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Wilson Lin

Publications and source records attributed to Wilson Lin.

3 recordsLinked to original sources

Spectral Sidorenko inequalities and edge-spectral supersaturation

We develop a spectral approach to Sidorenko-type inequalities and apply it to establish sharp edge-spectral supersaturation results. Let $H$ be a bipartite graph with $v$ vertices and $e$ edges, where $v\le e$, and write $M(G)=2e(G)$. We prove that Sidorenko's conjecture is equivalent to a spectral strengthening: $$ \hom(H,G)\ge M(G)^e |V(G)|^{v-2e} \quad \text{ if and only if }\quad \hom(H,G)\ge \lambda(G)^{2e-v}M(G)^{v-e}.$$ We also introduce an operator-norm certificate which, via the Riesz--Thorin interpolation, gives direct proofs of the spectral Sidorenko inequality in several cases. The converse direction in the equivalence theorem is proved by a tensor-power spectral regularization lemma. Our main result provides a unified framework to prove sharp asymptotic edge-spectral supersaturation results for degenerate bipartite graphs with the Sidorenko property, including complete bipartite graphs and even cycles. Let $S_{t-1,m}$ be the split graph with $m$ edges obtained by joining a clique $K_{t-1}$ to an independent set. For every $m$-edge graph $G$ with $\lambda(G)>\lambda(S_{t-1,m})$, $$\texttt{#} K_{t,t}(G) \ge \Big(\frac{2^{-(t-1)^2}}{(t!)^2}-o(1)\Big)m^t \quad \text{and}\quad \texttt{#}C_{2t}(G) \ge \Big(\frac{(t-1)!}{2t^t}-o(1)\Big)m^t.$$ Both constants are best possible: the first is attained asymptotically by random graphs, while the second is attained by split graphs. The supersaturation proofs combine spectral Sidorenko inequalities with a heavy-edge pruning process, a Perron-vector localized/delocalized dichotomy, and incidence-matrix inequalities.

math.CO

Using Neural Networks to Accelerate TALYS-2.0 Nuclear Reaction Simulations

Recent efforts to improve the predictability of TALYS-2.0 calculated charged-particle residual product cross sections have focused on adjusting parameters related to the optical model potential and pre-equilibrium process. Although adjusted TALYS-2.0 outputs show marked improvements in agreement with experimental data over the default parameters, the procedure is generally time-consuming due to the need for sequential TALYS-2.0 calculations. Since the models and model parameters must be defined and constrained prior to adjustment, we show in this work that an artificial neural network can serve as a surrogate model to successfully predict TALYS-2.0 outputs within this domain of input parameters. No practical differences were observed in the trained model's performance between uniform random, Latin hypercube and Sobol sequence sampling for generating the training datasets. Once validated, trained neural network models were used to adjust TALYS-2.0 nuclear model parameters, where a multi-parameter fitting procedure was not only feasible but optimal for this process. The neural network approach is >1000x faster at generating residual product cross sections than using TALYS-2.0 directly, and a high-fidelity surrogate model could be implemented with about 1500 TALYS-2.0 files to achieve adjusted cross sections comparable to the previous publication.

physics.comp-ph

Characterizing Secondary Neutrons at BLIP for Isotope Production Applications

Fast secondary neutrons created at the Brookhaven Linac Isotope Producer (BLIP) facility following proton irradiation were characterized by the foil activation technique and compared with FLUKA Monte Carlo simulations. The FLUKA-simulated neutron flux was spectrally adjusted following the maximum entropy formalism using the International Reactor Dosimetry and Fusion File (IRDFF-II), with predictions agreeing with experimental measurements to within 9% following the adjustment procedure. A multitude of degrader configurations were simulated to assess the feasibility of improving the fast (En > 20 MeV) secondary neutron yield at the proposed neutron target position (N-slot). A configuration where the N-slot is closest to the proton degrader produced the highest fast neutron yield, with tungsten degraders achieving the best performance. Assuming the optimized target-degrader configuration proposed in this work, we discuss potential isotope production opportunities with secondary neutrons. In most cases the yields are in the order of several mCi.

physics.app-ph