[PPT] - Cosmic fmow reconstructjon from deepest distance surveys (and PowerPoint Presentation

SLIDE 1

Cosmic fmow reconstructjon from deepest distance surveys (and redshifu surveys)

C

l

l a b

r

a t i

n

: J e n s J a s c h e ( T U M / E x C ) , M i c h a e l H u d s

n

( U

W)

, B e n j a m i n D . Wa n d e l t ( U P M C / I A P ) , F l

r

e n t L e c l e r c q ( I A P → P

r

t s m

u

t h )

I n s t i t u t d ' A s t r

p

h y s i q u e d e P a r i s ( C N R S )

SLIDE 2

July 5th 2016 2

O u t l i n e

C
n

t e x t a n d p r

b

l e m s

T

h e s t a t i s t i c a l m

d

e l s : B O R G ( s p e c t r

s

c

p

i c ) a n d V I R B I U S ( d i s t a n c e + s p e c t r

s

c

p

i c )

A

p p l i c a t i

n

t

C
s

m i c F l

w

s , C O M P O S I T E a n d 2 M + +

R

e s u l t s

C
n

c l u s i

n

SLIDE 3

July 5th 2016 3

C

n

t e x t

C
s

m i c d i s t a n c e s u r v e y s c

m

i n g

u

t

f

i n f a n c y

S

u p e r b s p e c t r

s

c

p

i c s u r v e y s

S D S S 3 / B O S S ( S D S S w e b s i t e ) 6 d F v s u r v e y ( S p r i n g

b

e t a l . 2 1 4 ) C

s

m i c fm

w

s 3 ( T u l l y e t a l . 2 1 6 )

SLIDE 4

July 5th 2016 4

C

n

t e x t

C
s

m i c d i s t a n c e s u r v e y s c

m

i n g

u

t

f

i n f a n c y

S

u p e r b s p e c t r

s

c

p

i c s u r v e y s

S

t i l l n a i v e d a t a a n a l y s i s

SLIDE 5

July 5th 2016 5

C

n

t e x t

C
s

m i c d i s t a n c e s u r v e y s c

m

i n g

u

t

f

i n f a n c y

S

u p e r b s p e c t r

s

c

p

i c s u r v e y s

S

t i l l n a i v e d a t a a n a l y s i s

I

s i t p

s

s i b l e t

d
a

f u l l a n d s t a t i s t i c a l l y a c c u r a t e a n a l y s i s

f

s u r v e y s ? O b t a i n v e l

c

i t y fj e l d s ?

SLIDE 6

July 5th 2016 6

O u t l i n e

C
n

t e x t a n d p r

b

l e m s

T

h e s t a t i s t i c a l m

d

e l s : B O R G ( s p e c t r

s

c

p

i c ) a n d V I R B I U S ( d i s t a n c e + s p e c t r

s

c

p

i c )

A

p p l i c a t i

n

t

C
s

m i c F l

w

s , C O M P O S I T E a n d 2 M + +

R

e s u l t s

C
n

c l u s i

n

SLIDE 7

Bayesian Origins Reconstruction from Galaxies (BORG)

SLIDE 8

July 5th 2016 8

B i a s m

d

e l

B O R G 2 m

d

e l

G a l a x y r e d s h i f t s s u r v e y C

s

m

l
g

i c a l p r i

r

F

r

w a r d m

d

e l :

✔ P

e r t u r b a t i

n

T h e

r

y

✔ P

a r t i c l e M e s h

M a t t e r D e n s i t y b u i l d i n g p a r t i c l e s → d e n s i t y P

i

s s

n

s t a t i s t i c s G a l a x y d e n s i t y : g a l a x y p

s

i t i

n

→ d e n s i t y I n i t i a l c

n

d i t i

n

s → p a r t i c l e s

L a v a u x & J a s c h e , 2 1 5 , M N R A S J a s c h e & Wa n d e l t , 2 1 3 , M N R A S

~ O p e n M P p a r a l l e l

SLIDE 9

July 5th 2016 9

B i a s m

d

e l

B O R G 3 m

d

e l

G a l a x y r e d s h i f t s s u r v e y C

s

m

l
g

i c a l p r i

r

F

r

w a r d m

d

e l :

✔ P

e r t u r b a t i

n

T h e

r

y

✔ P

a r t i c l e M e s h

✔ R

e d s h i f t S p a c e D i s t

r

t i

n

M a t t e r D e n s i t y b u i l d i n g p a r t i c l e s → d e n s i t y P

i

s s

n

s t a t i s t i c s G a l a x y d e n s i t y : g a l a x y p

s

i t i

n

→ d e n s i t y I n i t i a l c

n

d i t i

n

s → p a r t i c l e s

L a v a u x & J a s c h e , 2 1 6 , i n p r e p . J a s c h e & L a v a u x , 2 1 6 , i n p r e p .

