The Pandora SmallSat is a NASA flight project aimed at studying the atmospheres of exoplanets-planets orbiting stars outside our Solar System. Pandora will provide the first dataset of simultaneous, multiband (visible and NIR), long-baseline observations of exoplanets and their host stars. Pan-dora is an ambitious project that will fly a 0.44 m telescope in a small form factor. To achieve the scientific goals, the mission requires a departure from the traditional cost-schedule paradigm of half-meter-class observatories. Pandora achieves this by leveraging existing capabilities that necessitate minimal engineering development, disruptive and agile management, trusted partnerships with vendors, and strong support from the lead institutions. The Pandora team has developed a suite of high-fidelity parameterized simulation and modeling tools to estimate the performance of both imaging channels. This has enabled a unique bottom-up approach to deriving trades and system requirements. Pandora is a partnership between NASA and Lawrence Livermore National Laboratory. The project completed its Critical Design Review in October 2023 and is slated for launch into Sun-synchronous, low-Earth orbit in Fall 2025.

Stellar occultation observations from space can probe the stratosphere and mesosphere at a fine vertical scale around the globe. Unlike other measurement techniques like radiosondes and aircraft, stellar occultation has the potential to observe the atmosphere above 30 km, and unlike radio occultation, stellar occultation probes fine-scale phenomena with potential to observe atmospheric turbulence. We imaged the refractive bending angle of a star centroid for a series of occultations by the atmosphere. Atmospheric refractivity, density, and then temperature are retrieved from the bending observations with the Abel transformation and Edlen's law, the hydrostatic equation, and the ideal gas law. The retrieval technique is applied to data collected by two nanosatellites operated by Terran Orbital. Measurements were primarily taken by the GEOStare SV2 mission, with a dedicated imaging telescope, supplemented with images captured by spacecraft bus sensors, namely the star trackers on other Terran Orbital missions. The bending angle noise floor is 10 and30 arcsec for the star tracker and GEOStare SV2 data, respectively. The most significant sources of uncertainty are due to centroiding errors due to the fairly low-resolution stellar images and telescope pointing knowledge derived from noisy satellite attitude sensors. The former mainly affects the star tracker data, while the latter limits the GEOStare SV2 accuracy, with both providing low vertical resolution. This translates to a temperature profile retrieval up to roughly 20 km for both star tracker and GEOStare SV2 datasets. In preparation of an upcoming 2023 mission designed to correct these deficiencies, SOHIP, we simulated bending angle measurements with varying magnitudes of error. The expected maximum altitude of retrieved temperature is 41 km on average for these simulated measurements with a noise floor of 0.39 arcsec. Our work highlights the capabilities of stellar occultation observations from nanosatellites for atmospheric sounding. Future work will investigate high-frequency observations of atmospheric gravity waves and turbulence, mitigating the major uncertainties observed in these datasets.

We present the final design, environmental testing, and launch history of MiniCarb, a 6U CubeSat developed through a partnership between NASA Goddard Space Flight Center and Lawrence Livermore National Laboratory. MiniCarb’s science payload, developed at Goddard, was an occultation-viewing, passive laser heterodyne radiometer for observing methane, carbon dioxide, and water vapor in Earth’s atmosphere at ∼1.6 µm s −1 . MiniCarb’s satellite, developed at Livermore, implemented their CubeSat Next Generation Bus plug-and-play architecture to produce a modular platform that could be tailored to a range of science payloads. Following the launch on 5 December 2019, MiniCarb traveled to the International Space Station and was set into orbit on 1 February 2020 via Northrop Grumman’s Cygnus capsule which deployed MiniCarb with tipoff rotation of about 20° s −1 (significantly higher than the typical rate of 3° s −1 from prior CubeSats), from which the attitude control system was unable to recover resulting in a loss of power. In spite of this early failure, MiniCarb had many successes including rigorous environmental testing, successful deployment of its solar panels, and a successful test of the radio and communication through the Iridium network. This prior work and enticing cost (approximately $2 M for the satellite and $250 K for the payload) makes MiniCarb an ideal candidate for a low-cost and rapid rebuild as a single orbiter or constellation to globally observe key greenhouse gases.

Pulse shape discrimination (PSD) is the task of classifying electronic pulse shapes for different particle types such as gamma rays and fast neutrons interacting in scintillators and read out by photo sensitive detectors. This field has been limited in its adoption of techniques found in the statistical learning community. Methods initially employed in the 1960s for analog electronic circuitry persist in the current PSD literature describing operations performed on digitized pulses, which are amenable to statistical rigor. Despite vast amounts of data collected at low energy levels, traditional PSD methods are unable to discriminate particles below a certain threshold. In this work, Gaussian mixture models (GMMs) are used as a clustering technique for fast neutron detection in the absence of labeled data. GMMs yield improvements spanning the energy spectrum in a desirably efficient, unsupervised fashion. An extension, the Dirichlet Process GMM, provides further flexibility and classification improvement.

