arXiv - PHYS - Data Analysis, Statistics and Probability最新文献

{"title":"Network inference from oscillatory signals based on circle map","authors":"Akari Matsuki, Hiroshi Kori, Ryota Kobayashi","doi":"arxiv-2407.07445","DOIUrl":"https://doi.org/arxiv-2407.07445","url":null,"abstract":"To understand and control the dynamics of coupled oscillators, it is\u0000important to reveal the structure of the interaction network from observed\u0000data. While various techniques have been developed for inferring the network of\u0000asynchronous systems, it remains challenging to infer the network of\u0000synchronized oscillators without external stimulations. In this study, we\u0000develop a method for non-invasively inferring the network of synchronized\u0000and/or de-synchronized oscillators. An approach to network inference would be\u0000to fit the data to a set of differential equations describing the dynamics of\u0000phase oscillators. However, we show that this method fails to infer the true\u0000network due to the problems that arise when we use short-time phase\u0000differences. Therefore, we propose a method based on the circle map, which\u0000describes the phase change in one oscillatory cycle. We demonstrate the\u0000efficacy of the proposed method through the successful inference of the network\u0000structure from simulated data of limit cycle oscillator models. Our method\u0000provides a unified and concise framework for network estimation for a wide\u0000class of oscillator systems.","PeriodicalId":501065,"journal":{"name":"arXiv - PHYS - Data Analysis, Statistics and Probability","volume":"35 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141587674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Study of the decay and production properties of $D_{s1}(2536)$ and $D_{s2}^*(2573)$","authors":"M. Ablikim, M. N. Achasov, P. Adlarson, O. Afedulidis, X. C. Ai, R. Aliberti, A. Amoroso, Q. An, Y. Bai, O. Bakina, I. Balossino, Y. Ban, H. -R. Bao, V. Batozskaya, K. Begzsuren, N. Berger, M. Berlowski, M. Bertani, D. Bettoni, F. Bianchi, E. Bianco, A. Bortone, I. Boyko, R. A. Briere, A. Brueggemann, H. Cai, X. Cai, A. Calcaterra, G. F. Cao, N. Cao, S. A. Cetin, X. Y. Chai, J. F. Chang, G. R. Che, Y. Z. Che, G. Chelkov, C. Chen, C. H. Chen, Chao Chen, G. Chen, H. S. Chen, H. Y. Chen, M. L. Chen, S. J. Chen, S. L. Chen, S. M. Chen, T. Chen, X. R. Chen, X. T. Chen, Y. B. Chen, Y. Q. Chen, Z. J. Chen, Z. Y. Chen, S. K. Choi, G. Cibinetto, F. Cossio, J. J. Cui, H. L. Dai, J. P. Dai, A. Dbeyssi, R. E. de Boer, D. Dedovich, C. Q. Deng, Z. Y. Deng, A. Denig, I. Denysenko, M. Destefanis, F. De Mori, B. Ding, X. X. Ding, Y. Ding, Y. Ding, J. Dong, L. Y. Dong, M. Y. Dong, X. Dong, M. C. Du, S. X. Du, Y. Y. Duan, Z. H. Duan, P. Egorov, Y. H. Fan, J. Fang, J. Fang, S. S. Fang, W. X. Fang, Y. Fang, Y. Q. Fang, R. Farinelli, L. Fava, F. Feldbauer, G. Felici, C. Q. Feng, J. H. Feng, Y. T. Feng, M. Fritsch, C. D. Fu, J. L. Fu, Y. W. Fu, H. Gao, X. B. Gao, Y. N. Gao, Yang Gao, S. Garbolino, I. Garzia, L. Ge, P. T. Ge, Z. W. Ge, C. Geng, E. M. Gersabeck, A. Gilman, K. Goetzen, L. Gong, W. X. Gong, W. Gradl, S. Gramigna, M. Greco, M. H. Gu, Y. T. Gu, C. Y. Guan, A. Q. Guo, L. B. Guo, M. J. Guo, R. P. Guo, Y. P. Guo, A. Guskov, J. Gutierrez, K. L. Han, T. T. Han, F. Hanisch, X. Q. Hao, F. A. Harris, K. K. He, K. L. He, F. H. Heinsius, C. H. Heinz, Y. K. Heng, C. Herold, T. Holtmann, P. C. Hong, G. Y. Hou, X. T. Hou, Y. R. Hou, Z. L. Hou, B. Y. Hu, H. M. Hu, J. F. Hu, S. L. Hu, T. Hu, Y. Hu, G. S. Huang, K. X. Huang, L. Q. Huang, X. T. Huang, Y. P. Huang, Y. S. Huang, T. Hussain, F. Hölzken, N. Hüsken, N. in der