M P I + O p e n M P p a r a l l e l , e x a c t s u p e r s a m p l i n g , e n t i r e c

d

e r e w r i t i n g

SLIDE 10

July 5th 2016 10

B O R G 3 m

d

e l ( e fg e c t i v e l y )

−log P(δ ,α ,~ n)=A+∑

i

~ n Ri[(1+δNL,i[δ])

α×...]−Ni[α log(1+δNL,i[δ])+...]+

∑

k

| ^ δk |

2

2 Pk

sampled using Hamiltonian Monte Carlo algorithm Linear response operator (mask, radial selection) « Mean » density of tracers bias NGP binned data Poisson probability Gaussian prior Jasche & Wandelt (2013) LJ, JL, 2016 in prep.

SLIDE 11

July 5th 2016 11

B O R G 3 s a m p l i n g t e c h n i q u e

S a v e M a r k

v

C h a i n s t a t e S a v e M a r k

v

C h a i n s t a t e B i a s p a r a m e t e r H a m i l t

n

i a n S a m p l e r I n i t i a l d e n s i t y fj e l d F i n a l d e n s i t y fj e l d B i a s m

d

e l D a t a P

i

s s

n

L i k e l i h

d

F

r

w a r d F

r

w a r d G r a d i e n t G r a d i e n t N

m e a n

p a r a m e t e r

SLIDE 12

Velocity Reconstruction using Bayesian Inference Scheme

SLIDE 13

July 5th 2016 13

V I R B I U S m

d

e l

D i s t a n c e m

d

u l i / I n d i c a t

r

s R e d s h i f t s ( + e r r

r

s ) G a l a x y s u r v e y V e l

c

i t y fj e l d p r i

r

: P

t

e n t i a l , G a u s s i a n D i s t a n c e p r i

r

T r u e D i s t a n c e s C

s

m

l
g

i c a l p r i

r

B u l k fm

w

: u n i f

r

m p r i

r

V e l

c

i t y / d e n s i t y fj e l d L a v a u x , 2 1 6 , M N R A S

SLIDE 14

July 5th 2016 14

V I R B I U S m

d

e l

D i s t a n c e m

d

u l i / I n d i c a t

r

s R e d s h i f t s ( + e r r

r

s ) G a l a x y s u r v e y V e l

c

i t y fj e l d p r i

r

: P

t

e n t i a l , G a u s s i a n D i s t a n c e p r i

r

T r u e D i s t a n c e s M e t a p a r a m e t e r s ( c a l i b r a t i

n

, e r r

r

s , c l a s s i fj c a t i

n

) C

s

m

l
g

i c a l p r i

r

B u l k fm

w

: u n i f

r

m p r i

r

P r e d i c t e d v e l

c

i t i e s L

g

n

r

m a l m

d

e l G a u s s i a n e r r

r

m

d

e l V e l

c

i t y / d e n s i t y fj e l d O p e n M P p a r a l l e l L a v a u x , 2 1 6 , M N R A S

SLIDE 15

July 5th 2016 15

V I R B I U S m

d

e l

D i s t a n c e m

d

u l i / I n d i c a t

r

s R e d s h i f t s ( + e r r

r

s ) G a l a x y s u r v e y V e l

c

i t y fj e l d p r i

r

: P

t

e n t i a l , G a u s s i a n D i s t a n c e p r i

r

C

s

m

l
g

i c a l p r i

r

B u l k fm

w

: u n i f

r

m p r i

r

V e l

c

i t y / d e n s i t y fj e l d O p e n M P p a r a l l e l L C D M a s s u m p t i

n

s N

L

C D M a s s u m p t i

n

s

SLIDE 16

July 5th 2016 16

V I R B I U S m

d

e l ( e fg e c t i v e l y )

