maximally extracting information from spectra full spectral fitting · 2019-02-27 · ∼exp(ndim)...
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Full Spectral Fitting :Full Spectral Fitting :
Institute for Advanced Study, PrincetonPrinceton UniversityCarnegie Observatories
Hubble FellowCarnegie-Princeton-IAS Fellow
Yuan-Sen TingYuan-Sen Ting
Maximally extracting information from spectraMaximally extracting information from spectra
Why is it necessary ?Why is it necessary ?
How can it be achieve ?How can it be achieve ?
What does it mean for stellar spectroscopy ?What does it mean for stellar spectroscopy ?
Fitting the Fitting the entireentire spectrum spectrum
Why full spectral �tting is necessaryWhy full spectral �tting is necessary
Are we using Are we using allall information in spectra ? information in spectra ?
Are we using Are we using allall information in spectra ? information in spectra ?Clean, strong lines
Are we using Are we using allall information in spectra ? information in spectra ?Clean, strong lines
But blended features are usually excluded
Fitting strong/unblended features onlyFitting strong/unblended features onlyharnesses harnesses 10%10% of the spectral information of the spectral information
Clean strong lines /all information
APOGEE survey
Elemental Abundance YST+ 2016bYST+ 2016b
Fitting strong/unblended features onlyFitting strong/unblended features onlyharnesses harnesses 10%10% of the spectral information of the spectral information
Clean strong lines /all information
Unblended stronglines only contain10% of theinformation
APOGEE survey
Elemental Abundance YST+ 2016bYST+ 2016b
Why high-resolution is thought to beWhy high-resolution is thought to benecessary for stellar spectroscopynecessary for stellar spectroscopy
Clean, strong lines
High resolution R = λ/Δλ = 24 000
Most features are blended at Most features are blended at low-resolutionlow-resolution
Low resolution R = λ/Δλ = 6000
Most features are blended at Most features are blended at low-resolutionlow-resolution
Low resolution R = λ/Δλ = 6000
Need to �t the full spectrum
Survey coverage
e.g., most MSE sample will be the cool M-giant stars
Fitting the full spectra is a Fitting the full spectra is a necessitynecessity. Stars further. Stars furtheraway (cool giant stars) only have blended featuresaway (cool giant stars) only have blended features
M-dwarfs M-dwarfs (e.g., exoplanet hosts) are cool.(e.g., exoplanet hosts) are cool.Their spectral features are mostly blendedTheir spectral features are mostly blended
How full spectral �tting is made possibleHow full spectral �tting is made possible
Problems with classical "interpolation" methodsProblems with classical "interpolation" methods
Problems with classical "interpolation" methodsProblems with classical "interpolation" methods
Need to �t all stellar parameters + elemental abundancessimultaneously
N =dim 20 − 50
Problems with classical "interpolation" methodsProblems with classical "interpolation" methods
Traditional interpolations require a "regular" grid of models
N ∼models exp(N )dim
N <dim 6
Need to �t all stellar parameters + elemental abundancessimultaneously
N =dim 20 − 50
Problems with classical "interpolation" methodsProblems with classical "interpolation" methods
Traditional interpolations require a "regular" grid of models
N ∼models exp(N )dim
N <dim 6
Need to �t all stellar parameters + elemental abundancessimultaneously
N =dim 20 − 50
Call for a fast and accurate "interpolation" methodvia an adaptive grid, emulator approach
x
y
f (x,y)
Classical interpolationClassical interpolation
x
y
N ∼models exp(N )dim
f (x,y)
Classical interpolationClassical interpolation
f (x,y)
x
y
N ∼models exp(N )dim
f (x,y)
x
y
= a x + b y + c
Generative models / emulatorsGenerative models / emulators
Generative models / emulatorsGenerative models / emulators
f (x,y)
x
y
N exp(N )models dim
= a x + b y + c≪
Ef�cient "interpolation" via Ef�cient "interpolation" via machine learningmachine learning
Rix, YST+ 2016Rix, YST+ 2016YST+ 2016b, 2017a,b, 2018a,cYST+ 2016b, 2017a,b, 2018a,c
The PayneThe Payne
Also see The Cannon:Also see The Cannon:Ness+16, Casey+ 17Ness+16, Casey+ 17
StarNet : Leung & Bovy, 2018StarNet : Leung & Bovy, 2018
Ef�cient "interpolation" via Ef�cient "interpolation" via machine learningmachine learning
