Distributed Space Traffic Management Solutions with Emerging New Space Industry

Day-to-day services, from weather forecast to logistics, rely on space-based infrastructures whose integrity is crucial to stakeholders and end-users worldwide. Current trends point towards congestion of the near-Earth space environment increasing at a rate greater than existing systems support, and thus demand novel cost-efficient approaches to traffic detection, characterization, tracking, and management to ensure space remains a safe, integral part of societies and economies worldwide. Whereas machine-learning (ML) and artificial intelligence (AI) have been extensively proposed to address congestion and alleviate big-data problems of the future, little has been done so far to tackle the need for transnational coordination and conflict-resolution in the context of space traffic management (STM). In STM, there is an ever-growing need for distributing information and coordinating actions (e.g., avoidance manoeuvres) to reduce the operational costs borne by individual entities and to decrease the latencies of actionable responses taken upon the detection of hazardous conditions by one-to-two orders of magnitude. However, these needs are not exclusive to STM, as evidenced by the widespread adoption of solutions to distributing, coordinating, and automating actions in other industries such as air traffic management (ATM), where a short-range airborne collision avoidance system (ACAS) automatically coordinates evasive manoeuvres whenever a conjunction is detected. Within this context, this paper aims at establishing a roadmap of promising technologies (e.g., blockchain), protocols and processes that could be adapted from different domains (railway, automotive, aerial, and maritime) to build an integrated traffic coordination and communication architecture to simplify and harmonise stakeholders’ satellite operations. This paper is organised into seven sections. First, Section 1 introduces the problem of STM, highlighting its complexity. Following this introduction, Section 2 discusses needs and requirements of various stakeholders such as commercial operators, space situational awareness (SSA) service providers, launch-service providers, satellite and constellation owners, governmental agencies, regulators, and insurance companies. Then, Section 3 addresses existing gaps and challenges in STM, focusing on globally coordinated approaches. Next, Section 4 reviews technologies for distributed, secure, and persistent communications, and proposed solutions to address some of these challenges from non-space sectors. Thereafter, Section 5 briefly covers the history of STM proposals and presents the state-of-the-art solution being proposed for modern STM. Following this review, Section 6 devises a step-by-step plan for exploiting and deploying some of the identified technologies within a five-to-ten-year timeline to close several existing gaps. Finally, Section 7 concludes the paper

Topological and flat bands states induced by hybridized interactions in one-dimensional photonic lattices

We report on a study of a one-dimensional linear photonic lattice hosting, simultaneously, fundamental and dipolar modes at every site. We show how, thanks to the interaction between the different orbital modes, this minimal model exhibits rich transport and topological properties. By varying the detuning coefficient we find a regime where bands become flatter (with reduced transport) and, a second regime, where both bands connect on at a gap-closing transition (with enhanced transport). We detect an asymmetric transport due to the asymmetric inter-mode coupling and a linear energy exchange mechanism between modes. Further analysis show that the bands have a topological transition with a non-trivial Zak phase which leads to the appeareance of edge states in a finite system. Finally, for zero detuning, we found a symmetric condition for coupling constants, where the linear spectrum becomes completely flat, with states fully localized in space occupying only two lattice sites.Comment: 8 pages, 5 figure

TCF periodogram's high sensitivity: A method for optimizing detection of small transiting planets

We conduct a methodological study for statistically comparing the sensitivities of two periodograms for weak signal planet detection in transit surveys: the widely used Box-Least Squares (BLS) algorithm following light curve detrending and the Transit Comb Filter (TCF) algorithm following autoregressive ARIMA modeling. Small depth transits are injected into light curves with different simulated noise characteristics. Two measures of spectral peak significance are examined: the periodogram signal-to-noise ratio (SNR) and a False Alarm Probability (FAP) based on the generalized extreme value distribution. The relative performance of the BLS and TCF algorithms for small planet detection is examined for a range of light curve characteristics, including orbital period, transit duration, depth, number of transits, and type of noise. The TCF periodogram applied to ARIMA fit residuals with the SNR detection metric is preferred when short-memory autocorrelation is present in the detrended light curve and even when the light curve noise had white Gaussian noise. BLS is more sensitive to small planets only under limited circumstances with the FAP metric. BLS periodogram characteristics are inferior when autocorrelated noise is present. Application of these methods to TESS light curves with small exoplanets confirms our simulation results. The study ends with a decision tree that advises transit survey scientists on procedures to detect small planets most efficiently. The use of ARIMA detrending and TCF periodograms can significantly improve the sensitivity of any transit survey with regularly spaced cadence.Comment: 30 pages, 13 figures, submitted to AAS Journal

DIAmante TESS AutoRegressive Planet Search (DTARPS): I. Analysis of 0.9 Million Light Curves

