On the Strichartz estimates for orthonormal systems of initial data with regularity (1708.05588v1)
Abstract: The classical Strichartz estimates for the free Schr\"odinger propagator have recently been substantially generalised to estimates of the form [ \bigg|\sum_j\lambda_j|e{it\Delta}f_j|2\bigg|{Lp_tLq_x}\lesssim|\lambda|{\ell\alpha} ] for orthonormal systems $(f_j)_j$ of initial data in $L2$, firstly in work of Frank--Lewin--Lieb--Seiringer and later by Frank--Sabin. The primary objective is identifying the largest possible $\alpha$ as a function of $p$ and $q$, and in contrast to the classical case, for such estimates the critical case turns out to be $(p,q) = (\frac{d+1}{d},\frac{d+1}{d-1})$. We consider the case of orthonormal systems $(f_j)_j$ in the homogeneous Sobolev spaces $\dot{H}s$ for $s \in (0,\frac{d}{2})$ and we establish the sharp value of $\alpha$ as a function of $p$, $q$ and $s$, except possibly an endpoint in certain cases, at which we establish some weak-type estimates. Furthermore, at the critical case $(p,q) = (\frac{d+1}{d-2s},\frac{d(d+1)}{(d-1)(d-2s)})$ for general $s$, we show the veracity of the desired estimates when $\alpha = p$ if we consider frequency localised estimates, and the failure of the (non-localised) estimates when $\alpha = p$; this exhibits the difficulty of upgrading from frequency localised estimates in this context, again in contrast to the classical setting.
Collections
Sign up for free to add this paper to one or more collections.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.