Wiesche, J. Jackson, S. Janchiv, J. H. Jeong, Q. Ji, Q. P. Ji, W. Ji, X. B. Ji, X. L. Ji, Y. Y. Ji, X. Q. Jia, Z. K. Jia, D. Jiang, H. B. Jiang, P. C. Jiang, S. S. Jiang, T. J. Jiang, X. S. Jiang, Y. Jiang, J. B. Jiao, J. K. Jiao, Z. Jiao, S. Jin, Y. Jin, M. Q. Jing, X. M. Jing, T. Johansson, S. Kabana, N. Kalantar-Nayestanaki, X. L. Kang, X. S. Kang, M. Kavatsyuk, B. C. Ke, V. Khachatryan, A. Khoukaz, R. Kiuchi, O. B. Kolcu, B. Kopf, M. Kuessner, X. Kui, N. Kumar, A. Kupsc, W. Kühn, J. J. Lane, L. Lavezzi, T. T. Lei, Z. H. Lei, M. Lellmann, T. Lenz, C. Li, C. Li, C. H. Li, Cheng Li, D. M. Li, F. Li, G. Li, H. B. Li, H. J. Li, H. N. Li, Hui Li, J. R. Li, J. S. Li, K. Li, K. L. Li, L. J. Li, L. K. Li, Lei Li, M. H. Li, P. R. Li, Q. M. Li, Q. X. Li, R. Li, S. X. Li, T. Li, W. D. Li, W. G. Li, X. Li, X. H. Li, X. L. Li, X. Y. Li, X. Z. Li, Y. G. Li, Z. J. Li, Z. Y. Li, C. Liang, H. Liang, H. Liang, Y. F. Liang, Y. T. Liang, G. R. Liao, Y. P. Liao, J. Libby, A. Limphirat, C. C. Lin, D. X. Lin, T. Lin, B. J. Liu, B. X. Liu, C. Liu, C. X. Liu, F. Liu, F. H. Liu, Feng Liu, G. M. Liu, H. Liu, H. B. Liu, H. H. Liu, H. M. Liu, Huihui Liu, J. B. Liu, J. Y. Liu, K. Liu, K. Y. Liu, Ke Liu, L. Liu, L. C. Liu, Lu Liu, M. H. Liu, P. L. Liu, Q. Liu, S. B. Liu, T. Liu, W. K. Liu, W. M. Liu, X. Liu, X. Liu, Y. Liu, Y. Liu, Y. B. Liu, Z. A. Liu, Z. D. Liu, Z. Q. Liu, X. C. Lou, F. X. Lu, H. J. Lu, J. G. Lu, X. L. Lu, Y. Lu, Y. P. Lu, Z. H. Lu, C. L. Luo, J. R. Luo, M. X. Luo, T. Luo, X. L. Luo, X. R. Lyu, Y. F. Lyu, F. C. Ma, H. Ma, H. L. Ma, J. L. Ma, L. L. Ma, L. R. Ma, M. M. Ma, Q. M. Ma, R. Q. Ma, T. Ma, X. T. Ma, X. Y. Ma, Y. M. Ma, F. E. Maas, I. MacKay, M. Maggiora, S. Malde, Y. J. Mao, Z. P. Mao, S. Marcello, Z. X. Meng, J. G. Messchendorp, G. Mezzadri, H. Miao, T. J. Min, R. E. Mitchell, X. H. Mo, B. Moses, N. Yu. Muchnoi, J. Muskalla, Y. Nefedov, F. Nerling, L. S. Nie, I. B. Nikolaev, Z. Ning, S. Nisar, Q. L. Niu, W. D. Niu, Y. Niu, S. L. Olsen, S. L. Olsen, Q. Ouyang, S. Pacetti, X. Pan, Y. Pan, A. Pathak, Y. P. Pei, M. Pelizaeus, H. P. Peng, Y. Y. Peng, K. Peters, J. L. Ping, R. G. Ping, S. Plura, V. Prasad, F. Z. Qi, H. Qi, H. R. Qi, M. Qi, T. Y. Qi, S. Qian, W. B. Qian, C. F. Qiao, X. K. Qiao, J. J. Qin, L. Q. Qin, L. Y. Qin, X. P. Qin, X. S. Qin, Z. H. Qin, J. F. Qiu, Z. H. Qu, C. F. Redmer, K. J. Ren, A. Rivetti, M. Rolo, G. Rong, Ch. Rosner, M. Q. Ruan, S. N. Ruan, N. Salone, A. Sarantsev, Y. Schelhaas, K. Schoenning, M. Scodeggio, K. Y. Shan, W. Shan, X. Y. Shan, Z. J. Shang, J. F. Shangguan, L. G. Shao, M. Shao, C. P. Shen, H. F. Shen, W. H. Shen, X. Y. Shen, B. A. Shi, H. Shi, H. C. Shi, J. L. Shi, J. Y. Shi, Q. Q. Shi, S. Y. Shi, X. Shi, J. J. Song, T. Z. Song, W. M. Song, Y. J. Song, Y. X. Song, S. Sosio, S. Spataro, F. Stieler, S. S Su, Y. J. Su, G. B. Sun, G. X. Sun, H. Sun, H. K. Sun, J. F. Sun, K. Sun, L. Sun, S. S. Sun, T. Sun, W. Y. Sun, Y. Sun, Y. J. Sun, Y. Z. Sun, Z. Q. Sun, Z. T. Sun, C. J. Tang, G. Y. Tang, J. Tang, M. Tang, Y. A. Tang, L. Y. Tao, Q. T. Tao, M. Tat, J. X. Teng, V. Thoren, W. H. Tian, Y. Tian, Z. F. Tian, I. Uman, Y. Wan, S. J. Wang, B. Wang, B. L. Wang, Bo Wang, D. Y. Wang, F. Wang, H. J. Wang, J. J. Wang, J. P. Wang, K. Wang, L. L. Wang, M. Wang, N. Y. Wang, S. Wang, S. Wang, T. Wang, T. J. Wang, W. Wang, W. Wang, W. P. Wang, X. Wang, X. F. Wang, X. J. Wang, X. L. Wang, X. N. Wang, Y. Wang, Y. D. Wang, Y. F. Wang, Y. L. Wang, Y. N. Wang, Y. Q. Wang, Yaqian Wang, Yi Wang, Z. Wang, Z. L. Wang, Z. Y. Wang, Ziyi