SlideShare uma empresa Scribd logo
1 de 26
Outline Stochastic Approach to Inflation Warm Inflation Stochastic approach to Warm Inflation
Stochastic Approach to Warm Inflation
Leandro Alexandre da Silva
1
Rio de Janeiro State University
Department of Theoretical Physics
XXXI Encontro Nacional de F´ısica de Part´ıculas e Campos
01/09/2010
1
in collaboration with R.O. Ramos
Outline Stochastic Approach to Inflation Warm Inflation Stochastic approach to Warm Inflation
1 Stochastic Approach to Inflation
2 Warm Inflation
3 Stochastic approach to Warm Inflation
Outline Stochastic Approach to Inflation Warm Inflation Stochastic approach to Warm Inflation
Stochastic Approach to Inflation
When?
Stochastic Inflation: ∼ 1987
Why?
Exponentially rapid expansion → “freeze” of inflaton quantum
fluctuations on super-horizon scales
⇓
Inflaton fluctuations behave effectivelly as classical fluctuation
modes with random amplitudes
⇓
Emulates the growth of vacuum fluctuations by an effective
stochastic noise field which drives the dynamics of the
volume-smoothed inflaton → effective dynamics for
coarse-grained field φ
Outline Stochastic Approach to Inflation Warm Inflation Stochastic approach to Warm Inflation
Stochastic Approach to Inflation
How?
Usual approach → decomposition of φ in a classical,
coarse-grained component and in a quantum fluctuation part:
Φ(x, t) → φ(x, t) + q(x, t) .
φ → coarse-grained scalar field averaged over approximatelly
all de Sitter horizon size 1/χ
q(x, t) → summarizes high frequency (k kh ≈ χ) quantum
fluctuations.
q(x, t) aproximated as a free, massless scalar field.
Outline Stochastic Approach to Inflation Warm Inflation Stochastic approach to Warm Inflation
Stochastic Approach to Inflation
de Sitter metric:
ds2
= dxµ
dxν
gµν = −dt2
+ e2χt
dx2
,
Lagrangian density:
L =
1
2
√
−g [gµν
∂µΦ∂νφ − 2V (Φ)]
Equation of motion(EoM):
−3χ
∂
∂t
−
∂2
∂t2
+ e−2χt 2
Φ(x, t) −
∂V (Φ)
∂Φ
= 0
Outline Stochastic Approach to Inflation Warm Inflation Stochastic approach to Warm Inflation
Stochastic Approach to Inflation
Making the field decomposition (slow-roll: ¨φ(x, t) ≈ 0):
3χ
∂
∂t
− e−2χt 2
[φ(x, t) + q(x, t)] +
∂V (φ)
∂φ
= 0
3χ
∂
∂t
− e−2χt 2
φ(x, t) +
∂V (φ)
∂φ
= 3χη(x, t) ,
Noise term:
η(x, t) ≡ −
∂
∂t
+
e−2χt
3χ
2
q(x, t)
Fourier mode expansion in de Sitter background:
q(x, t) ≡ d3
kWχ(k) σk
(t)e−ik·x
ˆak
+ σ∗
k
(t)eik·x
ˆa†
k
.
Wχ(k) → filter or window function. Sharp momentum cutoff
implementation: Wχ(k) ≡ θ(k − χeχt).
Outline Stochastic Approach to Inflation Warm Inflation Stochastic approach to Warm Inflation
Stochastic Approach to Inflation
σk
(t) ≡
1
2k(2π)3
χτ − i
χ
k
e−ikτ
.
Commutator:
[η(x, τ), η(y, τ)] = 0
⇓
Classical behaviour of quantum noise!
Outline Stochastic Approach to Inflation Warm Inflation Stochastic approach to Warm Inflation
Stochastic Approach to Inflation
σk
(t) ≡
1
2k(2π)3
χτ − i
χ
k
e−ikτ
.
Commutator:
[η(x, τ), η(y, τ)] = 0
⇓
Classical behaviour of quantum noise!
Propagator:
0 | η(x, t)η(y, t ) | 0 =
χ3
4π2
δ(t − t )
sin τ | x − y |
τ | x − y |
.
Outline Stochastic Approach to Inflation Warm Inflation Stochastic approach to Warm Inflation
Warm Inflation
Same basic ideas of cold inflation
Inflaton interacts with other fields → radiation production
A reheating process is no more necessary
Smooth transition to the radiation dominated regime
Thermal origin for the density perturbations
A. Berera and L. Z. Fang,Phys. Rev. Lett. 74, 1912 (1995):
Inflaton dynamics → Langevin-like equation
Outline Stochastic Approach to Inflation Warm Inflation Stochastic approach to Warm Inflation
Microscopic motivation to Warm Inflation
Example:
S[φ, χ, σ] = d4
x
1
2
(∂µφ)2
−
1
2
m2
φφ2
−
λ
4!
φ4
+
1
2
(∂µχ)2
−
1
2
m2
χχ2
+
1
2
(∂µσ)2
−
1
2
m2
σσ2
−
g2
2
φ2
χ2
− f χσ2
.
φ → classical field in which dynamics we are interested in
χ → intermediate field that couples to σ and φ
σ → Thermally equilibrated field at temperature T
Outline Stochastic Approach to Inflation Warm Inflation Stochastic approach to Warm Inflation
Microscopic motivation to Warm Inflation
Detailed calculation: Berera and Ramos, PRD63, 103509
(2001),Gleiser and Ramos, PRD50, 2441 (1994):
General Effective Equation of Motion (Homogeneous
approximation)
d2φ(t)
dt2
= −
dVeff(φ)
dφ
− φn
(t)
t
−∞
dt φn
(t ) ˙φ(t )Kχ(t − t )
+ φn
(t) ξ (t) ,
n = 0: additive noise→ φχ2 interaction
