论文标题

通过第n个集中的时刻来界定消失的尖锐新形式

Bounding the Order of Vanishing of Cuspidal Newforms via the nth Centered Moments

论文作者

Dutta, Sohom, Miller, Steven J.

论文摘要

在Iwaniec,Luo和Sarnak的工作的基础上,我们使用$ n $级别的密度限制了消失的可能性,以订购至少$ r $的中央点,用于质量水平$ n \ to \ infty $的cuspidal Newforms家族,按符号分裂。有三种方法可以提高消失的顺序:优化测试功能,增加支持并增加所研究的$ n $级别密度。先前的工作确定了在某些支持范围内的$ 1 $和$ 2 $级别密度的最佳测试功能,从而导致界限的边缘改善,并使之成为进一步研究的富有成效的途径。同样,支持已尽可能增加,进一步的进步与数量理论中的微妙和困难的猜想有关。因此,我们专注于第三种方法,并研究较高的中心矩(与$ n $级别的密度相似,但在组合上更容易)。我们发现,在消失顺序的每个等级处的水平是最好的,因此每一个$ r> 2 $的消失顺序产生的世界纪录界限至少排名至少$ r $(例如,我们消失的界限至少订购5或至少6个比以前的范围少了一半,这是一个显着改进的范围)。此外,我们明确计算了以前工作的$ 1 $级别密度的最佳测试功能,并将其与更高级别的幼稚测试功能进行比较。这样一来,我们发现某些级别的最佳测试功能不是其他级别的最佳测试功能,并且某些测试功能可能在某些级别上优于其他级别,而在其他级别上则不能优于其他级别。最后,我们明确计算确定界限所需的积分,从而通过将$ n $维的积分转换为$ 1 $维的积分并大大降低了该过程中的计算成本。

Building on the work of Iwaniec, Luo and Sarnak, we use the $n$-level density to bound the probability of vanishing to order at least $r$ at the central point for families of cuspidal newforms of prime level $N \to \infty$, split by sign. There are three methods to improve bounds on the order of vanishing: optimizing the test functions, increasing the support, and increasing the $n$-level density studied. Previous work determined the optimal test functions for the $1$ and $2$-level densities in certain support ranges, leading to marginal improvements in bounds and making it not a productive avenue for further research. Similarly the support has been increased as far as possible, and further progress is shown to be related to delicate and difficult conjectures in number theory. Thus we concentrate on the third method, and study the higher centered moments (which are similar to the $n$-level densities but combinatorially easier). We find the level at each rank for which the bounds on the order of vanishing is the best, thus producing world-record bounds on the order of vanishing to rank at least $r$ for every $r > 2$ (for example, our bounds for vanishing to order at least 5 or at least 6 are less than half the previous bounds, a significant improvement). Additionally, we explicitly calculate the optimal test function for the $1$-level density from previous work and compare it to the naive test functions for higher levels. In doing so, we find that the optimal test function for certain levels are not the optimal for other levels, and some test functions may outperform others for some levels but not in others. Finally, we explicitly calculate the integrals needed to determine the bounds, doing so by transforming an $n$-dimensional integral to a $1$-dimensional integral and greatly reducing the computation cost in the process.

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