Wang, D. H. Wei, F. Weidner, S. P. Wen, Y. R. Wen, U. Wiedner, G. Wilkinson, M. Wolke, L. Wollenberg, C. Wu, J. F. Wu, L. H. Wu, L. J. Wu, X. Wu, X. H. Wu, Y. Wu, Y. H. Wu, Y. J. Wu, Z. Wu, L. Xia, X. M. Xian, B. H. Xiang, T. Xiang, D. Xiao, G. Y. Xiao, S. Y. Xiao, Y. L. Xiao, Z. J. Xiao, C. Xie, X. H. Xie, Y. Xie, Y. G. Xie, Y. H. Xie, Z. P. Xie, T. Y. Xing, C. F. Xu, C. J. Xu, G. F. Xu, H. Y. Xu, M. Xu, Q. J. Xu, Q. N. Xu, W. Xu, W. L. Xu, X. P. Xu, Y. Xu, Y. C. Xu, Z. S. Xu, F. Yan, L. Yan, W. B. Yan, W. C. Yan, X. Q. Yan, H. J. Yang, H. L. Yang, H. X. Yang, T. Yang, Y. Yang, Y. F. Yang, Y. F. Yang, Y. X. Yang, Z. W. Yang, Z. P. Yao, M. Ye, M. H. Ye, J. H. Yin, Junhao Yin, Z. Y. You, B. X. Yu, C. X. Yu, G. Yu, J. S. Yu, M. C. Yu, T. Yu, X. D. Yu, Y. C. Yu, C. Z. Yuan, J. Yuan, J. Yuan, L. Yuan, S. C. Yuan, Y. Yuan, Z. Y. Yuan, C. X. Yue, A. A. Zafar, F. R. Zeng, S. H. Zeng, X. Zeng, Y. Zeng, Y. J. Zeng, Y. J. Zeng, X. Y. Zhai, Y. C. Zhai, Y. H. Zhan, A. Q. Zhang, B. L. Zhang, B. X. Zhang, D. H. Zhang, G. Y. Zhang, H. Zhang, H. Zhang, H. C. Zhang, H. H. Zhang, H. H. Zhang, H. Q. Zhang, H. R. Zhang, H. Y. Zhang, J. Zhang, J. Zhang, J. J. Zhang, J. L. Zhang, J. Q. Zhang, J. S. Zhang, J. W. Zhang, J. X. Zhang, J. Y. Zhang, J. Z. Zhang, Jianyu Zhang, L. M. Zhang, Lei Zhang, P. Zhang, Q. Y. Zhang, R. Y. Zhang, S. H. Zhang, Shulei Zhang, X. M. Zhang, X. Y Zhang, X. Y. Zhang, Y. Zhang, Y. Zhang, Y. T. Zhang, Y. H. Zhang, Y. M. Zhang, Yan Zhang, Z. D. Zhang, Z. H. Zhang, Z. L. Zhang, Z. Y. Zhang, Z. Y. Zhang, Z. Z. Zhang, G. Zhao, J. Y. Zhao, J. Z. Zhao, L. Zhao, Lei Zhao, M. G. Zhao, N. Zhao, R. P. Zhao, S. J. Zhao, Y. B. Zhao, Y. X. Zhao, Z. G. Zhao, A. Zhemchugov, B. Zheng, B. M. Zheng, J. P. Zheng, W. J. Zheng, Y. H. Zheng, B. Zhong, X. Zhong, H. Zhou, J. Y. Zhou, L. P. Zhou, S. Zhou, X. Zhou, X. K. Zhou, X. R. Zhou, X. Y. Zhou, Y. Z. Zhou, Z. C. Zhou, A. N. Zhu, J. Zhu, K. Zhu, K. J. Zhu, K. S. Zhu, L. Zhu, L. X. Zhu, S. H. Zhu, T. J. Zhu, W. D. Zhu, Y. C. Zhu, Z. A. Zhu, J. H. Zou, J. Zu","doi":"arxiv-2407.07651","DOIUrl":"https://doi.org/arxiv-2407.07651","url":null,"abstract":"The $e^+e^-rightarrow D_s^+D_{s1}(2536)^-$ and $e^+e^-rightarrow\u0000D_s^+D^*_{s2}(2573)^-$ processes are studied using data samples collected with\u0000the BESIII detector at center-of-mass energies from 4.530 to 4.946~GeV. The\u0000absolute branching fractions of $D_{s1}(2536)^- rightarrow bar{D}^{*0}K^-$\u0000and $D_{s2}^*(2573)^- rightarrow bar{D}^0K^-$ are measured for the first time\u0000to be $(35.9pm 4.8pm 3.5)%$ and $(37.4pm 3.1pm 4.6)%$, respectively. The\u0000measurements are in tension with predictions based on the assumption that the\u0000$D_{s1}(2536)$ and $D_{s2}^*(2573)$ are dominated by a bare $cbar{s}$\u0000component. The $e^+e^-rightarrow D_s^+D_{s1}(2536)^-$ and $e^+e^-rightarrow\u0000D_s^+D^*_{s2}(2573)^-$ cross sections are measured, and a resonant structure at\u0000around 4.6~GeV with a width of 50~MeV is observed for the first time with a\u0000statistical significance of $15sigma$ in the $e^+e^-rightarrow\u0000D_s^+D^*_{s2}(2573)^-$ process. It could be the $Y(4626)$ found by the Belle\u0000collaboration in the $D_s^+D_{s1}(2536)^{-}$ final state, since they have\u0000similar masses and widths. There is also evidence for a structure at around\u00004.75~GeV in both processes.","PeriodicalId":501065,"journal":{"name":"arXiv - PHYS - Data Analysis, Statistics and Probability","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141587673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rapid Parameter Estimation for Merging Massive Black Hole Binaries Using ODE-Based Generative Models","authors":"Bo Liang, Minghui Du, He Wang, Yuxiang Xu, Chang Liu, Xiaotong Wei, Peng Xu, Li-e Qiang, Ziren Luo","doi":"arxiv-2407.07125","DOIUrl":"https://doi.org/arxiv-2407.07125","url":null,"abstract":"Detecting the coalescences of massive black hole binaries (MBHBs) is one of\u0000the primary targets for space-based gravitational wave