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Kevin H. Knuth

Publications and source records attributed to Kevin H. Knuth.

At least 19 recordsLinked to original sources

Critical Evaluation of Studies Alleging Evidence for Technosignatures in the POSS1-E Photographic Plates

Recent studies by B. Villarroel et al. have assembled and analysed datasets of unidentified features measured from digital scans of pre-Sputnik photographic plates. We have examined the claims in these papers using two previously published datasets that are closely related to those used in the Villarroel et al. studies. For these datasets, the assumption of a spatially uniform-random background distribution of features, essential to the Earth shadow analysis, is shown to be false. After finding the null distribution of feature count deviations from the background, we find no statistically significant deficit in the shadow. We find that the reported correlation between the timing of feature observations and nuclear tests becomes insignificant after properly normalizing by the relevant number of observation days, and is almost completely determined by the observation schedule of the Palomar telescope. We uncover important inconsistencies in the definitions of the datasets used in these studies, as well as the use of unvalidated datasets containing catalogue stars, scan artefacts, and plate defects. We find an overall gradual increase in number density of features toward the corners and edges of plates, as well as examples of (i) empty north-south strips that span multiple plates; (ii) clusters and voids having geometric shapes; and (iii) amorphous clusters. We also highlight a circular argument used in these studies, that leverages the results of an inferential analysis to justify conclusions about the origin of the features as well as the validity of the measurements. Finally, we also review the literature concerning historical searches for optical transients in photographic plates corresponding to gamma ray bursts (GRBs); following decades of work, researchers were unable to make a confident identification of a GRB-associated optical transient.

astro-ph.IM

Understanding Exoplanet Habitability: A Bayesian ML Framework for Predicting Atmospheric Absorption Spectra

The evolution of space technology in recent years, fueled by advancements in computing such as Artificial Intelligence (AI) and machine learning (ML), has profoundly transformed our capacity to explore the cosmos. Missions like the James Webb Space Telescope (JWST) have made information about distant objects more easily accessible, resulting in extensive amounts of valuable data. As part of this work-in-progress study, we are working to create an atmospheric absorption spectrum prediction model for exoplanets. The eventual model will be based on both collected observational spectra and synthetic spectral data generated by the ROCKE-3D general circulation model (GCM) developed by the climate modeling program at NASA's Goddard Institute for Space Studies (GISS). In this initial study, spline curves are used to describe the bin heights of simulated atmospheric absorption spectra as a function of one of the values of the planetary parameters. Bayesian Adaptive Exploration is then employed to identify areas of the planetary parameter space for which more data are needed to improve the model. The resulting system will be used as a forward model so that planetary parameters can be inferred given a planet's atmospheric absorption spectrum. This work is expected to contribute to a better understanding of exoplanetary properties and general exoplanet climates and habitability.

astro-ph.EP

The New Science of Unidentified Aerospace-Undersea Phenomena (UAP)

After decades of dismissal and secrecy, it has become clear that a significant number of the world's governments take Unidentified Aerospace-Undersea Phenomena (UAP), formerly known as Unidentified Flying Objects (UFOs), seriously -- yet still seem to know little about them. As a result, these phenomena are increasingly attracting the attention of scientists around the world, some of whom have recently formed research efforts to monitor and scientifically study UAP. In this paper, we review and summarize approximately 20 historical government studies dating from 1933 to the present (in Scandinavia, WWII, US, Canada, France, Russia, China), several historical private research studies (France, UK, US), and both recent and current scientific research efforts (Ireland, Germany, Norway, Sweden, US). In doing so, our objective is to clarify the existing global and historical scientific narrative around UAP. Studies range from field station development and deployment to the collection and analysis of witness reports from around the world. We dispel the common misconception that UAPs are an American phenomenon and show that UAP can be, and have been, scientifically investigated. Our aim here is to enable future studies to draw on the great depth of prior documented experience.

astro-ph.IM

The Scientific Investigation of Unidentified Aerial Phenomena (UAP) Using Multimodal Ground-Based Observatories

