Charlotte H. Müller, Miguel Steiner, Jan P. Unsleber, Thomas Weymuth, Moritz Bensberg, Katja-Sophia Csizi, Maximilian Mörchen, Paul L. Türtscher, Markus Reiher
Automated and high-throughput quantum chemical investigations into chemical processes have become feasible in great detail and broad scope. This results in an increase in complexity of the tasks and in the amount of generated data. An efficient and intuitive way for an operator to interact with these data and to steer virtual experiments is required. Here, we introduce Heron, a graphical user interface that allows for advanced human-machine interactions with quantum chemical exploration campaigns into molecular structure and reactivity. Heron offers access to interactive and automated explorations of chemical reactions with standard electronic structure modules, haptic force feedback, microkinetic modeling, and refinement of data by automated correlated calculations including black-box complete active space calculations. It is tailored to the exploration and analysis of vast chemical reaction networks. We show how interoperable modules enable advanced workflows and pave the way for routine low-entrance-barrier access to advanced modeling techniques.
{"title":"Heron: Visualizing and Controlling Chemical Reaction Explorations and Networks","authors":"Charlotte H. Müller, Miguel Steiner, Jan P. Unsleber, Thomas Weymuth, Moritz Bensberg, Katja-Sophia Csizi, Maximilian Mörchen, Paul L. Türtscher, Markus Reiher","doi":"arxiv-2406.09541","DOIUrl":"https://doi.org/arxiv-2406.09541","url":null,"abstract":"Automated and high-throughput quantum chemical investigations into chemical\u0000processes have become feasible in great detail and broad scope. This results in\u0000an increase in complexity of the tasks and in the amount of generated data. An\u0000efficient and intuitive way for an operator to interact with these data and to\u0000steer virtual experiments is required. Here, we introduce Heron, a graphical\u0000user interface that allows for advanced human-machine interactions with quantum\u0000chemical exploration campaigns into molecular structure and reactivity. Heron\u0000offers access to interactive and automated explorations of chemical reactions\u0000with standard electronic structure modules, haptic force feedback, microkinetic\u0000modeling, and refinement of data by automated correlated calculations including\u0000black-box complete active space calculations. It is tailored to the exploration\u0000and analysis of vast chemical reaction networks. We show how interoperable\u0000modules enable advanced workflows and pave the way for routine\u0000low-entrance-barrier access to advanced modeling techniques.","PeriodicalId":501065,"journal":{"name":"arXiv - PHYS - Data Analysis, Statistics and Probability","volume":"93 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141500982","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}
The weighted average of inconsistent data is a common and tedious problem that many scientists have encountered. The standard weighted average is not recommended for these cases, and different alternative methods are proposed in the literature. Here, we introduce a new method based on Bayesian statistics for a broad application that keeps the number of assumptions to a minimum. The uncertainty associated with each input value is considered just a lower bound of the true unknown uncertainty. By assuming a non-informative (Jeffreys') prior for true uncertainty and marginalising over its value, a modified Gaussian distribution is obtained with smoothly decreasing wings, which allows for a better treatment of scattered data and outliers. The proposed method is tested on a series of data sets: simulations, CODATA recommended value of the Newtonian gravitational constant, and some particle properties from the Particle Data Group, including the proton charge radius and the mass of the W boson. For the latter in particular, contrary to other works, our prediction lies in good agreement with the Standard Model. A freely available Python library is also provided for a simple implementation of our averaging method.
