Name: simulating imaging of large scale radio arrays on the lunar surface
Uploaded: 2020-07-30T14:00:00.000+00:00
Duration: 6 min 14 s
Description: Watch this Scientific Journal Video about Simulating Imaging of Large Scale Radio Arrays on the Lunar Surface at JoVE.com

Thanks to the Lunar Reconnaissance Orbiter (LRO) and Lunar Orbiter Laser Altimeter (LOLA) teams for providing the Lunar Digital Elevation Maps. This work was directly supported by the NASA Solar System Exploration Research Virtual Institute cooperative agreement number 80ARC017M0006, as part of the Network for Exploration and Space Science (NESS) team.

1. Software setup
<ol>
	<li>First, go to https://deepblue.lib.umich.edu/data/concern/data_sets/bg257f178?locale=en and download the software package. This software has only been tested in a UNIX environment, and may not fully function in other environments. The README in this package will help guide one through the rest of the software needed and its uses.</li>
	<li>Make sure python 2.7 or greater is installed. A link is provided in the README. Several common python libraries are also needed including numpy, matplotlib, pylab, scipy, subprocess, ephem, and datetime.</li>
	<li>Make sure CASA 4.7.1 or greater is installed. A link in provided in the README.</li>
	<li>Make sure gcc 4.8.5 or greater is installed. A link is provided in the README.</li>
	<li>Make sure the C toolkit for SPICE is installed. This software is used to align different astronomical reference frames and track the relative positions of planets, moons, and satellites. A link to download this software is also included in the README.
	<ol>
		<li>Download several kernels that contain information on astronomical and lunar reference frames, as well as the orbital dynamics of the Moon, Earth, and Sun. The specific kernels needed are listed in the README alongside a link of where to download them.</li>
	</ol>
	</li>
	<li>Obtain the final prerequisite data needed: Digital Elevation Models (DEMs) of the lunar surface created from LRO LOLA measurements. The specific file needed is listed and linked in the README.</li>
</ol>
2. Creating the array configuration
<ol>
	<li>Customize the createArrayConfig.py script.
	<ol>
		<li>Choose the configuration of the array by providing a list of Longitude and Latitude coordinates for each antenna. 
		NOTE: The script is currently formatted for a 10 km diameter array with 1024 elements, 32 arms with 32 log space spaced antenna each, using a constant factor to convert between meters and degrees of longitude/latitude near 0 degrees latitude. The site of the array, (-1.04&#176;, -0.43&#176;), was chosen because it is the center of the 10x10 km patch with the lowest elevation variation (&#963; = 5.6 m) close to the sub-Earth point (0&#176;, 0&#176;) in the Moon ME frame.</li>
		<li>Change the lunarPath variable in the script to reflect the new download location of the Digital Elevation Model containing the elevation data of the lunar surface.</li>
	</ol>
	</li>
	<li>Run the createArrayConfig.py script with &#8220;python createArrayConfig.py&#8221;. This will use the lunar Digital Elevation Model to solve for the elevation at each longitude and latitude for each antenna. Save the longitude, latitude, and elevation to files and print to the screen for easy copying and pasting into the next script. Make figures showing the array configuration on top of the local lunar topography (Figure 1).</li>
</ol>
3. Using SPICE to align coordinates
<ol>
	<li>Customize the eqArrOverTimeEarth.c script.
	<ol>
		<li>Take the output from the previous script, the Longitude, Latitude, and elevation of each antenna and copy them over into the corresponding lists in the script, also updating the variable &#8216;numsc&#8217; with the number of receivers and corresponding coordinates. 
		NOTE: Since C does not have dynamic array allocation, there was no easy way to flexibly read in the data automatically, so manual copying must be done.</li>
		<li>Update the lunar_furnsh.txt included in the package with the new path names for the required frame and ephemeris files.</li>
		<li>Specify what set of dates to observe on. This will inform the ephemerides within SPICE to accurately track where the Earth and Sun are in relation to the defined array for those dates. In the script currently 48 dates occurring roughly weekly over the year 2025 are selected.</li>
		<li>Specify the targeted area of the sky for the array to track and image. Currently the script saves the RA Dec of the Earth as seen from the lunar surface, but one may easily just put in static RA Dec coordinates instead.</li>
	</ol>
	</li>
	<li>Compile the eqArrOverTime.c script
	<ol>
		<li>Compile the script using the gcc command in the comment at the top of the script. It will be something like &#8220;gcc eqArrOverTimeEarth.c -o eqArrOverTimeEarth -I/home/alexhege/SPICE/cspice/include /home/alexhege/SPICE/cspice/lib/cspice.a -lm -std=c99&#8221;. Change the paths to reflect where the cspice libraries are located.</li>
	</ol>
	</li>
	<li>Run the eqArrOverTime executable with &#8220;./eqArrOverTime&#8221;. This should result in a number of files each with a set of variables in them. Most important are the XYZ position of each antenna in J2000 coordinates, and the Right Ascension and Declination (RA and Dec) coordinates of the targeted area in the sky (currently those of the Earth from the Moon&#8217;s perspective). The output variables are saved to .txt files containing the data for all the requested dates.</li>
</ol>
4. Using CASA to simulate array response
<ol>