observatories such as\u0000LISA, Taiji, and Tianqin. The fast and accurate parameter estimation of merging\u0000MBHBs is of great significance for both astrophysics and the global fitting of\u0000all resolvable sources. However, such analyses entail significant computational\u0000costs. To address these challenges, inspired by the latest progress in\u0000generative models, we proposed a novel artificial intelligence (AI) based\u0000parameter estimation method called Variance Preserving Flow Matching Posterior\u0000Estimation (VPFMPE). Specifically, we utilize triangular interpolation to\u0000maintain variance over time, thereby constructing a transport path for training\u0000continuous normalization flows. Compared to the simple linear interpolation\u0000method used in flow matching to construct the optimal transport path, our\u0000approach better captures continuous temporal variations, making it more\u0000suitable for the parameter estimation of MBHBs. Additionally, we creatively\u0000introduce a parameter transformation method based on the symmetry in the\u0000detector's response function. This transformation is integrated within VPFMPE,\u0000allowing us to train the model using a simplified dataset, and then perform\u0000parameter estimation on more general data, hence also acting as a crucial\u0000factor in improving the training speed. In conclusion, for the first time,\u0000within a comprehensive and reasonable parameter range, we have achieved a\u0000complete and unbiased 11-dimensional rapid inference for MBHBs in the presence\u0000of astrophysical confusion noise using ODE-based generative models. In the\u0000experiments based on simulated data, our model produces posterior distributions\u0000comparable to those obtained by nested sampling.","PeriodicalId":501065,"journal":{"name":"arXiv - PHYS - Data Analysis, Statistics and Probability","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141587675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A unified machine learning approach for reconstructing hadronically decaying tau leptons","authors":"Laurits Tani, Nalong-Norman Seeba, Hardi Vanaveski, Joosep Pata, Torben Lange","doi":"arxiv-2407.06788","DOIUrl":"https://doi.org/arxiv-2407.06788","url":null,"abstract":"Tau leptons serve as an important tool for studying the production of Higgs\u0000and electroweak bosons, both within and beyond the Standard Model of particle\u0000physics. Accurate reconstruction and identification of hadronically decaying\u0000tau leptons is a crucial task for current and future high energy physics\u0000experiments. Given the advances in jet tagging, we demonstrate how tau lepton\u0000reconstruction can be decomposed into tau identification, kinematic\u0000reconstruction, and decay mode classification in a multi-task machine learning\u0000setup.Based on an electron-positron collision dataset with full detector\u0000simulation and reconstruction, we show that common jet tagging architectures\u0000can be effectively used for these subtasks. We achieve comparable momentum\u0000resolutions of 2-3% with all the tested models, while the precision of\u0000reconstructing individual decay modes is between 80-95%. This paper also serves\u0000as an introduction to a new publicly available Fu{tau}ure dataset and provides\u0000recipes for the development and training of tau reconstruction algorithms,\u0000while allowing to study resilience to domain shifts and the use of foundation\u0000models for such tasks.","PeriodicalId":501065,"journal":{"name":"arXiv - PHYS - Data Analysis, Statistics and Probability","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141575441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Combination