A Gaussian Mixture Model (GMM) based machine learning algorithm has been applied to the problem of gamma/neutron pulse shape discrimination (PSD). The algorithm has been successfully implemented on a standard PC as well as a field programmable gate array (FPGA). Here we describe the GMM classifier and its implementation on these two different types of hardware. We compare the performance of the algorithm on these two platforms against each other, along with other standard techniques applied in PSD. Our results show that the FPGA-based GMM classifier outperforms the standard PSD techniques in terms of classification accuracy at low particle energy and executes more quickly than its CPU-based counterpart.

In this paper, we present novel methods for automated pulse shape discrimination. The classifiers are trained using simple radionuclide sources and do not require ground truth labeling. We test their performance using labels derived from time of flight experiments and present the results in terms of energy-dependent Receiver Operating Characteristic (ROC) curves. In addition, we also train and test standard pulse shape discrimination methods on the same data for comparison. We find multiple methods that can yield similar false neutron and true neutron rates at 24 keVee as tail-to-total or Gatti's optimal linear filter yield at 54 keVee.

A precise measurement of the Cosmic X-ray Background (CXB) is crucial for constraining models of the evolution and composition of the universe. While several large, expensive satellites have measured the CXB as a secondary mission, there is still disagreement about normalization of its spectrum. The Cosmic X-ray Background NanoSat (CXBN) is a small, low-cost satellite whose primary goal is to measure the CXB over its two-year lifetime. Benefiting from a low instrument-induced background due to its small mass and size, CXBN will use a novel, pixelated Cadmium Zinc Telluride (CZT) detector with energy resolution < 1 keV over the range 1-60 keV to measure the CXBN with unprecedented accuracy. This paper describes CXBN and its science payload, including the GEANT4 model that has been used to predict overall performance and the backgrounds from secondary particles in Low Earth Orbit. It also addresses the strategy for scanning the sky and calibrating the data, and presents the expected results over the two-year mission lifetime.

The Space-based Telescopes for Actionable Refinement of Ephemeris (STARE) program will collect the information needed to help satellite operators avoid collisions in space by using a network of nanosatellites to determine more accurate trajectories for selected space objects orbiting the Earth. In the first phase of the STARE program, two pathfinder cube-satellites (CubeSats) equipped with an optical imaging payload are being developed and deployed to demonstrate the main elements of the STARE concept. We first give an overview of the STARE program. The details of the optical imaging payload for the STARE pathfinder CubeSats are then described, followed by a description of the track detection algorithm that will be used on the images it acquires. Finally, simulation results that highlight the effectiveness of the mission are presented. (C) 2012 Society of Photo-Optical Instrumentation Engineers (SPIE). [DOI: 10.1117/1.OE.51.1.011004]

: Galaxy 15, a geostationary telecommunications satellite, ceased responding to ground operators commands on April 5, 2010. The satellite went into eastward longitudinal drift interfering with operation of other geostationary satellites. Before and after the control of Galaxy 15 was re-established on December 27, 2010, some optical observations of the satellite were conducted using a small-aperture telescope, at sidereal tracking rate, to derive more accurate orbital parameters for Galaxy 15. Here we present the optical imagery for Galaxy 15 satellite and analyze the data. Applying state-of-the-art streak extraction algorithm, and using batch/sequential least squares orbital fitting, we quantify the orbital improvement derived from these data. In addition, we study photometric signatures of Galaxy 15 in both attitude-controlled and uncontrolled regimes, comparing the light curves. This case study demonstrates the usefulness of optical observations by smallaperture telescopes for improving the orbits of GEO satellites.

An algorithm for determining satellite track end points with subpixel resolution in spaced-based images is presented. The algorithm allows for significant curvature in the imaged track due to rotation of the spacecraft capturing the image. The motivation behind the subpixel end point determination is first presented, followed by a description of the methodology used. Results from running the algorithm on real ground-based and simulated spaced-based images are shown to highlight its effectiveness.

The Space-based Telescopes for Actionable Refinement of Ephemeris (STARE) program will collect the information needed to help satellite operators avoid collisions in space by using a network of nano-satellites to determine more accurate trajectories for selected space objects orbiting the Earth. In the first phase of the STARE program, two pathfinder cube-satellites (CubeSats) equipped with an optical imaging payload are being developed and deployed to demonstrate the main elements of the STARE concept. In this paper, we first give an overview of the STARE program. We then describe the details of the optical imaging payload for the STARE pathfinder CubeSats, including the optical design and the sensor characterization. Finally, we discuss the track detection algorithm that will be used on the images acquired by the payload.

We report on long exposure results obtained with a Teledyne HyViSI H2RG detector operating in guide mode. The sensor simultaneously obtained nearly seeing-limited data while also guiding the Kitt Peak 2.1 m telescope. Results from unguided and guided operation are presented and used to place lower limits on flux/fluence values for accurate centroid measurements. We also report on significant noise reduction obtained in recent laboratory measurements that should further improve guiding capability with higher magnitude stars.