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5 results on '"Dogru, Anil"'

1. CALPAGAN: Calorimetry for Particles using GANs

Author

Simsek, Ebru, Isildak, Bora, Dogru, Anil, Reyhan, Bayrak, Aydogan Burak, and Ertekin, Seyda

Subjects
High Energy Physics - Experiment
Abstract

In this study, a novel approach is demonstrated for converting calorimeter images from fast simulations to those akin to comprehensive full simulations, utilizing conditional Generative Adversarial Networks (GANs). The concept of pix2pix is tailored for CALPAGAN, where images from fast simulations serve as the basis(condition) for generating outputs that closely resemble those from detailed simulations. The findings indicate a strong correlation between the generated images and those from full simulations, especially in terms of key observables like jet transverse momentum distribution, jet mass, jet subjettiness, and jet girth. Additionally, the paper explores the efficacy of this method and its intrinsic limitations. This research marks a significant step towards exploring more efficient simulation methodologies in High Energy Particle Physics.

Published
2024

2. Time Series Predictive Models for Opponent Behavior Modeling in Bilateral Negotiations

Author

Yesevi, Gevher, Keskin, Mehmet Onur, Doğru, Anıl, Aydoğan, Reyhan, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Aydoğan, Reyhan, editor, Criado, Natalia, editor, Lang, Jérôme, editor, Sanchez-Anguix, Victor, editor, and Serramia, Marc, editor

Published
2023
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3. CALPAGAN: Calorimetry for Particles Using Generative Adversarial Networks.

Author

Simsek, Ebru, Isildak, Bora, Dogru, Anil, Aydogan, Reyhan, Bayrak, Burak, and Ertekin, Seyda

Subjects
GENERATIVE adversarial networks, MOMENTUM distributions, PARTICLE physics, CALORIMETRY, CALORIMETERS
Abstract

In this study, a novel approach is demonstrated for converting calorimeter images from fast simulations to those akin to comprehensive full simulations, utilizing conditional Generative Adversarial Networks (GANs). The concept of Pix2pix is tailored for CALPAGAN, where images from fast simulations serve as the basis (condition) for generating outputs that closely resemble those from detailed simulations. The findings indicate a strong correlation between the generated images and those from full simulations, especially in terms of key observables like jet transverse momentum distribution, jet mass, jet subjettiness, and jet girth. Additionally, the paper explores the efficacy of this method and its intrinsic limitations. This research marks a significant step towards exploring more efficient simulation methodologies in high-energy particle physics. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
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4. CALPAGAN: Calorimetry for Particles using GANs

Author

Dogru, Anil, Aydogan, Reyhan, Bayrak, Burak, Ertekin, Seyda, Isildak, Bora, Simsek, Ebru, Dogru, Anil, Aydogan, Reyhan, Bayrak, Burak, Ertekin, Seyda, Isildak, Bora, and Simsek, Ebru

Abstract

In this study, a novel approach is demonstrated for converting calorimeter images from fast simulations to those akin to comprehensive full simulations, utilizing conditional Generative Adversarial Networks (GANs). The concept of pix2pix is tailored for CALPAGAN, where images from fast simulations serve as the basis(condition) for generating outputs that closely resemble those from detailed simulations. The findings indicate a strong correlation between the generated images and those from full simulations, especially in terms of key observables like jet transverse momentum distribution, jet mass, jet subjettiness, and jet girth. Additionally, the paper explores the efficacy of this method and its intrinsic limitations. This research marks a significant step towards exploring more efficient simulation methodologies in High Energy Particle Physics.

Published
2024

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