n = 1: multiplicative noise → φ2χ2 interaction
Outline Stochastic Approach to Inflation Warm Inflation Stochastic approach to Warm Inflation
Markovian Approximation
Non-Markovian Equation of Motion:
∂2
t + m2
φ +
λ
3!
φ(t)2
φ(t) + φn
(t)
t
t0
dt K(t − t )φn
(t ) ˙φ(t )
= φn
(t)ξ(t) .
Markovian Approximation:
φn
(t)
t
t0
dt K(t − t )φn
(t ) ˙φ(t ) φ2n
(t) ˙φ(t)
t
t0→−∞
dt K(t − t )
→ Υ φ2n
(t) ˙φ(t) .
Markovian Equation of Motion:
¨φ + Υ φ2n ˙φ + m2
φφ +
λ
6
φ3
= φn
(t) ξ(t)
Outline Stochastic Approach to Inflation Warm Inflation Stochastic approach to Warm Inflation
Markovian Approximation
Figure: (a) mχ = 50H(0), (b) mχ = 150H(0) and (c) mχ = 250H(0)
Outline Stochastic Approach to Inflation Warm Inflation Stochastic approach to Warm Inflation
Markovian Approximation
More details about Markovian dynamics reliability:
R. L. S. Farias, R. O. Ramos and L. A. da Silva, Nonlinear
effects in the dynamics governed by non-Markovian stochastic
Langevin-like equations, JPCS, in press
R. L. S. Farias, R. O. Ramos and L. A. da Silva, Numerical
Solutions for non-Markovian Stochastic Equations of Motion
Comp. Phys. Comm. 180, 574 (2009).
R. L. S. Farias, L. A. da Silva and R. O. Ramos,
Non-Markovian stochastic Langevin equations: Markovian and
non-Markovian dynamics, Phys. Rev. E 80, 031143 (2009)
R. L. S. Farias, R. O. Ramos and L. A. da Silva,Langevin
Simulations with Colored Noise and Non-Markovian
Dissipation Brazilian Journal of Physics, vol. 38 , no. 3B,
(2008)
Outline Stochastic Approach to Inflation Warm Inflation Stochastic approach to Warm Inflation
Markovian Approximation
Considering the additive case:
¨φ + [3H + Υ] ˙φ + V (φ) = ξ ,
¨a = −
8π
3m2
pl
ρr + ˙φ2
− V (φ) a ,
˙ρφ = −3
˙a
a
˙φ2
− Υ ˙φ2
+ ν ˙φ , ˙ρr = −4
˙a
a
ρr + Υ ˙φ2
− ξ ˙φ .
Outline Stochastic Approach to Inflation Warm Inflation Stochastic approach to Warm Inflation
Stochastic approach to Warm Inflation
Back to stochastic approach: implement Warm Inflation
Modification of EoM:
∂φ(x, t)
∂t
=
1
3χ + Υ
e−2χt 2
φ(x, t) −
∂V (φ)
∂φ
+η(x, t)+ξ(x, t) .
Outline Stochastic Approach to Inflation Warm Inflation Stochastic approach to Warm Inflation
Stochastic approach to Warm Inflation
Back to stochastic approach: implement Warm Inflation
Modification of EoM:
∂φ(x, t)
∂t
=
1
3χ + Υ
e−2χt 2
φ(x, t) −
∂V (φ)
∂φ
+η(x, t)+ξ(x, t) .
Global analysis:
∂φ(t)
∂t
= −
1
3χ + Υ
∂V (φ)
∂φ
+ η(t) + ξ(t) ,
η(t)η(t ) =
χ3
4π2
δ(t − t )
ξ(t)ξ(t ) = βδ(t − t ) .
Outline Stochastic Approach to Inflation Warm Inflation Stochastic approach to Warm Inflation
Stochastic approach to Warm Inflation
Considering a general SDE of the form
˙φ = h(φ, t) + g(φ, t)η(t) + f (φ, t)ξ(t) ,
we obtain a Fokker-Planck equation for η(t)ξ(t ) = 0 of the form
∂P(φ, t)
∂t
= −
∂
∂φ
D(1)
+
∂2
∂φ2
D(2)
P(φ, t) .
where the Kramers-Moyal coefficients are:
D(1)
(φ, t) = h(φ, t) +
α
2
∂g(φ, t)
∂φ
g(φ, t) +
β
2
∂f (φ, t)
∂φ
f (φ, t)
D(2)
(φ, t) =
α
2
g(φ, t)2
+
β
2
f (φ, t)2
.
(1)
Outline Stochastic Approach to Inflation Warm Inflation Stochastic approach to Warm Inflation
Stochastic approach to Warm Inflation
In our particular case:
∂P(φ, t)
∂t
= −
∂
∂φ
−
1
3χ + Υ
∂V (φ)
∂φ
+
∂2
∂φ2
χ3
4π2
+
β
2
P(φ, t) .
A trick to obtain φn(t) :
∂
∂t
φn
(t) ≡
∞
−∞
dφφn ∂
∂t
P(φ, t) .
∂
∂t
φn
(t) =
χ3
8π2
+
β
2
n(n−1) φn−2
(t) −
n
3χ + Υ
φn−1
(t)
∂V (φ)
∂t
.
φ2
(t) =
3χ + Υ
2m2
φ
χ3
4π2
+ β 1 − exp −
2m2
φ
3χ + Υ
t .
Outline Stochastic Approach to Inflation Warm Inflation Stochastic approach to Warm Inflation
Stochastic approach to Warm Inflation
The Fokker-Planck operator
LFP ≡ −
∂
∂φ
−
1
3χ + Υ
∂V (φ)
∂φ
+
∂2
∂φ2
χ3
4π2
+
β
2
,
is so that LFP = L†
FP.
⇒ Transformation of variables:
t → (3χ + Υ)t
φ → (3χ + Υ)
χ3
4π2
+
β
ψ
Outline Stochastic Approach to Inflation Warm Inflation Stochastic approach to Warm Inflation
Stochastic approach to Warm Inflation
The Fokker-Planck becomes:
∂P(ψ, t )
∂t
=
2
∂2P(ψ, t )
∂ψ2
+
∂
∂ψ
∂ ˜V (ψ)
∂ψ
P(ψ, t ) ,
where
˜V (ψ) ≡ (3χ + Υ)
χ3
4π2
+
β
−1
V φ = (3χ + Υ)
χ3
4π2
+
β
ψ
One more transformation
P(ψ, t ) = exp
1 ˜V (0) − ˜V (ψ) F(ψ, t ) ,
and then the Fokker-Planck equation becomes...
Outline Stochastic Approach to Inflation Warm Inflation Stochastic approach to Warm Inflation
Stochastic approach to Warm Inflation
...a Schr¨odinger-like equation with imaginary time:
−
2
∂2
∂ψ2
+ U(ψ) F(ψ, t ) = −
∂F(ψ, t )
∂t
, (2)
with
U(ψ) ≡
1
2

