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The Instrumentation Frontier was set up as a part of the Snowmass 2013 Community Summer Study to examine the instrumentation R&D needed to support particle physics research over the coming decade. This report summarizes the findings of the Energy Frontier subgroup of the Instrumentation Frontier.
This report summarizes findings of the 2013 Snowmass Community Summer Study Instrumentation Frontier's subgroup on the Intensity Frontier. This report is directed at identifying instrumentation R&D needed to support particle physics research over the coming decades at the Intensity Frontier.
As frontier artificial intelligence (AI) systems become more capable, it becomes more important that developers can explain why their systems are sufficiently safe. One way to do so is via safety cases: reports that make a structured argument, supported by evidence, that a system is safe enough in a given operational context. Safety cases are already common in other safety-critical industries such as aviation and nuclear power. In this paper, we explain why they may also be a useful tool in frontier AI governance, both in industry self-regulation and government regulation. We then discuss the practicalities of safety cases, outlining how to produce a frontier AI safety case and discussing what still needs to happen before safety cases can substantially inform decisions.
The data storage and data management needs are summarized for the energy frontier, intensity frontier, cosmic frontier, lattice field theory, perturbative QCD and accelerator science. The outlook for data storage technologies and costs is then outlined, followed by a summary of the current state of data, software and physics analysis capability preservation. The HEP outlook is summarized, pointing out where future data volumes may strain against what is technologically and financially feasible. Finally recommendations for areas of particular attention and action are made.
Mitigating the risks from frontier AI systems requires up-to-date and reliable information about those systems. Organizations that develop and deploy frontier systems have significant access to such information. By reporting safety-critical information to actors in government, industry, and civil society, these organizations could improve visibility into new and emerging risks posed by frontier systems. Equipped with this information, developers could make better informed decisions on risk management, while policymakers could design more targeted and robust regulatory infrastructure. We outline the key features of responsible reporting and propose mechanisms for implementing them in practice.
In this paper, we analyze the asymptotic behavior of the main characteristics of the mean-variance efficient frontier employing random matrix theory. Our particular interest covers the case when the dimension $p$ and the sample size $n$ tend to infinity simultaneously and their ratio $p/n$ tends to a positive constant $c\in(0,1)$. We neither impose any distributional nor structural assumptions on the asset returns. For the developed theoretical framework, some regularity conditions, like the existence of the $4$th moments, are needed. It is shown that two out of three quantities of interest are biased and overestimated by their sample counterparts under the high-dimensional asymptotic regime. This becomes evident based on the asymptotic deterministic equivalents of the sample plug-in estimators. Using them we construct consistent estimators of the three characteristics of the efficient frontier. It it shown that the additive and/or the multiplicative biases of the sample estimates are solely functions of the concentration ratio $c$. Furthermore, the asymptotic normality of the considered estimators of the parameters of the efficient frontier is proved. Verifying the theoretical res
The frontier of a planar Brownian motion is the boundary of the unbounded component of the complement of its range. In this paper we find the Hausdorff dimension of the set of double points on the frontier.
This report summarizes the current status of Cosmic Frontier physics and the broad and exciting future prospects identified for the Cosmic Frontier as part of the 2021 Snowmass Process.
We propose a quasi-Bayesian method to conduct inference for the production frontier. This approach combines multiple first-stage extreme quantile estimates by the quasi-Bayesian method to produce the point estimate and confidence interval for the production frontier. We show the asymptotic properties of the proposed estimator and the validity of the inference procedure. The finite sample performance of our method is illustrated through simulations and an empirical application.
We present multiwavelength photometric catalogues (HST, Spitzer and Hawk-I K band) for the first two of the Frontier Fields, Abell2744 and MACSJ0416 (plus their parallel fields). To detect faint sources even in the central regions of the clusters, we develop a robust and repeatable procedure that uses the public codes Galapagos and Galfit to model and remove most of the light contribution from both the brightest cluster members as well as the ICL. We perform the detection on the HST H160 processed image to obtain a pure H-selected sample. We also add a sample of sources which are undetected in the H160 image but appear on a stacked infrared image. Photometry in the other HST bands is obtained using SExtractor, performed again on residual images after the Galfit procedure for foreground light removal. Photometry on the Hawk-I and IRAC bands has been obtained using our PSF-matching deconfusion code T-PHOT. A similar procedure, but without the need for the foreground light removal, is adopted for the Parallel fields. The procedure allows for the detection and the photometric measurements of ~2500 sources per field. We deliver and release complete photometric H-detected catalogues, wit