Zobrazeno 1 - 10
of 1 294
pro vyhledávání: '"Saito , Masahiko"'
This paper presents a parameter scan technique for BSM signal models based on normalizing flow. Normalizing flow is a type of deep learning model that transforms a simple probability distribution into a complex probability distribution as an invertib
Externí odkaz:
http://arxiv.org/abs/2409.13201
This study aims to improve the performance of event classification in collider physics by introducing a pre-training strategy. Event classification is a typical problem in collider physics, where the goal is to distinguish the signal events of intere
Externí odkaz:
http://arxiv.org/abs/2312.06909
The goal of event classification in collider physics is to distinguish signal events of interest from background events to the extent possible to search for new phenomena in nature. We propose a decay-aware neural network based on a multi-task learni
Externí odkaz:
http://arxiv.org/abs/2212.08759
Autor:
Jang, Wonho, Terashi, Koji, Saito, Masahiko, Bauer, Christian W., Nachman, Benjamin, Iiyama, Yutaro, Okubo, Ryunosuke, Sawada, Ryu
Publikováno v:
Quantum 6, 798 (2022)
There is no unique way to encode a quantum algorithm into a quantum circuit. With limited qubit counts, connectivity, and coherence times, a quantum circuit optimization is essential to make the best use of near-term quantum devices. We introduce a n
Externí odkaz:
http://arxiv.org/abs/2209.02322
Autor:
Saito, Masahiko, Kishimoto, Tomoe, Kaneta, Yuya, Itoh, Taichi, Umeda, Yoshiaki, Tanaka, Junichi, Iiyama, Yutaro, Sawada, Ryu, Terashi, Koji
Publikováno v:
EPJ Web of Conferences 251, 03036 (2021)
The usefulness and value of Multi-step Machine Learning (ML), where a task is organized into connected sub-tasks with known intermediate inference goals, as opposed to a single large model learned end-to-end without intermediate sub-tasks, is present
Externí odkaz:
http://arxiv.org/abs/2106.02301
Autor:
Jang, Wonho, Terashi, Koji, Saito, Masahiko, Bauer, Christian W., Nachman, Benjamin, Iiyama, Yutaro, Kishimoto, Tomoe, Okubo, Ryunosuke, Sawada, Ryu, Tanaka, Junichi
There is no unique way to encode a quantum algorithm into a quantum circuit. With limited qubit counts, connectivities, and coherence times, circuit optimization is essential to make the best use of near-term quantum devices. We introduce two separat
Externí odkaz:
http://arxiv.org/abs/2102.10008
Autor:
Kishimoto, Tomoe, Saito, Masahiko, Tanaka, Junichi, Iiyama, Yutaro, Sawada, Ryu, Terashi, Koji
Connecting multiple machine learning models into a pipeline is effective for handling complex problems. By breaking down the problem into steps, each tackled by a specific component model of the pipeline, the overall solution can be made accurate and
Externí odkaz:
http://arxiv.org/abs/2101.07571
Autor:
Iwata, Shuhei, Yamaguchi, Satoshi, Kimura, Seiji, Hattori, Soichi, Mikami, Yukio, Kawasaki, Yohei, Shiko, Yuki, Akagi, Ryuichiro, Amaha, Kentaro, Atsuta, Tomonori, Ikegawa, Naoshi, Koyama, Minoru, Nakagawa, Ryosuke, Omodani, Toru, Ouchi, Hiroshi, Saito, Masahiko, Takahashi, Kenji, Watanabe, Shotaro, Sasho, Takahisa, Ohtori, Seiji
Publikováno v:
In Journal of Orthopaedic Science January 2024 29(1):243-248
Autor:
Terashi, Koji, Kaneda, Michiru, Kishimoto, Tomoe, Saito, Masahiko, Sawada, Ryu, Tanaka, Junichi
Publikováno v:
Comput. Softw. Big Sci. 5, 2 (2021)
We present studies of quantum algorithms exploiting machine learning to classify events of interest from background events, one of the most representative machine learning applications in high-energy physics. We focus on variational quantum approach
Externí odkaz:
http://arxiv.org/abs/2002.09935
We discuss a possibility to measure the lifetime of charged Wino in supersymmetric model at future 100 TeV pp colliders, assuming that (neutral) Wino is the lightest superparticle (LSP). In the Wino LSP scenario, the charged Wino has a lifetime of ab
Externí odkaz:
http://arxiv.org/abs/1912.00592