	<li>Customize the LunarEarthPicFreqIntegration.py script.
	<ol>
		<li>Specify the observing frequency for the array to make an image at. This is currently set to 0.75 MHz.</li>
		<li>Specify a CASA compatible truth image (or create from a .fits image file) with Jansky/pixel values for the array to reconstruct (e.g., Figure 2). Constants (res, res1, width, arcMinDiv) in the code will need to be changed to reflect the size and resolution of the input truth image. 
		NOTE: If using&#160;the SPICE method to provide&#160;the RA Dec coordinates, one&#160;may comment out the &#8216;import&#160;ephem&#8217; statement in this&#160;script. This library requires the&#160;use of casa-pip from the casa-python package to install, but 
		allows for tracking of other&#160;astronomical objects within&#160;python.</li>
	</ol>
	</li>
	<li>Run the LunarEarthPic.py script. Commented at the top of the script are examples on how to run the script. The following command is one example on how to run the script from the command line: 
	&#8220;nohup casa --nologger --nologfile --nogui --agg -c LunarEarthPicFreqIntegration.py -outDir . -correlate True -numSC 1024 | tee earth.out &#38;&#8221; 
	The -numSC flag is used to inform the code how many antenna/receivers are being used, and helps unpack the data from the .txt files containing the receiver coordinates. 
	NOTE: The antenna baseline vector, measured in units of the observing wavelength (&#955;), has length D&#955; and components (&#965;, &#957;, w) = (&#8710;x,&#8710;y,&#8710;z)/&#955; . The pipeline then calculates the visibilities, or observed cross correlated voltages for each pair of antennas. Here the small field of view approximation is used to calculate the visibilities, following the standard formula from Thompson et al.2 for an infinitesimal bandwidth at frequency &#957;. 
	<img alt="Equation 2" src="/files/ftp_upload/61540/61540equ02.jpg" /> 
	The sky coordinates of the target the array is imaging is deemed the phase center, to which the z, or w, axis of the frame is pointed. (l, m, n) are the direction cosines from the (U, V, W) coordinate system. The sky brightness pattern around the source under observation is I&#957;(l, m). Spectral flux density is often presented in the derived unit 1 Jansky (Jy) = 10&#8722;26 W/m2/Hz. Spectral brightness is simply Jy/steradian to represent the amount of flux coming from a particular area in the sky. A&#957;(l, m) is the normalized antenna primary beam pattern, or how sensitive it is to radiation coming from that point in the sky. 
	This script calculates the antenna separations in the appropriately projected reference frame from the coordinates output from the previous script. It then uses Equation 2 to calculate the visibility data for each pair of antenna. The resulting visibilities are stored alongside the baselines in a CASA Measurement Set file (.ms). This MS file is the primary output of this script.</li>
</ol>
5. Imaging the data &#8211; noiseless and noisy
<ol>
	<li>Customize the noiseCopies.py script.
	<ol>
		<li>Set the System Equivalent Flux Density (SEFD), referred to as avNoise in the script. The SEFD is a convenient way to talk about the total noise of a radio antenna since it ties in both the system temperature and the effective area, and provides a way to directly compare the signal and the noise. It is currently set to 1.38e7 Jansky, which is an optimistic noise level for 0.75 MHz. 
		NOTE: For the low frequency radio regime, there are three main sources on constant noise: amplifier noise, quasithermal noise from free electrons (estimated by Meyer-Vernet et al.24 to be 6.69e4 Jy at 0.75 MHz, using a electrically short dipole approximation), and Galactic background radiation from the Milky Way (estimated by Novacco &#38; Brown25 to be 4.18e6 Jansky at 0.75 MHz for the full sky, of which a lunar array will only see some portion). This optimal noise level of 1.38e7 Jy assumes that amplifier noise dominates the other terms. See Hegedus et al. for a more detailed discussion.</li>
		<li>Set the bandwidth being integrated over in variable &#8216;noise&#8217; line 200. Set to 500 kHz.</li>
		<li>Set the integration time in variable &#8216;noise&#8217; line 200.</li>
	</ol>
	</li>
	<li>Run the noiseCopies.py script with &#8220;nohup casa --nologger --nologfile --nogui --agg -c noiseCopies.py | tee noise.out &#38;&#8221;.
	<ol>
		<li>The script will first create an image from the noiseless visibility data, calling standard radio astronomy algorithm CLEAN26 to create an image like Figure 3.</li>
		<li>The script will then create copies of the MS and add the appropriate noise level to the complex visibility data and image it using CLEAN. The script currently makes images for a range of integration times up to 24 hours and over several robust weighting scheme values. Depending on the configuration of the array, image quality may vary with the choice of data weighting schemes. These noisy images will look something like Figure 4, which used an integration time of 4 hours. 
		NOTE: The noise is added with standard Signal to Noise formulas. From Taylor2 the interferometric noise for a single polarization is 
		<img alt="Equation 3" src="/files/ftp_upload/61540/61540equ03.jpg" /> 
		Here, &#951;s is the system efficiency or correlator efficiency, which has been set to a conservative value of 0.8. Nant is the number of antennas in the array (Nant = 2 for each individual visibility), &#8710;&#957; is the bandwidth being integrated over in Hz, and &#8710;t is the integration time in seconds.</li>
	</ol>
	</li>
</ol>

