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Jackson Zariski

Publications and source records attributed to Jackson Zariski.

3 recordsLinked to original sources

Recursive Record Filtering and Longest Decreasing Subsequences

We consider a recursive record-filtering procedure, which we informally call Disappear-Sort, acting as a sort of parallel to traditional patience sorting. Let $D_n$ denote the number of passes required to eliminate a sequence of length $n$ sampled as i.i.d.\ copies of a continuous random variable, where each pass retains the left-to-right records and applies the same rule recursively to the remaining entries. For the non-resampling procedure, we associate to a permutation $p_n\in S_n$ a natural poset and show that the recursive Disappear-Sort layers form an antichain decomposition of this poset. This provides an order-theoretic interpretation of the procedure and identifies the total number of passes with $L(p_n)$, the length of the longest decreasing subsequence of $p_n$. Equivalently, after reversing the comparison direction, the Disappear-Sort layers coincide with the piles arising in classical patience sorting. For a uniformly random permutation, the pass count therefore has the same distribution as the first-column length of the tableau shape produced by the Robinson--Schensted correspondence. We use this classical connection to express $\mathbb{E}[D_n]$ as a sum over partitions and standard Young tableaux. Established results on Plancherel-random Young diagrams then imply $\mathbb{E}[D_n]\sim 2\sqrt{n}$, with fluctuations on the $n^{1/6}$ scale governed by the Tracy--Widom distribution. We also consider a resampling variant in which the nonrecord entries are replaced after each pass by a fresh independent sample of the same size, and derive an exact recurrence for its expected number of passes involving the unsigned Stirling numbers of the first kind. We conclude with an $O(n\log n)$ implementation for computing the non-resampling pass count.

math.CO

Deep learning solutions to telescope pointing and guiding

The WIYN 3.5m Telescope at Kitt Peak National Observatory hosts a suite of optical and near infrared instruments, including an extreme precision, optical spectrograph, NEID, built for exoplanet radial velocity studies. In order to achieve sub ms precision, NEID has strict requirements on survey efficiency, stellar image positioning, and guiding performance, which have exceeded the native capabilities of the telescope's original pointing and tracking system. In order to improve the operational efficiency of the telescope we have developed a novel telescope pointing system, built on a recurrent neural network, that does not rely on the usual pointing models (TPoint or other quasi physical bases). We discuss the development of this system, how the intrinsic properties of the pointing problem inform our network design, and show preliminary results from our best models. We also discuss plans for the generalization of this framework, so that it can be applied at other sites.

astro-ph.IM

Inelastic Particle Clusters from Cumulative Momenta

We consider a physical system comprising discrete massive particles on the real line whose trajectories interact via perfectly inelastic collision, also known as sticky particles. It turns out that polygons formed in a convex "cumulative momentum diagram" of the initial conditions allow us to easily predict how many particle clusters form as time $t\to\infty$. We explore an application of this to a unit mass system with $\pm 1$ velocities, which has ties to simple symmetric random walks and lattice path counting.

math.DS