of operational modal analysis algorithms to identify modal parameters of an actual centrifugal compressor","authors":"Leandro O. Zague, Daniel A. Castello, Carlos F. T. Matt","doi":"arxiv-2407.07273","DOIUrl":"https://doi.org/arxiv-2407.07273","url":null,"abstract":"The novelty of the current work is precisely to propose a statistical\u0000procedure to combine estimates of the modal parameters provided by any set of\u0000Operational Modal Analysis (OMA) algorithms so as to avoid preference for a\u0000particular one and also to derive an approximate joint probability distribution\u0000of the modal parameters, from which engineering statistics of interest such as\u0000mean value and variance are readily provided. The effectiveness of the proposed\u0000strategy is assessed considering measured data from an actual centrifugal\u0000compressor. The statistics obtained for both forward and backward modal\u0000parameters are finally compared against modal parameters identified during\u0000standard stability verification testing (SVT) of centrifugal compressors prior\u0000to shipment, using classical Experimental Modal Analysis (EMA) algorithms. The\u0000current work demonstrates that combination of OMA algorithms can provide quite\u0000accurate estimates for both the modal parameters and the associated\u0000uncertainties with low computational costs.","PeriodicalId":501065,"journal":{"name":"arXiv - PHYS - Data Analysis, Statistics and Probability","volume":"37 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141587672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Deep Learning Approach for Modeling and Hindcasting Lake Michigan Ice Cover","authors":"Hazem Abdelhady, Cary Troy","doi":"arxiv-2407.04937","DOIUrl":"https://doi.org/arxiv-2407.04937","url":null,"abstract":"In large lakes, ice cover plays an important role in shipping and navigation,\u0000coastal erosion, regional weather and climate, and aquatic ecosystem function.\u0000In this study, a novel deep learning model for ice cover concentration\u0000prediction in Lake Michigan is introduced. The model uses hindcasted\u0000meteorological variables, water depth, and shoreline proximity as inputs, and\u0000NOAA ice charts for training, validation, and testing. The proposed framework\u0000leverages Convolution Long Short-Term Memory (ConvLSTM) and Convolution Neural\u0000Network (CNN) to capture both spatial and temporal dependencies between model\u0000input and output to simulate daily ice cover at 0.1{deg} resolution. The model\u0000performance was assessed through lake-wide average metrics and local error\u0000metrics, with detailed evaluations conducted at six distinct locations in Lake\u0000Michigan. The results demonstrated a high degree of agreement between the\u0000model's predictions and ice charts, with an average RMSE of 0.029 for the daily\u0000lake-wide average ice concentration. Local daily prediction errors were\u0000greater, with an average RMSE of 0.102. Lake-wide and local errors for weekly\u0000and monthly averaged ice concentrations were reduced by almost 50% from daily\u0000values. The accuracy of the proposed model surpasses currently available\u0000physics-based models in the lake-wide ice concentration prediction, offering a\u0000promising avenue for enhancing ice prediction and hindcasting in large lakes.","PeriodicalId":501065,"journal":{"name":"arXiv - PHYS - Data Analysis, Statistics and Probability","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141575442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data-driven modeling from biased small training data using periodic orbits","authors":"Kengo Nakai, Yoshitaka