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The authors have nothing to disclose.

Each step of the simulation pipeline is necessary and feeds into the next, taking an array configuration on the lunar surface, aligning the reference frame correctly to orient the array to the target area in the sky, calculating the visibility data, adding the appropriate noise levels, and running imaging algorithms on the resulting data.
For each step, customizations may be made. In step 2, the user defined array configuration may be any list of longitudes and latitudes. This then feeds into the SPICE script in step 3, where one may choose the exact time of the planned measurements, as well as where in the sky the array should be focused. In step 3, one can specify the simulated truth emission that the array is attempting to image by providing a suitable CASA .truth file. Then in step 4 one may change the expected level of noise depending on the observing frequency and expected hardware capabilities. This set of codes constitutes a flexible simulation framework that may be used to iterate array design for any number of uses, depending on the targeted science. These codes can all be run on an average laptop or workstation, though computation time increases with number of antennas. The slowest parts of the process are predicting the visibilities, followed by imaging. For small arrays, the entire process can be done in minutes, while for larger arrays of a few hundred or thousand receivers, hours or days may be needed.
Some next steps that could be taken with this pipeline to increase its realism include adding a channel-dependent foreground removal system. This requires building up a global sky model, dominated at low frequencies by galactic synchrotron emission and a few bright sources like Cas A, tracking which part of the sky is visible to the receivers, and convolving that brightness pattern with the primary beam, with the phase center of the array aligned towards the imaging target. For longer integration times, tracking the apparent motion of the sky is also an issue. Another improvement that could be added is a transient event/radio frequency interference (RFI) flagging system that can remove flagged channels from normal imaging, and send them to a specialized pipeline that images and characterizes the flagged data. This transient event pipeline could then use special algorithms like uvmodelfit that can take advantage of the high signal to noise ratio of these events to characterize them better than the normal resolution of the array27.
There are also additional effects that need to be taken into account for a full array calibration, one of which is mutual coupling. As discussed in Ellingson28, this can lead to a decrease in sensitivity in arrays if they have receivers that are within a few wavelengths of each other. This is seen in a decrease in sensitivity for the array, or equivalently, an increase in the SEFD. This is especially true for beams greater than 10 degrees away from zenith. The example array in this work targets Earth, which is always near zenith by design, so mutual coupling should not affect this particular imaging target, but studies of the SEFD over the full range of elevation angles and frequencies will need to be done in commissioning for any real array to unlock its full potential. Another shortcoming of this array simulation pipeline lies in the imperfect lunar surface maps used. DEMs from LRO LOLA measurements have at best a resolution of 60x60 meters/pixel in the 512 pixels/degree maps. One can interpolate these data for simulated arrays, but for real arrays there will need to be a commissioning/calibration period where sources with a known position will be used to determine the relative separations between all the antenna to high precision. Possible calibration sources include Cas A, periodic low frequency emission from Jupiter or Earth, or potentially the Lunar Gateway29.
There is also the response of the lunar surface to consider. There is a layer of lunar topsoil called the regolith that acts like a lossy dielectric that can reflect incoming emission with some efficiency, above the lunar bedrock which can also reflect incoming emission with some better efficiency30,31. This response is dependent on the ambient temperature and incoming frequency, as well as the chemical composition of the regolith. Studies30,31 have found that at lower temperatures below 100 K, the regolith is nearly transparent to radio emission, and reflection occurs at the bedrock level with a reflection coefficient of around 0.5-0.6. At higher temperatures 150-200 K, the regolith can absorb emission and reflect incoming radiation at the surface with a reflection coefficient of around 0.2-0.3. At temperatures above 200 K, it is found that the dielectric properties of the regolith are diminished, and variation from reflection can be ignored. These effects can reduce the effective area of the array, reducing sensitivity and requiring longer integration times. This effect can be modeled with electromagnetic simulation software packages such as NEC4.232 given models of relative permittivity/dielectric constant as a function of lunar depth. This will output the SEFD of a receiver for a given frequency, which can be given to the array simulation pipeline to calculate the correct noise to add to the simulated signal. Adding a grounding grid between the receiver and the lunar surface may help diminish the effect of reflected waves, but adds its own set of complications in the form of deployment.
Many of the hypothetical or fuzzy details around the implementation of a radio receiver on the lunar surface will finally solidify into reality with recent funding of single low frequency antenna projects like Radio wave Observations on the Lunar Surface of the photoElectron Sheath (ROLSES) and the Lunar Surface Electromagnetics Experiment (LuSEE)33. LuSEE was recently funded by NASA as part of the Commercial Lunar Payload Services program. Both suites of antenna will consist mainly of flight spares for past instruments like STEREO/WAVES or PSP FIELDS and are planned for a 2021 delivery. Measurements from these receivers will finally solidify the level of quasithermal noise from the photoelectron sheath from ionized dust on the lunar surface and how it changes over the course of a lunar day. These measurements will also characterize the level of reflection and absorption from the lunar surface, and quantify how it changes the SEFD of the receiver. They will also provide statistics on the number of transient events or RFI that are received on the lunar surface. These missions will pave the way for arrays of antennas that will finally be able to make a multitude of novel scientific observations such as low frequency emission from solar radio bursts, far away galaxies, and planetary magnetospheres. The simulation pipeline described in this work provides a flexible way to iterate the design of these future arrays for a variety of scientific targets.