 ∂ ˜V (ψ)
∂ψ
2
−
∂2 ˜V (ψ)
∂ψ2

 . (3)
Analogy with quantum mechanics:
ψ | α, t =
λ
ψ | λ λ | α, t → F(ψ, t ) =
λ
cλe− i
Eλt
ϕλ(ψ) ,
with
Hϕλ(ψ) = Eλϕλ(ψ) , H ≡ −
2
∂2
∂ψ2
+ U(ψ) , t →
i
t
Outline Stochastic Approach to Inflation Warm Inflation Stochastic approach to Warm Inflation
Stochastic approach to Warm Inflation
Then using standard operator techniques and recovering the
original variables φ and t, we get:
P(φ, t) =
λ
cλe−m2
φσ−1
λt
×
Λ
1
2
π
1
4
√
2λλ!
m2
φ
(λ
2 − 3
4 )
m2
φΛ−1
φ −
∂
∂φ
λ
exp
m2
φΛ−1
φ2
,
And the propagator K(φ2, t2; φ1, t1):
K(φ2, t2; φ1, t1) = 1 − e−2m2
φσ−1
(t2−t1)
− 1
2
×
exp



−
m2
φΛ−1
φ2
2 + φ2
1 − 2φ2φ1e−m2
φσ−1
(t2−t1)
1 − e−2m2
φσ−1(t2−t1)



with Λ ≡ (3χ + Υ) χ3
4π2 + β
and σ ≡ 3χ + Υ
Outline Stochastic Approach to Inflation Warm Inflation Stochastic approach to Warm Inflation
Discussions and perspectives
Planck energy scale at V (φ = φmax ) → breakdown of
semiclassical picture of spacetime
⇓
P(φmax ) = 0
⇓
Bounded from above eigenvalues
H eigenvalues ∈ N → LFP eingenvalues ∈ Z−
⇓
Highest eigenvalue (˜λmax ) of volume-weighted FP equation
(LFP → LFP + 3H) can be negative or positive: warm inflation
→ ˜λmax > 0 → number of inflating domains increases without
limit. (to appear in JCAP)
Outline Stochastic Approach to Inflation Warm Inflation Stochastic approach to Warm Inflation
Discussions and perspectives
Work out the local (φ(x, t)) analysis
Study the pathway to classicalization: when the thermal
fluctuations overcome the quantum ones?
Better quantify eternal inflation based on Warm Inflation
Outline Stochastic Approach to Inflation Warm Inflation Stochastic approach to Warm Inflation
Thanks for your attention!!!

Mais conteúdo relacionado

Mais procurados

Talk in BayesComp 2018
Talk in BayesComp 2018Talk in BayesComp 2018
Talk in BayesComp 2018JeremyHeng10
 
160511 hasegawa lab_seminar
160511 hasegawa lab_seminar160511 hasegawa lab_seminar
160511 hasegawa lab_seminarTomohiro Koana
 
The lattice Boltzmann equation: background, boundary conditions, and Burnett-...
The lattice Boltzmann equation: background, boundary conditions, and Burnett-...The lattice Boltzmann equation: background, boundary conditions, and Burnett-...
The lattice Boltzmann equation: background, boundary conditions, and Burnett-...Tim Reis
 
Freezing of energy of a soliton in an external potential
Freezing of energy of a soliton in an external potentialFreezing of energy of a soliton in an external potential
Freezing of energy of a soliton in an external potentialAlberto Maspero
 
The lattice Boltzmann equation: background and boundary conditions
The lattice Boltzmann equation: background and boundary conditionsThe lattice Boltzmann equation: background and boundary conditions
The lattice Boltzmann equation: background and boundary conditionsTim Reis
 
short course at CIRM, Bayesian Masterclass, October 2018
short course at CIRM, Bayesian Masterclass, October 2018short course at CIRM, Bayesian Masterclass, October 2018
short course at CIRM, Bayesian Masterclass, October 2018Christian Robert
 
Understanding lattice Boltzmann boundary conditions through moments
Understanding lattice Boltzmann boundary conditions through momentsUnderstanding lattice Boltzmann boundary conditions through moments
Understanding lattice Boltzmann boundary conditions through momentsTim Reis
 
Convergence methods for approximated reciprocal and reciprocal-square-root
Convergence methods for approximated reciprocal and reciprocal-square-rootConvergence methods for approximated reciprocal and reciprocal-square-root
Convergence methods for approximated reciprocal and reciprocal-square-rootKeigo Nitadori
 
Workshop presentations l_bworkshop_reis
Workshop presentations l_bworkshop_reisWorkshop presentations l_bworkshop_reis
Workshop presentations l_bworkshop_reisTim Reis
 
Unbiased Bayes for Big Data
Unbiased Bayes for Big DataUnbiased Bayes for Big Data
Unbiased Bayes for Big DataChristian Robert
 
RSS discussion of Girolami and Calderhead, October 13, 2010
RSS discussion of Girolami and Calderhead, October 13, 2010RSS discussion of Girolami and Calderhead, October 13, 2010
RSS discussion of Girolami and Calderhead, October 13, 2010Christian Robert
 
Pinning and facetting in multiphase LBMs
Pinning and facetting in multiphase LBMsPinning and facetting in multiphase LBMs
Pinning and facetting in multiphase LBMsTim Reis
 
Hermite integrators and Riordan arrays
Hermite integrators and Riordan arraysHermite integrators and Riordan arrays
Hermite integrators and Riordan arraysKeigo Nitadori
 
Natalini nse slide_giu2013
Natalini nse slide_giu2013Natalini nse slide_giu2013
Natalini nse slide_giu2013Madd Maths
 
A short remark on Feller’s square root condition.
A short remark on Feller’s square root condition.A short remark on Feller’s square root condition.
A short remark on Feller’s square root condition.Ilya Gikhman
 

Mais procurados (19)

Talk in BayesComp 2018
Talk in BayesComp 2018Talk in BayesComp 2018
Talk in BayesComp 2018
 
160511 hasegawa lab_seminar
160511 hasegawa lab_seminar160511 hasegawa lab_seminar
160511 hasegawa lab_seminar
 
The lattice Boltzmann equation: background, boundary conditions, and Burnett-...
The lattice Boltzmann equation: background, boundary conditions, and Burnett-...The lattice Boltzmann equation: background, boundary conditions, and Burnett-...
The lattice Boltzmann equation: background, boundary conditions, and Burnett-...
 
