Optimization of Detector-Preamplifier for Cryogenic Spectrometry

The design optimization of the detector-preamplifier subsystem is critical to the achievement of sensitive infrared spectrometers. The application illustrated is for cryogenically- cooled detectors, but the optimal approach based upon an operational preamplifier is general for detector operation under background limited conditions

Evaluation and attribution of OCO-2 XCO_2 uncertainties

Evaluating and attributing uncertainties in total column atmospheric CO_2 measurements (XCO_2) from the OCO-2 instrument is critical for testing hypotheses related to the underlying processes controlling XCO_2 and for developing quality flags needed to choose those measurements that are usable for carbon cycle science. Here we test the reported uncertainties of version 7 OCO-2 XCO_2 measurements by examining variations of the XCO_2 measurements and their calculated uncertainties within small regions (∼  100 km  ×  10.5 km) in which natural CO_2 variability is expected to be small relative to variations imparted by noise or interferences. Over 39 000 of these small neighborhoods comprised of approximately 190 observations per neighborhood are used for this analysis. We find that a typical ocean measurement has a precision and accuracy of 0.35 and 0.24 ppm respectively for calculated precisions larger than  ∼  0.25 ppm. These values are approximately consistent with the calculated errors of 0.33 and 0.14 ppm for the noise and interference error, assuming that the accuracy is bounded by the calculated interference error. The actual precision for ocean data becomes worse as the signal-to-noise increases or the calculated precision decreases below 0.25 ppm for reasons that are not well understood. A typical land measurement, both nadir and glint, is found to have a precision and accuracy of approximately 0.75 and 0.65 ppm respectively as compared to the calculated precision and accuracy of approximately 0.36 and 0.2 ppm. The differences in accuracy between ocean and land suggests that the accuracy of XCO2 data is likely related to interferences such as aerosols or surface albedo as they vary less over ocean than land. The accuracy as derived here is also likely a lower bound as it does not account for possible systematic biases between the regions used in this analysis

Using Machine Learning to Reduce Observational Biases When Detecting New Impacts on Mars

The current inventory of recent (fresh) impacts on Mars shows a strong bias towards areas of low thermal inertia. These areas are generally visually bright, and impacts create dark scours and rays that make them easier to detect. It is expected that impacts occur at a similar rate in areas of higher thermal inertia, but those impacts are under-detected. This study investigates the use of a trained machine learning classifier to increase the detection of fresh impacts on Mars using CTX data. This approach discovered 69 new fresh impacts that have been confirmed with follow-up HiRISE images. We found that examining candidates partitioned by thermal inertia (TI) values, which is only possible due to the large number of machine learning candidates, helps reduce the observational bias and increase the number of known high-TI impacts.Comment: 17 pages, 10 figures, 2 tables (Author's preprint, accepted version

The IPAC Image Subtraction and Discovery Pipeline for the intermediate Palomar Transient Factory

We describe the near real-time transient-source discovery engine for the intermediate Palomar Transient Factory (iPTF), currently in operations at the Infrared Processing and Analysis Center (IPAC), Caltech. We coin this system the IPAC/iPTF Discovery Engine (or IDE). We review the algorithms used for PSF-matching, image subtraction, detection, photometry, and machine-learned (ML) vetting of extracted transient candidates. We also review the performance of our ML classifier. For a limiting signal-to-noise ratio of 4 in relatively unconfused regions, "bogus" candidates from processing artifacts and imperfect image subtractions outnumber real transients by ~ 10:1. This can be considerably higher for image data with inaccurate astrometric and/or PSF-matching solutions. Despite this occasionally high contamination rate, the ML classifier is able to identify real transients with an efficiency (or completeness) of ~ 97% for a maximum tolerable false-positive rate of 1% when classifying raw candidates. All subtraction-image metrics, source features, ML probability-based real-bogus scores, contextual metadata from other surveys, and possible associations with known Solar System objects are stored in a relational database for retrieval by the various science working groups. We review our efforts in mitigating false-positives and our experience in optimizing the overall system in response to the multitude of science projects underway with iPTF.Comment: 66 pages, 21 figures, 7 tables, accepted by PAS

