionarytracks, have the advantage of reducing uncertainties on the derivedmass and radius, at the price of some model dependence that weprefer to avoid in this study.
2.1 Error estimates
The formal uncertainties (σ i) on the masses (Mi) of the stars wereused to compute a weighted average mass for stars belonging to theRGB and for stars in the RC:
M =$N
1 Mi/σ2i$N
1 1/σ 2i
.
C⃝ 2011 The Authors, MNRAS 419, 2077–2088Monthly Notices of the Royal Astronomical Society C⃝ 2011 RAS
Belkacem et al. 2011
2000 2200 2400 2600 2800 3000 3200 3400 3600 3800 40000
0.02
0.04
0.06
0.08
0.1
0.12
ν [µHz]
m2 s
−2µ
Hz−
1
2000 2200 2400 2600 2800 3000 3200 3400 3600 3800 40000
0.02
0.04
0.06
0.08
0.1
0.12
ν [µHz]
m2 s
−2µ
Hz−
1
BiSON data
�⌫ =⇣2R R0
drc(r)
⌘�1
/�M/R3
�1/2
Davies et al. 2014
SOLAR-LIKE OSCILLATIONS
NON-RADIAL MODES IN K GIANTS
He core
H-burning shell
H-rich radiative core
convective envelope
gravity mode acoustic mode
An asteroseismic membership study of NGC 6791, NGC 6819, and NGC 6811 3
FIG. 2.— Color-magnitude diagrams of the clusters. Photometry is fromStetson et al. (2003) (NGC 6791), Hole et al. (2009) (NGC 6819), and theWebda database (http://www.univie.ac.at/webda/) (NGC 6811). Representa-tive isochrones from Pietrinferni et al. (2004) (NGC 6791 and NGC 6811) andMarigo et al. (2008) (NGC 6819) are matched to the red giant stars to guidethe eye. Near horizontal dashed lines mark the sampling limit for solar-likeoscillations with Kepler’s long-cadence mode. Large black dots show starsfor which Kepler data are currently available, while large colored dots (red,purple and blue) indicate the subset of these which show evidence of solar-like oscillations. All large dots in the two lower panels show likely membersfrom radial velocity surveys (Hole et al. 2009 and unpublished work by Mei-bom). Vertical dashed lines mark the approximate location of the classicalinstability strip. Stars marked as ’Clump star’ in Tables 1 and 2 are encircled.
The photometric time series data presented here were ob-tained in ’long cadence’ (∆t ∼ 30min, Jenkins et al. (2010a))between 2009 May 12 and 2010 March 20, known as ob-serving quarters 1–4 (Q1–Q4). Within this period the space-craft’s long-cadence mode provided approximately 14,000data points per star. The raw images were processed by thestandard Kepler Science Pipeline and included steps to re-move signatures in the data from sources such as pointingdrifts, focus changes, and thermal variations all performedduring the Pre-search Data Conditioning (PDC) procedure(Jenkins et al. 2010b). PDC also corrects for flux from neigh-boring stars within each photometric aperture based on astatic aperture model. However, this model is not adequatefor all stars, due to small changes in the telescope point-spread-function and pointing between subsequent quarterlyrolls when the spacecraft is rotated 90 degrees to align itssolar panels. As a result the light curves show jumps in theaverage flux level from one quarter to the next. To correctfor that we shifted the average flux levels for each quarterto match that of the raw (pre-PDC) data before stitching to-gether the time series from all four quarters. This ensured thatthe relative flux variations were consistent from one quarterto the next. We compared our corrected (post-PDC) data withthe raw data and also after we performed a number of “man-ual” corrections based entirely on the appearance of the lightcurves (hence not taking auxiliary house-keeping data such aspointing into account). These corrections included removalof outliers, jumps, and slow trends in a similar way as the ap-proach by Garcia et al. (2011). The comparison revealed thatfor a few stars PDC did not perform well, in which case wechose the raw or “manually” corrected raw data.