不一致数据的加权平均是许多科学家都遇到过的一个常见而又繁琐的问题。标准加权平均法不推荐用于这些情况，文献中提出了不同的替代方法。在此，我们介绍一种基于贝叶斯统计的新方法，它应用广泛，能将假设的数量保持在最低水平。与每个输入值相关的不确定性被视为真实未知不确定性的下限。通过假设真实不确定性的非信息（杰弗里斯）先验值并对其值进行边际化处理，可以得到一个具有平滑递减翼的修正高斯分布，从而可以更好地处理分散数据和异常值。所提出的方法在一系列数据集上进行了测试：模拟、牛顿引力常数的 CODATA 推荐值，以及粒子数据组的一些粒子特性，包括质子电荷半径和 W 玻色子质量。特别是后者，与其他工作相反，我们的预测与标准模型非常一致。我们还提供了一个免费的 Python 库，用于简单实现我们的平均方法。
{"title":"A minimalistic and general weighted averaging method for inconsistent data","authors":"Martino Trassinelli, Marleen Maxton","doi":"arxiv-2406.08293","DOIUrl":"https://doi.org/arxiv-2406.08293","url":null,"abstract":"The weighted average of inconsistent data is a common and tedious problem\u0000that many scientists have encountered. The standard weighted average is not\u0000recommended for these cases, and different alternative methods are proposed in\u0000the literature. Here, we introduce a new method based on Bayesian statistics\u0000for a broad application that keeps the number of assumptions to a minimum. The\u0000uncertainty associated with each input value is considered just a lower bound\u0000of the true unknown uncertainty. By assuming a non-informative (Jeffreys')\u0000prior for true uncertainty and marginalising over its value, a modified\u0000Gaussian distribution is obtained with smoothly decreasing wings, which allows\u0000for a better treatment of scattered data and outliers. The proposed method is\u0000tested on a series of data sets: simulations, CODATA recommended value of the\u0000Newtonian gravitational constant, and some particle properties from the\u0000Particle Data Group, including the proton charge radius and the mass of the W\u0000boson. For the latter in particular, contrary to other works, our prediction\u0000lies in good agreement with the Standard Model. A freely available Python\u0000library is also provided for a simple implementation of our averaging method.","PeriodicalId":501065,"journal":{"name":"arXiv - PHYS - Data Analysis, Statistics and Probability","volume":"41 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141500984","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}
Didier P. Monselesan, James S. Risbey, Benoit Legresy, Sophie Cravatte, Bastien Pagli, Takeshi Izumo, Christopher C. Chapman, Mandy Freund, Abdelwaheb Hannachi, Damien Irving, P. Jyoteeshkumar Reddy, Doug Richardson, Dougal T. Squire, Carly R. Tozer
El Ni~no-Southern Oscillation global (ENSO) imprint on sea surface temperature comes in many guises. To identify its tropical fingerprints and impacts on the rest of the climate system, we propose a global approach based on archetypal analysis (AA), a pattern recognition method based on the identification of extreme configurations in the dataset under investigation. Relying on detrended sea surface temperature monthly anomalies over the 1982 to 2022 period, the technique recovers central and eastern Pacific ENSO types identified by more traditional methods and allows one to hierarchically add extra flavours and nuances to both persistent and transient phases of the phenomenon. Archetypal patterns found compare favorably to phase identification from K-means, fuzzy C-means and recently published network-based machine-learning algorithms. The AA implementation is modified for the identification of ENSO phases in sub-seasonal-to-seasonal prediction systems and complements current alert systems in characterising the diversity of ENSO and its teleconnections. Tropical and extra-tropical teleconnection composites from various oceanic and atmospheric fields derived from the analysis are shown to be robust and physically relevant. Extending AA to sub-surface ocean fields improves the discrimination between phases when the characterisation of ENSO based on sea surface temperature is uncertain. We show that AA on detrended sea-level monthly anomalies provides a clearer expression of ENSO types.
