Astronomers have been measuring the separations and position angles between the two components of binary stars since William Herschel began his observations in 1781. In 1970, Anton Labeyrie pioneered a method, speckle interferometry, that overcomes the usual resolution limits induced by atmospheric turbulence by taking hundreds or thousands of short exposures and reducing them in Fourier space. Our 2022 automation of speckle interferometry allowed us to use a fully robotic 1.0-meter PlaneWave Instruments telescope, located at the El Sauce Observatory in the Atacama Desert of Chile, to obtain observations of many known binaries with established orbits. The long-term objective of these observations is to establish the precision, accuracy, and limitations of this telescope's automated speckle interferometry measurements. This paper provides an early overview of the Known Binaries Project and provide example results on a small-separation (0.27") binary, WDS 12274-2843 B 228.

Stella Spendida is a project to develop the science and engineering workforce of the 21st century by bringing science missions into the undergraduate classroom.

CubeSat astronomical telescopes could become a major contributor to research. Four programs could enhance this evolutionary process: (1) an autonomous telescope research constellation, (2) advancing telescope technology, (3) encouraging CubeSat telescope commercial ventures, and (4) developing a supportive community of practice.

A team of nine students from Cuesta College studied double star STF 2128 (WDS 17033+5935) using ten CCD images obtained at the Sierra Remote Observatories. Calculations of these ten observations yielded an average separation of 12.203′′ and an average position angle of 42.957°. By comparing these values with past observations from the Washington Double Star Catalogue, we concluded that STF 2128 is likely a true binary system. Vol. 14 No. 1 January 1, 2018 Page 55 Journal of Double Star Observations Exploring the Binary Nature of STF 2128 Using Separation and Position Angle Measurements Equipment and Procedures Measurements of STF 2128 were obtained using the charge-coupled device camera (CCD) astrometry method. CCD astrometry of double stars typically involves 1 second or longer exposures and a field of view (FOV) of several arcminutes to produce high-resolution images of the target star and surrounding stars. Ten images of STF 2128 were captured on April 9, 2017 at the Sierra Remote Observatories (SRO) (shown in Figure 3). SRO is located 4,610 feet above sea level in California’s Sierra Nevada Mountains, providing prime atmospheric conditions and preventing the interference of fog and the thermal inversion layer. A PlaneWave Instruments CDK17 Astrograph telescope was utilized to obtain these measurements (shown in Figure 4). The CDK17 is a 17 inch (0.43 meter), f/6.8 corrected Dall-Kirkham model. It encompasses a 70 millimeter field view without any field curFigure 2: (left) Friedrich Georg Wilhelm von Struve and (right) William Herschel and his sister, Caroline. Figure 1: Orbital plot of past 69 observations made on STF 2128 provided by Brian Mason of the United States Naval Observatory (USNO). Vol. 14 No. 1 January 1, 2018 Page 56 Journal of Double Star Observations Exploring the Binary Nature of STF 2128 Using Separation and Position Angle Measurements vature, off-axis coma, or astigmatism. The camera used to obtain the ten images was an Apogee Alta F16M front illuminated KAF-16803 monochrome CCD camera. This camera has a 16.8 Megapixel sensor with 9 mm pixels. There were no filters used when capturing the ten images. Astrometric plate-solving was done on Astrometry.net, in order to transform the pixel (X, Y) coordinates into celestial (RA, Dec) coordinates for each image. The aperture size was reduced, then the team obtained calculated values for the separation and position angle of STF 2128 Then, the ten images were uploaded onto AstroImageJ, a downloadable imaging program. We obtained calculated values for the separation and position angle of STF 2128. These measurements from all ten images were averaged using the statistical software, Minitab. We constructed a position angle versus time graph, as well as a separation versus time graph from our new data and data from past observations.

Investigations of the main asteroid belt and efforts to constrain that population’s physical characteristics involve the daunting task of studying hundreds of thousands of small bodies. Taxonomic systems are routinely employed to study the large-scale nature of the asteroid belt because they utilize common observational parameters, but asteroid taxonomies only define broadly observable properties and are not compositionally diagnostic. This work builds upon the results of work by Hardersen et al., which has the goal of constraining the abundance and distribution of basaltic asteroids throughout the main asteroid belt. We report on the near-infrared (NIR: 0.7 to 2.5 μ m) reflectance spectra, surface mineralogical characterizations, analysis of spectral band parameters, and meteorite analogs for 33 V p asteroids. NIR reflectance spectroscopy is an effective remote sensing technique to detect most pyroxene group minerals, which are spectrally distinct with two very broad spectral absorptions at ∼0.9 and ∼1.9 μ m. Combined with the results from Hardersen et al., we identify basaltic asteroids for ∼95% (39/41) of our inner-belt V p sample, but only ∼25% (2/8) of the outer-belt V p sample. Inner-belt basaltic asteroids are most likely associated with (4) Vesta and represent impact fragments ejected from previous collisions. Outer-belt V p asteroids exhibit disparate spectral, mineralogical, and meteorite analog characteristics and likely originate from diverse parent bodies. The discovery of two additional likely basaltic asteroids provides additional evidence for an outer-belt basaltic asteroid population.