4. BLENDING AND LIGHT CURVE VERIFICATIONThe super stamps in Figure 1 clearly illustrate that blend-
ing is an issue we need to address before proceeding withthe analysis of these cluster data. Some stars show clearsignatures of blending arising from the relatively large pixelscale (∼ 4′′) of the Kepler photometer compared to the fairlycrowded cluster fields. Blending will give rise to additionallight in the photometric aperture, which will reduce the rela-tive stellar variability, and increase the photon counting noise.In severe cases, the detected stellar variability arises from ablending star and not the target.We have studied the effects from blending by looking at
correlations between light curves of all the target stars (black,red, purple, and blue dots in Figure 2). The light curve corre-lations show no significant increase unless the stars are withinapproximately five pixels of each other and the blending staris at least as bright as the target (Figure 3). We visuallyassessed the light curves of all stars separated by less thanfive pixels, and identified those that showed clear correlationover extended periods of time as blends. We list the blend-ing stars in Table 1 (column-3), Table 2 (column-4), and Ta-ble 3 (column-4). Also listed here, is the variability type ofblending stars identified from single light curves that clearlyshowed variability from two stars. If the variability includedthe expected seismic signal of the target, under the assumptionthat the target was a cluster member, we interpreted the addi-tional signal as caused by a blend. We see no blending forour NGC 6811 targets. We note that the stars for which wecurrently have light curves are far from all stars in the vicinityof the clusters (see Figure 1). It is therefore likely that ourcorrelation analysis has not revealed all blends. For the twomost crowded clusters (NGC 6791 and NGC 6819) there are
An asteroseismic membership study of NGC 6791, NGC 6819, and NGC 6811 3
FIG. 2.— Color-magnitude diagrams of the clusters. Photometry is fromStetson et al. (2003) (NGC 6791), Hole et al. (2009) (NGC 6819), and theWebda database (http://www.univie.ac.at/webda/) (NGC 6811). Representa-tive isochrones from Pietrinferni et al. (2004) (NGC 6791 and NGC 6811) andMarigo et al. (2008) (NGC 6819) are matched to the red giant stars to guidethe eye. Near horizontal dashed lines mark the sampling limit for solar-likeoscillations with Kepler’s long-cadence mode. Large black dots show starsfor which Kepler data are currently available, while large colored dots (red,purple and blue) indicate the subset of these which show evidence of solar-like oscillations. All large dots in the two lower panels show likely membersfrom radial velocity surveys (Hole et al. 2009 and unpublished work by Mei-bom). Vertical dashed lines mark the approximate location of the classicalinstability strip. Stars marked as ’Clump star’ in Tables 1 and 2 are encircled.
The photometric time series data presented here were ob-tained in ’long cadence’ (∆t ∼ 30min, Jenkins et al. (2010a))between 2009 May 12 and 2010 March 20, known as ob-serving quarters 1–4 (Q1–Q4). Within this period the space-craft’s long-cadence mode provided approximately 14,000data points per star. The raw images were processed by thestandard Kepler Science Pipeline and included steps to re-move signatures in the data from sources such as pointingdrifts, focus changes, and thermal variations all performedduring the Pre-search Data Conditioning (PDC) procedure(Jenkins et al. 2010b). PDC also corrects for flux from neigh-boring stars within each photometric aperture based on astatic aperture model. However, this model is not adequatefor all stars, due to small changes in the telescope point-spread-function and pointing between subsequent quarterlyrolls when the spacecraft is rotated 90 degrees to align itssolar panels. As a result the light curves show jumps in theaverage flux level from one quarter to the next. To correctfor that we shifted the average flux levels for each quarterto match that of the raw (pre-PDC) data before stitching to-gether the time series from all four quarters. This ensured thatthe relative flux variations were consistent from one quarterto the next. We compared our corrected (post-PDC) data withthe raw data and also after we performed a number of “man-ual” corrections based entirely on the appearance of the lightcurves (hence not taking auxiliary house-keeping data such aspointing into account). These corrections included removalof outliers, jumps, and slow trends in a similar way as the ap-proach by Garcia et al. (2011). The comparison revealed thatfor a few stars PDC did not perform well, in which case wechose the raw or “manually” corrected raw data.