厄尔尼诺/南方涛动（ENSO）对海面温度的全球影响有多种形式。为了识别它的热带指纹和对气候系统其他部分的影响，我们提出了一种基于原型分析（AA）的全球方法，这是一种基于识别调查数据集中极端配置的模式识别方法。依靠 1982 年至 2022 年期间的去趋势海面温度月度异常，该技术恢复了用更多传统方法识别的中太平洋和东太平洋厄尔尼诺/南方涛动类型，并允许人们分层次地为该现象的持续和瞬时阶段添加额外的味道和细微差别。所发现的原型模式与 K-均值、模糊 C-均值和最近发布的基于网络的机器学习算法的相位识别相比，效果更佳。AA 实现经过修改，可用于识别亚季节到季节预报系统中的厄尔尼诺/南方涛动阶段，并在描述厄尔尼诺/南方涛动及其远程联系的多样性方面对当前警报系统进行补充。从分析中得出的各种海洋和大气场的热带和热带外远缘联系组合显示出其稳健性和物理相关性。当基于海表温度的厄尔尼诺/南方涛动特征不确定时，将 AA 扩展到次表层海洋场可提高阶段之间的判别能力。我们表明，对去趋势海平面月度异常的 AA 可以更清晰地表达 ENSO 类型。
{"title":"On the archetypal `flavours', indices and teleconnections of ENSO revealed by global sea surface temperatures","authors":"Didier P. Monselesan, James S. Risbey, Benoit Legresy, Sophie Cravatte, Bastien Pagli, Takeshi Izumo, Christopher C. Chapman, Mandy Freund, Abdelwaheb Hannachi, Damien Irving, P. Jyoteeshkumar Reddy, Doug Richardson, Dougal T. Squire, Carly R. Tozer","doi":"arxiv-2406.08694","DOIUrl":"https://doi.org/arxiv-2406.08694","url":null,"abstract":"El Ni~no-Southern Oscillation global (ENSO) imprint on sea surface\u0000temperature comes in many guises. To identify its tropical fingerprints and\u0000impacts on the rest of the climate system, we propose a global approach based\u0000on archetypal analysis (AA), a pattern recognition method based on the\u0000identification of extreme configurations in the dataset under investigation.\u0000Relying on detrended sea surface temperature monthly anomalies over the 1982 to\u00002022 period, the technique recovers central and eastern Pacific ENSO types\u0000identified by more traditional methods and allows one to hierarchically add\u0000extra flavours and nuances to both persistent and transient phases of the\u0000phenomenon. Archetypal patterns found compare favorably to phase identification\u0000from K-means, fuzzy C-means and recently published network-based\u0000machine-learning algorithms. The AA implementation is modified for the\u0000identification of ENSO phases in sub-seasonal-to-seasonal prediction systems\u0000and complements current alert systems in characterising the diversity of ENSO\u0000and its teleconnections. Tropical and extra-tropical teleconnection composites\u0000from various oceanic and atmospheric fields derived from the analysis are shown\u0000to be robust and physically relevant. Extending AA to sub-surface ocean fields\u0000improves the discrimination between phases when the characterisation of ENSO\u0000based on sea surface temperature is uncertain. We show that AA on detrended\u0000sea-level monthly anomalies provides a clearer expression of ENSO types.","PeriodicalId":501065,"journal":{"name":"arXiv - PHYS - Data Analysis, Statistics and Probability","volume":"76 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141500983","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}
We perform a comparative analysis of quintessence and $k$-essence scalar field models in the data analysis perspective. We study the quintessence field with an exponential potential and the $k$-essence field with an inverse square potential in the present work. Before delving into data analysis, we provide a brief perspective on dynamical evolution on both of the models and obtain the stability constraints on the model parameters. We adopt Bayesian inference procedure to estimate the model parameters that best-fit the data. A comprehensive analysis utilizing Observational Hubble data (OHD) and Pantheon+ compilation of Type Ia supernovae (SNIa) shows that $k$-essence model fits the data slightly better than the quintessence model while the evidence of these models in comparison with the $Lambda$CDM model is weak. The value of the Hubble constant predicted by both the models is in close agreement with the value obtained by the Planck2018 collaboration assuming the $Lambda$CDM model.