Freezing of energy of a soliton in an external potential
Freezing of energy of a soliton in an external potentialFreezing of energy of a soliton in an external potential
Freezing of energy of a soliton in an external potential
 
The lattice Boltzmann equation: background and boundary conditions
The lattice Boltzmann equation: background and boundary conditionsThe lattice Boltzmann equation: background and boundary conditions
The lattice Boltzmann equation: background and boundary conditions
 
short course at CIRM, Bayesian Masterclass, October 2018
short course at CIRM, Bayesian Masterclass, October 2018short course at CIRM, Bayesian Masterclass, October 2018
short course at CIRM, Bayesian Masterclass, October 2018
 
Understanding lattice Boltzmann boundary conditions through moments
Understanding lattice Boltzmann boundary conditions through momentsUnderstanding lattice Boltzmann boundary conditions through moments
Understanding lattice Boltzmann boundary conditions through moments
 
Convergence methods for approximated reciprocal and reciprocal-square-root
Convergence methods for approximated reciprocal and reciprocal-square-rootConvergence methods for approximated reciprocal and reciprocal-square-root
Convergence methods for approximated reciprocal and reciprocal-square-root
 
Workshop presentations l_bworkshop_reis
Workshop presentations l_bworkshop_reisWorkshop presentations l_bworkshop_reis
Workshop presentations l_bworkshop_reis
 
Unbiased Bayes for Big Data
Unbiased Bayes for Big DataUnbiased Bayes for Big Data
Unbiased Bayes for Big Data
 
RSS discussion of Girolami and Calderhead, October 13, 2010
RSS discussion of Girolami and Calderhead, October 13, 2010RSS discussion of Girolami and Calderhead, October 13, 2010
RSS discussion of Girolami and Calderhead, October 13, 2010
 
QMC: Operator Splitting Workshop, Thresholdings, Robustness, and Generalized ...
QMC: Operator Splitting Workshop, Thresholdings, Robustness, and Generalized ...QMC: Operator Splitting Workshop, Thresholdings, Robustness, and Generalized ...
QMC: Operator Splitting Workshop, Thresholdings, Robustness, and Generalized ...
 
Pinning and facetting in multiphase LBMs
Pinning and facetting in multiphase LBMsPinning and facetting in multiphase LBMs
Pinning and facetting in multiphase LBMs
 
Master theorem
Master theoremMaster theorem
Master theorem
 
QMC Program: Trends and Advances in Monte Carlo Sampling Algorithms Workshop,...
QMC Program: Trends and Advances in Monte Carlo Sampling Algorithms Workshop,...QMC Program: Trends and Advances in Monte Carlo Sampling Algorithms Workshop,...
QMC Program: Trends and Advances in Monte Carlo Sampling Algorithms Workshop,...
 
Hermite integrators and Riordan arrays
Hermite integrators and Riordan arraysHermite integrators and Riordan arrays
Hermite integrators and Riordan arrays
 
Natalini nse slide_giu2013
Natalini nse slide_giu2013Natalini nse slide_giu2013
Natalini nse slide_giu2013
 
hone_durham
hone_durhamhone_durham
hone_durham
 
A short remark on Feller’s square root condition.
A short remark on Feller’s square root condition.A short remark on Feller’s square root condition.
A short remark on Feller’s square root condition.
 

Destaque

Inflação estocástica não-isentrópica (defesa de tese)
Inflação estocástica não-isentrópica (defesa de tese)Inflação estocástica não-isentrópica (defesa de tese)
Inflação estocástica não-isentrópica (defesa de tese)Leandro da Silva
 
Dinâmica estocástica em neurociência teórica
Dinâmica estocástica em neurociência teóricaDinâmica estocástica em neurociência teórica
Dinâmica estocástica em neurociência teóricaLeandro da Silva
 
Dinâmica não-markoviana: uma abordagem via equação de Langevin
Dinâmica não-markoviana: uma abordagem via equação de LangevinDinâmica não-markoviana: uma abordagem via equação de Langevin
Dinâmica não-markoviana: uma abordagem via equação de LangevinLeandro da Silva
 
Dinâmica estocástica em neurociência teórica II
Dinâmica estocástica em neurociência teórica IIDinâmica estocástica em neurociência teórica II
Dinâmica estocástica em neurociência teórica IILeandro da Silva
 
Efeitos de memória em teoria de campos
Efeitos de memória em teoria de camposEfeitos de memória em teoria de campos
Efeitos de memória em teoria de camposLeandro da Silva
 

Destaque (11)

Formação de estruturas
Formação de estruturasFormação de estruturas
Formação de estruturas
 
ENFPC 2013
ENFPC 2013ENFPC 2013
ENFPC 2013
 
Defesa de dissertação
Defesa de dissertaçãoDefesa de dissertação
Defesa de dissertação
 
Supercondutividade
SupercondutividadeSupercondutividade
Supercondutividade
 
Supercondutividade II
Supercondutividade IISupercondutividade II
Supercondutividade II
 
Inflação estocástica não-isentrópica (defesa de tese)
Inflação estocástica não-isentrópica (defesa de tese)Inflação estocástica não-isentrópica (defesa de tese)
Inflação estocástica não-isentrópica (defesa de tese)
 
Dinâmica estocástica em neurociência teórica
Dinâmica estocástica em neurociência teóricaDinâmica estocástica em neurociência teórica
Dinâmica estocástica em neurociência teórica
 
ENFPC 2012
ENFPC 2012 ENFPC 2012
ENFPC 2012
 
Dinâmica não-markoviana: uma abordagem via equação de Langevin
Dinâmica não-markoviana: uma abordagem via equação de LangevinDinâmica não-markoviana: uma abordagem via equação de Langevin
Dinâmica não-markoviana: uma abordagem via equação de Langevin
 
Dinâmica estocástica em neurociência teórica II
Dinâmica estocástica em neurociência teórica IIDinâmica estocástica em neurociência teórica II
Dinâmica estocástica em neurociência teórica II
 
Efeitos de memória em teoria de campos
Efeitos de memória em teoria de camposEfeitos de memória em teoria de campos
Efeitos de memória em teoria de campos
 

Semelhante a ENFPC 2010

Approximate Bayesian Computation with Quasi-Likelihoods
Approximate Bayesian Computation with Quasi-LikelihoodsApproximate Bayesian Computation with Quasi-Likelihoods
Approximate Bayesian Computation with Quasi-LikelihoodsStefano Cabras
 
Ray : modeling dynamic systems
Ray : modeling dynamic systemsRay : modeling dynamic systems
Ray : modeling dynamic systemsHouw Liong The
 
Linear response theory and TDDFT
Linear response theory and TDDFT Linear response theory and TDDFT
Linear response theory and TDDFT Claudio Attaccalite
 
Norm-variation of bilinear averages
Norm-variation of bilinear averagesNorm-variation of bilinear averages
Norm-variation of bilinear averagesVjekoslavKovac1
 
A Fibonacci-like universe expansion on time-scale
A Fibonacci-like universe expansion on time-scaleA Fibonacci-like universe expansion on time-scale
A Fibonacci-like universe expansion on time-scaleOctavianPostavaru
 
Allele Frequencies as Stochastic Processes: Mathematical & Statistical Approa...
Allele Frequencies as Stochastic Processes: Mathematical & Statistical Approa...Allele Frequencies as Stochastic Processes: Mathematical & Statistical Approa...
Allele Frequencies as Stochastic Processes: Mathematical & Statistical Approa...Gota Morota
 