The field of radio astronomy began in 1932 with the accidental detection of galactic radio emission by Karl G. Jansky1 at 20 MHz, in a range now commonly called the low frequency radio. Ever since then, radio astronomy has grown rapidly, catching up with higher frequency optical observations that have been going on for centuries longer. Another breakthrough was the utilization of radio interferometry, where groups of antenna separated by large distances are used to create a synthetic aperture, providing a way to scale up the sensitivity and resolution of radio observations2,3. This can intuitively be thought of as an extension of the regular resolution formula for optical observations:
<img alt="Equation 1" src="/files/ftp_upload/61540/61540equ01.jpg" />
For an observing dish of size D meters, and an observing wavelength of &#955; meters, &#920;HPBW is the angular size in radians of the Half Power Beam Width (HPBW), defining the resolution on the sky. This process of synthesizing a fraction of a large full dish with only scattered points across a mostly empty area is also called aperture synthesis. In the realm of radio interferometry, the resolution of an array is determined by the furthest distance between any two receivers in the array, and this distance is used as D in Equation 1.
The mathematics behind interferometry has been well documented in classic texts like Thompson&#8217;s Interferometry and Synthesis in Radio Astronomy3. The basic insight can be communicated informally as &#8220;(for planar arrays observing a small field of view) the cross correlation of signals between any 2 receivers (a visibility) will yield information about a 2D Fourier coefficient of the sky brightness pattern.&#8221; What Fourier mode is sampled depends on the separation of the receivers (the baseline), normalized by the observing wavelength. Receivers that are further apart (in the standard UVW coordinate system oriented towards the imaging target) sample higher spatial frequency features, yielding higher resolution details at smaller scales. Conversely, receivers that are close together in the same UVW frame sample lower spatial frequencies, giving information of larger scale structures at a lower resolution.
For the lowest radio frequencies, free electrons in Earth&#8217;s ionosphere prevent radio waves below 10 MHz from travelling from space to the ground, and vice versa. This so-called &#8220;ionospheric cutoff&#8221; has long prevented ground-based observations of the sky for this frequency range. The obvious answer to this limitation is to put radio receivers into space where they can record data free of the influence of Earth&#8217;s atmosphere and free electrons in its ionosphere. This has been done before with single antennas on spacecraft like Wind4 and STEREO5, which have revealed many astrophysical processes that produce emissions in this low frequency radio range. This includes emissions from the interactions of electrons with the Earth&#8217;s magnetosphere, electron acceleration from solar eruptions, and from the galaxy itself. Single antenna observations can measure the total flux density of such events, but cannot pinpoint where the emission is coming from. In order to localize this low frequency emission and make images in this frequency regime for the first time, many antennas will have to be sent to space and have their data combined to make a synthetic aperture.
Doing this would open a new window through which humankind can observe the universe, enabling a number of scientific measurements that require images of the sky in these lowest frequencies. The Moon is one possible site for a synthetic aperture in space, and it comes with pros and cons when compared to free flying orbiting arrays. The lunar far side has a unique radio quiet zone that blocks all of the usual interference coming from man-made signals, while the near side provides a static place for Earth observing arrays, and if constructed at the lunar sub-Earth point, the Earth will always be at the zenith of the sky. With a static array, it is easier to obtain short baselines to measure large scale emissions, as they are in no danger of colliding, unlike free flying arrays. The drawbacks of a lunar array are chiefly difficulties in cost and power. A large-scale array on the Moon would require a substantial amount of infrastructure and money, while smaller orbiting arrays would require far fewer resources. There is also the issue of power; most places on the Moon are exposed to sufficient sunlight for solar power generation for 1/3 of each lunar day. Surviving the large swings in temperature from lunar day to night is also an engineering concern. Putting aside these difficulties, there is still the problem of making sure that the proposed array design is suitable for its specified science target(s). The response of any given array is dependent on the structure of the emission being observed along with the configuration and sensitivity of the array.
Several conceptual arrays to go on the lunar surface have been drawn up over the decades. Early designs were not the most detailed, but still recognized the scientific advances that could be attained by such arrays6,7,8,9,10. More arrays have also been put forth in recent years, some of which, like FARSIDE11, DEX12, and DALI13 seek to measure the absorption troughs of the redshifted neutral hydrogen 21-cm signal in the 10-40 MHz range to probe the so called &#8220;Dark Ages&#8221; and constrain cosmological models of the early universe. Others like ROLSS14 call out tracking bright solar type II radio bursts far into the heliosphere to identify the site of solar energetic particle acceleration within coronal mass ejections as their compelling science case. Smaller scale arrays have also been described like the 2-element interferometer RIF15, which would use a single lander and a moving rover to sample many baselines as it moves outward from the lander. RIF focuses on the ability to make a sky map of these low frequencies for the first time, and calculates the UV coverage and synthesized beam for integrated observations.
Space-based radio arrays could also enable low frequency imaging of distant radio galaxies to determine magnetic fields and astrometric measurements16. Low frequency images of these bodies would provide a more complete picture of the physics governing these systems, in particular yielding synchrotron emission data for the lower end of the electron energy distribution. There are also a range of various magnetospheric emissions that occur at these low frequencies, providing both global (constant synchrotron emission) and local (bursts, auroral kilometric radiation) signatures of electron dynamics that are not detectable from the ground17. The brightest recorded emissions of these types have come from Earth and Jupiter, as these are the nearest planets with strong magnetospheres. However, arrays with sufficient sensitivity and resolution could observe magnetospheric emission from other outer planets, or even extrasolar planets18. This topic in particular was called out as an area of interest at the recent Planetary Sciences Vision 2050 workshop.
This work focuses on simulating the response of radio arrays on the Moon consisting of anywhere from just a few antennas, to hundreds or thousands of antennas. This simulation framework is useful for iterating array design for imaging any given scientific target in a small field of view (a few square degrees) but does not currently support all sky imaging. Accurate estimates of the predicted brightness maps along with realistic noise profiles must be used to ensure that a given array size/configuration is sufficient to observe the target to a certain noise level or resolution. The geometry of the array must also be known to a high degree so that the baselines are computed accurately to enable correct imaging of the data. Currently, the best maps of the Lunar surface are Digital Elevation Models (DEMs) from Lunar Reconnaissance Orbiter&#8217;s (LRO&#8217;s)19 Lunar Orbiter Laser Altimeter (LOLA)20. The simulation pipeline accepts longitude latitude coordinates for each receiver and interpolates the elevation at these points from existing DEMs to calculate the full 3D position.
From these coordinates the baselines are computed and inserted into a Common Astronomy Software Applications (CASA)21 Measurement Set (MS) file. The MS format can be used with many existing analysis and imaging algorithms, and holds information about the array configuration, visibility data, and alignment with the sky. However, many of these software routines are hard coded to work with arrays that rotate with the Earth&#8217;s surface, and do not work for orbiting or Lunar arrays. To circumvent this, this pipeline manually calculates the baselines and visibilities for a given array and imaging target, and inserts the data into the MS format. The SPICE22 library is used to correctly align the lunar and sky coordinate systems and track the motions of the Moon, Earth, and Sun.
The simulation framework described here follows Hegedus et al.17, and the software is archived by the University of Michigan library in the Deep Blue archive23, stored at https://deepblue.lib.umich.edu/data/concern/data_sets/bg257f178?locale=en. Any patches or updates to this archived software can be found at https://github.com/alexhege/LunarSynchrotronArray. The following section will describe the requirements for this software, and walk through the process of forming an array, setting the appropriate noise levels, feeding the array a simulated truth image of the targeted emission, and simulating the array&#8217;s noiseless and noisy reconstructions of the emission using a CASA script.