{"title":"Comparative Analysis of $k$-essence and Quintessence Scalar Field Models: A Data Analysis Approach","authors":"Saddam Hussain, Sarath Nelleri, Kaushik Bhattacharya","doi":"arxiv-2406.07179","DOIUrl":"https://doi.org/arxiv-2406.07179","url":null,"abstract":"We perform a comparative analysis of quintessence and $k$-essence scalar\u0000field models in the data analysis perspective. We study the quintessence field\u0000with an exponential potential and the $k$-essence field with an inverse square\u0000potential in the present work. Before delving into data analysis, we provide a\u0000brief perspective on dynamical evolution on both of the models and obtain the\u0000stability constraints on the model parameters. We adopt Bayesian inference\u0000procedure to estimate the model parameters that best-fit the data. A\u0000comprehensive analysis utilizing Observational Hubble data (OHD) and Pantheon+\u0000compilation of Type Ia supernovae (SNIa) shows that $k$-essence model fits the\u0000data slightly better than the quintessence model while the evidence of these\u0000models in comparison with the $Lambda$CDM model is weak. The value of the\u0000Hubble constant predicted by both the models is in close agreement with the\u0000value obtained by the Planck2018 collaboration assuming the $Lambda$CDM model.","PeriodicalId":501065,"journal":{"name":"arXiv - PHYS - Data Analysis, Statistics and Probability","volume":"45 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141500993","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}
Jinbo Hu, Hong Yuan, Letian Chen, Nan Zhao, C. P. Sun
The optimal fingerprint method serves as a potent approach for detecting and attributing climate change. However, its experimental validation encounters challenges due to the intricate nature of climate systems. Here, we experimentally examine the optimal fingerprint method simulated by a precisely controlled magnetic resonance system of spins. The spin dynamic under an applied deterministic driving field and a noise field is utilized to emulate the complex climate system with external forcing and internal variability. Our experimental results affirm the theoretical prediction regarding the existence of an optimal detection direction which maximizes the signal-to-noise ratio, thereby validating the optimal fingerprint method. This work offers direct empirical verification of the optimal fingerprint method, crucial for comprehending climate change and its societal impacts.
{"title":"Experimental verification of the optimal fingerprint method for detecting climate change","authors":"Jinbo Hu, Hong Yuan, Letian Chen, Nan Zhao, C. P. Sun","doi":"arxiv-2406.11879","DOIUrl":"https://doi.org/arxiv-2406.11879","url":null,"abstract":"The optimal fingerprint method serves as a potent approach for detecting and\u0000attributing climate change. However, its experimental validation encounters\u0000challenges due to the intricate nature of climate systems. Here, we\u0000experimentally examine the optimal fingerprint method simulated by a precisely\u0000controlled magnetic resonance system of spins. The spin dynamic under an\u0000applied deterministic driving field and a noise field is utilized to emulate\u0000the complex climate system with external forcing and internal variability. Our\u0000experimental results affirm the theoretical prediction regarding the existence\u0000of an optimal detection direction which maximizes the signal-to-noise ratio,\u0000thereby validating the optimal fingerprint method. This work offers direct\u0000empirical verification of the optimal fingerprint method, crucial for\u0000comprehending climate change and its societal impacts.","PeriodicalId":501065,"journal":{"name":"arXiv - PHYS - Data Analysis, Statistics and Probability","volume":"63 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141500996","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}
Luana Liberato, Paolo Tanga, David Mary, Kate Minker, Benoit Carry, Federica Spoto, Przemyslaw Bartczak, Bruno Sicardy, Dagmara Oszkiewicz, Josselin Desmars
Asteroids with companions constitute an excellent sample for studying the collisional and dynamical evolution of minor planets. The currently known binary population were discovered by different complementary techniques that produce, for the moment, a strongly biased distribution, especially in a range of intermediate asteroid sizes (approximately 20 to 100 km) where both mutual photometric events and high-resolution adaptive optic imaging are poorly efficient. A totally independent technique of binary asteroid discovery, based on astrometry, can help to reveal new binary systems and populate a range of sizes and separations that remain nearly unexplored. In this work, we describe a dedicated period detection method and its results for the Gaia DR3 data set. This method looks for the presence of a periodic signature in the orbit post-fit residuals. After conservative filtering and validation based on statistical and physical criteria, we are able to present a first sample of astrometric binary candidates, to be confirmed by other observation techniques such as photometric light curves and stellar occultations.
