With A. Pontzen & H. V. Peiris — arXiv:2012.02201
New-Horizon simulation — Dubois+20
Tempel+13
Focus on closed systems: Lagrangian patch
Predict one value for L
Focus on open systems: dark matter haloes
Predict p(L)
L=a2D˙L0
White 84, Codis+15
Vitvitska+02
AM can be predicted
AM is stochastic/chaotic
Focus on closed systems: Lagrangian patch
Predict one value for L
Focus on open systems: dark matter haloes
Predict p(L)
AM can be predicted from the initial conditions
AM is stochastic/chaotic
Not chaotic if for a given region,
L(t)=f(t)×L0,(1+ϵ),
with f indep. of L0.
Not chaotic if for a given region,
L(t)=f(t)×L0,(1+ϵ),
with f indep. of L0.
How to change L0 all other things being equal?
ΛCDM gives you =L0 … all other things different.
*Only an illustration, ICs do not correspond to the halos below.
N-body numerical integration
N-body numerical integration
+
Original L0
Genetic modif.
N-body numerical integration
Contrast x50
Roth+15, Stopyra+20
Can compute
P(δδ(x)=v)
or
P(δi∑αiδi=v)
or
P(δLx=v)
+ minimization of Δχ2
⇒ unique solution
= "genetically modified density field"
+
=
Original L0
Genetic modif.
New initial L0′
N-body numerical integration
N-body numerical integration
Same region & environment
Different L0
Contrast x50
Roth+15, Stopyra+20
Original L0
New initial L0′
Original L
New initial L′
We can predict/control AM down to z=0.*
How does it compare to predictions from linear theory?
*AM of regions, not halos
Prediction from linear theory:
Lpred.=timea2D˙×spaceL0.
Simulations with genetically modified initial conditions
Lpred.=timef(t)×spaceL0,
where f(t) is the measured growth rate of a given region in the ref. simu.
Prediction from linear theory:
Lpred.=timea2D˙×spaceL0.
Simulations with genetically modified initial conditions
Lpred.=timef(t)×spaceL0,where f(t) is the measured growth rate of a given region in the ref. simu.
Median of deviations
Prediction from linear theory:
Lpred.=timea2D˙×spaceL0.
Simulations with genetically modified initial conditions
Lpred.=timef(t)×spaceL0,where f(t) is the measured growth rate of a given region in the ref. simu.
Median of deviations
Scatter of deviations
Prediction from linear theory:
Lpred.=timea2D˙×spaceL0.
Simulations with genetically modified initial conditions
Lpred.=timef(t)×spaceL0,where f(t) is the measured growth rate of a given region in the ref. simu.
Median of deviations
Scatter of deviations
Explored how L of given fixed regions changes with changed L0
Read more in Cadiou, Pontzen & Peiris arXiv:2012.02201.
What's next?
Read more in Cadiou, Pontzen & Peiris arXiv:2012.02201.
Explored how L of given fixed regions changes with changed L0
Initial L given in initial conditions by
Lz,0∝i,j,k∑[(qxijk−qˉx)∇yϕijk−(qyijk−qˉy)∇xϕijk].
Genetic modifications: find u such that
u⋅δ=L0,
with δ={δi0j0k0,…,δinjnkn} and solve for
u⋅δ′=L0′
with minimal change.