<table><tbody><tr><td>No physical materials are needed, this is a purely computational work.</td><td></td><td></td><td></td></tr></tbody></table>

simulating imaging of large scale radio arrays on the lunar surface

In recent years there has been a renewed interest in returning to the Moon for reasons both scientific and exploratory in nature. The Moon provides the perfect training ground for building large scale bases that one may apply to other planets like Mars. The existence of a radio quiet zone on the lunar far side has promise for early universe studies and exoplanet searches, while the near side provides a stable base that may be used to observe low frequency emissions from Earth&#8217;s magnetosphere that may help gauge its response to incoming space weather. The construction of a large-scale radio array would provide large scientific returns as well as acting as a test of humanity&#8217;s ability to build structures on other planets. This work focuses on simulating the response of small to large-scale radio arrays on the Moon consisting of hundreds or thousands of antennas. The response of the array is dependent on the structure of the emission along with the configuration and sensitivity of the array. A set of locations are selected for the simulated radio receivers, using Digital Elevation Models from the Lunar Orbiter Laser Altimeter instrument on Lunar Reconnaissance Orbiter to characterize the elevation of the receiver locations. A custom Common Astronomy Software Applications code is described and used to process the data from the simulated receivers, aligning the lunar and sky coordinate frames using SPICE to ensure the proper projections are used for imaging. This simulation framework is useful for iterating array design for imaging any given scientific target in a small field of view. This framework does not currently support all sky imaging.

Following the software pipeline should be fairly straightforward, and it should be obvious that each step is working as it should. Running createArrayConfig.py from step 2 should create a figure resembling Figure 1, where configuration of the defined array is plotted on top of the local topography of the lunar surface, as derived from the LRO LOLA derived Digital Elevation Model.
Step 3 should give key output files eqXYZ_EarthCentered.txt, RAs.txt, and Decs.txt, among others. Examples of these files are located in the downloaded package.
Step 4 should create a truth image that is similar to Figure 2, which is then used to calculate the visibility data. It should also output a CASA Measurement Set (.ms) file that one may browse with the usual CASA command of casabrowser to see that both the baselines and visibility data were calculated and saved.
Step 5 should output figures similar to Figure 3 and Figure 4 for the noiseless and noisy images respectively. The noisy images should look less clear than the noiseless image.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/61540/61540fig01.jpg" /> 
Figure 1: Configuration of the array over elevation map of the lunar surface. 
This is an example array configuration consisting of a logarithmically spaced circular array over 10 km. The configuration has 32 arms of 32 logarithmically spaced antenna for a total of 1024 antenna. The site of the array, (-1.04&#176;, -0.43&#176;) was chosen because it is the center of the 10x10 km patch with the lowest elevation variation (&#963; = 5.6 m) close to the sub-Earth point (0&#176;, 0&#176;) in the Moon Mean Earth (ME) frame. The elevation data was obtained from a Digital Elevation Map derived from LRO LOLA measurements. This figure was taken from Hegedus et al.13. <a href="https://www.jove.com/files/ftp_upload/61540/61540fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/61540/61540fig02.jpg" /> 
Figure 2: Truth image of synchrotron emission from radiation belts at lunar distances. 
This is an example of a science target for the array to image. The recovered image is then compared to this input to determine the performance of the array. The brightness map was created from Salammb&#244; electron simulation data and run through a calculation for determining the synchrotron emission that would be observed at lunar distances. The 1.91&#176; Earth is added in for a scale indicator. This figure was taken from Hegedus et al.13. <a href="https://www.jove.com/files/ftp_upload/61540/61540fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/61540/61540fig03.jpg" /> 
Figure 3: Noiseless response of 10 km diameter array to input truth image. 
This is one of the outputs from Step 5, applying standard radio astronomy imaging algorithm CLEAN, using a Briggs weighting scheme with a robustness parameter of &#8722;0.5. This figure was taken from Hegedus et al.13. <a href="https://www.jove.com/files/ftp_upload/61540/61540fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/61540/61540fig04.jpg" /> 
Figure 4: Noisy response of 10 km diameter array to input truth image. 
This is one of the outputs from Step 5, applying standard radio astronomy CLEAN, using a Briggs weighting scheme with a robustness parameter of &#8722;0.5. For this image, a System Equivalent Flux Density of 1.38e7 Jansky was used, an integration bandwidth of 500 kHz, and an integration time of 4 hours. The noise was also reduced by a factor of 16 to simulate the response of a 16K antenna array instead of a 1K antenna array. This figure was taken from Hegedus et al.13. <a href="https://www.jove.com/files/ftp_upload/61540/61540fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about Simulating Imaging of Large Scale Radio Arrays on the Lunar Surface at JoVE.com

Simulating Imaging of Large Scale Radio Arrays on the Lunar Surface

A simulation framework for testing the imaging capabilities of large-scale radio arrays on the lunar surface is presented. Major noise components are discussed, and a software pipeline is walked through with details on how to customize it for novel scientific uses.

In recent years there has been a renewed interest in returning to the Moon for reasons both scientific and exploratory in nature. The Moon provides the ...

simulating-imaging-of-large-scale-radio-arrays-on-the-lunar-surface

Research

JoVE Journal

Engineering

4.8K Views.  University of Michigan. A simulation framework for testing the imaging capabilities of large-scale radio arrays on the lunar surface is presented. Major noise components are discussed, and a software pipeline is walked through with details on how to customize it for novel scientific uses.