{"title":"Binary asteroid candidates in Gaia DR3 astrometry","authors":"Luana Liberato, Paolo Tanga, David Mary, Kate Minker, Benoit Carry, Federica Spoto, Przemyslaw Bartczak, Bruno Sicardy, Dagmara Oszkiewicz, Josselin Desmars","doi":"arxiv-2406.07195","DOIUrl":"https://doi.org/arxiv-2406.07195","url":null,"abstract":"Asteroids with companions constitute an excellent sample for studying the\u0000collisional and dynamical evolution of minor planets. The currently known\u0000binary population were discovered by different complementary techniques that\u0000produce, for the moment, a strongly biased distribution, especially in a range\u0000of intermediate asteroid sizes (approximately 20 to 100 km) where both mutual\u0000photometric events and high-resolution adaptive optic imaging are poorly\u0000efficient. A totally independent technique of binary asteroid discovery, based\u0000on astrometry, can help to reveal new binary systems and populate a range of\u0000sizes and separations that remain nearly unexplored. In this work, we describe\u0000a dedicated period detection method and its results for the Gaia DR3 data set.\u0000This method looks for the presence of a periodic signature in the orbit\u0000post-fit residuals. After conservative filtering and validation based on\u0000statistical and physical criteria, we are able to present a first sample of\u0000astrometric binary candidates, to be confirmed by other observation techniques\u0000such as photometric light curves and stellar occultations.","PeriodicalId":501065,"journal":{"name":"arXiv - PHYS - Data Analysis, Statistics and Probability","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141500985","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}
We systematically analyze the implications of statistical noise within numerical derivatives on simulation-based Fisher forecasts for large scale structure surveys. Noisy numerical derivatives resulting from a finite number of simulations, $N_{sims}$, act to bias the associated Fisher forecast such that the resulting marginalized constraints can be significantly tighter than the noise-free limit. We show the source of this effect can be traced to the influence of the noise on the marginalization process. Parameters such as the neutrino mass, $M$, for which higher-order forward differentiation schemes are commonly used, are more prone to noise; the predicted constraints can be akin to those purely from a random instance of statistical noise even using $(1mathrm{Gpc}/h)^{3}$ simulations with $N_{sims}=500$ realizations. We demonstrate how derivative noise can artificially reduce parameter degeneracies and seemingly null the effects of adding nuisance parameters to the forecast, such as HOD fitting parameters. We mathematically characterize these effects through a full statistical analysis, and demonstrate how confidence intervals for the true noise-free, $N_{sims} rightarrow infty$, Fisher constraints can be recovered even when noise comprises a consequential component of the measured signal. The findings and approaches developed here are important for ensuring simulation-based analyses can be accurately used to assess upcoming survey capabilities.
