 Divergences for computing exp()

Hi
I am working on a multivariate hierarchical model where I need to compute exp of sampled values (the sampled parameters are on the log scale).
However, computing the exp() gives many divergences. The issue is not with sampling the parameters, but computing the exponential of theta in the transformed parameters block.

How do I overcome the divergences? Or is there something incorrect with the way the exponential is computed?

``````
data {
int<lower=1> J;
int<lower =1> K;
vector[K] mu_a;
}
parameters{
matrix[J,K] a;
vector<lower = 0>[K] tau;       // sd of b
cholesky_factor_corr[K] Omega;
matrix[K,J] z;                  //standard normal variates
}
transformed parameters {
matrix[J,K] b;
matrix[J,K] theta_raw;

b = (diag_pre_multiply(tau, Omega)*z)';     //covariance matrix
theta_raw = exp(a + b);

}
model {
//hyper-priors
tau ~ cauchy(0,1);
Omega ~ lkj_corr_cholesky(2);
to_vector(z) ~ normal(0,1);
for(j in 1:J){
a[j,] ~ normal(mu_a, 1);
}

}
``````

The R code used is

``````stan_data <- list(J = J,
K = 8,
mu_a = c(log(1.4e6), log(6e-11), log(6),log(5), log(1e4), log(3),log(4e4),log(1e-4)),
)

fit <- stan("model_name.stan",
data = stan_data,
chains = 1, iter = 1000
control = list(adapt_delta = 0.8, max_treedepth = 10),
seed=4838282)

``````

Thanks