Saiki","doi":"arxiv-2407.06229","DOIUrl":"https://doi.org/arxiv-2407.06229","url":null,"abstract":"In this study, we investigate the effect of reservoir computing training data\u0000on the reconstruction of chaotic dynamics. Our findings indicate that a\u0000training time series comprising a few periodic orbits of low periods can\u0000successfully reconstruct the Lorenz attractor. We also demonstrate that biased\u0000training data does not negatively impact reconstruction success. Our method's\u0000ability to reconstruct a physical measure is much better than the so-called\u0000cycle expansion approach, which relies on weighted averaging. Additionally, we\u0000demonstrate that fixed point attractors and chaotic transients can be\u0000accurately reconstructed by a model trained from a few periodic orbits, even\u0000when using different parameters.","PeriodicalId":501065,"journal":{"name":"arXiv - PHYS - Data Analysis, Statistics and Probability","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141575440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient Materials Informatics between Rockets and Electrons","authors":"Adam M. Krajewski","doi":"arxiv-2407.04648","DOIUrl":"https://doi.org/arxiv-2407.04648","url":null,"abstract":"The true power of computational research typically can lay in either what it\u0000accomplishes or what it enables others to accomplish. In this work, both\u0000avenues are simultaneously embraced across several distinct efforts existing at\u0000three general scales of abstractions of what a material is - atomistic,\u0000physical, and design. At each, an efficient materials informatics\u0000infrastructure is being built from the ground up based on (1) the fundamental\u0000understanding of the underlying prior knowledge, including the data, (2)\u0000deployment routes that take advantage of it, and (3) pathways to extend it in\u0000an autonomous or semi-autonomous fashion, while heavily relying on artificial\u0000intelligence (AI) to guide well-established DFT-based ab initio and\u0000CALPHAD-based thermodynamic methods. The resulting multi-level discovery infrastructure is highly generalizable as\u0000it focuses on encoding problems to solve them easily rather than looking for an\u0000existing solution. To showcase it, this dissertation discusses the design of\u0000multi-alloy functionally graded materials (FGMs) incorporating ultra-high\u0000temperature refractory high entropy alloys (RHEAs) towards gas turbine and jet\u0000engine efficiency increase reducing CO2 emissions, as well as hypersonic\u0000vehicles. It leverages a new graph representation of underlying mathematical\u0000space using a newly developed algorithm based on combinatorics, not subject to\u0000many problems troubling the community. Underneath, property models and phase\u0000relations are learned from optimized samplings of the largest and highest\u0000quality dataset of HEA in the world, called ULTERA. At the atomistic level, a\u0000data ecosystem optimized for machine learning (ML) from over 4.5 million\u0000relaxed structures, called MPDD, is used to inform experimental observations\u0000and improve thermodynamic models by providing stability data enabled by a new\u0000efficient featurization framework.","PeriodicalId":501065,"journal":{"name":"arXiv - PHYS - Data Analysis, Statistics and Probability","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141575445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exotic and physics-informed support vector machines for high energy physics","authors":"A. Ramirez-Morales, A. Gutiérrez-Rodríguez, T. Cisneros-Pérez, H. Garcia-Tecocoatzi, A. Dávila-Rivera","doi":"arxiv-2407.03538","DOIUrl":"https://doi.org/arxiv-2407.03538","url":null,"abstract":"In