Introducing Zap Q-Learning
Introducing Zap Q-Learning   Introducing Zap Q-Learning
Introducing Zap Q-Learning Sean Meyn
 
Stochastic Gravity in Conformally-flat Spacetimes
Stochastic Gravity in Conformally-flat SpacetimesStochastic Gravity in Conformally-flat Spacetimes
Stochastic Gravity in Conformally-flat SpacetimesRene Kotze
 
Tales on two commuting transformations or flows
Tales on two commuting transformations or flowsTales on two commuting transformations or flows
Tales on two commuting transformations or flowsVjekoslavKovac1
 
Seminar Talk: Multilevel Hybrid Split Step Implicit Tau-Leap for Stochastic R...
Seminar Talk: Multilevel Hybrid Split Step Implicit Tau-Leap for Stochastic R...Seminar Talk: Multilevel Hybrid Split Step Implicit Tau-Leap for Stochastic R...
Seminar Talk: Multilevel Hybrid Split Step Implicit Tau-Leap for Stochastic R...Chiheb Ben Hammouda
 
Theoretical Spectroscopy Lectures: real-time approach 2
Theoretical Spectroscopy Lectures: real-time approach 2Theoretical Spectroscopy Lectures: real-time approach 2
Theoretical Spectroscopy Lectures: real-time approach 2Claudio Attaccalite
 
Scattering theory analogues of several classical estimates in Fourier analysis
Scattering theory analogues of several classical estimates in Fourier analysisScattering theory analogues of several classical estimates in Fourier analysis
Scattering theory analogues of several classical estimates in Fourier analysisVjekoslavKovac1
 
D. Vulcanov - On Cosmologies with non-Minimally Coupled Scalar Field and the ...
D. Vulcanov - On Cosmologies with non-Minimally Coupled Scalar Field and the ...D. Vulcanov - On Cosmologies with non-Minimally Coupled Scalar Field and the ...
D. Vulcanov - On Cosmologies with non-Minimally Coupled Scalar Field and the ...SEENET-MTP
 

Semelhante a ENFPC 2010 (20)

Approximate Bayesian Computation with Quasi-Likelihoods
Approximate Bayesian Computation with Quasi-LikelihoodsApproximate Bayesian Computation with Quasi-Likelihoods
Approximate Bayesian Computation with Quasi-Likelihoods
 
Ray : modeling dynamic systems
Ray : modeling dynamic systemsRay : modeling dynamic systems
Ray : modeling dynamic systems
 
002 ray modeling dynamic systems
002 ray modeling dynamic systems002 ray modeling dynamic systems
002 ray modeling dynamic systems
 
Linear response theory and TDDFT
Linear response theory and TDDFT Linear response theory and TDDFT
Linear response theory and TDDFT
 
Rdnd2008
Rdnd2008Rdnd2008
Rdnd2008
 
Adc
AdcAdc
Adc
 
Norm-variation of bilinear averages
Norm-variation of bilinear averagesNorm-variation of bilinear averages
Norm-variation of bilinear averages
 
Linear response theory
Linear response theoryLinear response theory
Linear response theory
 
A Fibonacci-like universe expansion on time-scale
A Fibonacci-like universe expansion on time-scaleA Fibonacci-like universe expansion on time-scale
A Fibonacci-like universe expansion on time-scale
 
Allele Frequencies as Stochastic Processes: Mathematical & Statistical Approa...
Allele Frequencies as Stochastic Processes: Mathematical & Statistical Approa...Allele Frequencies as Stochastic Processes: Mathematical & Statistical Approa...
Allele Frequencies as Stochastic Processes: Mathematical & Statistical Approa...
 
Introducing Zap Q-Learning
Introducing Zap Q-Learning   Introducing Zap Q-Learning
Introducing Zap Q-Learning
 
Stochastic Gravity in Conformally-flat Spacetimes
Stochastic Gravity in Conformally-flat SpacetimesStochastic Gravity in Conformally-flat Spacetimes
Stochastic Gravity in Conformally-flat Spacetimes
 
Signal Processing Homework Help
Signal Processing Homework HelpSignal Processing Homework Help
Signal Processing Homework Help
 
Tales on two commuting transformations or flows
Tales on two commuting transformations or flowsTales on two commuting transformations or flows
Tales on two commuting transformations or flows
 
spectralmethod
spectralmethodspectralmethod
spectralmethod
 
Seminar Talk: Multilevel Hybrid Split Step Implicit Tau-Leap for Stochastic R...
Seminar Talk: Multilevel Hybrid Split Step Implicit Tau-Leap for Stochastic R...Seminar Talk: Multilevel Hybrid Split Step Implicit Tau-Leap for Stochastic R...
Seminar Talk: Multilevel Hybrid Split Step Implicit Tau-Leap for Stochastic R...
 
Theoretical Spectroscopy Lectures: real-time approach 2
Theoretical Spectroscopy Lectures: real-time approach 2Theoretical Spectroscopy Lectures: real-time approach 2
Theoretical Spectroscopy Lectures: real-time approach 2
 
Sildes buenos aires
Sildes buenos airesSildes buenos aires
Sildes buenos aires
 
Scattering theory analogues of several classical estimates in Fourier analysis
Scattering theory analogues of several classical estimates in Fourier analysisScattering theory analogues of several classical estimates in Fourier analysis
Scattering theory analogues of several classical estimates in Fourier analysis
 
D. Vulcanov - On Cosmologies with non-Minimally Coupled Scalar Field and the ...
D. Vulcanov - On Cosmologies with non-Minimally Coupled Scalar Field and the ...D. Vulcanov - On Cosmologies with non-Minimally Coupled Scalar Field and the ...
D. Vulcanov - On Cosmologies with non-Minimally Coupled Scalar Field and the ...
 

