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Simulation-based inference methods that feature correct conditional coverage of confidence sets based on observations that have been compressed to a scalar test statistic require accurate modeling of either the p-value function or the cumulative distribution function (cdf) of the test statistic. If the model of the cdf, which is typically a deep neural network, is a function of the test statistic then the derivative of the neural network with respect to the test statistic furnishes an approximation of the sampling distribution of the test statistic. We explore whether this approach to modeling conditional 1-dimensional sampling distributions is a viable alternative to the probability density-ratio method, also known as the likelihood-ratio trick. Relatively simple, yet effective, neural network models are used whose predictive uncertainty is quantified through a variety of methods.

A fundamental goal of astrobiology is to detect life outside of Earth. This proves to be an exceptional challenge outside of our solar system, where strong assumptions must be made about how life would manifest and interact with its planet. Such assumptions are required because of the lack of a consensus theory of living systems, or an understanding of the possible extent of planetary dynamics. Here we explore a model of life spreading between planetary systems via panspermia and terraformation. Our model shows that as life propagates across the galaxy, correlations emerge between planetary characteristics and location, and can function as a population-scale agnostic biosignature. This biosignature is agnostic because it is independent of strong assumptions about any particular instantiation of life or planetary characteristic--by focusing on a specific hypothesis of what life may do, rather than what life may be. By clustering planets based on their observed characteristics, and examining the spatial extent of these clusters, we demonstrate (and evaluate) a way to prioritize specific planets for further observation--based on their potential for containing life. We consider obstacles that must be overcome to practically implement our approach, including identifying specific ways in which better understanding astrophysical and planetary processes would improve our ability to detect life. Finally, we consider how this model leads us to think in novel ways about hierarchies of life and planetary scale replication., Comment: 16 pages, 20 figures

Understanding the origin(s) of life (OoL) is a fundamental challenge for science in the 21st century. Research on OoL spans many disciplines, including chemistry, physics, biology, planetary sciences, computer science, mathematics and philosophy. The sheer number of different scientific perspectives relevant to the problem has resulted in the coexistence of diverse tools, techniques, data, and software in OoL studies. This has made communication between the disciplines relevant to the OoL extremely difficult because the interpretation of data, analyses, or standards of evidence can vary dramatically. Here, we hope to bridge this wide field of study by providing common ground via the consolidation of tools and techniques rather than positing a unifying view on how life emerges. We review the common tools and techniques that have been used significantly in OoL studies in recent years. In particular, we aim to identify which information is most relevant for comparing and integrating the results of experimental analyses into mathematical and computational models. This review aims to provide a baseline expectation and understanding of technical aspects of origins research, rather than being a primer on any particular topic. As such, it spans broadly -- from analytical chemistry to mathematical models -- and highlights areas of future work that will benefit from a multidisciplinary approach to tackling the mystery of life's origin. Ultimately, we hope to empower a new generation of OoL scientists by reviewing how they can investigate life's origin, rather than dictating how to think about the problem.

High-fidelity simulators that connect theoretical models with observations are indispensable tools in many sciences. When coupled with machine learning, a simulator makes it possible to infer the parameters of a theoretical model directly from real and simulated observations without explicit use of the likelihood function. This is of particular interest when the latter is intractable. In this work, we introduce a simple extension of the recently proposed likelihood-free frequentist inference (LF2I) approach that has some computational advantages. Like LF2I, this extension yields provably valid confidence sets in parameter inference problems in which a high-fidelity simulator is available. The utility of our algorithm is illustrated by applying it to three pedagogically interesting examples: the first is from cosmology, the second from high-energy physics and astronomy, both with tractable likelihoods, while the third, with an intractable likelihood, is from epidemiology.