Mars Image Content Classification: Three Years of NASA Deployment and Recent Advances

The NASA Planetary Data System hosts millions of images acquired from the planet Mars. To help users quickly find images of interest, we have developed and deployed content-based classification and search capabilities for Mars orbital and surface images. The deployed systems are publicly accessible using the PDS Image Atlas. We describe the process of training, evaluating, calibrating, and deploying updates to two CNN classifiers for images collected by Mars missions. We also report on three years of deployment including usage statistics, lessons learned, and plans for the future.Comment: Published at the Thirty-Third Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-21). IAAI Innovative Application Award. 10 pages, 11 figures, 6 table

Onboard Science Instrument Autonomy for the Detection of Microscopy Biosignatures on the Ocean Worlds Life Surveyor

The quest to find extraterrestrial life is a critical scientific endeavor with civilization-level implications. Icy moons in our solar system are promising targets for exploration because their liquid oceans make them potential habitats for microscopic life. However, the lack of a precise definition of life poses a fundamental challenge to formulating detection strategies. To increase the chances of unambiguous detection, a suite of complementary instruments must sample multiple independent biosignatures (e.g., composition, motility/behavior, and visible structure). Such an instrument suite could generate 10,000x more raw data than is possible to transmit from distant ocean worlds like Enceladus or Europa. To address this bandwidth limitation, Onboard Science Instrument Autonomy (OSIA) is an emerging discipline of flight systems capable of evaluating, summarizing, and prioritizing observational instrument data to maximize science return. We describe two OSIA implementations developed as part of the Ocean Worlds Life Surveyor (OWLS) prototype instrument suite at the Jet Propulsion Laboratory. The first identifies life-like motion in digital holographic microscopy videos, and the second identifies cellular structure and composition via innate and dye-induced fluorescence. Flight-like requirements and computational constraints were used to lower barriers to infusion, similar to those available on the Mars helicopter, "Ingenuity." We evaluated the OSIA's performance using simulated and laboratory data and conducted a live field test at the hypersaline Mono Lake planetary analog site. Our study demonstrates the potential of OSIA for enabling biosignature detection and provides insights and lessons learned for future mission concepts aimed at exploring the outer solar system.Comment: 49 pages, 18 figures, submitted to The Planetary Science Journal on 2023-04-2

1990: Abilene Christian College Bible Lectures - Full Text

LUKE: A GOSPEL FOR THE WORLD Being the Abilene Christian University Annual Bible Lectures 1990 Published by ACU PRESS 1634 Campus Court Abilene, Texas 7960

Corn Cob Residue Carbon and Nutrient Dynamics during Decomposition

The cob fraction of corn (Zea mays L.) residue has characteristics that reduce concerns associated with residue removal making it a potential biofuel feedstock. The contribution the cob makes to soil C and nutrient dynamics is unknown. A litterbag study was conducted in no-tillage plots under irrigated and rain fed conditions in eastern Nebraska. Litterbags containing cobs were placed in corn rows on the soil surface or vertically in the 0- to 10-cm soil depth following grain harvest and collected aft er 63, 122, 183, 246, 304, and 370 d. Samples were analyzed for dry matter, C, N, P, K, S, Ca, Mg, Fe, Mn, Cu, and Zn. Dry matter loss was greater for buried (59% loss rain fed site vs. 64% irrigated site) than surface cobs (49% loss rain fed site vs. 42% irrigated site). Cob N, P, S, content did not change over the duration of the study and these nutrients would play a limited role in nutrition for the subsequent crop. Cob K content declined exponentially over the study suggesting that cob K would be available to the subsequent crop. Cob Ca, Mg, Zn, Fe, Mn, and Cu content increased during the study representing immobilization. With the exception of K, nutrients contained in the cob are immobilized the year following harvest and play a minor role in mineral nutrition of the subsequent crop. As cellulosic conversion technology becomes available cobs represent a feedstock that can be harvested with minor effect on crop nutrient availability