4. BLENDING AND LIGHT CURVE VERIFICATIONThe super stamps in Figure 1 clearly illustrate that blend-
ing is an issue we need to address before proceeding withthe analysis of these cluster data. Some stars show clearsignatures of blending arising from the relatively large pixelscale (∼ 4′′) of the Kepler photometer compared to the fairlycrowded cluster fields. Blending will give rise to additionallight in the photometric aperture, which will reduce the rela-tive stellar variability, and increase the photon counting noise.In severe cases, the detected stellar variability arises from ablending star and not the target.We have studied the effects from blending by looking at
correlations between light curves of all the target stars (black,red, purple, and blue dots in Figure 2). The light curve corre-lations show no significant increase unless the stars are withinapproximately five pixels of each other and the blending staris at least as bright as the target (Figure 3). We visuallyassessed the light curves of all stars separated by less thanfive pixels, and identified those that showed clear correlationover extended periods of time as blends. We list the blend-ing stars in Table 1 (column-3), Table 2 (column-4), and Ta-ble 3 (column-4). Also listed here, is the variability type ofblending stars identified from single light curves that clearlyshowed variability from two stars. If the variability includedthe expected seismic signal of the target, under the assumptionthat the target was a cluster member, we interpreted the addi-tional signal as caused by a blend. We see no blending forour NGC 6811 targets. We note that the stars for which wecurrently have light curves are far from all stars in the vicinityof the clusters (see Figure 1). It is therefore likely that ourcorrelation analysis has not revealed all blends. For the twomost crowded clusters (NGC 6791 and NGC 6819) there are
An asteroseismic membership study of NGC 6791, NGC 6819, and NGC 6811 3
FIG. 2.— Color-magnitude diagrams of the clusters. Photometry is fromStetson et al. (2003) (NGC 6791), Hole et al. (2009) (NGC 6819), and theWebda database (http://www.univie.ac.at/webda/) (NGC 6811). Representa-tive isochrones from Pietrinferni et al. (2004) (NGC 6791 and NGC 6811) andMarigo et al. (2008) (NGC 6819) are matched to the red giant stars to guidethe eye. Near horizontal dashed lines mark the sampling limit for solar-likeoscillations with Kepler’s long-cadence mode. Large black dots show starsfor which Kepler data are currently available, while large colored dots (red,purple and blue) indicate the subset of these which show evidence of solar-like oscillations. All large dots in the two lower panels show likely membersfrom radial velocity surveys (Hole et al. 2009 and unpublished work by Mei-bom). Vertical dashed lines mark the approximate location of the classicalinstability strip. Stars marked as ’Clump star’ in Tables 1 and 2 are encircled.
The photometric time series data presented here were ob-tained in ’long cadence’ (∆t ∼ 30min, Jenkins et al. (2010a))between 2009 May 12 and 2010 March 20, known as ob-serving quarters 1–4 (Q1–Q4). Within this period the space-craft’s long-cadence mode provided approximately 14,000data points per star. The raw images were processed by thestandard Kepler Science Pipeline and included steps to re-move signatures in the data from sources such as pointingdrifts, focus changes, and thermal variations all performedduring the Pre-search Data Conditioning (PDC) procedure(Jenkins et al. 2010b). PDC also corrects for flux from neigh-boring stars within each photometric aperture based on astatic aperture model. However, this model is not adequatefor all stars, due to small changes in the telescope point-spread-function and pointing between subsequent quarterlyrolls when the spacecraft is rotated 90 degrees to align itssolar panels. As a result the light curves show jumps in theaverage flux level from one quarter to the next. To correctfor that we shifted the average flux levels for each quarterto match that of the raw (pre-PDC) data before stitching to-gether the time series from all four quarters. This ensured thatthe relative flux variations were consistent from one quarterto the next. We compared our corrected (post-PDC) data withthe raw data and also after we performed a number of “man-ual” corrections based entirely on the appearance of the lightcurves (hence not taking auxiliary house-keeping data such aspointing into account). These corrections included removalof outliers, jumps, and slow trends in a similar way as the ap-proach by Garcia et al. (2011). The comparison revealed thatfor a few stars PDC did not perform well, in which case wechose the raw or “manually” corrected raw data.