In 1998, based exclusively on visual observations, Wulff Heintz calculated an orbital path, with a period of 234 years and a semi-major axis of 0.55ʺ, and determined that WDS 18224+4545 (A700) was a gravitationally bound binary star system. The research presented here disputes this and presents an optical double star system solution with a linear trajectory. Using data from the first speckle interferometry observation for A700, collected on October 22, 2013, with the 2.1-meter telescope at Kitt Peak National Observatory (KPNO), new separation and position angles were determined to be 0.623ʺ and 127.872°, respectively. These results diverge radically from Heintz’s predicted orbit, and when correlated with past observational data, they follow a linear trend that returns a fit with an R value of 0.977. New calculations predict a minimum separation of 0.076ʺ in the year 1953. Figure 1. Team picture From left to right: Russell M. Genet, Jordan Steed, Ian R. Parent, Michelle Williams, Daniel Orman, Ellery Conover, S. Taylor Vaughn, Nels Siverson, Jessica C. Gardella,

Recent advances in high-speed low-noise CCD and CMOS cameras, coupled with breakthroughs in data reduction software that runs on desktop PCs, has opened the domain of speckle interferometry and high-accuracy CCD measurements of double stars to amateurs, allowing them to do useful science of high quality. This paper describes how to use a speckle interferometry reduction program, the Speckle Tool Box (STB), to achieve this level of result. For over a year the author (Harshaw) has been using STB (and its predecessor, Plate Solve 3) to obtain measurements of double stars based on CCD camera technology for pairs that are either too wide (the stars not sharing the same isoplanatic patch, roughly 5 arc-seconds in diameter) or too faint to image in the coherence time required for speckle (usually under 40ms). This same approach using speckle reduction software to measure CCD pairs with greater accuracy than possible with lucky imaging has been used, it turns out, for several years by the U. S. Naval Observatory. Vol. 13 No. 1 January 1, 2017 Page 53 Journal of Double Star Observations The Speckle Toolbox: A Powerful Data Reduction Tool for CCD Astrometry an integration time (shutter speed) that is as short as possible to help “freeze” the star image during moments of superb seeing. This file of images is then processed by selecting a small percentage of the best frames based on different criteria best signal-to-noise ratio (where frame selection is based on the ratio of the signal-to-noise versus star density and is best used on noisy frames), or best maximum (where frame selection is based on the strength of the central concentration of light in the star’s image, best used with low-noise frames and files with small star images). There may be other options available, depending on the software package used to select the frames used for a lucky image. Once the frames have been selected, they can be “aligned and stacked”, meaning the software will recenter each frame based on the centroid of the primary star. All selected frames are then blended into one final image, which often shows very clean stars that are easy to measure. However, lucky imaging suffers from the fact that it is very difficult to use with accuracy on very close pairs (closer than about 5 arc seconds) or where one (or both) of the stars is bright, resulting in large star images that may overlap or in which it may be difficult to determine the centroids. Yet this is the domain where most of the interest lies in visual double star astrometry. It also requires fairly bright stars in order to get shutter speeds fast enough to freeze the star images. This is where speckle data reduction software can be of immense help. Over the last 16 months, I have been gathering data on hundreds of double stars (with these observations being reported in this Journal) using two cameras—a Skyris 618C color CCD, and most recently, the ZWO ASI290 monochrome CMOS camera. I have been reducing my data and making measurements using a speckle reduction program written by David Rowe, chief technical officer at PlainWave Instruments. The original program provided to me by Rowe was called Plate Solve 3, and was a robust multipurpose program that did many things besides speckle reduction. About six months ago, Rowe released a special sub-set of Plate Solve, called Speckle Tool Box (STB for short in this paper). I will describe in this paper how to use STB to do accurate astrometry on close double stars, whether with speckle interferometry or CCD imaging, and explain how to obtain a free copy for use in your own observing program. If you have ever requested data from the Washington Double Star Catalog for a particular pair of stars, the reply you got included a text file titled “datarequest_key”. If you read that file, you will find a translation key for the methods used to report measurements. Two of those codes are Cu and Su, which are described in the datarequest_key file as “USNO CCD imaging (speckle-style reduction).” (The C and S refer to two different cameras used for the data collection.) Wanting to be sure if this method was like the one I have been using, I wrote Brian Mason at the USNO and asked him about this method. It is, indeed, the method I have been using, in which a CCD image of a double star is analyzed using speckle reduction software in order to obtain more precise measurements than possible with lucky imaging. Mason (2007) writes, “Most of the systems observed with this camera (the “Cu” camera at the U. S. Naval Observatory in Washington, D. C.) have separations well beyond the regime in which there is any expectation of isoplanicity, so we classify the observing technique for all of these measures as just “CCD astrometry,” rather than speckle interferometry. Despite this classification, there is an expectation that the resulting measurements have smaller errors than classical CCD astrometry. Each measurement is the result of many hundreds of correlations per frame, and up to several thousand frames per observation.” 