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Kepler Planet Detection Metrics: Robovetter Completeness and Effectiveness for Data Release 25

In general, the Kepler pipeline identifies a list of Threshold Crossing Events (TCEs), which are periodic flux decrements meeting certain criteria (Jenkins, 2017). These TCEs are reviewed and those that appear consistent with astrophysically transiting or eclipsing systems are classified as Kepler Objects of Interest (KOIs). Further review is given to KOIs, which are then dispositioned as Planet Candidates (PCs) or False Positive (FPs). FPs are further denoted by four major flags that indicate if the signal is Not Transit-Like (NTL), due to a Stellar Eclipse (SS; previously referred to as Significant Secondary), and/or due to contamination from a source other than the target as evidenced by a Centroid Offset (CO) oran Ephemeris Match (EM) with another object. This entire TCE review process is known as dispositioning or vetting.In the first five Kepler mission planet candidate catalogs (Borucki et al., 2011a,b; Batalha et al., 2013; Burke et al., 2014; Rowe et al., 2015), TCEs were manually examined on an individual basis and dispositioned using various plots and quantitative diagnostic tests (see e.g., Coughlin, 2017). In the sixth catalog, Mullally et al. (2015a) employed partial automation via simple parameter cuts to automatically disposition a large fraction of TCEs as not transit-like. Mullally et al. (2015a) also used an automated technique known as the centroid Robovetter (Mullally, 2017) to automatically identify some FP KOIs due to centroid offsets - a telltale signature of light contamination from another target. The remaining targets were manually dispositioned. In the seventh catalog, Coughlin et al. (2016) automated theentire dispositioning process using what is collectively known simply as the Robovetter.In the eighth and final mission catalog, Thompson et al. (2017) use a revised Robovetter to automate the dispositioning of all TCEs with an emphasis on creating a catalog suitable for accurately determining planet occurrence rates. In order to calculate accurate occurrence rates, the completeness and effectiveness of the Robovetter must be characterized. We define these terms as applied to the Robovetter, following Thompson et al. (2017), as:1. Completeness: The fraction of transiting planets detected by the pipeline that are classified as planet candidates by the Robovetter.2. Effectiveness: The fraction of false positives detected by the pipeline that are classified as false positives by the Robovetter.The remainder of this document describes products that can be used to quantitatively assess Robovetter completeness and effectiveness for an arbitrary set of Kepler stars.

Robovetter↗

Developing Concepts of Operations Using Multi-Step Tool Techniques With Large Language Models

The National Aeronautics and Space Administration (NASA) Air Mobility Pathfinders (AMP) project is developing and evaluating concepts of operations (ConOps) for safe, secure, and scalable Urban Air Mobility (UAM) operations. The AMP project’s Operational Concepts, Architecture, and Requirements Integration (OCARI) Team is using a Model Based System Engineering (MBSE) approach for integration, interoperability, and traceability of Advanced Air Mobility (AAM) ecosystems centered around urban air taxi services. The team’s goal is to define structures and behaviors needed for system feasibility, readiness, and interoperability, establish a UAM knowledge base, and trace and validate assumptions and requirements relevant to AAM. NASA Langley Research Center (LaRC) is spearheading an innovative digital engineering approach to integrate, communicate, and facilitate the research of multi-modal transportation systems. The Knowledge-based Digital Platform (KbDP) is a concept being developed that ties the workflows of Project Managers (PM), Principal Investigators (PI), and System Engineers together across organizational boundaries. It does so through the management of an information database defined by mathematical, data science, and system engineering principles. Machine Learning (ML) algorithms play a key role in this concept by extracting meaningful knowledge from relational and graph databases, document repositories, and system artifacts, which the human user leverages to greatly improve the efficiency and effectiveness of their research. Recent advancements in the field of Large Language Models (LLMs), specifically models trained for tool use, such as Command-R , now allow for the reliable implementation of single-step and multi-step tool-centric systems. These techniques provide the LLM with a set of tools, in our case Python functions, that can be called on to answer a much wider range of questions compared to LLMs implemented using a traditional single-source or Retrieval Augmented Generation (RAG) approach. Through this method, the LLM can pull information from multiple data sources, such as relational or graph databases, document repositories, application programming interfaces (APIs), and SysML artifacts depending on the user’s question. The LLM can also output the information in a variety of different formats, using output generation tools, such as CSV, UML, or SysML artifacts. Additionally, tools can be assigned roles and can work together to provide answers to queries in an “agent” like approach, similar to that implemented by Microsoft’s AutoGen framework where different agents can converse with each other to accomplish tasks. Previously, our team developed a chatbot system with “agent like” functionality in the form of different “modes” the user could select from a user interface (UI), this architecture can be seen on the left in figure 1. Three different modes were implemented, the first mode allowed the LLM to utilize the structures and algorithms within a graph database to trace UAM requirements. The second mode gave the LLM access to a vector search capable of providing relevant information from thousands of document pages related to UAM ConOps and requirements. The third mode served as a general assistant where users could enter open-ended questions and custom prompts to utilize the LLM for different use-cases. This system improved the process surrounding generating and analyzing information related to UAM requirements, however, the implementation provided a clunky user experience. Users were required to know what mode to select within the UI in advance before entering their question to the selected tool. Moreover, the different tools were isolated from each other, they lacked bidirectional links that would allow for tools to collaborate to generate better responses. Our team is working on a new architecture, seen on the right in the below figure, with the goal to address many of the UX shortcomings of our original system while improving the accuracy and depth of responses from the LLM. This new system will automatically select the appropriate tool to use based off the user’s question. Each tool will be capable of calling on any of the other tools available to the LLM, resulting in a collaborative pipeline where tools can pass data between other tools until enough data is received to generate an answer to the user’s question. Using a locally deployed, open-source, LLM, the NASA OCARI team, in collaboration with Collins Aerospace, will implement a prototype application that will bridge knowledge across multiple sources to assist System Engineers (SEs) with requirements discovery and tracing, research question and use case identification, and assumption validation. Such a system will also allow SEs to more easily, and intuitively, explore the AAM ecosystem, ultimately improving the efficiency and effectiveness of the SE's research and decision-making processes surrounding ConOps development and validation. In this session, our team will provide a video demonstration of our new prototype architecture in action. We will also present an overview of our prototype system architecture and talk about its advantages over traditional LLM deployments along with how those advantages can provide additional value to the field of System Engineering.