4. BLENDING AND LIGHT CURVE VERIFICATIONThe super stamps in Figure 1 clearly illustrate that blend-
ing is an issue we need to address before proceeding withthe analysis of these cluster data. Some stars show clearsignatures of blending arising from the relatively large pixelscale (∼ 4′′) of the Kepler photometer compared to the fairlycrowded cluster fields. Blending will give rise to additionallight in the photometric aperture, which will reduce the rela-tive stellar variability, and increase the photon counting noise.In severe cases, the detected stellar variability arises from ablending star and not the target.We have studied the effects from blending by looking at
correlations between light curves of all the target stars (black,red, purple, and blue dots in Figure 2). The light curve corre-lations show no significant increase unless the stars are withinapproximately five pixels of each other and the blending staris at least as bright as the target (Figure 3). We visuallyassessed the light curves of all stars separated by less thanfive pixels, and identified those that showed clear correlationover extended periods of time as blends. We list the blend-ing stars in Table 1 (column-3), Table 2 (column-4), and Ta-ble 3 (column-4). Also listed here, is the variability type ofblending stars identified from single light curves that clearlyshowed variability from two stars. If the variability includedthe expected seismic signal of the target, under the assumptionthat the target was a cluster member, we interpreted the addi-tional signal as caused by a blend. We see no blending forour NGC 6811 targets. We note that the stars for which wecurrently have light curves are far from all stars in the vicinityof the clusters (see Figure 1). It is therefore likely that ourcorrelation analysis has not revealed all blends. For the twomost crowded clusters (NGC 6791 and NGC 6819) there are
~8 Gyr
~2.5 Gyr
~1 Gyr
acoustic spectrum of a R~10 Rsun star
M~ 1.1 Msun
ν~ 35 μHz
M~ 1.6 Msun
ν~ 55 μHz
M~ 2.5 Msun
ν~ 80 μHz
overmassiveundermassive
SOUNDING K GIANTS IN CLUSTERS
OVERMASSIVE STARS
0.4 0.6 0.8 1 1.2 1.4
12
12.5
13
13.5
14
14.5
15
15.5
16
16.5
V
B−V
NGC6819
Hole et al. photometryRGB stars used in this studyRC stars used in this studyOvermassive stars
Masses ~ 2-3 MSUN
Handberg et al. 2015, in preparationBrogaard et al. 2015
in depth studies of their structuree.g. internal rotational profile,
core structure
compare with expectations from binary evolution
detection
A LI-RICH 1.6 MSUN RGB STAR
Anthony-Twarog et al. 2013
*
Rosvick & VandenBerg 1998
Carlberg et al. 2015
✗ ✗0.7 RC
Asteroseismology of old open clusters 2079
0.8 0.9 1 1.1 1.2 1.3 1.4 1.5 1.6
13
14
15
16
17
18
19
20
V
B−V
NGC6791
Stetson et al. photometryRGB stars used in this studyRC stars used in this study
0.4 0.6 0.8 1 1.2 1.4
12
12.5
13
13.5
14
14.5
15
15.5
16
16.5
V
B−V
NGC6819
Hole et al. photometryRGB stars used in this studyRC stars used in this study
Figure 1. CMD of NGC 6791 (left-hand panel) and NGC 6819 (right-hand panel). Photometric data are taken from Stetson, Bruntt & Grundahl (2003) andHole et al. (2009), respectively. RGB stars used in this work are marked by open squares and RC stars by open circles. See Section 3.2 for a description of thetarget selection.
et al. 1991; Kjeldsen & Bedding 1995; Mosser et al. 2010; Belkacemet al. 2011), and therefore
νmax ≃ M/M⊙(R/R⊙)2
!Teff/Teff,⊙
νmax,⊙ , (2)
where νmax,⊙ = 3100 µHz and Teff,⊙ = 5777 K.These scaling relations are widely used to estimate masses and
radii of red giants (see e.g. Stello et al. 2008; Kallinger et al. 2010;Mosser et al. 2010), but they are based on simplifying assumptionsthat remain to be independently verified. Recent advances have beenmade on providing a theoretical basis for the relation between theacoustic cut-off frequency and νmax (Belkacem et al. 2011), andpreliminary investigations with stellar models (Stello et al. 2009)indicate that the scaling relations hold to within ∼3 per cent on themain sequence and RGB (see also White et al. 2011).
Depending on the observational constraints available, we mayderive mass estimates from equations (1) and (2) alone, or via theircombination with other available information from non-seismic ob-servations. When no information on distance/luminosity is avail-able, which is usually the case for field stars, equations (1) and (2)may be solved to derive M and R (see e.g. Kallinger et al. 2010;Mosser et al. 2010):
M
M⊙≃
"νmax
νmax,⊙
#3 ""ν
"ν⊙
#−4 "Teff
Teff,⊙
#3/2
, (3)
R
R⊙≃
"νmax
νmax,⊙
# ""ν
"ν⊙
#−2 "Teff
Teff,⊙
#1/2
. (4)
In the case of clusters, we can use the distance/luminosities esti-mated with independent methods (i.e. via isochrone fitting or eclips-ing binaries) as an additional constraint. Including this informationallows M to be estimated also from equation (1) or equation (2)
alone (see equations 5 and 6, respectively), or in combination lead-ing to a mass estimate with no explicit dependence on Teff (as inequation 7):
M
M⊙≃
""ν
"ν⊙
#2 "L
L⊙
#3/2 "Teff
Teff,⊙
#−6
, (5)
M
M⊙≃
"νmax
νmax,⊙
# "L
L⊙
# "Teff
Teff,⊙
#−7/2
, (6)
M
M⊙≃
"νmax
νmax,⊙
#12/5 ""ν
"ν⊙
#−14/5 "L
L⊙
#3/10
. (7)
In the following sections, we use equations (3)–(7) directly toestimate M (and R) without adding any extra dependence on stellarmodels. As illustrated in detail e.g. by Gai et al. (2011), additional(so-called ‘grid-based’) methods to estimate M and R can be de-signed. These procedures are also based on equations (1) and (2)but, by searching solutions for M and R in grids of evolutionarytracks, have the advantage of reducing uncertainties on the derivedmass and radius, at the price of some model dependence that weprefer to avoid in this study.
