dataesr/scientific-paragraphs-categorization huggingface dataset:
Symmetry is imposed such that there is reflection symmetry in the direction normal to the plane of deflection within each metagrating unit cell (Figure  1b ). In the database, devices are labeled and categorized based on operating wavelength, polarization, deflection angle, material, device thickness, dimensionality, design method, and efficiency. It is possible to search and filter devices in MetaNet based on these parameters, and device files can be downloaded individually or as filtered groups. For the current batch of uploaded devices, the wavelength range spans 500 to 1300 nm and experimentally-measured refractive indices for polycrystalline silicon are posted in the database. To simplify our analyses, we have dropped the imaginary part of the refractive index when designing and evaluating our devices.
In the future, we will expand the database to include a wider scope of materials, including other variants of silicon, such as crystalline  [37]  and amorphous silicon  [38] , as well as titanium dioxide  [39, 40]  and silicon nitride  [41, 42] , which are commonly used for visible light applications. We will also include devices with more diverse functionalities, such as polarization control, as well as wavelength-scale scattering elements that can be stitched together to create aperiodic structures  [19].
Metagrating optimization algorithms We generate metagrating datasets in MetaNet using two different topology optimization methods. The first is with gradient-based topology optimization based on the adjoint variables method. This optimization method is local in that it uses the method of gradient descent to improve the performance of an initial dielectric distribution. The second is with global topology optimization networks (GLOnets), which is a global optimizer. With GLOnets, the optimization problem is reframed as the training of a generative neural network. Open source code for both methods is available at: http://metanet. stanford. edu/code/. We hope that the sharing of baseline optimization code becomes a more mainstream practice within the nanophotonics community, as it would facilitate the advancement of optimization algorithm development.
Gradient-based topology optimization. The basis for most of our dataset generation is gradient-based topology optimization. In this method, the objective is to maximize a Figure of Merit, which in our case is the intensity of light deflected to the desired deflection angle. For a given device dielectric distribution, the Figure of Merit is iteratively improved by computing a gradient, based on forward and adjoint simulations, and using it to perturb the dielectric values of each voxel in the device each iteration. For dielectric metagratings, the method is described in more detail in Ref. [23]. As a method of gradient descent, the optimization itself is deterministic and fully dependent on the initial dielectric distribution. We generate large datasets by optimizing a batch of devices, each with different random initial dielectric distributions.
In the posted version of this code in MetaNet, we have made efforts to ensure that the code is fast, accurate, readable, and easy to use and modify by researchers unfamiliar with topology optimization. First, we have organized the code with clear naming conventions. Variable names and definitions are assigned so they are consistent with descriptions of the methods in our prior publications  [23, 24]  as well as devices currently uploaded to MetaNet. Second, we include significant in-line documentation to make the code easy to use and modify. Alongside parameter and function definitions, we have included a set of default hyperparameter values such as the learning rate, binarization coefficient, and robustness-related parameters, so that the code is accessible at all levels of usage and can be run "out of the box.
" Third, the code has been structured to be compatible with native Matlab parallel computing capabilities. With only minor modifications, the code can be run on multiple cores on a single workstation or across multiple nodes in a computing cluster. Finally, we couple our algorithms with a mainstream, high-speed, open source electromagnetic solver, Reticolo  [43]. Reticolo is Matlab-based and can efficiently and accurately compute the electromagnetic fields required in our optimization. With our combined optimization and simulation platform, there is no complicated installation or requirement of additional libraries needed to run Reticolo and the entire package can be downloaded from a single location. The overall setup time from download to running an optimization should take less than five minutes.
In addition to being easily accessible, we have found Reticolo to be amongst the fastest and most accurate RCWA solvers available. Reticolo is capable of running efficiently on different computing systems ranging from personal computers to computing clusters, without the need for expensive GPUs and excessive memory, which is an often overlooked feature in newer electromagnetic solvers. At present, our code can be configured to apply to a broad range of metagrating optimization cases, including devices that operate with different polarizations and at single or broadband wavelengths. Further code refactorization, such as that featured in Ref. [44] , can provide added modularity and even interchangability of electromagnetic solvers. Code refinement for diffractive optics optimization, including refractorization, will be the subject of future study.