(Abridged) Unidentified Aerial Phenomena (UAP) have resisted explanation and have received little formal scientific attention for 75 years. A primary objective of the Galileo Project is to build an integrated software and instrumentation system designed to conduct a multimodal census of aerial phenomena and to recognize anomalies. Here we present key motivations for the study of UAP and address historical objections to this research. We describe an approach for highlighting outlier events in the high-dimensional parameter space of our census measurements. We provide a detailed roadmap for deciding measurement requirements, as well as a science traceability matrix (STM) for connecting sought-after physical parameters to observables and instrument requirements. We also discuss potential strategies for deciding where to locate instruments for development, testing, and final deployment. Our instrument package is multimodal and multispectral, consisting of (1) wide-field cameras in multiple bands for targeting and tracking of aerial objects and deriving their positions and kinematics using triangulation; (2) narrow-field instruments including cameras for characterizing morphology, spectra, polarimetry, and photometry; (3) passive multistatic arrays of antennas and receivers for radar-derived range and kinematics; (4) radio spectrum analyzers to measure radio and microwave emissions; (5) microphones for sampling acoustic emissions in the infrasonic through ultrasonic frequency bands; and (6) environmental sensors for characterizing ambient conditions (temperature, pressure, humidity, and wind velocity), as well as quasistatic electric and magnetic fields, and energetic particles. The use of multispectral instruments and multiple sensor modalities will help to ensure that artifacts are recognized and that true detections are corroborated and verifiable.

astro-ph.IM

The arithmetic of uncertainty unifies quantum formalism and relativistic spacetime

The theories of quantum mechanics and relativity dramatically altered our understanding of the universe ushering in the era of modern physics. Quantum theory deals with objects probabilistically at small scales, whereas relativity deals classically with motion in space and time. We show here that the mathematical structures of quantum theory and of relativity follow together from pure thought, defined and uniquely constrained by the same elementary "combining and sequencing" symmetries that underlie standard arithmetic and probability. The key is uncertainty, which inevitably accompanies observation of quantity and imposes the use of pairs of numbers. The symmetries then lead directly to the use of complex "$\surd\mathord-1$" arithmetic, the standard calculus of quantum mechanics, and the Lorentz transformations of relativistic spacetime. One dimension of time and three dimensions of space are thus derived as the profound and inevitable framework of physics.

physics.gen-ph

The symmetrical foundation of Measure, Probability and Quantum theories

Quantification starts with sum and product rules that express combination and partition. These rules rest on elementary symmetries that have wide applicability, which explains why arithmetical adding up and splitting into proportions are ubiquitous. Specifically, measure theory formalises addition, and probability theory formalises inference in terms of proportions. Quantum theory rests on the same simple symmetries, but is formalised in two dimensions, not just one, in order to track an object through its binary interactions with other objects. The symmetries still require sum and product rules (here known as the Feynman rules), but they apply to complex numbers instead of real scalars, with observable probabilities being modulus-squared (known as the Born rule). The standard quantum formalism follows. There is no mystery or weirdness, just ordinary probabilistic inference.

quant-ph

Lattices and Their Consistent Quantification

This paper introduces the order-theoretic concept of lattices along with the concept of consistent quantification where lattice elements are mapped to real numbers in such a way that preserves some aspect of the order-theoretic structure. Symmetries, such as associativity, constrain consistent quantification and lead to a constraint equation known as the sum rule. Distributivity in distributive lattices also constrains consistent quantification and leads to a product rule. The sum and product rules, which are familiar from, but not unique to, probability theory, arise from the fact that logical statements form a distributive (Boolean) lattice, which exhibits the requisite symmetries.

cs.LO

An Introduction to Influence Theory: Kinematics and Dynamics

Influence theory is a foundational theory of physics that is not based on traditional empirically defined concepts, such as positions in space and time, mass, energy, or momentum. Instead, the aim is to derive these concepts, and their empirically determined relationships, from a more primitive model. It is postulated that there exist things, which we call particles, that influence one another in a discrete and directed fashion resulting in a partially ordered set of influence events. We consider the problem of consistent quantification of the influence events. Observers are modeled as particle chains (observer chains) as if an observer were able to track a particle and quantify the influence events that the particle experiences. From these quantified influence events, we study consistent quantification of the universe of events based on the observer chains. In this paper, we both review and further develop the kinematics and dynamics of particles from the perspective of influence theory.

physics.gen-ph

Bayesian Model Testing of Ellipsoidal Variations on Stars due to Hot Jupiters

A massive planet closely orbiting its host star creates tidal forces that distort the typically spherical stellar surface. These distortions, known as ellipsoidal variations, result in changes in the photometric flux emitted by the star, which can be detected within the data from the Kepler Space Telescope. Currently, there exist several models describing such variations and their effect on the photometric flux. By using Bayesian model testing in conjunction with the Bayesian-based exoplanet characterization software package EXONEST, the most probable representation for ellipsoidal variations was determined for synthetic data and the confirmed hot Jupiter exoplanet Kepler-13Ab.The most preferred model for ellipsoidal variations observed in the Kepler-13 light curve was determined to be EVIL-MC. Among the trigonometric models, the Modified Kane & Gelino model provided the best representation of ellipsoidal variations for the Kepler-13 system and may serve as a fast alternative to the more computationally intensive EVIL-MC. The computational feasibility of directly modeling the ellipsoidal variations of a star are examined and future work is outlined. Providing a more accurate model of ellipsoidal variations is expected to result in better planetary mass estimations.