Último

My INSURER PTE LTD - Insurtech Innovation Award 2024
My INSURER PTE LTD - Insurtech Innovation Award 2024My INSURER PTE LTD - Insurtech Innovation Award 2024
My INSURER PTE LTD - Insurtech Innovation Award 2024The Digital Insurer
 
Bun (KitWorks Team Study 노별마루 발표 2024.4.22)
Bun (KitWorks Team Study 노별마루 발표 2024.4.22)Bun (KitWorks Team Study 노별마루 발표 2024.4.22)
Bun (KitWorks Team Study 노별마루 발표 2024.4.22)Wonjun Hwang
 
WordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your BrandWordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your Brandgvaughan
 
Integration and Automation in Practice: CI/CD in Mule Integration and Automat...
Integration and Automation in Practice: CI/CD in Mule Integration and Automat...Integration and Automation in Practice: CI/CD in Mule Integration and Automat...
Integration and Automation in Practice: CI/CD in Mule Integration and Automat...Patryk Bandurski
 
Unleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding ClubUnleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding ClubKalema Edgar
 
SAP Build Work Zone - Overview L2-L3.pptx
SAP Build Work Zone - Overview L2-L3.pptxSAP Build Work Zone - Overview L2-L3.pptx
SAP Build Work Zone - Overview L2-L3.pptxNavinnSomaal
 
CloudStudio User manual (basic edition):
CloudStudio User manual (basic edition):CloudStudio User manual (basic edition):
CloudStudio User manual (basic edition):comworks
 
"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr Bagan"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr BaganFwdays
 
Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage Cost
Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage CostLeverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage Cost
Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage CostZilliz
 
Powerpoint exploring the locations used in television show Time Clash
Powerpoint exploring the locations used in television show Time ClashPowerpoint exploring the locations used in television show Time Clash
Powerpoint exploring the locations used in television show Time Clashcharlottematthew16
 
Anypoint Exchange: It’s Not Just a Repo!
Anypoint Exchange: It’s Not Just a Repo!Anypoint Exchange: It’s Not Just a Repo!
Anypoint Exchange: It’s Not Just a Repo!Manik S Magar
 
Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!Commit University
 
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024BookNet Canada
 
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks..."LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...Fwdays
 
Artificial intelligence in cctv survelliance.pptx
Artificial intelligence in cctv survelliance.pptxArtificial intelligence in cctv survelliance.pptx
Artificial intelligence in cctv survelliance.pptxhariprasad279825
 
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024BookNet Canada
 
"Federated learning: out of reach no matter how close",Oleksandr Lapshyn
"Federated learning: out of reach no matter how close",Oleksandr Lapshyn"Federated learning: out of reach no matter how close",Oleksandr Lapshyn
"Federated learning: out of reach no matter how close",Oleksandr LapshynFwdays
 
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek SchlawackFwdays
 
Developer Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLDeveloper Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLScyllaDB
 

Último (20)

My INSURER PTE LTD - Insurtech Innovation Award 2024
My INSURER PTE LTD - Insurtech Innovation Award 2024My INSURER PTE LTD - Insurtech Innovation Award 2024
My INSURER PTE LTD - Insurtech Innovation Award 2024
 
Bun (KitWorks Team Study 노별마루 발표 2024.4.22)
Bun (KitWorks Team Study 노별마루 발표 2024.4.22)Bun (KitWorks Team Study 노별마루 발표 2024.4.22)
Bun (KitWorks Team Study 노별마루 발표 2024.4.22)
 
WordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your BrandWordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your Brand
 
Integration and Automation in Practice: CI/CD in Mule Integration and Automat...
Integration and Automation in Practice: CI/CD in Mule Integration and Automat...Integration and Automation in Practice: CI/CD in Mule Integration and Automat...
Integration and Automation in Practice: CI/CD in Mule Integration and Automat...
 
Unleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding ClubUnleash Your Potential - Namagunga Girls Coding Club
Unleash Your Potential - Namagunga Girls Coding Club
 
SAP Build Work Zone - Overview L2-L3.pptx
SAP Build Work Zone - Overview L2-L3.pptxSAP Build Work Zone - Overview L2-L3.pptx
SAP Build Work Zone - Overview L2-L3.pptx
 
CloudStudio User manual (basic edition):
CloudStudio User manual (basic edition):CloudStudio User manual (basic edition):
CloudStudio User manual (basic edition):
 
"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr Bagan"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr Bagan
 
Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage Cost
Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage CostLeverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage Cost
Leverage Zilliz Serverless - Up to 50X Saving for Your Vector Storage Cost
 
Powerpoint exploring the locations used in television show Time Clash
Powerpoint exploring the locations used in television show Time ClashPowerpoint exploring the locations used in television show Time Clash
Powerpoint exploring the locations used in television show Time Clash
 
DMCC Future of Trade Web3 - Special Edition
DMCC Future of Trade Web3 - Special EditionDMCC Future of Trade Web3 - Special Edition
DMCC Future of Trade Web3 - Special Edition
 
Anypoint Exchange: It’s Not Just a Repo!
Anypoint Exchange: It’s Not Just a Repo!Anypoint Exchange: It’s Not Just a Repo!
Anypoint Exchange: It’s Not Just a Repo!
 
Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!
 
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
 
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks..."LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...
"LLMs for Python Engineers: Advanced Data Analysis and Semantic Kernel",Oleks...
 
Artificial intelligence in cctv survelliance.pptx
Artificial intelligence in cctv survelliance.pptxArtificial intelligence in cctv survelliance.pptx
Artificial intelligence in cctv survelliance.pptx
 
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
 
"Federated learning: out of reach no matter how close",Oleksandr Lapshyn
"Federated learning: out of reach no matter how close",Oleksandr Lapshyn"Federated learning: out of reach no matter how close",Oleksandr Lapshyn
"Federated learning: out of reach no matter how close",Oleksandr Lapshyn
 
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
 
Developer Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLDeveloper Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQL
 