{"title":"Fisher's Mirage: Noise Tightening of Cosmological Constraints in Simulation-Based Inference","authors":"Christopher Wilson, Rachel bean","doi":"arxiv-2406.06067","DOIUrl":"https://doi.org/arxiv-2406.06067","url":null,"abstract":"We systematically analyze the implications of statistical noise within\u0000numerical derivatives on simulation-based Fisher forecasts for large scale\u0000structure surveys. Noisy numerical derivatives resulting from a finite number\u0000of simulations, $N_{sims}$, act to bias the associated Fisher forecast such\u0000that the resulting marginalized constraints can be significantly tighter than\u0000the noise-free limit. We show the source of this effect can be traced to the\u0000influence of the noise on the marginalization process. Parameters such as the\u0000neutrino mass, $M$, for which higher-order forward differentiation schemes are\u0000commonly used, are more prone to noise; the predicted constraints can be akin\u0000to those purely from a random instance of statistical noise even using\u0000$(1mathrm{Gpc}/h)^{3}$ simulations with $N_{sims}=500$ realizations. We\u0000demonstrate how derivative noise can artificially reduce parameter degeneracies\u0000and seemingly null the effects of adding nuisance parameters to the forecast,\u0000such as HOD fitting parameters. We mathematically characterize these effects\u0000through a full statistical analysis, and demonstrate how confidence intervals\u0000for the true noise-free, $N_{sims} rightarrow infty$, Fisher constraints can\u0000be recovered even when noise comprises a consequential component of the\u0000measured signal. The findings and approaches developed here are important for\u0000ensuring simulation-based analyses can be accurately used to assess upcoming\u0000survey capabilities.","PeriodicalId":501065,"journal":{"name":"arXiv - PHYS - Data Analysis, Statistics and Probability","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141500995","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}
Real-world non-autonomous systems are open, out-of-equilibrium systems that evolve in and are driven by temporally varying environments. Such systems can show multiple timescale and transient dynamics together with transitions to very different and, at times, even disastrous dynamical regimes. Since such critical transitions disrupt the systems' intended or desired functionality, it is crucial to understand the underlying mechanisms, to identify precursors of such transitions and to reliably detect them in time series of suitable system observables to enable forecasts. This review critically assesses the various steps of investigation involved in time-series-analysis-based detection of critical transitions in real-world non-autonomous systems: from the data recording to evaluating the reliability of offline and online detections. It will highlight pros and cons to stimulate further developments, which would be necessary to advance understanding and forecasting nonlinear behavior such as critical transitions in complex systems.
{"title":"Time-series-analysis-based detection of critical transitions in real-world non-autonomous systems","authors":"Klaus Lehnertz","doi":"arxiv-2406.05195","DOIUrl":"https://doi.org/arxiv-2406.05195","url":null,"abstract":"Real-world non-autonomous systems are open, out-of-equilibrium systems that\u0000evolve in and are driven by temporally varying environments. Such systems can\u0000show multiple timescale and transient dynamics together with transitions to\u0000very different and, at times, even disastrous dynamical regimes. Since such\u0000critical transitions disrupt the systems' intended or desired functionality, it\u0000is crucial to understand the underlying mechanisms, to identify precursors of\u0000such transitions and to reliably detect them in time series of suitable system\u0000observables to enable forecasts. This review critically assesses the various\u0000steps of investigation involved in time-series-analysis-based detection of\u0000critical transitions in real-world non-autonomous systems: from the data\u0000recording to evaluating the reliability of offline and online detections. It\u0000will highlight pros and cons to stimulate further developments, which would be\u0000necessary to advance understanding and forecasting nonlinear behavior such as\u0000critical transitions in complex systems.","PeriodicalId":501065,"journal":{"name":"arXiv - PHYS - Data Analysis, Statistics and Probability","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141500994","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}
Mikołaj Kita, Jan Dubiński, Przemysław Rokita, Kamil Deja
In High Energy Physics simulations play a crucial role in unraveling the complexities of particle collision experiments within CERN's Large Hadron Collider. Machine learning simulation methods have garnered attention as promising alternatives to traditional approaches. While existing methods mainly employ Variational Autoencoders (VAEs) or Generative Adversarial Networks (GANs), recent advancements highlight the efficacy of diffusion models as state-of-the-art generative machine learning methods. We present the first simulation for Zero Degree Calorimeter (ZDC) at the ALICE experiment based on diffusion models, achieving the highest fidelity compared to existing baselines. We perform an analysis of trade-offs between generation times and the simulation quality. The results indicate a significant potential of latent diffusion model due to its rapid generation time.