2.1 Error estimates
The formal uncertainties (σ i) on the masses (Mi) of the stars wereused to compute a weighted average mass for stars belonging to theRGB and for stars in the RC:
M =$N
1 Mi/σ2i$N
1 1/σ 2i
.
C⃝ 2011 The Authors, MNRAS 419, 2077–2088Monthly Notices of the Royal Astronomical Society C⃝ 2011 RAS
Stello et al. 2010,2011
Corsaro et al. 2012Miglio et al. 2012
Basu et al. 2011
[Fe/H]~0.3-0.4 [Fe/H]~0
Hekker et al. 2011
EXPLOITING ENSEMBLES
14.4 14.6 14.8 15 15.2 15.4 15.6 15.8 16 16.20.6
0.8
1
1.2
1.4
1.6
1.8Eq. 6
V
M/M
sun
RGB
14.5 14.6 14.7 14.8 14.9V
RC
14.5 14.6 14.7 14.8 14.90.6
0.8
1
1.2
1.4
1.6
1.8
V
M/M
sun
RC correction applied
Mass(RGB) vs Mass(RC)
quantitative estimate of integrated RGB mass loss∆M = 0.09±0.03 (random) ±0.04 (systematic) MSUN
INTEGRATED RGB MASS LOSS
NGC6791
Miglio et al. 2012
2 3 4 5 6 7 8 9 10 11 120
50
100
150
200
250
300
350
∆ν
∆Π
1
2 3 4 5 6 7 8 9 10 11 120
50
100
150
200
250
300
350
∆ν
∆Π
1
NGC6791 NGC6819bare-schwarzschild1Hp overshooting1Hp penetrative convection
TESTING HECB MODELS
Vrad, et al., in preparation Bossini, et al., in preparationArentoft, et al., in preparationBossini, et al., 2015
Handberg, et al, in preparation
16 18 20 22 24 26 28 30 32 342.8
2.9
3
3.1
3.2
3.3
3.4
3.5
3.6
Frequency (µHz)
∆ν
(µH
z)
KIC 5023732 − ∆νfit = 3.0823 µHz
l=0l=1l=2
25 30 35 40 45 503.6
3.7
3.8
3.9
4
4.1
4.2
4.3
Frequency (µHz)
∆ν
(µH
z)
KIC 5113041 − ∆νfit = 3.9943 µHz
l=0l=1l=2
40 45 50 55 60 65 70 75
4.9
5
5.1
5.2
5.3
5.4
5.5
5.6
5.7
5.8
Frequency (µHz)
∆ν
(µH
z)
KIC 5023889 − ∆νfit = 5.3663 µHz
l=0l=1l=2
Kepler giants in NGC6819
ACOUSTIC GLITCHES IN GIANTS
ANY HOPE FOR SEISMOLOGY IN GCS?
A&A proofs: manuscript no. M4
Fig. 2.
Fig. 5.
initial helium abundance Y = 0.25 (Marino et al. 2008; Villanovaet al. 2012; Malavolta et al. 2014).
Mention multiple populations.
We proceed by adopting the recently determined values ofdistance, extinction and reddening, and mass of RGB stars from
isochrone fitting as described in the literature (M = 0.87 M�,see Malavolta et al. 2014 ). For each star in the sample we de-termine Te↵ using the de-reddened (B � V) colours and the re-lations by Ramírez & Meléndez (2005) and the photosphericradius/luminosity by combining distance, apparent magnitude
Article number, page 2 of 5
M4: a first glimpse
Miglio, Chaplin, Brogaard, et al. in preparation
ANY HOPE FOR SEISMOLOGY IN GCS?
K2P2 pipeline, Lund et al. 2015
Miglio, Chaplin, Brogaard, et al. in preparation
M4: a first glimpse
ANY HOPE FOR SEISMOLOGY IN GCS?