H a r d t

w

r i t e c

m

p l e t e l y h e r e . . . T h e f

l

l

w

i n g i s

n

l y t h e c

r

e p a r t : L i k e l i h

d

P

s

t e r i

r

d i s t r i b u t i

n

SLIDE 17

July 5th 2016 17

V I R B I U S s a m p l i n g t e c h n i q u e

G i b b s s a m p l i n g P a r t i a l l y c

l

l a p s e d G i b b s s a m p l i n g B

x

b u l k fm

w

Θ d e n s i t y fj e l d D i s t a n c e s T y p e C l a s s i fj e r S e l e c t i

n

f u n c t i

n

T y p e p r

b

a b i l i t y C l a s s i fj e r S a v e M a r k

v

C h a i n s t a t e S a v e M a r k

v

C h a i n s t a t e E x p e n s i v e

SLIDE 18

July 5th 2016 18

V I R B I U S s a m p l i n g t e c h n i q u e

G i b b s s a m p l i n g P a r t i a l l y c

l

l a p s e d G i b b s s a m p l i n g B

x

b u l k fm

w

Θ d e n s i t y fj e l d D i s t a n c e s T y p e C l a s s i fj e r S e l e c t i

n

f u n c t i

n

T y p e p r

b

a b i l i t y C l a s s i fj e r S a v e M a r k

v

C h a i n s t a t e S a v e M a r k

v

C h a i n s t a t e

C

d

e f u l l y t e s t e d

n

N

b
d

y s i m u l a t i

n

+ m

c

k c a t a l

g

s C

d

e f u l l y t e s t e d

n

N

b
d

y s i m u l a t i

n

+ m

c

k c a t a l

g

s

L a v a u x , 2 1 6 , M N R A S

SLIDE 19

July 5th 2016 19

O u t l i n e

C
n

t e x t a n d p r

b

l e m s

T

h e s t a t i s t i c a l m

d

e l s : B O R G ( s p e c t r

s

c

p

i c ) a n d V I R B I U S ( d i s t a n c e + s p e c t r

s

c

p

i c )

A

p p l i c a t i

n

t

C
s

m i c F l

w

s , C O M P O S I T E a n d 2 M + +

R

e s u l t s

C
n

c l u s i

n

SLIDE 20

July 5th 2016 20

C

s

m i c F l

w

s 2 . 1 d a t a

T u l l y

F

i s h e r F u n d a m e n t a l p l a n e S u p e r n

v

a e S u r f . b r i g h t n e s s fm u c t u a t i

n

M i s c T R G B / C e p h e i d T u l l y e t a l . ( A J , 2 1 3 ) 8 1 8 8 g a l a x i e s 4 s u b s e t s :

"

H i g h p r e c i s i

n

" : 5 5 8

S

u p e r n

v

a e : 2 2 1

F

u n d a m e n t a l p l a n e : 1 4 5 5

T

u l l y

F

i s h e r : 5 9 5 4 M a x v e l

c

i t y : 2 9 3 1 3 k m / s

SLIDE 21

July 5th 2016 21

C O M P O S I T E d a t a

Wa t k i n s , F e l d m a n & H u d s

n

( M N R A S , 2 9 ) 4 4 8 1 g a l a x i e s 1 s u b s e t :

M

i x e d b a g M a x v e l

c

i t y : 4 2 5 8 k m / s

SLIDE 22

July 5th 2016 22

2 M + + c

m

p i l a t i

n

M p c / h G a l a x y d i s t r i b u t i

n

S D S S 6 d F 2 M R S 2 5 M p c / h R e d s h i f t c

m

p l e t e n e s s

L a v a u x & H u d s

n

( M N R A S , 2 1 1 )