systems engineering↗

Semantic Web Data Discovery of Earth Science Data at NASA Goddard Earth Sciences Data and Information Services Center (GES DISC)

Mirador is a web interface for searching Earth Science data archived at the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). Mirador provides keyword-based search and guided navigation for providing efficient search and access to Earth Science data. Mirador employs the power of Google's universal search technology for fast metadata keyword searches, augmented by additional capabilities such as event searches (e.g., hurricanes), searches based on location gazetteer, and data services like format converters and data sub-setters. The objective of guided data navigation is to present users with multiple guided navigation in Mirador is an ontology based on the Global Change Master directory (GCMD) Directory Interchange Format (DIF). Current implementation includes the project ontology covering various instruments and model data. Additional capabilities in the pipeline include Earth Science parameter and applications ontologies.

Hegde, Mahabaleshwara↗

High-Energy Electromagnetic Offline Follow-Up of Ligo-Virgo Gravitational-Wave Binary Coalescence Candidate Events

We present two different search methods for electromagnetic counterparts to gravitational-wave (GW) events from ground-based detectors using archival NASA high-energy data from the Fermi Gamma-ray Burst Monitor (GBM) and RXTE All-sky Monitor (ASM) instruments. To demonstrate the methods, we use a limited number of representative GW background noise events produced by a search for binary neutron star coalescence over the last two months of the LIGO-Virgo S6/VSR3 joint science run. Time and sky location provided by the GW data trigger a targeted search in the high-energy photon data. We use two custom pipelines: one to search for prompt gamma-ray counterparts in GBM, and the other to search for a variety of X-ray afterglow model signals in ASM. We measure the efficiency of the joint pipelines to weak gamma-ray burst counterparts, and a family of model X-ray afterglows. By requiring a detectable signal in either electromagnetic instrument coincident with a GW event, we are able to reject a large majority of GW candidates. This reduces the signal-to-noise ratio of the loudest surviving GW background event by around 15-20 percent.

gamma-ray burst: general↗

NASA’s Unsteady Pressure-Sensitive Paint Research and Operational Capability Developments

To address challenges in the field of unsteady aerodynamics, NASA has developed a new state-of-the-art capability called Unsteady Pressure-Sensitive Paint (uPSP). It has been developed as an operational surface-pressure measurement capability for deployment in NASA’s AETC ground test facilities. Offering unprecedented spatiotemporal resolution, uPSP is an extremely powerful tool for investigating unsteady separated flows. To accelerate scientific discovery, uPSP data is to be processed and distributed as rapidly as it is acquired. Several demonstrations of the technology collected valuable data that has been used to develop data products, a robust processing pipeline, and other computational capabilities. A collection of papers documents the most recent research and development work on the uPSP technology and capability. This paper summarizes the current state of this effort at NASA.