astro-ph.EP

EXONEST: The Bayesian Exoplanetary Explorer

The fields of astronomy and astrophysics are currently engaged in an unprecedented era of discovery as recent missions have revealed thousands of exoplanets orbiting other stars. While the Kepler Space Telescope mission has enabled most of these exoplanets to be detected by identifying transiting events, exoplanets often exhibit additional photometric effects that can be used to improve the characterization of exoplanets. The EXONEST Exoplanetary Explorer is a Bayesian exoplanet inference engine based on nested sampling and originally designed to analyze archived Kepler Space Telescope and CoRoT (Convection Rotation et Transits planétaires) exoplanet mission data. We discuss the EXONEST software package and describe how it accommodates plug-and-play models of exoplanet-associated photometric effects for the purpose of exoplanet detection, characterization and scientific hypothesis testing. The current suite of models allows for both circular and eccentric orbits in conjunction with photometric effects, such as the primary transit and secondary eclipse, reflected light, thermal emissions, ellipsoidal variations, Doppler beaming and superrotation. We discuss our new efforts to expand the capabilities of the software to include more subtle photometric effects involving reflected and refracted light. We discuss the EXONEST inference engine design and introduce our plans to port the current MATLAB-based EXONEST software package over to the next generation Exoplanetary Explorer, which will be a Python-based open source project with the capability to employ third-party plug-and-play models of exoplanet-related photometric effects.

astro-ph.EP

Analyzing Exoplanet Phase Curve Information Content: Toward Optimized Observing Strategies

Secondary eclipses and phase curves reveal information about the reflectivity and heat distribution in exoplanet atmospheres. The phase curve is composed of a combination of reflected, and thermally emitted light from the planet, and for circular orbits the phase curve peaks during the secondary eclipse or at an orbital phase of 0.5. Physical mechanisms have been discovered which shift the phase curve maximum of tidally locked close in planets to the right, or left, of the secondary eclipse. These mechanisms include cloud formations, and atmospheric superrotation, both of which serve to shift the thermally bright hot-spot, or highly reflective bright spot, of the atmosphere away from the sub-stellar point. Here we present a methodology for optimizing observing strategies for both secondary eclipses and phase curves with the goal of maximizing the information gained about the planetary atmosphere while minimizing the (assumed) continuous observation time. We show that we can increase the duty cycle of observations aimed at the measurements of phase curve characteristics (Amplitude, Phase offset) by up to $50\%$ for future platforms such as CHEOPS and JWST. We apply this methodology to the test cases of the Spitzer phase curve of 55-Cancri-e, which displays an eastward shift in its phase curve maximum, as well as model-generated observations of an ultra-short period planet observed with CHEOPS.

astro-ph.IM

A Classification of Event Sequences in the Influence Network

We build on the classification in [1] of event sequences in the influence network as respecting collinearity or not, so as to determine in future work what phenomena arise in each case. Collinearity enables each observer to uniquely associate each particle event of influencing with one of the observer's own events, even in the case of events of influencing the other observer. We further classify events as to whether they are spacetime events that obey in the fine-grained case the coarse-grained conditions of [2], finding that Newton's First and Second Laws of motion are obeyed at spacetime events. A proof of Newton's Third Law under particular circumstances is also presented.

physics.class-ph

An Information Physics Derivation of Equations of Geodesic Form from the Influence Network

Information physics considers physical laws to result from the consistent quantification and processing of information about physical phenomena. In previous efforts, one of us (Knuth) has shown that a simple model of particles that directly influence one another results in a partially ordered set referred to as the influence network, from which emerge the Minkowski metric and Lorentz transformations of special relativity. Here, we extend earlier work on receipt of influence to the case of one particle influencing another, finding that this gives rise to equations of the form of geodesic equations from general relativity in 1+1 dimensions. Future work will test the equivalence of the current result to general relativity in 1+1 dimensions.