SLIDE 23

July 5th 2016 23

V I R B I U S r e c

n

s t r u c t e d fj e l d s

PRELIMINARY (!) C F 2 . 1 , m e a n d e n s i t y a n d v e l

c

i t y fj e l d g i v e n d a t a S u p e r g a l a c t i c p l a n e S h a p l e y c

n

c e n t r a t i

n

N

r

m a H y d r a

C

e n t a u r u s V i r g

P

e r s e u s c l u s t e r P i s c e s c l u s t e r C

m

a B

t

e s v

i

d

SLIDE 24

July 5th 2016 24

B O R G 3 d e n s i t y fj e l d

S u p e r g a l a c t i c p l a n e , fj n a l d e n s i t y fj e l d s m

t

h e d t

5

M p c / h ( G a u s s i a n ) C

m

a S h a p l e y c

n

c e n t r a t i

n

P e r s e u s c l u s t e r P i s c e s c l u s t e r V i r g

N
r

m a H y d r a

C

e n t a u r u s B

t

e s v

i

d PRELIMINARY 2 M + + , m e a n fj n a l m a t t e r d e n s i t y fj e l d S u p e r g a l a c t i c p l a n e

SLIDE 25

July 5th 2016 25

B O R G 3 d e n s i t y fj e l d

S u p e r g a l a c t i c p l a n e , fj n a l d e n s i t y fj e l d , n

s

m

t

h i n g C

m

a S h a p l e y c

n

c e n t r a t i

n

P e r s e u s c l u s t e r P i s c e s c l u s t e r V i r g

N
r

m a H y d r a

C

e n t a u r u s B

t

e s v

i

d PRELIMINARY S u p e r g a l a c t i c p l a n e 2 M + + , m e a n fj n a l m a t t e r d e n s i t y fj e l d

SLIDE 26

July 5th 2016 26

D e n s i t y + v e l

c

i t y fj e l d

PRELIMINARY S u p e r g a l a c t i c p l a n e 2 M + + , m e a n fj n a l m a t t e r d e n s i t y fj e l d C F 2 . 1 , m e a n d i v e r g e n c e fj e l d + v e l

c

i t y

SLIDE 27

July 5th 2016 27

M e t a

c

h a r a c t e r i s t i c s f

u

n d b y V I R B I U S

Z e r

p
i

n t C a l i b r a t i

n

H u b b l e c

n

s t a n t A d d i t i

n

a l e r r

r

P

p

u l a t i

n

f r a c t i

n

V e r y h i g h n

i

s e = n

c
n

s t r a i n t s ~ 2 %

f

t h e w h

l

e s a m p l e A l

t
f

T F s

A r b i t r a r y u n i t s

1 1 1

( k m / s )

S i g n i fj c a n t n u m b e r

f

l

w

v e l

c

i t y d i s p e r s i

n

SLIDE 28

July 5th 2016 28

B u l k fm

w

s ( C

s

m i c F l

w

s 2 . 1 )

PRELIMINARY C

m

p a t i b l e w i t h H

fg

m a n , C

u

r t

i

s & T u l l y ( 2 1 5 ) G a u s s i a n k e r n e l

SLIDE 29

July 5th 2016 29

B u l k fm

w

s ( C O M P O S I T E )

PRELIMINARY G a u s s i a n k e r n e l

SLIDE 30

July 5th 2016 30

C

n

c l u s i

n

✔ P

s

s i b i l i t y

f

fj n d i n g

u

t l i e r s ✔ C

n

s i s t e n t a n a l y s i s

f

d i fg e r e n t d i s t a n c e d a t a ✔ H u b b l e c

n

s t a n t i n C F 2 . 1 i s m

r

e l i k e ~ 7 k m / s / M p c ✔ C

m

p a r i s

n

w i t h B O R G , s i m i l a r t

p
l
g

y b u t d e t a i l s a r e s t i l l s

r

t e d

u

t ✔ B O R G 3

p

e n s n e w p

s

s i b i l i t i e s

f

f u l l y n

n
l

i n e a r r e c

n

s t r u c t i

n

, i n c l u d i n g a l s

d

i s t a n c e d a t a ✔ B u l k fm

w

s s t i l l h i g h i n C F 2 . 1 , n

t

c

m

p a t i b l e w i t h L C D M ✔ C O M P O S I T E h a s n

r

e s i d u a l n

n
L

C D M b u l k fm

w

✔ B

t

h s u r v e y s c

m

p a t i b l e i n t h e i n n e r ~ 6 M p c / h ( G a u s s i a n ) r e g i

n

✔ N

c
s

m

l
g

i c a l p a r a m e t e r s fj t t i n g y e t

SLIDE 31