Video: Simulating Imaging of Large Scale Radio Arrays on the Lunar Surface

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

We propose a method for increasing the resolution of an object and overcoming the diffraction limit of an optical system installed on top of a moving imaging system, such as an airborne platform or satellite. The resolution improvement is obtained in a two-step process. First, three low resolution differently defocused images are being captured and the optical phase is retrieved using an improved iterative Gerchberg-Saxton based algorithm. The phase retrieval allows to numerically back propagate the field to the aperture plane. Second, the imaging system is shifted and the first step is repeated. The obtained optical fields at the aperture plane are combined and a synthetically increased lens aperture is generated along the direction of movement, yielding higher imaging resolution. The method resembles a well-known approach from the microwave regime called the Synthetic Aperture Radar (SAR) in which the antenna size is synthetically increased along the platform propagation direction. The proposed method is demonstrated through laboratory experiment.

A method for overcoming the optical diffraction limit is presented. The method includes a two-step process: optical phase retrieval using iterative Gerchberg-Saxton algorithm, and imaging system shifting followed by repetition of the first step. A synthetically increased lens aperture is generated along the direction of movement, yielding higher imaging resolution.

We propose a method for increasing the resolution of an object and overcoming the diffraction limit of an optical system installed on top of a moving ...

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

A Ground Penetrating Radar (GPR) system based on a ground-coupled, densely populated antenna array was used to collect data during an infiltration experiment conducted at a test site near the Tottori Sand Dune, Japan. The antenna array used in this study consists of 10 transmitting antennas (Tx) and 11 receiving antennas (Rx). For this experiment, the system was configured to use all possible Tx-Rx pairings, resulting in a Multi-Offset Gather (MOG) consisting of 110 Tx-Rx combinations. The array was left stationary at a position directly above the infiltration area and data were collected every 1.5 seconds using a time-based trigger. Common-Offset Gather (COG) and Common Mid-Point (CMP) data cubes were reconstructed from the MOG data during post-processing. There have been few studies that used time-lapse CMP data to estimate changes in velocity of propagation. In this study, electromagnetic (EM) wave velocity was estimated heuristically at 1-minute intervals from the reconstructed CMP data through curve fitting, using the hyperbola equation. We then proceeded to calculate the depth of the wetting front. The evolution of the wetting front over time obtain through this method is consistent with the observations from a soil moisture sensor which was placed at a depth below 20 cm. The results obtained in this study demonstrate the ability of such array GPR system to monitor a subsurface dynamic process like water infiltration accurately and quantitatively.

Here we present a Ground Penetrating Radar (GPR) system based on a ground-coupled, densely populated antenna array for monitoring the dynamic process of subsurface water infiltration. A time-lapse radar image of the infiltration process allowed estimating the depth of the wetting front during the course of the infiltration process.

A Ground Penetrating Radar (GPR) system based on a ground-coupled, densely populated antenna array was used to collect data during an infiltration ...

Environment

Using Synchrotron Radiation Microtomography to Investigate Multi-scale Three-dimensional Microelectronic Packages

Synchrotron radiation micro-tomography (SR&#181;T) is a non-destructive three-dimensional (3D) imaging technique that offers high flux for fast data acquisition times with high spatial resolution. In the electronics industry there is serious interest in performing failure analysis on 3D microelectronic packages, many which contain multiple levels of high-density interconnections. Often in tomography there is a trade-off between image resolution and the volume of a sample that can be imaged. This inverse relationship limits the usefulness of conventional computed tomography (CT) systems since a microelectronic package is often large in cross sectional area 100-3,600 mm2, but has important features on the micron scale. The micro-tomography beamline at the Advanced Light Source (ALS), in Berkeley, CA USA, has a setup which is adaptable and can be tailored to a sample&#39;s properties, i.e., density, thickness, etc., with a maximum allowable cross-section of 36 x 36 mm. This setup also has the option of being either monochromatic in the energy range ~7-43 keV or operating with maximum flux in white light mode using a polychromatic beam. Presented here are details of the experimental steps taken to image an entire 16 x 16 mm system within a package, in order to obtain 3D images of the system with a spatial resolution of 8.7 &#181;m all within a scan time of less than 3 min. Also shown are results from packages scanned in different orientations and a sectioned package for higher resolution imaging. In contrast a conventional CT system would take hours to record data with potentially poorer resolution. Indeed, the ratio of field-of-view to throughput time is much higher when using the synchrotron radiation tomography setup. The description below of the experimental setup can be implemented and adapted for use with many other multi-materials.

For this study synchrotron radiation micro-tomography, a non-destructive three-dimensional imaging technique, is employed to investigate an entire microelectronic package with a cross-sectional area of 16 x 16 mm. Due to the synchrotron&#39;s high flux and brightness the sample was imaged in just 3 min&#160;with an 8.7 &#181;m spatial resolution.

Synchrotron radiation micro-tomography (SR&#181;T) is a non-destructive three-dimensional (3D) imaging technique that offers high flux for fast data ...

A Field Primer for Monitoring Benthic Ecosystems Using Structure-From-Motion Photogrammetry

Structure-from-motion (SfM) photogrammetry is a technique used to generate three-dimensional (3D) reconstructions from a sequence of two-dimensional (2D) images. SfM methods are becoming increasingly popular as a noninvasive way to monitor many systems, including anthropogenic and natural landscapes, geologic structures, and both terrestrial and aquatic ecosystems. Here, a detailed protocol is provided for collecting SfM imagery to generate 3D models of benthic habitats. Additionally, the cost, time efficiency, and output quality of employing a Digital Single Lens Reflex (DSLR) camera versus a less expensive action camera have been compared. A tradeoff between computational time and resolution was observed, with the DSLR camera producing models with more than twice the resolution, but taking approximately 1.4-times longer to produce than the action camera. This primer aims to provide a thorough description of the steps necessary to collect SfM data in benthic habitats for those who are unfamiliar with the technique as well as for those already using similar methods.