在欧洲核子研究中心的大型强子对撞机中，高能物理模拟在揭示粒子对撞实验的复杂性方面发挥着至关重要的作用。机器学习仿真方法作为传统方法的替代品备受关注。虽然现有方法主要采用变异自动编码器（VAE）或生成对抗网络（GAN），但最近的进展凸显了扩散模型作为最先进的生成机器学习方法的功效。我们首次基于扩散模型在 ALICE 实验中模拟了零度量热器（ZDC），与现有基线相比达到了最高的保真度。我们对生成时间和仿真质量之间的权衡进行了分析。结果表明，延迟扩散模型因其快速生成时间而具有巨大潜力。
{"title":"Generative Diffusion Models for Fast Simulations of Particle Collisions at CERN","authors":"Mikołaj Kita, Jan Dubiński, Przemysław Rokita, Kamil Deja","doi":"arxiv-2406.03233","DOIUrl":"https://doi.org/arxiv-2406.03233","url":null,"abstract":"In High Energy Physics simulations play a crucial role in unraveling the\u0000complexities of particle collision experiments within CERN's Large Hadron\u0000Collider. Machine learning simulation methods have garnered attention as\u0000promising alternatives to traditional approaches. While existing methods mainly\u0000employ Variational Autoencoders (VAEs) or Generative Adversarial Networks\u0000(GANs), recent advancements highlight the efficacy of diffusion models as\u0000state-of-the-art generative machine learning methods. We present the first\u0000simulation for Zero Degree Calorimeter (ZDC) at the ALICE experiment based on\u0000diffusion models, achieving the highest fidelity compared to existing\u0000baselines. We perform an analysis of trade-offs between generation times and\u0000the simulation quality. The results indicate a significant potential of latent\u0000diffusion model due to its rapid generation time.","PeriodicalId":501065,"journal":{"name":"arXiv - PHYS - Data Analysis, Statistics and Probability","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141513763","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}
We uncover two precise interpretations of traditional electroweak fine-tuning (FT) measures that were historically missed. (i) a statistical interpretation: the traditional FT measure shows the change in plausibility of a model in which a parameter was exchanged for the $Z$ boson mass relative to an untuned model in light of the $Z$ boson mass measurement. (ii) an information-theoretic interpretation: the traditional FT measure shows the exponential of the extra information, measured in nats, relative to an untuned model that you must supply about a parameter in order to fit the $Z$ mass. We derive the mathematical results underlying these interpretations, and explain them using examples from weak scale supersymmetry. These new interpretations shed fresh light on historical and recent studies using traditional FT measures.
我们揭示了传统电弱微调（FT）量度的两种精确解释，而这两种解释在历史上都被忽略了。(i) 统计学解释：传统的微调量度显示了一个模型的可信度的变化，在这个模型中，一个参数被交换为玻色子质量，相对于一个未调谐的模型，它是根据玻色子质量的测量值。(ii)信息论解释：传统的 FT 测量显示了相对于非调谐模型的信息外延指数（以纳特为单位），为了拟合 Z$玻色子质量，你必须提供一个参数。我们推导出这些解释所依据的数学结果，并用弱尺度超对称的例子来解释它们。这些新的解释为历史上和最近使用传统FT测量方法的研究提供了新的启示。
{"title":"Precise interpretations of traditional fine-tuning measures","authors":"Andrew Fowlie, Gonzalo Herrera","doi":"arxiv-2406.03533","DOIUrl":"https://doi.org/arxiv-2406.03533","url":null,"abstract":"We uncover two precise interpretations of traditional electroweak fine-tuning\u0000(FT) measures that were historically missed. (i) a statistical interpretation:\u0000the traditional FT measure shows the change in plausibility of a model in which\u0000a parameter was exchanged for the $Z$ boson mass relative to an untuned model\u0000in light of the $Z$ boson mass measurement. (ii) an information-theoretic\u0000interpretation: the traditional FT measure shows the exponential of the extra\u0000information, measured in nats, relative to an untuned model that you must\u0000supply about a parameter in order to fit the $Z$ mass. We derive the\u0000mathematical results underlying these interpretations, and explain them using\u0000examples from weak scale supersymmetry. These new interpretations shed fresh\u0000light on historical and recent studies using traditional FT measures.","PeriodicalId":501065,"journal":{"name":"arXiv - PHYS - Data Analysis, Statistics and Probability","volume":"35 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141548085","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}
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