The ultimate goal of astrobiology is to determine the distribution and diversity of life in the universe. But as the word "biosignature" suggests, what will be detected is not life itself, but an observation implicating a particular process associated with living systems. Technical constraints and our limited access to other worlds suggest we are more likely to detect an out-of-equilibrium suite of gasses than a writhing octopus. Yet, anything short of a writhing octopus will raise skepticism among astrobiologists about what has been detected. Resolving that skepticism requires a theory to delineate processes due to life and those due solely to abiotic mechanisms. This poses an existential question for the endeavor of life detection: How do astrobiologists plan to detect life via features shared between non-living and living systems? We argue that you cannot without an underlying theory of life. We illustrate this by analyzing the hypothetical detection of an "Earth 2.0" exoplanet. In the absence of a theory of life, we argue the community should focus on identifying unambiguous features of life via four areas of active research: understanding the principles of life on Earth, building life in the lab, detecting life in the solar system and searching for technosignatures. Ultimately, we ask, what exactly do astrobiologists hope to learn by searching for life?, Comment: 15 pages, 2 figures, 1 box

Data analysis at the LHC has a very steep learning curve, which erects a formidable barrier between data and anyone who wishes to analyze data, either to study an idea or to simply understand how data analysis is performed. To make analysis more accessible, we designed the so-called Analysis Description Language (ADL), a domain specific language capable of describing the contents of an LHC analysis in a standard and unambiguous way, independent of any computing frameworks. ADL has an English-like highly human-readable syntax and directly employs concepts relevant to HEP. Therefore it eliminates the need to learn complex analysis frameworks written based on general purpose languages such as C++ or Python, and shifts the focus directly to physics. Analyses written in ADL can be run on data using a runtime interpreter called CutLang, without the necessity of programming. ADL and CutLang are designed for use by anyone with an interest in, and/or knowledge of LHC physics, ranging from experimentalists and phenomenologists to non-professional enthusiasts. ADL/CutLang are originally designed for research, but are also equally intended for education and public use. This approach has already been employed to train undergraduate students with no programming experience in LHC analysis in two dedicated schools in Turkey and Vietnam, and is being adapted for use with LHC Open Data. Moreover, work is in progress towards piloting an educational module in particle physics data analysis for high school students and teachers. Here, we introduce ADL and CutLang and present the educational activities based on these practical tools., Comment: 6 pages, 2 figures. Contribution to the proceedings of EPS-HEP 2021, European Physical Society conference on High Energy Physics, 26-30 July 2021

We propose to adopt a declarative domain specific language for describing the physics algorithm of a high energy physics (HEP) analysis in a standard and unambiguous way decoupled from analysis software frameworks, and argue that this approach provides an accessible and sustainable environment for analysis design, use and preservation. Prototype of such a language called Analysis Description Language (ADL) and its associated tools are being developed and applied in various HEP physics studies. We present the motivations for using a DSL, design principles of ADL and its runtime interpreter CutLang, along with current physics studies based on this approach. We also outline ideas and prospects for the future. Recent physics studies, hands-on workshops and surveys indicate that ADL is a feasible and effective approach with many advantages and benefits, and offers a direction to which the HEP field should give serious consideration., Comment: contribution to Snowmass 2021

The Astrobiology Graduate Conference (AbGradCon) is an annual conference both organized for and by early career researchers, postdoctoral fellows, and students as a way to train the next generation of astrobiologists and develop a robust network of cohorts moving forward. AbGradCon 2021 was held virtually on September 14-17, 2021, hosted by the Earth-Life Science Institute (ELSI) of Tokyo Institute of Technology after postponement of the in-person event in 2020 due to the COVID-19 pandemic. The meeting consisted of presentations by 120 participants from a variety of fields, two keynote speakers, and other career building events and workshops. Here, we report on the organizational and executional aspects of AbGradCon 2021, including the meeting participant demographics, various digital aspects introduced specifically for a virtual edition of the meeting, and the abstract submission and evaluation process. The abstract evaluation process of AbGradCon 2021 is unique in that all evaluations are done by the peers of the applicants, and as astrobiology is inherently a broad discipline, the abstract evaluation process revealed a number of trends related to multidisciplinarity of the astrobiology field. We believe that meetings like AbGradCon can provide a unique opportunity for students and early career researchers in astrobiology to experience community building, inter- and multidisciplinary collaboration, and career training and would be a welcome sight in other fields as well. We hope that this report provides inspiration and a basic roadmap for organizing future conferences in any field with similar goals.

