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--- dlcp2026:program [05/07/2026 18:22] – [44. Texture Based Explainable Machine Learning for Automated Glacial Crevasse Detection in GPR Radargrams] admin
+++ dlcp2026:program [05/07/2026 18:51] (current) – [7. Shower Core Reconstruction in the HiSCORE Experiment using Neural Networks fed by Autoencoder-Derived Essential Features] admin
@@ Line 1: / Line 1: @@
 {{ :dlcp2026:dlcp26-logo.png?200|}}
-====== Program (Draft) ======
+====== Program (final) ======
-//23.06.2025//
+//05.06.2025//
-**The final list of accepted reports will be publish later.**
 <color /orange>The first author is the presenter.</color>
-//If someone did not find themselves in the list, please inform us by email [[dlcp@sinp.msu.ru]]//
+{{ :dlcp2026:dlcp2026_program.pdf |DLCP2026 program}} (final).
-{{ :dlcp2026:dlcp2026_program.pdf |DLCP2026 program}} (draft).
 ===== Section 1. Machine Learning in Fundamental Physics =====
@@ Line 107: / Line 103: @@
 and plans. Physics of Atomic Nuclei. — 2021 — Vol. 84, no. 6 — P. 966–974.
-==== 7. Shower Core Reconstruction in the HiSCORE Experiment using Neural Networks fed by Autoencoder-Derived Essential Features ====
+==== 7. Neural Network-Based Shower Core Reconstruction in the HiSCORE Experiment using Autoencoder-Derived Essential Features ====
 //**Ю.Ю. Дубенская**(1), А.П. Крюков(1), П.А. Волчугов(1), Е.О. Гресь(1,2), А.П. Демичев(1), Д.П. Журов(1,2), С.П. Поляков(1), Е.Б. Постников(1), А.Ю. Разумов(1) \\ (1) НИИЯФ МГУ, Москва, (2) НИИПФ ИГУ, Иркутск //