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Letian Shen

Publications and source records attributed to Letian Shen.

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Quasiregular maps of Sierpi\'nski carpet Julia sets

We prove that if $f$ and $g$ are postcritically finite rational maps whose Julia sets $\mathcal{J}(f), \mathcal{J}(g)$, respectively, are Sierpi\'nski carpets, and if $\xi$ is a quasiregular map of the Riemann sphere $\widehat{\mathbb{C}}$ with $\xi^{-1}(\mathcal{J}(g))=\mathcal{J}(f)$, then $\xi$ is the restriction of a rational map to the Julia set $\mathcal{J}(f)$. Moreover, when $g=f$ we prove that, for some positive integers $k$ and $l$, $f^k\circ \xi^l=f^{2k}$. These conclusions extend the main results of M. Bonk, M. Lyubich, S. Merenkov, Quasisymmetries of Sierpi\'nski carpet Julia sets, Adv. Math, 301 (2016), 383-422. Finally, we demonstrate that when Julia sets of postcritically finite rational maps are not Sierpi\'nski carpets, say they are tree-like or gaskets, the above conclusions no longer hold.

math.DS

Vision Calorimeter for Anti-neutron Reconstruction: A Baseline

In high-energy physics, anti-neutrons ($\bar{n}$) are fundamental particles that frequently appear as final-state particles, and the reconstruction of their kinematic properties provides an important probe for understanding the governing principles. However, this confronts significant challenges instrumentally with the electromagnetic calorimeter (EMC), a typical experimental sensor but recovering the information of incident $\bar{n}$ insufficiently. In this study, we introduce Vision Calorimeter (ViC), a baseline method for anti-neutron reconstruction that leverages deep learning detectors to analyze the implicit relationships between EMC responses and incident $\bar{n}$ characteristics. Our motivation lies in that energy distributions of $\bar{n}$ samples deposited in the EMC cell arrays embody rich contextual information. Converted to 2-D images, such contextual energy distributions can be used to predict the status of $\bar{n}$ ($i.e.$, incident position and momentum) through a deep learning detector along with pseudo bounding boxes and a specified training objective. Experimental results demonstrate that ViC substantially outperforms the conventional reconstruction approach, reducing the prediction error of incident position by 42.81% (from 17.31$^{\circ}$ to 9.90$^{\circ}$). More importantly, this study for the first time realizes the measurement of incident $\bar{n}$ momentum, underscoring the potential of deep learning detectors for particle reconstruction. Code is available at https://github.com/yuhongtian17/ViC.

hep-ex