GLOnets. GLOnet is a global optimizer based on the training of a generative neural network and can output highly efficient topology-optimized metasurfaces that operate near or at the physical limits of structured media engineering  [26, 45]. A key feature of the optimization approach is that the network initially generates a distribution of devices that broadly samples the design space, and it then shifts and refines this distribution towards favorable design space regions over the course of optimization. Training is performed by calculating the forward and adjoint electromagnetic simulations of outputted devices and using the subsequent efficiency gradients for backpropagation. The GLOnets code posted to MetaNet is from Ref. [45]. GLOnet code consists of two parts. First, the neural network is defined and trained in Python utilizing the PyTorch library.
Benchmarking 2D and 3D devices With the MetaNet library, it is straightforward to directly compare and benchmark different methods and device types. Here, we compare 2D and 3D metagratings performing the same task of deflecting light of specific wavelength to a target angle. In Figure  2 , we plot the efficiencies of the highest performing 2D and 3D deflectors generated by gradient-based topology optimization featured in the database. During optimization, the 2D devices are restricted to a minimum feature size of 30 nm. As it is difficult to add strict minimal feature size constraint on 3D devices, the 3D devices are optimized with robustness control of 5 nm edge deviation  [25]  to ensure fabricability. All devices have the same thickness of 325 nm and operate with TE polarization. We compare deflectors across a wavelength range of 500 to 1300 nm and deflection angles from 35 to 85 degrees.
Statistical analysis of dataset Principle component analysis (PCA) is a technique for analyzing high-dimensional datasets by reducing them to a more manageable number of dimensions. In this section, we present the PCA of 2D deflectors generated by unconditional GLOnets. [45]  The lower dimensional PCA representation allows us to directly visualize the variance in GLOnet-generated device patterns. An important consideration in this analysis is that the individual metagrating unit cells are invariant to arbitrary spatial translations and wrapping. As an example, the left part of Figure  4a  shows three physically identical unit cell patterns exhibiting different levels of spatial translation and wrapping. If we naively apply conventional PCA directly to these patterns, these equivalent patterns will appear as distinctive devices.
With our translationally invariant device representations, we apply PCA to better understand and map out the distribution of globally optimized devices within the design space. For a given operating wavelength and deflection angle pair, we first train twenty GLOnets and build a "globally optimized" dataset by generating five hundred devices and selecting the top five devices from each GLOnet. We then use the aforementioned preprocessing procedure to transform this dataset from x(n) to f (k). Finally, we perform PCA on f (k). Representative PCA results are shown in Figure  4. Figure  4b  shows results for devices operating at a wavelength of 850 nm and a deflection angle of 65 degrees. The majority of GLOnets converge to the same global optimum (i. e. , cluster of red dots in the figure), which is a device possessing three ridges that operates with an efficiency of 97%.
Conclusions and Outlook We have introduced the MetaNet database as a tool to facilitate design in the nanophotonics community. Currently, we have uploaded over one hundred thousand designs of metagratings, created using both gradient-based topology optimization and global topology optimization, and have categorized the devices by geometry and function. We have also demonstrated applications of MetaNet using our own metagrating dataset by performing aperiodic metasurface device design and using principal component analysis to better understand the design space. Beyond simply adding more devices and device categories, MetaNet can be still be improved in a variety of ways. For example, if we wish to utilize the maximum potential of MetaNet for machine learning, MetaNet should include not only final device patterns but also optimization trajectory information such as the intermediate patterns and gradients.
Ultimately, we anticipate that contributions from the broader community, together with coordinated dialogue and agreement in specifying good model systems and device parameters for study, will be required for MetaNet to have maximum efficacy in our community. The creation of a central database for photonics devices raises questions regarding standardization and data-sharing that must be collectively addressed by the photonics community in the future. For instance, tracking and quantifying the minimum feature size and robustness in freeform devices is important because there exist practical limits to fabrication resolution and intrinsic trade offs between feature size, robustness, and device performance. However, there currently is no well-defined metric to specify and compare features sizes in freeform devices.
Another issue is the non-uniformity of data storage, as different research groups often use entirely different file formats. For the current dataset, we have chosen the HDF5 format, the primary storage format of Matlab's ". mat" data files in newer versions. HDF5 is also supported by Python, Java, C, Julia, and many other common languages. This format streamlines data management by allowing device layout data to be readily combined with other device-relevant data. However, other formats such as GDSII files are more accessible and more conducive to implementation in a fabrication write file. The trades offs between various formats will have to be discussed, and it will likely be necessary to develop new software that can quickly convert design files between different file formats under a fixed set of conventions.