ENFPC 2010

  • 1. Outline Stochastic Approach to Inflation Warm Inflation Stochastic approach to Warm Inflation Stochastic Approach to Warm Inflation Leandro Alexandre da Silva 1 Rio de Janeiro State University Department of Theoretical Physics XXXI Encontro Nacional de F´ısica de Part´ıculas e Campos 01/09/2010 1 in collaboration with R.O. Ramos
  • 2. Outline Stochastic Approach to Inflation Warm Inflation Stochastic approach to Warm Inflation 1 Stochastic Approach to Inflation 2 Warm Inflation 3 Stochastic approach to Warm Inflation
  • 3. Outline Stochastic Approach to Inflation Warm Inflation Stochastic approach to Warm Inflation Stochastic Approach to Inflation When? Stochastic Inflation: ∼ 1987 Why? Exponentially rapid expansion → “freeze” of inflaton quantum fluctuations on super-horizon scales ⇓ Inflaton fluctuations behave effectivelly as classical fluctuation modes with random amplitudes ⇓ Emulates the growth of vacuum fluctuations by an effective stochastic noise field which drives the dynamics of the volume-smoothed inflaton → effective dynamics for coarse-grained field φ
  • 4. Outline Stochastic Approach to Inflation Warm Inflation Stochastic approach to Warm Inflation Stochastic Approach to Inflation How? Usual approach → decomposition of φ in a classical, coarse-grained component and in a quantum fluctuation part: Φ(x, t) → φ(x, t) + q(x, t) . φ → coarse-grained scalar field averaged over approximatelly all de Sitter horizon size 1/χ q(x, t) → summarizes high frequency (k kh ≈ χ) quantum fluctuations. q(x, t) aproximated as a free, massless scalar field.
  • 5. Outline Stochastic Approach to Inflation Warm Inflation Stochastic approach to Warm Inflation Stochastic Approach to Inflation de Sitter metric: ds2 = dxµ dxν gµν = −dt2 + e2χt dx2 , Lagrangian density: L = 1 2 √ −g [gµν ∂µΦ∂νφ − 2V (Φ)] Equation of motion(EoM): −3χ ∂ ∂t − ∂2 ∂t2 + e−2χt 2 Φ(x, t) − ∂V (Φ) ∂Φ = 0
  • 6. Outline Stochastic Approach to Inflation Warm Inflation Stochastic approach to Warm Inflation Stochastic Approach to Inflation Making the field decomposition (slow-roll: ¨φ(x, t) ≈ 0): 3χ ∂ ∂t − e−2χt 2 [φ(x, t) + q(x, t)] + ∂V (φ) ∂φ = 0 3χ ∂ ∂t − e−2χt 2 φ(x, t) + ∂V (φ) ∂φ = 3χη(x, t) , Noise term: η(x, t) ≡ − ∂ ∂t + e−2χt 3χ 2 q(x, t) Fourier mode expansion in de Sitter background: q(x, t) ≡ d3 kWχ(k) σk (t)e−ik·x ˆak + σ∗ k (t)eik·x ˆa† k . Wχ(k) → filter or window function. Sharp momentum cutoff implementation: Wχ(k) ≡ θ(k − χeχt).
  • 7. Outline Stochastic Approach to Inflation Warm Inflation Stochastic approach to Warm Inflation Stochastic Approach to Inflation σk (t) ≡ 1 2k(2π)3 χτ − i χ k e−ikτ . Commutator: [η(x, τ), η(y, τ)] = 0 ⇓ Classical behaviour of quantum noise!
  • 8. Outline Stochastic Approach to Inflation Warm Inflation Stochastic approach to Warm Inflation Stochastic Approach to Inflation σk (t) ≡ 1 2k(2π)3 χτ − i χ k e−ikτ . Commutator: [η(x, τ), η(y, τ)] = 0 ⇓ Classical behaviour of quantum noise! Propagator: 0 | η(x, t)η(y, t ) | 0 = χ3 4π2 δ(t − t ) sin τ | x − y | τ | x − y | .
  • 9. Outline Stochastic Approach to Inflation Warm Inflation Stochastic approach to Warm Inflation Warm Inflation Same basic ideas of cold inflation Inflaton interacts with other fields → radiation production A reheating process is no more necessary Smooth transition to the radiation dominated regime Thermal origin for the density perturbations A. Berera and L. Z. Fang,Phys. Rev. Lett. 74, 1912 (1995): Inflaton dynamics → Langevin-like equation
  • 10. Outline Stochastic Approach to Inflation Warm Inflation Stochastic approach to Warm Inflation Microscopic motivation to Warm Inflation Example: S[φ, χ, σ] = d4 x 1 2 (∂µφ)2 − 1 2 m2 φφ2 − λ 4! φ4 + 1 2 (∂µχ)2 − 1 2 m2 χχ2 + 1 2 (∂µσ)2 − 1 2 m2 σσ2 − g2 2 φ2 χ2 − f χσ2 . φ → classical field in which dynamics we are interested in χ → intermediate field that couples to σ and φ σ → Thermally equilibrated field at temperature T
  • 11. Outline Stochastic Approach to Inflation Warm Inflation Stochastic approach to Warm Inflation Microscopic motivation to Warm Inflation Detailed calculation: Berera and Ramos, PRD63, 103509 (2001),Gleiser and Ramos, PRD50, 2441 (1994): General Effective Equation of Motion (Homogeneous approximation) d2φ(t) dt2 = − dVeff(φ) dφ − φn (t) t −∞ dt φn (t ) ˙φ(t )Kχ(t − t ) + φn (t) ξ (t) , n = 0: additive noise→ φχ2 interaction n = 1: multiplicative noise → φ2χ2 interaction
  • 12. Outline Stochastic Approach to Inflation Warm Inflation Stochastic approach to Warm Inflation Markovian Approximation Non-Markovian Equation of Motion: ∂2 t + m2 φ + λ 3! φ(t)2 φ(t) + φn (t) t t0 dt K(t − t )φn (t ) ˙φ(t ) = φn (t)ξ(t) . Markovian Approximation: φn (t) t t0 dt K(t − t )φn (t ) ˙φ(t ) φ2n (t) ˙φ(t) t t0→−∞ dt K(t − t ) → Υ φ2n (t) ˙φ(t) . Markovian Equation of Motion: ¨φ + Υ φ2n ˙φ + m2 φφ + λ 6 φ3 = φn (t) ξ(t)
  • 13. Outline Stochastic Approach to Inflation Warm Inflation Stochastic approach to Warm Inflation Markovian Approximation Figure: (a) mχ = 50H(0), (b) mχ = 150H(0) and (c) mχ = 250H(0)