0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2
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17
V−I
V
Cluster membersDetections
0 10 20 30 40 50 600
10
20
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40
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60
ν max Expected [µHz]
ν max
Obs
erve
d [µ
Hz]
0 1 2 3 4 5 6 70
1
2
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7
∆ν Expected [µHz]
∆ν
Obs
erve
d [µ
Hz]
Asteroseismology of old open clusters 2079
0.8 0.9 1 1.1 1.2 1.3 1.4 1.5 1.6
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V
B−V
NGC6791
Stetson et al. photometryRGB stars used in this studyRC stars used in this study
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NGC6819
Hole et al. photometryRGB stars used in this studyRC stars used in this study
Figure 1. CMD of NGC 6791 (left-hand panel) and NGC 6819 (right-hand panel). Photometric data are taken from Stetson, Bruntt & Grundahl (2003) andHole et al. (2009), respectively. RGB stars used in this work are marked by open squares and RC stars by open circles. See Section 3.2 for a description of thetarget selection.
et al. 1991; Kjeldsen & Bedding 1995; Mosser et al. 2010; Belkacemet al. 2011), and therefore
νmax ≃ M/M⊙(R/R⊙)2
!Teff/Teff,⊙
νmax,⊙ , (2)
where νmax,⊙ = 3100 µHz and Teff,⊙ = 5777 K.These scaling relations are widely used to estimate masses and
radii of red giants (see e.g. Stello et al. 2008; Kallinger et al. 2010;Mosser et al. 2010), but they are based on simplifying assumptionsthat remain to be independently verified. Recent advances have beenmade on providing a theoretical basis for the relation between theacoustic cut-off frequency and νmax (Belkacem et al. 2011), andpreliminary investigations with stellar models (Stello et al. 2009)indicate that the scaling relations hold to within ∼3 per cent on themain sequence and RGB (see also White et al. 2011).
Depending on the observational constraints available, we mayderive mass estimates from equations (1) and (2) alone, or via theircombination with other available information from non-seismic ob-servations. When no information on distance/luminosity is avail-able, which is usually the case for field stars, equations (1) and (2)may be solved to derive M and R (see e.g. Kallinger et al. 2010;Mosser et al. 2010):
M
M⊙≃
"νmax
νmax,⊙
#3 ""ν
"ν⊙
#−4 "Teff
Teff,⊙
#3/2
, (3)
R
R⊙≃
"νmax
νmax,⊙
# ""ν
"ν⊙
#−2 "Teff
Teff,⊙
#1/2
. (4)
In the case of clusters, we can use the distance/luminosities esti-mated with independent methods (i.e. via isochrone fitting or eclips-ing binaries) as an additional constraint. Including this informationallows M to be estimated also from equation (1) or equation (2)
alone (see equations 5 and 6, respectively), or in combination lead-ing to a mass estimate with no explicit dependence on Teff (as inequation 7):
M
M⊙≃
""ν
"ν⊙
#2 "L
L⊙
#3/2 "Teff
Teff,⊙
#−6
, (5)
M
M⊙≃
"νmax
νmax,⊙
# "L
L⊙
# "Teff
Teff,⊙
#−7/2
, (6)
M
M⊙≃
"νmax
νmax,⊙
#12/5 ""ν
"ν⊙
#−14/5 "L
L⊙
#3/10
. (7)
In the following sections, we use equations (3)–(7) directly toestimate M (and R) without adding any extra dependence on stellarmodels. As illustrated in detail e.g. by Gai et al. (2011), additional(so-called ‘grid-based’) methods to estimate M and R can be de-signed. These procedures are also based on equations (1) and (2)but, by searching solutions for M and R in grids of evolutionarytracks, have the advantage of reducing uncertainties on the derivedmass and radius, at the price of some model dependence that weprefer to avoid in this study.
2.1 Error estimates
The formal uncertainties (σ i) on the masses (Mi) of the stars wereused to compute a weighted average mass for stars belonging to theRGB and for stars in the RC:
M =$N
1 Mi/σ2i$N
1 1/σ 2i
.
C⃝ 2011 The Authors, MNRAS 419, 2077–2088Monthly Notices of the Royal Astronomical Society C⃝ 2011 RAS
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SUMMARY
asteroseismology of K giants in clusters:
overmassive starsexposing camouflage:
first look at M4 with K2: detection of solar-like oscillations
undermassive Li-rich RC star
mass loss, mass loss dispersion
testing stellar models
age-mass relations