2. How Speckle Reduction Software Works The Speckle Tool Box does speckle reduction by working on a FITS cube. A FITS cube is a set of FITS images bound into a single file. Normally, one should use several hundred to several thousand FITS images and bind them into a FITS cube. Since most camera control software simply captures FITS images and does not bind them into cubes, STB does that for you (I will explain the menu of processes later). Once the FITS cubes have been compiled, it is best to pre-process the cubes. This is not a requirement in STB, but it does make for much faster solutions when it is time to do the speckle reduction. Pre-processing consists of STB reading each frame in the FITS cube and then computing its power spectrum using a Fourier transform and then taking the squared modulus of each complex pixel value. The frames are then averaged and saved as a file with a special suffix (_PSD) added to the file name. During speckle reduction, a processed file is loaded into STB and the power spectrum is then graphically displayed on screen as an autocorellogram. See Figure 1. Note that the autocorellogram displays radial symmetry. It is not an actual image of the double star, but rather a graphical portrayal of the two-dimensional autocorellation of the averaged power spectrum. The symmetric nature of the display results from the fact that autocorellation of any real function is inherently symVol. 13 No. 1 January 1, 2017 Page 54 Journal of Double Star Observations The Speckle Toolbox: A Powerful Data Reduction Tool for CCD Astrometry metrical. Rowe is working on a new feature for STB that can generate a recovered, high-resolution image by a method called Bispectrum analysis, which is extremely demanding on the computer’s processor, normally requiring a special co-processor to be installed to allow the program to generate a solution in a reasonable amount of time. (Currently, bispectrum analysis is done mostly on high-speed mainframes, where it is still a time consuming process.) So how does one use STB to generate autocorellograms that can then be measured with higher precision that lucky imaging? 3. Using The Speckle Tool Box to Make FITS Cubes The Speckle Tool Box home screen is shown in Figure 2. At the top of the screen is a list of commands and below that, a palette of tool icons. I will explain each part of STB in detail and show how each part contributes to an astrometric solution. To make FITS cubes, click on TOOLS, then from the drop-down menu, select “Make FITS cube(s)...” A dialog box will appear, as shown in Figure 3. The instructions in the “Operation” window detail how to select files for binding into Cubes. STB allows you to specify whether the original files are monochrome or color and even allow the user to crop the files to a uniform size. Since STB works best on images that have dimensions that are a power of two (256 x 256 and 512 x 512 being the norms), this is an important feature. 4. Doing a Drift Calibration with STB A drift calibration is done by clicking on TOOLS and selecting “Drift Calibration Analysis...” This opens a powerful feature of STB: a simple way to determine the camera’s angle with reference to true north as well as the pixel scale for the camera (how many arc seconds each pixel spans). To obtain drift files for analysis, you must select a bright star near the meridian and at a medium declinaFigure 1. An autocorellogram generated by STB. Figure 3: Dialog box to make FITS cubes Figure 2: The home screen of The Speckle Toolbox Vol. 13 No. 1 January 1, 2017 Page 55 Journal of Double Star Observations The Speckle Toolbox: A Powerful Data Reduction Tool for CCD Astrometry tion (between 30° and 60° works best, but any declination will work). Jot down the declination of the star for use later. Use the telescope’s slow motion controls to nudge the star just off the east edge of the camera chip (you may have to temporarily cut power to the polar axis drive motor to see which way the star drifts). Then (1) start recording the file and (2) kill power to the drive motor. When the star drifts off the west end of the chip, (3) stop the recording and (4) re-power the drive motor. Use the slow motion controls to return the star to just off the east edge of the chip and repeat the process. I suggest you do at least 12 drifts, and more is even better. (I normally use 20 drifts when calibrating my system.) Figure 4 shows the dialog box that starts the drift analysis process. This window is very important and requires considerable user input, so I will cover it step by step and illustrate with an actual drift analysis. The area tagged “1” in the red circle (Callout 1) is where you enter the maximum number of frames to use for the analysis. The default is 1,000 but you may specify any number you wish. For instance, if you have a camera chip whose long axis is east-to-west and you are shooting at very short integration t