We provide a detailed protocol for conducting underwater structure-from-motion photogrammetry surveys to generate 3D models and orthomosaics.

Structure-from-motion (SfM) photogrammetry is a technique used to generate three-dimensional (3D) reconstructions from a sequence of two-dimensional (2D) ...

Multimodal Hierarchical Imaging of Serial Sections for Finding Specific Cellular Targets within Large Volumes

Targeting specific cells at ultrastructural resolution within a mixed cell population or a tissue can be achieved by hierarchical imaging using a combination of light and electron microscopy. Samples embedded in resin are sectioned into arrays consisting of ribbons of hundreds of ultrathin sections and deposited on pieces of silicon wafer or conductively coated coverslips. Arrays are imaged at low resolution using a digital consumer like smartphone camera or light microscope (LM) for a rapid large area overview, or a wide field fluorescence microscope (fluorescence light microscopy (FLM)) after labeling with fluorophores. After post-staining with heavy metals, arrays are imaged in a scanning electron microscope (SEM). Selection of targets is possible from 3D reconstructions generated by FLM or from 3D reconstructions made from the SEM image stacks at intermediate resolution if no fluorescent markers are available. For ultrastructural analysis, selected targets are finally recorded in the SEM at high-resolution (a few nanometer image pixels). A ribbon-handling tool that can be retrofitted to any ultramicrotome is demonstrated. It helps with array production and substrate removal from the sectioning knife boat. A software platform that allows automated imaging of arrays in the SEM is discussed. Compared to other methods generating large volume EM data, such as serial block-face SEM (SBF-SEM) or focused ion beam SEM (FIB-SEM), this approach has two major advantages: (1) The resin-embedded sample is conserved, albeit in a sliced-up version. It can be stained in different ways and imaged with different resolutions. (2) As the sections can be post-stained, it is not necessary to use samples strongly block-stained with heavy metals to introduce contrast for SEM imaging or render the tissue blocks conductive. This makes the method applicable to a wide variety of materials and biological questions. Particularly prefixed materials e.g., from biopsy banks and pathology labs, can directly be embedded and reconstructed in 3D.

This protocol targets specific cells in tissue for imaging at nanoscale resolution using a scanning electron microscope (SEM). Large numbers of serial sections from resin-embedded biological material are first imaged in a light microscope to identify the target and then in a hierarchical manner in the SEM.

Targeting specific cells at ultrastructural resolution within a mixed cell population or a tissue can be achieved by hierarchical imaging using a ...

Developmental Biology

Scattering And Absorption of Light in Planetary Regoliths

Theoretical, numerical, and experimental methods are presented for multiple scattering of light in macroscopic discrete random media of densely-packed microscopic particles. The theoretical and numerical methods constitute a framework of Radiative Transfer with Reciprocal Transactions (R2T2). The R2T2 framework entails Monte Carlo order-of-scattering tracing of interactions in the frequency space, assuming that the fundamental scatterers and absorbers are wavelength-scale volume elements composed of large numbers of randomly distributed particles. The discrete random media are fully packed with the volume elements. For spherical and nonspherical particles, the interactions within the volume elements are computed exactly using the Superposition T-Matrix Method (STMM) and the Volume Integral Equation Method (VIEM), respectively. For both particle types, the interactions between different volume elements are computed exactly using the STMM. As the tracing takes place within the discrete random media, incoherent electromagnetic fields are utilized, that is, the coherent field of the volume elements is removed from the interactions. The experimental methods are based on acoustic levitation of the samples for non-contact, non-destructive scattering measurements. The levitation entails full ultrasonic control of the sample position and orientation, that is, six degrees of freedom. The light source is a laser-driven white-light source with a monochromator and polarizer. The detector is a mini-photomultiplier tube on a rotating wheel, equipped with polarizers. The R2T2 is validated using measurements for a mm-scale spherical sample of densely-packed spherical silica particles. After validation, the methods are applied to interpret astronomical observations for asteroid (4) Vesta and comet 67P/Churyumov-Gerasimenko (Figure 1) recently visited by the NASA Dawn mission and the ESA Rosetta mission, respectively.

Numerical and experimental methods are presented for multiple scattering of light in discrete random media of densely-packed particles. The methods are utilized to interpret the observations of asteroid (4) Vesta and comet 67P/Churyumov-Gerasimenko.

Theoretical, numerical, and experimental methods are presented for multiple scattering of light in macroscopic discrete random media of densely-packed ...

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

Digital imaging and processing technology have evolved to facilitate the expansion of large-area imaging surveys, which increase our capacity to study the status, trends, and dynamics of organisms living in subtidal habitats. By creating photo-realistic digital twins for ex situ analyses, these approaches allow small field teams to collect substantially more data than was previously possible. Here we present a four-step, large-area imaging survey pipeline and analysis methodology, including image collection, model construction, ecological analysis, and data curation, that has been developed and refined through experimentation over the past decade. Each step described has a consistent focus on the unique value of the original source imagery. While types of data extracted from large-area image surveys are vast, we include here workflows to extract ecological data for structural complexity, community composition, and demographic analyses valuable for monitoring and hypothesis-driven efforts. We additionally include recommendations for metadata standards, which complement the collection of large-area imaging data and support archival efforts facilitating transparency and collaboration between research groups.

This protocol covers a four-step large-area imaging survey methodology used to extract metrics of structural complexity, community composition, and population demographics for coral reef communities. The quality of imagery collected and integrated access to the source imagery are prioritized within each step of the protocol.

Digital imaging and processing technology have evolved to facilitate the expansion of large-area imaging surveys, which increase our capacity to study the ...