Emulators withneural networks
Rix, YST+ 2016Rix, YST+ 2016YST+ 2016b, 2017a,b, 2018a,cYST+ 2016b, 2017a,b, 2018a,c
The PayneThe Payne
Also see The Cannon:Also see The Cannon:Ness+16, Casey+ 17Ness+16, Casey+ 17
StarNet : Leung & Bovy, 2018StarNet : Leung & Bovy, 2018
Ef�cient "interpolation" via Ef�cient "interpolation" via machine learningmachine learning
Emulators withneural networks
Fitting 25D requires only 2000 model spectra
Rix, YST+ 2016Rix, YST+ 2016YST+ 2016b, 2017a,b, 2018a,cYST+ 2016b, 2017a,b, 2018a,c
The PayneThe Payne
Also see The Cannon:Also see The Cannon:Ness+16, Casey+ 17Ness+16, Casey+ 17
StarNet : Leung & Bovy, 2018StarNet : Leung & Bovy, 2018
What does it mean forWhat does it mean forstellar spectroscopystellar spectroscopy
The Payne attains 2 times more The Payne attains 2 times more preciseprecise elemental elementalabundances for the APOGEE surveyabundances for the APOGEE survey
YST+ 2018cYST+ 2018c
Most features are blended at low-resolutionMost features are blended at low-resolution
Low resolution R = λ/Δλ = 6000
Need to �t the full spectrum
Classical methodcan only measure
< 3 elements
YST+ 2017bYST+ 2017b
The Payne measured 16 elemental abundancesThe Payne measured 16 elemental abundancesfrom from LAMOST (MSE-like)LAMOST (MSE-like) low-resolution low-resolution spectra spectra
R = λ/Δλ = 1800
Ab
un
dan
ce P
reci
sio
n [d
ex]
Precision = 0.05-0.15 dex
The Payne measured16 elements
YST+ 2017bYST+ 2017b
The Payne measured 16 elemental abundancesThe Payne measured 16 elemental abundancesfrom from LAMOST (MSE-like)LAMOST (MSE-like) low-resolution low-resolution spectra spectra
R = λ/Δλ = 1800
Ab
un
dan
ce P
reci
sio
n [d
ex]
Xiang, YST+, in prep.Xiang, YST+, in prep.
16 elements16 elements from from77 million million low- low-
resolutionresolutionLAMOST spectraLAMOST spectra
LiLi CC
MgMg
CaCa
CrCr
BaBaYY
VV
SiSi
NaNaOO
AlAl
TiTi
MnMnMetallicity [Fe/H]
Ab
un
dan
ce r
atio
[X
/Fe]
YST+ 2017bYST+ 2017b
R = λ/Δλ = 1800
Low-resolution spectraLow-resolution spectra (R=2,000-6,000) (R=2,000-6,000)contain contain extensiveextensive information about > 20 information about > 20elemental abundances (and stellar ages)elemental abundances (and stellar ages)
And the new generation of spectral �ttingAnd the new generation of spectral �ttingideas can deliver themideas can deliver them
Summary :Summary :
How to deal withHow to deal withimperfectimperfect spectral models spectral models
I.e., are you sure that you areI.e., are you sure that you aremeasuring Ca from Ca lines?measuring Ca from Ca lines?
Precision
Pure data-driven
Dealing with imperfect spectral modelsDealing with imperfect spectral models
AccuracyNot via astrophysical
correlation ?
"empirical library"
Precision
Pure data-driven
Data-driven +theoretical prior
Dealing with imperfect spectral modelsDealing with imperfect spectral models
AccuracyNot via astrophysical
correlation ?
"empirical library"
(Ting+ 17)
Precision
Pure data-driven
Data-driven +theoretical prior
Dealing with imperfect spectral modelsDealing with imperfect spectral models
AccuracyNot via astrophysical
correlation ?
"empirical library"
(Ting+ 17)
Better line list(Ting+ 18, Cargile+)
Low-resolution spectra contain abundantLow-resolution spectra contain abundantinformation of various elemental abundancesinformation of various elemental abundances
Relative
1
10
100
Spectral Resolution
σ [X/H]
YST+ 2017aYST+ 2017a
Same exposure timeSame number of CCD pixels
Robust spectral models1 pixel / resolution element
Low-resolution spectra contain abundantLow-resolution spectra contain abundantinformation of various elemental abundancesinformation of various elemental abundances
Relative
1
10
100
Not much gaingoing beyondR >1000Flat trend
Spectral Resolution
σ [X/H]
YST+ 2017aYST+ 2017a
Same exposure timeSame number of CCD pixels
Robust spectral models1 pixel / resolution element
Low-resolution spectra contain abundantLow-resolution spectra contain abundantinformation of various elemental abundancesinformation of various elemental abundances
Relative
1
10
100
Not much gaingoing beyondR >1000Flat trend
Spectral Resolution
σ [X/H]
YST+ 2017aYST+ 2017a
Same exposure timeSame number of CCD pixels
Robust spectral models1 pixel / resolution element
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