Pressure-Sensitive Paint↗

NASA’s Unsteady Pressure-Sensitive Paint Research and Operational Capability Developments

To address challenges in the field of unsteady aerodynamics, NASA has developed a new stateof-the-art capability called Unsteady Pressure-Sensitive Paint (uPSP). It has been developed as an operational surface-pressure measurement capability for deployment in NASA’s AETC ground test facilities. Offering unprecedented spatiotemporal resolution, uPSP is an extremely powerful tool for investigating unsteady separated flows. To accelerate scientific discovery, uPSP data is to be processed and distributed as rapidly as it is acquired. Several demonstrations of the technology collected valuable data that has been used to develop data products, a robust processing pipeline, and other computational capabilities. A collection of papers documents the most recent research and development work on the uPSP technology and capability. This paper summarizes the current state of this effort at NASA.

Pressure-Sensitive Paint↗

Programable Pipelined-Image Processor

Computer serves as pipelined processor for imagery or other two-dimensional digital data. Processor does feature extraction, smoothing, edge detection, texture measurement, and stereoscoptic area correlation. Also plans routes for obstacle avoidance by robots and solves two-dimensional partial differential equations. Image processor consists of modular units: each includes set of computing elements of types particularly useful in pipelined-image processing. Flexible interconnection scheme used to route data to subsequent stages of pipeline.

Gennery, D. B.↗

Coronal Thermal Structure and Abundance of Super-Metal-Rich Late-Type Stars

This observation is for grating spectroscopy of 30 Ari, a late-type star with very high metallicity. The goal is to use extreme cases to help understand how abundances change from the photosphere to the corona. The only progress is to report to date is preparation for the analysis of the data. The SAO team has produced spectral model predictions for comparison with the observed spectra. The target was obtained by X-ray Multimirror Mission (XMM)-Newton on 2001 January 16 for 28000 sec. Pipeline processing is difficult and the data have not yet been available. Furthermore, we have been cautioned that the data cannot be correctly processed until at least September of this year, as there are problems with the RGS software to extract the spectrum. We have attended two workshops this summer in which results from XMM on late-type stellar coronae were presented including SMM results from GT team members. We noted that only members of the instrument teams are in a position to analyze XMM data.

Brickhouse, N.↗

TESS Data Release Notes: Sector 24, DR35

These Data Release Notes provide information on the processing and export of data from the Transiting Exoplanet Survey Satellite (TESS). The data products included in this data release are full frame images (FFIs), target pixel fi les, light curve fi les, collateral pixel fi les, cotrending basis vectors (CBVs), and Data Validation (DV) reports, time series, and associated xml fi les. These data products were generated by the TESS Science Processing Operations Center (SPOC, Jenkins et al., 2016) at NASA Ames Research Center from data collected by the TESS instrument, which is managed by the TESS Payload Operations Center (POC) at Massachusetts Institute of Technology (MIT). The format and content of these data products are documented in the Science Data Products Description Document (SDPDD). The SPOC science algorithms are based heavily on those of the Kepler Mission science pipeline, and are described in the Kepler Data Processing Handbook (Jenkins, 2019).2 The Data Validation algorithms are documented in Twicken et al. (2018) and Li et al. (2019). The TESS Instrument Handbook (Vanderspek et al., 2018) contains more information about the TESS instrument design, detector layout, data properties, and mission operations. The TESS Mission is funded by NASA's Science Mission Directorate.

TESS↗

TESS Data Release Notes: Sectors 1 – 13, Multi-Sector Search, DR20

These Data Release Notes provide information on the processing and export of data from the Transiting Exoplanet Survey Satellite (TESS). This data release is a combined, multi-sector transit search only. The underlying data products from individual observing sectors have been previously released. The data products included in this data release are the Data Validation (DV) reports, time series, and associated xml les for the threshold crossing events (TCEs) found by searching a combined data set including data from multiple observing sectors. These data products were generated by the TESS Science Processing Operations Center (SPOC, Jenkins et al., 2016) at NASA Ames Research Center from data collected by the TESS instrument, which is managed by the TESS Payload Operations Center (POC) at Massachusetts Institute of Technology (MIT). The format and content of these data products are documented in the Science Data Products Description Document (SDPDD)1. The SPOC science algorithms are based heavily on those of the Kepler Mission science pipeline, and are described in the Kepler Data Processing Handbook (Jenkins, 2017)2. The Data Validation algorithms are documented in Twicken et al. (2018) and Li et al. (2019). The TESS Instrument Handbook (Vanderspek et al., 2018)3 contains more information about the TESS instrument design, detector layout, data properties, and mission operations. The TESS Mission is funded by NASA's Science Mission Directorate.

