Quantitative Convergence for Sparse Ergodic Averages in $L^1$

We provide a unified framework to proving pointwise convergence of sparse sequences, deterministic and random, at the $L^1(X)$ endpoint. Specifically, suppose that $$a_n \in \{ \lfloor n^c \rfloor, \min\{ k : \sum_{j \leq k} X_j = n\} \}$$ where $X_j$ are Bernoulli random variables with expectations $\mathbb{E} X_j = n^{-\alpha}$, and we restrict to $1 < c < 7/6, \ 0 < \alpha < 1/2$. Then (almost surely) for any measure-preserving system, $(X,\mu,T)$, and any $f \in L^1(X)$, the ergodic averages $$\frac{1}{N} \sum_{n \leq N} T^{a_n} f$$ converge $\mu$-a.e. Moreover, our proof gives new quantitative estimates on the rate of convergence, using jump-counting/variation/oscillation technology, pioneered by Bourgain. This improves on previous work of Urban-Zienkiewicz, and Mirek, who established the above with $c = \frac{1001}{1000}, \ \frac{30}{29}$, respectively, and LaVictoire, who established the random result, all in a non-quantitative setting.