Binary Black Hole Merger

A central problem in data science is to use potentially noisy samples of an unknown function to predict function values for unseen inputs. In classical statistics, the predictive error is understood as a trade-off between the bias and the variance that balances model simplicity with its ability to fit complex functions. However, overparametrized models exhibit counterintuitive behaviors, such as “double descent” in which models of increasing complexity exhibit decreasing generalization error. Other models may exhibit more complicated patterns of predictive error with multiple peaks and valleys. Neither double descent nor multiple descent phenomena are well explained by the bias-variance decomposition. We introduce a decomposition that we call the generalized aliasing decomposition (GAD) to explain the relationship between predictive performance and model complexity. The GAD decomposes the predictive error into three parts: (1) model insufficiency, which dominates when the number of parameters is much smaller than the number of data points, (2) data insufficiency, which dominates when the number of parameters is much greater than the number of data points, and (3) generalized aliasing, which dominates between these two extremes. We demonstrate the applicability of the GAD to diverse applications, including random feature models from machine learning, Fourier transforms from signal processing, solution methods for differential equations, and predictive formation enthalpy in materials discovery. Because key components of the generalized aliasing decomposition can be explicitly calculated from the relationship between model class and samples without seeing any data labels, it can answer questions related to experimental design and model selection before collecting data or performing experiments. We further demonstrate this approach on several examples and discuss implications for predictive modeling and data science.

We report on roughly 16 yr of photometric monitoring of the trans-Neptunian binary system (120347) Salacia–Actaea, which provides significant evidence that Salacia and Actaea are tidally locked to the mutual orbital period in a fully synchronous configuration. The orbit of Actaea is updated, followed by a Lomb–Scargle periodogram analysis of the ground-based photometry, which reveals a synodic period similar to the orbital period and a peak-to-peak lightcurve amplitude of Δm = 0.0900 ± 0.0036 mag (1σ uncertainty). Incorporating archival Hubble Space Telescope photometry that resolves each component, we argue that the periodicity in the unresolved data is driven by a longitudinally varying surface morphology on Salacia, and we derive a sidereal rotation period that is within 1σ of the mutual orbital period. A rudimentary tidal evolution model is invoked that suggests synchronization occurred within 1.1 Gyr after Actaea was captured/formed.

In response to the 2020 Update of the European Strategy for Particle Physics, the Future Circular Collider (FCC) Feasibility Study was launched as an international collaboration hosted by CERN. This report describes the FCC integrated programme, which consists of two stages: an electron-positron collider (FCC-ee) in the first phase, serving as a high-luminosity Higgs, top, and electroweak factory; followed by a proton-proton collider (FCC-hh) at the energy frontier in the second phase. The FCC-ee is designed to operate at four key centre-of-mass energies: the Z pole, the WW pair production threshold, the ZH production peak, and the top/anti-top production threshold—each delivering the highest possible luminosities to four experiments. Over 15 years of operation, FCC-ee will produce more than 6 trillion Z bosons, 200 million WW pairs, nearly 3 million Higgs bosons, and 2 million top anti-top pairs. Precise energy calibration at the Z pole and WW threshold will be achieved through frequent resonant depolarisation of pilot bunches. The sequence of operation modes between the Z, WW, and ZH substages remains flexible. The FCC-hh will operate at a centre-of-mass energy of approximately 85 TeV—nearly an order of magnitude higher than the LHC—and is designed to deliver 5 to 10 times the integrated luminosity of the upcoming High-Luminosity LHC. Its mass reach for direct discovery extends to several tens of TeV. In addition to proton-proton collisions, the FCC-hh is capable of supporting ion-ion, ion-proton, and lepton-hadron collision modes. This second volume of the Feasibility Study Report presents the complete design of the FCC-ee collider, its operation and staging strategy, the full-energy booster and injector complex, required accelerator technologies, safety concepts, and technical infrastructure. It also includes the design of the FCC-hh hadron collider, development of high-field magnets, hadron injector options, and key technical systems for FCC-hh.

This book presents the result of an innovative challenge, to create a systematic literature overview driven by machine-generated content. Questions and related keywords were prepared for the machine to query, discover, collate and structure by Artificial Intelligence (AI) clustering. The AI-based approach seemed especially suitable to provide an innovative perspective as the topics are indeed both complex, interdisciplinary and multidisciplinary, for example, climate, planetary and evolution sciences. Springer Nature has published much on these topics in its journals over the years, so the challenge was for the machine to identify the most relevant content and present it in a structured way that the reader would find useful. The automatically generated literature summaries in this book are intended as a springboard to further discoverability. They are particularly useful to readers with limited time, looking to learn more about the subject quickly and especially if they are new to the topics. Springer Nature seeks to support anyone who needs a fast and effective start in their content discovery journey, from the undergraduate student exploring interdisciplinary content to Master- or PhD-thesis developing research questions, to the practitioner seeking support materials, this book can serve as an inspiration, to name a few examples. 

It is important to us as a publisher to make the advances in technology easily accessible to our authors and find new ways of AI-based author services that allow human-machine interaction to generate readable, usable, collated, research content.

Purpose

To examine the associations and substitutions of dietary sugars [extrinsic (free) or intrinsic (non-free)] as well as dietary starch and fiber intakes for indices of body fat and cardiometabolic health.

Methods

Dietary intake was assessed at multiple times using multi-day 24-hour recalls over 18-months for indices of body fat (body fat %, waist circumference, BMI, and weight change) (n = 1066) and at baseline and 12 months for cardiometabolic outcomes (LDL, HDL, HbA1c) (n = 736). Bayesian modeling was applied to analyze the probabilistic impact of dietary carbohydrate components using credible intervals for association and substitution analyses with repeated measures random effects modeling.

Results

A higher starch intake significantly associated with higher body fat %, BMI and waist circumference (WC) (all CrI > 0). Conversely, intrinsic sugar and fiber intakes were significantly linked to lower body fat indices, while free sugar showed no association. A 20 g substitution of free sugars with intrinsic sugars significantly associated with lower body fat (CrI: -4.2; -1.0%), BMI (CrI: -1.8; -0.4) and WC (CrI: -4.2; -1.0 cm), while substituting intrinsic sugars with starch resulted in significantly higher body fat, BMI, WC and weight change. Replacing starch with fiber associated with higher HDL-C (CrI: -0.0; 0.3) and lower LDL-C (CrI: -0.6; 0.1). Replacing free sugars with starch associated with a higher HbA1c level (CrI: 0.0;0.2).

Conclusion

These results underscore the importance of distinguishing between intrinsic versus extrinsic sugars and highlight the potential benefits of increasing intrinsic sugars and fiber while reducing starch for better body fat management and cardiometabolic health.

Obtaining microscopic structure-property relationships for grain boundaries is challenging due to their complex atomic structures. Recent efforts use machine learning to derive these relationships, but the way the atomic grain boundary structure is represented can have a significant impact on the predictions. Key steps for property prediction common to grain boundaries and other variable-sized atom clustered structures include: (1) describing the atomic structure as a feature matrix, (2) transforming the variable-sized feature matrix to a fixed length common to all structures, and (3) applying a machine learning algorithm to predict properties from the transformed matrices. We examine how these steps and different combinations of engineered features impact the accuracy of grain boundary energy predictions using a database of over 7000 grain boundaries. Additionally, we assess how different engineered features support interpretability, offering insights into the physics of the structure-property relationships.