{"id":3426,"date":"2025-10-30T11:56:19","date_gmt":"2025-10-30T02:56:19","guid":{"rendered":"https:\/\/www.kmi.nagoya-u.ac.jp\/eng\/?post_type=seminar&p=3426"},"modified":"2025-10-30T12:11:52","modified_gmt":"2025-10-30T03:11:52","slug":"advancing-event-reconstruction-and-calibration-via-differentiable-optical-detector-simulation","status":"publish","type":"seminar","link":"https:\/\/www.kmi.nagoya-u.ac.jp\/eng\/seminar\/3426\/","title":{"rendered":"Advancing Event Reconstruction and Calibration via Differentiable Optical Detector Simulation"},"content":{"rendered":"

Next-generation Water Cherenkov detectors aim to probe fundamental questions in neutrino physics. These measurements demand unprecedented precision in detector calibration and event reconstruction that push beyond traditional Monte Carlo techniques.<\/p>\n

We present LUCiD, the first differentiable ray-tracing framework for optical particle detectors. Rather than sampling discrete photon paths, LUCiD computes expected detector responses by propagating probability weights through the optical system, with all operations differentiable end-to-end. Processing one million photons with gradients takes tens of milliseconds on a single GPU, four orders of magnitude faster than traditional CPU-based simulations. <\/p>\n

This differentiable approach enables direct navigation through high-dimensional, strongly correlated parameter spaces where sampling methods struggle. We demonstrate simultaneous gradient-based calibration of critical detector parameters including scattering length, attenuation length, and reflection coefficients. For event reconstruction, LUCiD achieves performance comparable with state-of-the-art machine learning methods while maintaining complete visibility into the underlying physics. The framework seamlessly incorporates neural network surrogates for unknown phenomena without sacrificing interpretability. LUCiD\u2019s high-throughput capabilities also position it as an efficient platform for producing large-scale training datasets for machine learning foundation models in future neutrino physics research. This work represents a paradigm shift in how we approach next-generation neutrino experiments through differentiable programming.<\/p>\n

\n

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\n\u672c\u7814\u7a76\u306f\u3001\u6b21\u4e16\u4ee3\u30cb\u30e5\u30fc\u30c8\u30ea\u30ce\u5b9f\u9a13\u306e\u30a2\u30d7\u30ed\u30fc\u30c1\u306b\u304a\u3044\u3066\u3001\u5fae\u5206\u53ef\u80fd\u30d7\u30ed\u30b0\u30e9\u30df\u30f3\u30b0\u3092\u6d3b\u7528\u3057\u305f\u65b0\u305f\u306a\u30d1\u30e9\u30c0\u30a4\u30e0\u30b7\u30d5\u30c8\u3068\u306a\u308a\u307e\u3059\u3002<\/p>\n","protected":false},"featured_media":0,"template":"","tags":[],"seminar_category":[154],"acf":{"s_now_accepting":true,"s_date_order":"2025-11-06 16:00:00","s_date_end":"2025-11-06 17:00:00","s_date_text":"16:00 - 17:00","s_text":"Omar Alterkait (Tufts University)","s_place":"ES635 + Zoom","s_place_other":"","s_categoryother":"","s_poster":"","s_poster2":"","s_slide":""},"yoast_head":"\nAdvancing Event Reconstruction and Calibration via Differentiable Optical Detector Simulation - KMI - Nagoya University<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.kmi.nagoya-u.ac.jp\/eng\/seminar\/3426\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Advancing Event Reconstruction and Calibration via Differentiable Optical Detector Simulation - KMI - Nagoya University\" \/>\n<meta property=\"og:description\" content=\"Next-generation Water Cherenkov detectors aim to probe fundamental questions in neutrino physics. 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