Reliable modeling of conditional densities is important for quantitative scientific fields such as particle physics. In domains outside physics, implicit quantile neural networks (IQN) have been shown to provide accurate models of conditional densities. We present a successful application of IQNs to jet simulation and correction using the tools and simulated data from the Compact Muon Solenoid (CMS) Open Data portal., Comment: NeurIPS 2021 - Workshop on Machine Learning and the Physical Sciences, Dec 2021, Vancouver, Canada

The statistical models used to derive the results of experimental analyses are of incredible scientific value and are essential information for analysis preservation and reuse. In this paper, we make the scientific case for systematically publishing the full statistical models and discuss the technical developments that make this practical. By means of a variety of physics cases -- including parton distribution functions, Higgs boson measurements, effective field theory interpretations, direct searches for new physics, heavy flavor physics, direct dark matter detection, world averages, and beyond the Standard Model global fits -- we illustrate how detailed information on the statistical modelling can enhance the short- and long-term impact of experimental results., Comment: 60 pages, 15 figures

This paper presents an overview and features of an Analysis Description Language (ADL) designed for HEP data analysis. ADL is a domain specific, declarative language that describes the physics content of an analysis in a standard and unambiguous way, independent of any computing frameworks. It also describes infrastructures that render ADL executable, namely CutLang, a direct runtime interpreter (originally also a language), and adl2tnm, a transpiler converting ADL into C++ code. In ADL, analyses are described in human readable plain text files, clearly separating object, variable and event selection definitions in blocks, with a syntax that includes mathematical and logical operations, comparison and optimisation operators, reducers, four-vector algebra and commonly used functions. Recent studies demonstrate that adapting the ADL approach has numerous benefits for the experimental and phenomenological HEP communities. These include facilitating the abstraction, design, optimization, visualization, validation, combination, reproduction, interpretation and overall communication of the analysis contents and long term preservation of the analyses beyond the lifetimes of experiments. Here we also discuss some of the current ADL applications in physics studies and future prospects based on static analysis and differentiable programming., Comment: accepted for publication in vCHEP2021 conference proceedings. arXiv admin note: text overlap with arXiv:2101.09031

An analysis description language is a domain specific language capable of describing the contents of an LHC analysis in a standard and unambiguous way, independent of any computing framework. It is designed for use by anyone with an interest in, and knowledge of, LHC physics, i.e., experimentalists, phenomenologists and other enthusiasts. Adopting analysis description languages would bring numerous benefits for the LHC experimental and phenomenological communities ranging from analysis preservation beyond the lifetimes of experiments or analysis software to facilitating the abstraction, design, visualization, validation, combination, reproduction, interpretation and overall communication of the analysis contents. Here, we introduce the analysis description language concept and summarize the current efforts ongoing to develop such languages and tools to use them in LHC analyses., Comment: Accepted contribution to the proceedings of The 8th Annual Conference on Large Hadron Collider Physics, LHCP2020, 25-30 May, 2020, online

For the first time in human history, we will soon be able to apply the scientific method to the question "Are We Alone?" The rapid advance of exoplanet discovery, planetary systems science, and telescope technology will soon allow scientists to search for life beyond our Solar System through direct observation of extrasolar planets. This endeavor will occur alongside searches for habitable environments and signs of life within our Solar System. While the searches are thematically related and will inform each other, they will require separate observational techniques. The search for life on exoplanets holds potential through the great diversity of worlds to be explored beyond our Solar System. However, there are also unique challenges related to the relatively limited data this search will obtain on any individual world. This white paper reviews the scientific community's ability to use data from future telescopes to search for life on exoplanets. This material summarizes products from the Exoplanet Biosignatures Workshop Without Walls (EBWWW). The EBWWW was constituted by a series of online and in person activities, with participation from the international exoplanet and astrobiology communities, to assess state of the science and future research needs for the remote detection of life on planets outside our Solar System., Comment: This is a white paper that was submitted to the National Academies of Sciences Study: Astrobiology Science Strategy for the Search for Life in the Universe

The random grid search (RGS) is a simple, but efficient, stochastic algorithm to find optimal cuts that was developed in the context of the search for the top quark at Fermilab in the mid-1990s. The algorithm, and associated code, have been enhanced recently with the introduction of two new cut types, one of which has been successfully used in searches for supersymmetry at the Large Hadron Collider. The RGS optimization algorithm is described along with the recent developments, which are illustrated with two examples from particle physics. One explores the optimization of the selection of vector boson fusion events in the four-lepton decay mode of the Higgs boson and the other optimizes SUSY searches using boosted objects and the razor variables., Comment: 26 pages, 9 figures

