The strongly-coupled system like the quark-hadron transition (if it is of first order) is becoming an active play-yard for the physics of cosmological first-order phase transitions. However, the traditional field theoretic approach to strongly-coupled first-order phase transitions is of great challenge, driving recent efforts from holographic dual theories with explicit numerical simulations. These holographic numerical simulations have revealed an intriguing linear correlation between the phase pressure difference (pressure difference away from the wall) to the non-relativistic terminal velocity of an expanding planar wall, which has been reproduced analytically alongside both cylindrical and spherical walls from perfect-fluid hydrodynamics in our previous study but only for a bag equation of state. We have also found in our previous study a universal quadratic correlation between the wall pressure difference (pressure difference near the bubble wall) to the non-relativistic terminal wall velocity regardless of wall geometries. In this paper, we will generalize these analytic relations between the phase/wall pressure difference and terminal wall velocity into a more realistic equation of state beyond the simple bag model, providing the most general predictions so far for future tests from holographic numerical simulations of strongly-coupled first-order phase transitions
{"title":"General bubble expansion at strong coupling","authors":"Wang, Jun-Chen, Yuwen, Zi-Yan, Hao, Yu-Shi, Wang, Shao-Jiang","doi":"10.48550/arxiv.2311.07347","DOIUrl":"https://doi.org/10.48550/arxiv.2311.07347","url":null,"abstract":"The strongly-coupled system like the quark-hadron transition (if it is of first order) is becoming an active play-yard for the physics of cosmological first-order phase transitions. However, the traditional field theoretic approach to strongly-coupled first-order phase transitions is of great challenge, driving recent efforts from holographic dual theories with explicit numerical simulations. These holographic numerical simulations have revealed an intriguing linear correlation between the phase pressure difference (pressure difference away from the wall) to the non-relativistic terminal velocity of an expanding planar wall, which has been reproduced analytically alongside both cylindrical and spherical walls from perfect-fluid hydrodynamics in our previous study but only for a bag equation of state. We have also found in our previous study a universal quadratic correlation between the wall pressure difference (pressure difference near the bubble wall) to the non-relativistic terminal wall velocity regardless of wall geometries. In this paper, we will generalize these analytic relations between the phase/wall pressure difference and terminal wall velocity into a more realistic equation of state beyond the simple bag model, providing the most general predictions so far for future tests from holographic numerical simulations of strongly-coupled first-order phase transitions","PeriodicalId":496270,"journal":{"name":"arXiv (Cornell University)","volume":"114 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136353461","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}
Pub Date : 2023-11-13DOI: 10.48550/arxiv.2311.07452
Greenwell, Brandon M., Dahlmann, Annika, Dhoble, Saurabh
Compared to "black-box" models, like random forests and deep neural networks, explainable boosting machines (EBMs) are considered "glass-box" models that can be competitively accurate while also maintaining a higher degree of transparency and explainability. However, EBMs become readily less transparent and harder to interpret in high-dimensional settings with many predictor variables; they also become more difficult to use in production due to increases in scoring time. We propose a simple solution based on the least absolute shrinkage and selection operator (LASSO) that can help introduce sparsity by reweighting the individual model terms and removing the less relevant ones, thereby allowing these models to maintain their transparency and relatively fast scoring times in higher-dimensional settings. In short, post-processing a fitted EBM with many (i.e., possibly hundreds or thousands) of terms using the LASSO can help reduce the model's complexity and drastically improve scoring time. We illustrate the basic idea using two real-world examples with code.
{"title":"Explainable Boosting Machines with Sparsity -- Maintaining\u0000 Explainability in High-Dimensional Settings","authors":"Greenwell, Brandon M., Dahlmann, Annika, Dhoble, Saurabh","doi":"10.48550/arxiv.2311.07452","DOIUrl":"https://doi.org/10.48550/arxiv.2311.07452","url":null,"abstract":"Compared to \"black-box\" models, like random forests and deep neural networks, explainable boosting machines (EBMs) are considered \"glass-box\" models that can be competitively accurate while also maintaining a higher degree of transparency and explainability. However, EBMs become readily less transparent and harder to interpret in high-dimensional settings with many predictor variables; they also become more difficult to use in production due to increases in scoring time. We propose a simple solution based on the least absolute shrinkage and selection operator (LASSO) that can help introduce sparsity by reweighting the individual model terms and removing the less relevant ones, thereby allowing these models to maintain their transparency and relatively fast scoring times in higher-dimensional settings. In short, post-processing a fitted EBM with many (i.e., possibly hundreds or thousands) of terms using the LASSO can help reduce the model's complexity and drastically improve scoring time. We illustrate the basic idea using two real-world examples with code.","PeriodicalId":496270,"journal":{"name":"arXiv (Cornell University)","volume":"108 19","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136353470","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}