this article, we explore machine learning techniques using support vector\u0000machines with two novel approaches: exotic and physics-informed support vector\u0000machines. Exotic support vector machines employ unconventional techniques such\u0000as genetic algorithms and boosting. Physics-informed support vector machines\u0000integrate the physics dynamics of a given high-energy physics process in a\u0000straightforward manner. The goal is to efficiently distinguish signal and\u0000background events in high-energy physics collision data. To test our\u0000algorithms, we perform computational experiments with simulated Drell-Yan\u0000events in proton-proton collisions. Our results highlight the superiority of\u0000the physics-informed support vector machines, emphasizing their potential in\u0000high-energy physics and promoting the inclusion of physics information in\u0000machine learning algorithms for future research.","PeriodicalId":501065,"journal":{"name":"arXiv - PHYS - Data Analysis, Statistics and Probability","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141575443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of the Digital Annealer Unit in Optimizing Chemical Reaction Conditions for Enhanced Production Yields","authors":"Shih-Cheng Li, Pei-Hwa Wang, Jheng-Wei Su, Wei-Yin Chiang, Shih-Hsien Huang, Yen-Chu Lin, Chia-Ho Ou, Chih-Yu Chen","doi":"arxiv-2407.17485","DOIUrl":"https://doi.org/arxiv-2407.17485","url":null,"abstract":"Finding appropriate reaction conditions that yield high product rates in\u0000chemical synthesis is crucial for the chemical and pharmaceutical industries.\u0000However, due to the vast chemical space, conducting experiments for each\u0000possible reaction condition is impractical. Consequently, models such as QSAR\u0000(Quantitative Structure-Activity Relationship) or ML (Machine Learning) have\u0000been developed to predict the outcomes of reactions and illustrate how reaction\u0000conditions affect product yield. Despite these advancements, inferring all\u0000possible combinations remains computationally prohibitive when using a\u0000conventional CPU. In this work, we explore using a Digital Annealing Unit (DAU)\u0000to tackle these large-scale optimization problems more efficiently by solving\u0000Quadratic Unconstrained Binary Optimization (QUBO). Two types of QUBO models\u0000are constructed in this work: one using quantum annealing and the other using\u0000ML. Both models are built and tested on four high-throughput experimentation\u0000(HTE) datasets and selected Reaxys datasets. Our results suggest that the\u0000performance of models is comparable to classical ML methods (i.e., Random\u0000Forest and Multilayer Perceptron (MLP)), while the inference time of our models\u0000requires only seconds with a DAU. Additionally, in campaigns involving active\u0000learning and autonomous design of reaction conditions to achieve higher\u0000reaction yield, our model demonstrates significant improvements by adding new\u0000data, showing promise of adopting our method in the iterative nature of such\u0000problem settings. Our method can also accelerate the screening of billions of\u0000reaction conditions, achieving speeds millions of times faster than traditional\u0000computing units in identifying superior conditions. Therefore, leveraging the\u0000DAU with our developed QUBO models has the potential to be a valuable tool for\u0000innovative chemical synthesis.","PeriodicalId":501065,"journal":{"name":"arXiv - PHYS - Data Analysis, Statistics and Probability","volume":"140 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141784372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}