Surface Mapping of Earth-like Exoplanets using Single Point Light Curves

Spatially resolving exoplanet features from single-point observations is essential for evaluating the potential habitability of exoplanets. The ultimate goal of this protocol is to determine whether these planetary worlds harbor geological features and/or climate systems. We present a method of extracting information from multi-wavelength single-point light curves and retrieving surface maps. It uses singular value decomposition (SVD) to separate sources that contribute to light curve variations and infer the existence of partially cloudy climate systems. Through analysis of the time series obtained from SVD, physical attributions of principal components (PCs) could be inferred without assumptions of any spectral properties. Combining with viewing geometry, it is feasible to reconstruct surface maps if one of the PCs are found to contain surface information. Degeneracy originated from convolution of the pixel geometry and spectrum information determines the quality of reconstructed surface maps, which requires the introduction of regularization. For the purpose of demonstrating the protocol, multi-wavelength light curves of Earth, which serves as a proxy exoplanet, are analyzed. Comparison between the results and the ground truth is presented to show the performance and limitation of the protocol. This work provides a benchmark for future generalization of exoplanet applications.

The protocol extracts information from light curves of exoplanets and constructs their surface maps. It uses light curves of Earth, which serves as a proxy exoplanet, to demonstrate the approach.

Spatially resolving exoplanet features from single-point observations is essential for evaluating the potential habitability of exoplanets. The ultimate ...

Parallel Active Cantilever Arrays in AFMS to Enable High-Throughput Inspections

Active Probe Atomic Force Microscopy with Quattro-Parallel Cantilever Arrays for High-Throughput Large-Scale Sample Inspection

An Atomic Force Microscope&#160;(AFM) is&#160;a powerful and versatile tool for nanoscale surface studies to capture 3D topography images of samples. However, due to their limited imaging throughput, AFMs have not been widely adopted for large-scale inspection purposes. Researchers have developed high-speed AFM systems to record dynamic process videos in chemical and biological reactions at tens of frames per second, at the cost of a small imaging area of up to several square micrometers. In contrast, inspecting large-scale nanofabricated structures, such as semiconductor wafers, requires nanoscale spatial resolution imaging of a static sample over hundreds of square centimeters with high productivity. Conventional AFMs use a single passive cantilever probe with an optical beam deflection system, which can only collect one pixel at a time during AFM imaging, resulting in low imaging throughput. This work utilizes an array of active cantilevers with embedded piezoresistive sensors and thermomechanical actuators, which allows simultaneous multi-cantilever operation in parallel operation for increased imaging throughput. When combined with large-range nano-positioners and proper control algorithms, each cantilever can be individually controlled to capture multiple AFM images. With data-driven post-processing algorithms, the images can be stitched together, and defect detection can be performed by comparing them to the desired geometry. This paper introduces principles of the custom AFM using the active cantilever arrays, followed by a discussion on practical experiment considerations for inspection applications. Selected example images of silicon calibration grating, highly-oriented pyrolytic graphite, and extreme ultraviolet lithography masks are captured using an array of four active cantilevers (&#34;Quattro&#34;) with a 125 &#181;m tip separation distance. With more engineering integration, this high-throughput, large-scale imaging tool can provide 3D metrological data for extreme ultraviolet (EUV) masks, chemical mechanical planarization (CMP) inspection, failure analysis, displays, thin-film step measurements, roughness measurement dies, and laser-engraved dry gas seal grooves.

Author Spotlight: Introduction to Active Probe Atomic Force Microscopy with Quattro-Parallel Cantilever Arrays

Large-scale sample inspection with nanoscale resolution has a wide range of applications, especially for nanofabricated semiconductor wafers. Atomic force microscopes can be a great tool for this purpose, but are limited by their imaging speed. This work utilizes parallel active cantilever arrays in AFMs to enable high-throughput and large-scale inspections.

An Atomic Force Microscope&#160;(AFM) is&#160;a powerful and versatile tool for nanoscale surface studies to capture 3D topography images of samples. ...

Neutron Radiography and Computed Tomography of Biological Systems at the Oak Ridge National Laboratory's High Flux Isotope Reactor

Neutrons have historically been used for a broad range of biological applications employing techniques such as small-angle neutron scattering, neutron spin echo, diffraction, and inelastic scattering. Unlike neutron scattering techniques that obtain information in reciprocal space, attenuation-based neutron imaging measures a signal in real space that is resolved on the order of tens of micrometers. The principle of neutron imaging follows the Beer-Lambert law and is based on the measurement of the bulk neutron attenuation through a sample. Greater attenuation is exhibited by some light elements (most notably, hydrogen), which are major components of biological samples. Contrast agents such as deuterium, gadolinium, or lithium compounds can be used to enhance contrast in a similar fashion as it is done in medical imaging, including techniques such as optical imaging, magnetic resonance imaging, X-ray, and positron emission tomography. For biological systems, neutron radiography and computed tomography have increasingly been used to investigate the complexity of the underground plant root network, its interaction with soils, and the dynamics of water flux in situ. Moreover, efforts to understand contrast details in animal samples, such as soft tissues and bones, have been explored. This manuscript focuses on the advances in neutron bioimaging such as sample preparation, instrumentation, data acquisition strategy, and data analysis using the High Flux Isotope Reactor CG-1D neutron imaging beamline. The aforementioned capabilities will be illustrated using a selection of examples in plant physiology&#160;(herbaceous plant/root/soil system)&#160;and biomedical applications&#160;(rat femur and mouse lung).

This manuscript describes a protocol for neutron radiography and computed tomography of biological samples using a High Flux Isotope Reactor (HFIR) CG-1D beamline to measure a metal implant in a rat femur, a mouse lung, and an herbaceous plant root/soil system.

Neutrons have historically been used for a broad range of biological applications employing techniques such as small-angle neutron scattering, neutron ...