Nearly one million light curves from the TESS Year 1 southern hemisphere extracted from Full Frame Images with the DIAmante pipeline are processed through the AutoRegressive Planet Search statistical procedure. ARIMA models remove trends and lingering autocorrelated noise, the Transit Comb Filter identifies the strongest periodic signal in the light curve, and a Random Forest machine learning classifier is trained and applied to identify the best potential candidates. Classifier training sets include injections of both planetary transit signals and contaminating eclipsing binaries. The optimized classifier has a True Positive Rate of 92.8% and a False Positive Rate of 0.37% from the labeled training set. The result of this DIAmante TESS autoregressive planet search (DTARPS) analysis is a list of 7,377 potential exoplanet candidates. The classifier has a False Positive Rate of 0.3%, a 64% recall rate for previously confirmed exoplanets, and a 78% negative recall rate for known False Positives. The completeness map of the injected planetary signals shows high recall rates for planets with 8 - 30 R(Earth) radii and periods 0.6-13 days and poor completeness for planets with radii < 2 R(Earth) or periods < 1 day. The list has many False Alarms and False Positives that need to be culled with multifaceted vetting operations (Paper II).Comment: 46 pages, 21 figures, submitted to AAS Journals. A Machine Readable Table for Table 3 is available at https://drive.google.com/drive/folders/1DyxNcNlfcHHAoCdsaipxxIbP5A2FPey

DIAmante TESS AutoRegressive Planet Search (DTARPS): II. Hundreds of New TESS Candidate Exoplanets

The DIAmante TESS AutoRegressive Planet Search (DTARPS) project seeks to identify photometric transiting planets from 976,814 southern hemisphere stars observed in Year 1 of the TESS mission. This paper follows the methodology developed by Melton et al. (Paper I) using light curves extracted and pre-processed by the DIAmante project (Montalto et al. 2020). Paper I emerged with a list of 7,377 light curves with statistical properties characteristic of transiting planets but dominated by False Alarms and False Positives. Here a multistage vetting procedure is applied including: centroid motion and crowding metrics, False Alarm and False Positive reduction, photometric binary elimination, and ephemeris match removal. The vetting produces a catalog of 462 DTARPS Candidates across the southern ecliptic hemisphere and 310 objects in a spatially incomplete Galactic Plane list. Fifty-eight percent were not previously identified as transiting systems. Candidates are flagged for possible blending from nearby stars based on Zwicky Transient Facility data and for possible radial velocity variations based on Gaia satellite data. Orbital periods and planetary radii are refined using astrophysical modeling; the resulting parameters closely match published values for Confirmed Planets. Their properties are discussed in Paper III.Comment: 25 pages, 10 figures, submitted to AAS Journals. Machine Readable Tables and Figure Sets for Tables 1 and 4 are available at https://drive.google.com/drive/folders/1DyxNcNlfcHHAoCdsaipxxIbP5A2FPeyi?usp=share_lin

DIAmante TESS AutoRegressive Planet Search (DTARPS): III. Understanding the DTARPS Candidate Transiting Planet Catalogs

The DIAmante TESS AutoRegressive Planet Search (DTARPS) project, using novel statistical methods, has identified several hundred candidates for transiting planetary systems obtained from 0.9 million Full Frame Image light curves obtained in the TESS Year 1 southern hemisphere survey (Melton et al. 2022a and 2022b). Several lines of evidence, including limited reconnaissance spectroscopy, indicate that at least half are true planets rather than False Positives. Here various population properties of these objects are examined. Half of the DTARPS candidates are hot Neptunes, populating the 'Neptune desert' found in Kepler planet samples. The DTARPS samples also identify dozens of Ultra Short Period planets with orbital periods down to 5 hours, high priority systems for atmospheric transimssion spectroscopy, and planets orbiting low-mass M stars. DTARPS methodology is sufficiently well-characterized at each step that preliminary planet occurrence rates can be estimated. Except for the increase in hot Neptunes, DTARPS planet occurrence rates are consistent with Kepler rates. Overall, DTARPS provides one of the largest and most reliable catalog of TESS exoplanet candidates that can be tapped to improve our understanding of various exoplanetary populations and astrophysical processes.Comment: 29 pages, 16 figures, submitted to the AAS Journals February 13, 202

CRUSOE: Data Model for Cyber Situation Awareness

Attaining and keeping cyber situational awareness is crucial for the proper incident response, especially in critical infrastructures. Incident handlers need to process heterogeneous data, such as network topology and organisation's missions and objectives, to effectively mitigate the threats. The development of tools for attaining cyber situational awareness often faces the problem of effectively obtaining, correlating, and storing such heterogeneous data. In this paper, we present CRUSOE, an extensible layered data model for attaining and keeping information on cyber situational awareness. We conducted interviews with incident handlers from several security teams and evaluated existing requirements on cyber situational awareness to formalise the requirements on the proposed data model so that can be used in today's common network settings. The CRUSOE data model keeps track of missions, systems, networks, hosts, threats, detection and response capabilities, and access control in a network of an organisation. It is also designed to be filled primarily with the data that can be obtained in a semi- or fully-automated fashion in today's common network environments