quant-ph

Identifying Excessively Rounded or Truncated Data

All data are digitized, and hence are essentially integers rather than true real numbers. Ordinarily this causes no difficulties since the truncation or rounding usually occurs below the noise level. However, in some instances, when the instruments or data delivery and storage systems are designed with less than optimal regard for the data or the subsequent data analysis, the effects of digitization may be comparable to important features contained within the data. In these cases, information has been irrevocably lost in the truncation process. While there exist techniques for dealing with truncated data, we propose a straightforward method that will allow us to detect this problem before the data analysis stage. It is based on an optimal histogram binning algorithm that can identify when the statistical structure of the digitization is on the order of the statistical structure of the data set itself.

physics.data-an

Designing Intelligent Instruments

Remote science operations require automated systems that can both act and react with minimal human intervention. One such vision is that of an intelligent instrument that collects data in an automated fashion, and based on what it learns, decides which new measurements to take. This innovation implements experimental design and unites it with data analysis in such a way that it completes the cycle of learning. This cycle is the basis of the Scientific Method. The three basic steps of this cycle are hypothesis generation, inquiry, and inference. Hypothesis generation is implemented by artificially supplying the instrument with a parameterized set of possible hypotheses that might be used to describe the physical system. The act of inquiry is handled by an inquiry engine that relies on Bayesian adaptive exploration where the optimal experiment is chosen as the one which maximizes the expected information gain. The inference engine is implemented using the nested sampling algorithm, which provides the inquiry engine with a set of posterior samples from which the expected information gain can be estimated. With these computational structures in place, the instrument will refine its hypotheses, and repeat the learning cycle by taking measurements until the system under study is described within a pre-specified tolerance. We will demonstrate our first attempts toward achieving this goal with an intelligent instrument constructed using the LEGO MINDSTORMS NXT robotics platform.

cs.AI

Why Square Roots of Probabilities?

Square roots of probabilities appear in several contexts, which suggests that they are somehow more fundamental than probabilities. Square roots of probabilities appear in expressions of the Fisher-Rao Metric and the Hellinger-Bhattacharyya distance. They also come into play in Quantum Mechanics via the Born rule where probabilities are found by taking the squared modulus of the quantum amplitude. Why should this be the case and why do these square roots not arise in the various formulations of probability theory? In this short, inconclusive exploration, I consider quantifying a logical statement with a vector defined by a set of components each quantifying one of the atomic statements defining the hypothesis space. I show that conditional probabilities (bi-valuations), such as $P(x|y)$, can be written as the dot product of the two vectors quantifying the logical statements $x$ and $y$ each normalized with respect to the vector quantifying the conditional $y$. The components of the vectors are proportional to the square root of the probability. As a result, this formulation is shown to be consistent with a concept of orthogonality applied to the set of mutually exclusive atomic statements such that the sum rule is represented as the sum of the squares of the square roots of probability.

math.GM

Understanding the Electron

Well over a century after the discovery of the electron, we are still faced with serious conceptual issues regarding precisely what an electron is. Since the development of particle physics and the Standard Model, we have accumulated a great deal of knowledge about the relationships among various subatomic particles. However, this knowledge has not significantly aided in our understanding of the fundamental nature of any particular elementary subatomic particle. The fact that many particle properties, such as position, time, speed, energy, momentum, and component of spin, are observer-dependent suggests that these relevant variables do not represent properties per se, but rather the relationship between the observer and the observed. That is, they reflect details about how the electron influences the observer, and vice versa. Here we attempt to understand this by considering a simple model where particles influence one another in a discrete and direct fashion. The resulting framework, referred to as Influence Theory, is shown to faithfully reproduce a surprising amount of physics. While it would be naive to assume that the ideas presented here comprise anything resembling the final word on the matter, it is hoped that this work will demonstrate that a simple and understandable picture of particles, such as the electron, is indeed feasible and should be actively sought after.

physics.gen-ph

Bayesian Evidence and Model Selection

In this paper we review the concepts of Bayesian evidence and Bayes factors, also known as log odds ratios, and their application to model selection. The theory is presented along with a discussion of analytic, approximate and numerical techniques. Specific attention is paid to the Laplace approximation, variational Bayes, importance sampling, thermodynamic integration, and nested sampling and its recent variants. Analogies to statistical physics, from which many of these techniques originate, are discussed in order to provide readers with deeper insights that may lead to new techniques. The utility of Bayesian model testing in the domain sciences is demonstrated by presenting four specific practical examples considered within the context of signal processing in the areas of signal detection, sensor characterization, scientific model selection and molecular force characterization.

stat.ME