Burke, Christopher J.↗

TESS Data Release Notes: Sector 21 DR 29

These Data Release Notes provide information on the processing and export of data from the Transiting Exoplanet Survey Satellite (TESS). The data products included in this data release are full frame images (FFIs), target pixel les, light curve les, collateral pixel les, cotrending basis vectors (CBVs), and Data Validation (DV) reports, time series, and associated xml les. These data products were generated by the TESS Science Processing Operations Center (SPOC, Jenkins et al., 2016) at NASA Ames Research Center from data collected by the TESS instrument, which is managed by the TESS Payload Operations Center (POC) at Massachusetts Institute of Technology (MIT). The format and content of these data products are documented in the Science Data Products Description Document (SDPDD)1. The SPOC science algorithms are based heavily on those of the Kepler Mission science pipeline, and are described in the Kepler Data Processing Handbook (Jenkins, 2017).2 The Data Validation algorithms are documented in Twicken et al. (2018) and Li et al. (2019). The TESS Instrument Handbook (Vanderspek et al., 2018) contains more information about the TESS instrument design, detector layout, data properties, and mission operations. The TESS Mission is funded by NASA's Science Mission Directorate.

Michael M. Fausnaugh↗

TESS Data Release Notes: Sectors 1 – 36, Multi-sector Search, DR53

These Data Release Notes provide information on the processing and export of data from the Transiting Exoplanet Survey Satellite (TESS). This data release is a combined, multi-sector transit search only. The underlying data products from individual observing sectors have been previously released. The data products included in this data release are the Data Validation (DV) reports, time series, and associated xml files for the threshold crossing events (TCEs) found by searching a combined data set including data from multiple observing sectors. These data products were generated by the TESS Science Processing Operations Center (SPOC, Jenkins et al., 2016) at NASA Ames Research Center from data collected by the TESS instrument, which is managed by the TESS Payload Operations Center (POC) at Massachusetts Institute of Technology (MIT). The format and content of these data products are documented in the Science Data Products Description Document (SDPDD)1. The SPOC science algorithms are based heavily on those of the Kepler Mission science pipeline, and are described in the Kepler Data Processing Handbook (Jenkins, 2020)2. The Data Validation algorithms are documented in Twicken et al. (2018) and Li et al. (2019). The TESS Instrument Handbook (Vanderspek et al., 2018) contains more information about the TESS instrument design, detector layout, data properties, and mission operations. The TESS Mission is funded by NASA's Science Mission Directorate.

TESS↗

Real time pipelined system for forming the sum of products in the processing of video data

A 3-by-3 convolver utilizes 9 binary arithmetic units connected in cascade for multiplying 12-bit binary pixel values P sub i which are positive or two's complement binary numbers by 5-bit magnitide (plus sign) weights W sub i which may be positive or negative. The weights are stored in registers including the sign bits. For a negative weight, the one's complement of the pixel value to be multiplied is formed at each unit by a bank of 17 exclusive or gates G sub i under control of the sign of the corresponding weight W sub i, and a correction is made by adding the sum of the absolute values of all the negative weights for each 3-by-3 kernel. Since this correction value remains constant as long as the weights are constant, it can be precomputed and stored in a register as a value to be added to the product PW of the first arithmetic unit.

Wilcox, Brian↗

Status of the TESS Science Processing Operations Center

The Transiting Exoplanet Survey Satellite (TESS) science pipeline is being developed by the Science Processing Operations Center (SPOC) at NASA Ames Research Center based on the highly successful Kepler Mission science pipeline. Like the Kepler pipeline, the TESS science pipeline will provide calibrated pixels, simple and systematic error-corrected aperture photometry, and centroid locations for all 200,000+ target stars, observed over the 2-year mission, along with associated uncertainties. The pixel and light curve products are modeled on the Kepler archive products and will be archived to the Mikulski Archive for Space Telescopes (MAST). In addition to the nominal science data, the 30-minute Full Frame Images (FFIs) simultaneously collected by TESS will also be calibrated by the SPOC and archived at MAST. The TESS pipeline will search through all light curves for evidence of transits that occur when a planet crosses the disk of its host star. The Data Validation pipeline will generate a suite of diagnostic metrics for each transit-like signature discovered, and extract planetary parameters by fitting a limb-darkened transit model to each potential planetary signature. The results of the transit search will be modeled on the Kepler transit search products (tabulated numerical results, time series products, and pdf reports) all of which will be archived to MAST.