Exoplanet science promises a continued rapid accumulation of new observations in the near future, energizing a drive to understand and interpret the forthcoming wealth of data to identify signs of life beyond our Solar System. The large statistics of exoplanet samples, combined with the ambiguity of our understanding of universal properties of life and its signatures, necessitate a quantitative framework for biosignature assessment Here, we introduce a Bayesian framework for guiding future directions in life detection, which permits the possibility of generalizing our search strategy beyond biosignatures of known life. The Bayesian methodology provides a language to define quantitatively the conditional probabilities and confidence levels of future life detection and, importantly, may constrain the prior probability of life with or without positive detection. We describe empirical and theoretical work necessary to place constraints on the relevant likelihoods, including those emerging from stellar and planetary context, the contingencies of evolutionary history and the universalities of physics and chemistry. We discuss how the Bayesian framework can guide our search strategies, including determining observational wavelengths or deciding between targeted searches or larger, lower resolution surveys. Our goal is to provide a quantitative framework not entrained to specific definitions of life or its signatures, which integrates the diverse disciplinary perspectives necessary to confidently detect alien life., Comment: 125 pages (double spaced), 12 figures; The paper is the fourth in a series of 5 review manuscripts of the NExSS Exoplanet Biosignatures Workshop. Community comment was solicited at this url: https://nexss.info/groups/ebwww

We present and analyze two pathways to produce commercial optical-fiber patch cords with stable long-term transmission in the ultraviolet (UV) at powers up to $\sim$ 200 mW, and typical bulk transmission between 66-75\%. Commercial fiber patch cords in the UV are of great interest across a wide variety of scientific applications ranging from biology to metrology, and the lack of availability has yet to be suitably addressed. We provide a guide to producing such solarization-resistant, hydrogen-passivated, polarization-maintaining, connectorized and jacketed optical fibers compatible with demanding scientific and industrial applications. Our presentation describes the fabrication and hydrogen loading procedure in detail and presents a high-pressure vessel design, calculations of required \Ht\ loading times, and information on patch cord handling and the mitigation of bending sensitivities. Transmission at 313 nm is measured over many months for cumulative energy on the fiber output of > 10 kJ with no demonstrable degradation due to UV solarization, in contrast to standard uncured fibers. Polarization sensitivity and stability are characterized yielding polarization extinction ratios between 15 dB and 25 dB at 313 nm, where we find patch cords become linearly polarizing. We observe that particle deposition at the fiber facet induced by high-intensity UV exposure can (reversibly) deteriorate patch cord performance and describe a technique for nitrogen purging of fiber collimators which mitigates this phenomenon., Comment: 19 pages, 6 figures

These lectures introduce the basic ideas and practices of statistical analysis for particle physicists, using a real-world example to illustrate how the abstractions on which statistics is based are translated into practical application., Comment: 28 pages, contribution to the 2013 European School of High-Energy Physics, Paradfurdo, Hungary, 5-18 Jun 2013. arXiv admin note: substantial text overlap with arXiv:1504.00945

We introduce a few of the key ideas of statistical analysis using two real-world examples to illustrate how these ideas are used in practice., Comment: presented at the 2012 European School of High-Energy Physics, La Pommeraye, Anjou, France, 6-19 June 2012

The search for a new source of CP violation is one of the most important endeavors in particle physics. A particularly interesting way to perform this search is to probe the CP phase in the $h\tau\tau$ coupling, as the phase is currently completely unconstrained by all existing data. Recently, a novel variable $\Theta$ was proposed for measuring the CP phase in the $h\tau\tau$ coupling through the $\tau^\pm \to \pi^\pm \pi^0 \nu$ decay mode. We examine two crucial questions that the real LHC detectors must face, namely, the issue of neutrino reconstruction and the effects of finite detector resolution. For the former, we find strong evidence that the collinear approximation is the best for the $\Theta$ variable. For the latter, we find that the angular resolution is actually not an issue even though the reconstruction of $\Theta$ requires resolving the highly collimated $\pi^\pm$'s and $\pi^0$'s from the $\tau$ decays. Instead, we find that it is the missing transverse energy resolution that significantly limits the LHC reach for measuring the CP phase via $\Theta$. With the current missing energy resolution, we find that with $\sim 1000\,\textrm{fb}^{-1}$ the CP phase hypotheses $\Delta = 0^\circ$ (the standard model value) and $\Delta = 90^\circ$ can be distinguished, at most, at the 95\% confidence level., Comment: Minor wording changes. The version published in PRD