  • 14. Outline Stochastic Approach to Inflation Warm Inflation Stochastic approach to Warm Inflation Markovian Approximation More details about Markovian dynamics reliability: R. L. S. Farias, R. O. Ramos and L. A. da Silva, Nonlinear effects in the dynamics governed by non-Markovian stochastic Langevin-like equations, JPCS, in press R. L. S. Farias, R. O. Ramos and L. A. da Silva, Numerical Solutions for non-Markovian Stochastic Equations of Motion Comp. Phys. Comm. 180, 574 (2009). R. L. S. Farias, L. A. da Silva and R. O. Ramos, Non-Markovian stochastic Langevin equations: Markovian and non-Markovian dynamics, Phys. Rev. E 80, 031143 (2009) R. L. S. Farias, R. O. Ramos and L. A. da Silva,Langevin Simulations with Colored Noise and Non-Markovian Dissipation Brazilian Journal of Physics, vol. 38 , no. 3B, (2008)
  • 15. Outline Stochastic Approach to Inflation Warm Inflation Stochastic approach to Warm Inflation Markovian Approximation Considering the additive case: ¨φ + [3H + Υ] ˙φ + V (φ) = ξ , ¨a = − 8π 3m2 pl ρr + ˙φ2 − V (φ) a , ˙ρφ = −3 ˙a a ˙φ2 − Υ ˙φ2 + ν ˙φ , ˙ρr = −4 ˙a a ρr + Υ ˙φ2 − ξ ˙φ .
  • 16. Outline Stochastic Approach to Inflation Warm Inflation Stochastic approach to Warm Inflation Stochastic approach to Warm Inflation Back to stochastic approach: implement Warm Inflation Modification of EoM: ∂φ(x, t) ∂t = 1 3χ + Υ e−2χt 2 φ(x, t) − ∂V (φ) ∂φ +η(x, t)+ξ(x, t) .
  • 17. Outline Stochastic Approach to Inflation Warm Inflation Stochastic approach to Warm Inflation Stochastic approach to Warm Inflation Back to stochastic approach: implement Warm Inflation Modification of EoM: ∂φ(x, t) ∂t = 1 3χ + Υ e−2χt 2 φ(x, t) − ∂V (φ) ∂φ +η(x, t)+ξ(x, t) . Global analysis: ∂φ(t) ∂t = − 1 3χ + Υ ∂V (φ) ∂φ + η(t) + ξ(t) , η(t)η(t ) = χ3 4π2 δ(t − t ) ξ(t)ξ(t ) = βδ(t − t ) .
  • 18. Outline Stochastic Approach to Inflation Warm Inflation Stochastic approach to Warm Inflation Stochastic approach to Warm Inflation Considering a general SDE of the form ˙φ = h(φ, t) + g(φ, t)η(t) + f (φ, t)ξ(t) , we obtain a Fokker-Planck equation for η(t)ξ(t ) = 0 of the form ∂P(φ, t) ∂t = − ∂ ∂φ D(1) + ∂2 ∂φ2 D(2) P(φ, t) . where the Kramers-Moyal coefficients are: D(1) (φ, t) = h(φ, t) + α 2 ∂g(φ, t) ∂φ g(φ, t) + β 2 ∂f (φ, t) ∂φ f (φ, t) D(2) (φ, t) = α 2 g(φ, t)2 + β 2 f (φ, t)2 . (1)
  • 19. Outline Stochastic Approach to Inflation Warm Inflation Stochastic approach to Warm Inflation Stochastic approach to Warm Inflation In our particular case: ∂P(φ, t) ∂t = − ∂ ∂φ − 1 3χ + Υ ∂V (φ) ∂φ + ∂2 ∂φ2 χ3 4π2 + β 2 P(φ, t) . A trick to obtain φn(t) : ∂ ∂t φn (t) ≡ ∞ −∞ dφφn ∂ ∂t P(φ, t) . ∂ ∂t φn (t) = χ3 8π2 + β 2 n(n−1) φn−2 (t) − n 3χ + Υ φn−1 (t) ∂V (φ) ∂t . φ2 (t) = 3χ + Υ 2m2 φ χ3 4π2 + β 1 − exp − 2m2 φ 3χ + Υ t .
  • 20. Outline Stochastic Approach to Inflation Warm Inflation Stochastic approach to Warm Inflation Stochastic approach to Warm Inflation The Fokker-Planck operator LFP ≡ − ∂ ∂φ − 1 3χ + Υ ∂V (φ) ∂φ + ∂2 ∂φ2 χ3 4π2 + β 2 , is so that LFP = L† FP. ⇒ Transformation of variables: t → (3χ + Υ)t φ → (3χ + Υ) χ3 4π2 + β ψ
  • 21. Outline Stochastic Approach to Inflation Warm Inflation Stochastic approach to Warm Inflation Stochastic approach to Warm Inflation The Fokker-Planck becomes: ∂P(ψ, t ) ∂t = 2 ∂2P(ψ, t ) ∂ψ2 + ∂ ∂ψ ∂ ˜V (ψ) ∂ψ P(ψ, t ) , where ˜V (ψ) ≡ (3χ + Υ) χ3 4π2 + β −1 V φ = (3χ + Υ) χ3 4π2 + β ψ One more transformation P(ψ, t ) = exp 1 ˜V (0) − ˜V (ψ) F(ψ, t ) , and then the Fokker-Planck equation becomes...
  • 22. Outline Stochastic Approach to Inflation Warm Inflation Stochastic approach to Warm Inflation Stochastic approach to Warm Inflation ...a Schr¨odinger-like equation with imaginary time: − 2 ∂2 ∂ψ2 + U(ψ) F(ψ, t ) = − ∂F(ψ, t ) ∂t , (2) with U(ψ) ≡ 1 2   ∂ ˜V (ψ) ∂ψ 2 − ∂2 ˜V (ψ) ∂ψ2   . (3) Analogy with quantum mechanics: ψ | α, t = λ ψ | λ λ | α, t → F(ψ, t ) = λ cλe− i Eλt ϕλ(ψ) , with Hϕλ(ψ) = Eλϕλ(ψ) , H ≡ − 2 ∂2 ∂ψ2 + U(ψ) , t → i t
  • 23. Outline Stochastic Approach to Inflation Warm Inflation Stochastic approach to Warm Inflation Stochastic approach to Warm Inflation Then using standard operator techniques and recovering the original variables φ and t, we get: P(φ, t) = λ cλe−m2 φσ−1 λt × Λ 1 2 π 1 4 √ 2λλ! m2 φ (λ 2 − 3 4 ) m2 φΛ−1 φ − ∂ ∂φ λ exp m2 φΛ−1 φ2 , And the propagator K(φ2, t2; φ1, t1): K(φ2, t2; φ1, t1) = 1 − e−2m2 φσ−1 (t2−t1) − 1 2 × exp    − m2 φΛ−1 φ2 2 + φ2 1 − 2φ2φ1e−m2 φσ−1 (t2−t1) 1 − e−2m2 φσ−1(t2−t1)    with Λ ≡ (3χ + Υ) χ3 4π2 + β and σ ≡ 3χ + Υ
  • 24. Outline Stochastic Approach to Inflation Warm Inflation Stochastic approach to Warm Inflation Discussions and perspectives Planck energy scale at V (φ = φmax ) → breakdown of semiclassical picture of spacetime ⇓ P(φmax ) = 0 ⇓ Bounded from above eigenvalues H eigenvalues ∈ N → LFP eingenvalues ∈ Z− ⇓ Highest eigenvalue (˜λmax ) of volume-weighted FP equation (LFP → LFP + 3H) can be negative or positive: warm inflation → ˜λmax > 0 → number of inflating domains increases without limit. (to appear in JCAP)
  • 25. Outline Stochastic Approach to Inflation Warm Inflation Stochastic approach to Warm Inflation Discussions and perspectives Work out the local (φ(x, t)) analysis Study the pathway to classicalization: when the thermal fluctuations overcome the quantum ones? Better quantify eternal inflation based on Warm Inflation
  • 26. Outline Stochastic Approach to Inflation Warm Inflation Stochastic approach to Warm Inflation Thanks for your attention!!!