high performance computing↗

Kepler Planet Detection Metrics: Per-Target Detection Contours for Data Release 25

A necessary input to planet occurrence calculations is an accurate model for the pipeline completeness (Burke et al., 2015). This document describes the use of the Kepler planet occurrence rate products in order to calculate a per-target detection contour for the measured Data Release 25 (DR25) pipeline performance. A per-target detection contour measures for a given combination of orbital period, Porb, and planet radius, Rp, what fraction of transit signals are recoverable by the Kepler pipeline (Twicken et al., 2016; Jenkins et al., 2017). The steps for calculating a detection contour follow the procedure outlined in Burke et al. (2015), but have been updated to provide improved accuracy enabled by the substantially larger database of transit injection and recovery tests that were performed on the final version (i.e., SOC 9.3) of the Kepler pipeline (Christiansen, 2017; Burke Catanzarite, 2017a). In the following sections, we describe the main inputs to the per-target detection contour and provide a worked example of the python software released with this document (Kepler Planet Occurrence Rate Tools KeplerPORTs)1 that illustrates the generation of a detection contour in practice. As background material for this document and its nomenclature, we recommend the reader be familiar with the previous method of calculating a detection contour (Section 2 of Burke et al.,2015), input parameters relevant for describing the data quantity and quality of Kepler targets (Burke Catanzarite, 2017b), and the extensive new transit injection and recovery tests of the Kepler pipeline (Christiansen et al., 2016; Burke Catanzarite, 2017a; Christiansen, 2017).

Planet Detection Metrics↗

Thermal Performance of Cryogenic Piping Multilayer Insulation in Actual Field Installations

A standardized way of comparing the thermal performance of different pipelines in different sizes is needed. Vendor data for vacuum-insulated piping are typically given in heat leak rate per unit length (W/m) for a specific diameter pipeline. An overall k-value for actual field installations (k(sub oafi)) is therefore proposed as a more generalized measure for thermal performance comparison and design calculation. The k(sub oafi) provides a direct correspondence to the k-values reported for insulation materials and illustrates the large difference between ideal multilayer insulation (MLI) and actual MLI performance. In this experimental research study, a section of insulated piping was tested under cryogenic vacuum conditions, including simulated spacers and bending. Several different insulation systems were tested using a 1-meter-long cylindrical cryostat test apparatus. The simulated spacers tests showed significant degradation in the thermal performance of a given insulation system. An 18-meter-long pipeline test apparatus is now in operation at the Cryogenics Test Laboratory, NASA Kennedy Space Center, for conducting liquid nitrogen thermal performance tests.

Fesmire, J.↗

A Complete UV Atlas of Standard Stars

The general objective of this project is to provide a comprehensive ultraviolet spectral atlas of stars based on the data in the IUE Final Archive. The data have been uniformly processed by the IUE NEWSIPS pipeline system. The NEWSIPS gives an improved signal to noise ratio for the data that will be especially beneficial to low signal portions of the spectra; and it also gives more accurate fluxes for stars by adopting the latest absolute calibration and measured instrument parameters. Our new atlas, therefore, provides more uniform and accurate data than the version previously published. In addition, considerable efforts were devoted to obtain reliable spectral types, V,B-V, and E(B-V). Our goal is to provide the information which can be used with reasonable confidence by scientists.

Wu, Chi-Chao↗

Probing the Reverse Shock in an Oxygen-Rich Supernova Remnant

The aim of this proposal was to examine the O VI emission at three positions around the X-ray bright ring of the remnant in order to investigate the relation between the O VI emission, the X-ray O VII and O VIII emission, and the optical [O III] emission, and how these vary around the rim of the remnant. So far, only one Far Ultraviolet Spectroscopic Explorer (FUSE) pointing at the remnant has been performed, together with the background pointing needed to subtract the diffuse scattered light from nearby O stars. The data were recalibrated using the CalFUSE calibration pipeline, but the emission is faint enough that hand processing will be needed for this pointing to optimize the extraction. Rereduction of the data is underway. A postdoctoral fellow has also been hired, and in part she will assist with the analysis.

Sonneborn, George↗