There are very effective techniques for removing the stellar background but these techniques cannot remove the noise introduced by the stars. This noise component effectively dictates the limiting magnitude of the subtracted images. Data with better seeing do reach fainter limits but our best ground-based search data missed detecting Arrokoth by about one stellar magnitude. The New Horizons mission team needed as much information about the target as possible prior to the flyby of Arrokoth on 2019 Jan 01 (UT). The spatial resolution of HST is about 1200 km per pixel in our imaging data. Stellar occultations provide higher spatial resolution data, typically at or better than 1 km, that greatly exceed what is possible with HST. However, a successful occultation has its own challenges. The target body must pass close enough to a star when it can be seen from somewhere on Earth that has good weather.
With a sufficiently accurate star position, this information translates to astrometric data that is at least a factor of 100 better than a single HST image and subject to completely different potential systematic errors. These astrometric constraints were expected to be very important for improved orbit estimates prior to the encounter for both navigation of New Horizons as well as pointing information for the cameras. The albedo of Arrokoth was clearly one of our measurement objectives but it also played a role in building a successful observing strategy for the deployment for the occultation observations. Using our absolute magnitude estimate of H V = 11. 1, a 4% albedo implies a diameter of 40 km. Ignoring the photometric errors on the absolute magnitude, this represented a practical upper limit to its size.
In addition, the University of Virginia (UVA) provided three additional systems, all of differing designs. We assigned these systems codes T23 to T25 and we will describe these separately. We relied heavily on astrometric support catalogs for this project. During the project we used many different catalogs. However, getting a good prediction required using the same catalog to calibrate the HST astrometry and obtain the position of the occultation star. In the earliest phase of this project, we used a special catalog developed by S. Gwyn at Canadian Astronomy Data Centre (CADC) using data from the Canada France Hawaii Telescope (CFHT) Megacam system. This catalog had better internal consistency than any other catalog available at the time; however, this catalog did not have useful proper motion information.
We obtained HST images of all candidate occultation stars. The HST images showed no signs of stellar duplicity. The images captured the positions of the stars near the epoch of the occultation so that the projected uncertainty from proper motion would not dominate the prediction uncertainty. As we were working to extract this information, the Gaia Mission graciously agreed to provide pre-release positions from the Gaia Data Release 2 (DR2) catalog which had just finished its initial processing. With DR2 in hand, there was no need to get positions from the HST data. The Gaia DR2 pre-release sub-catalog covered an area of the sky encompassing all of the HST observations of Arrokoth from the discovery epoch in 2014 through the end of 2017. The area also included the occultation stars. This catalog contained proper motions and uncertainties for all listed stars.
The catalog density in these areas was high enough that the final uncertainty of the WCS calibration for the HST images was a negligible component of the occultation predictions. Having Gaia DR2 information on the occultation stars was fundamentally important because it meant all of the astrometry for Arrokoth and all of the occultation stars were in the same catalog system. More significantly, DR2 was referenced to the same ICRF as used for navigation of New Horizons. This allowed us to quantify the uncertainties for the HST observations, the orbit estimation, and for the occultation predictions themselves, and ultimately provide useful positional data that would aid with navigation for the New Horizons flyby. Table  1  provides the final positions of the stars used in support of the occultation campaigns.
Another important component of event predictions is the orbit estimation for Arrokoth. We had an on-going program with HST to observe the object periodically and collect additional astrometry, starting in 2014 with its discovery and continuing through October 2018. Our baseline observing cadence was five epochs of data per year, spread out over • • • • • • • • • • • • Note-Positions are all referenced to EME2000. the apparition. Each epoch consisted of five 370-sec exposures, usually within a single visibility window from HST (one orbit). In 2017, a special lightcurve campaign added an additional 24 orbits of astrometric data just prior to the SOFIA occultation attempt. We assembled the twenty-two mobile T01-T22 systems with commercially available components plus custom storage and shipping crates.
This software can write each image to a separate FITS file while also recording the GPS-based start time for each exposure. The software writes the latitude and longitude to the header of each file. Because the camera does not pass out the altitude, we had to manually record the altitude information. All of our systems used SharpCap version 3. 0. 3938. 0 for the entirety of the 2017 campaigns. The GPS position in this version of the software was not always accurate, so we instructed all teams to use another means (usually a cell phone) to record their location. The camera has a fast 12-bit A/D converter but SharpCap shifts the data to the most significant bits of a 16-bit integer. This version of SharpCap allowed a variable system gain setting between 1 and 48; the higher the gain number, the fewer photons per count.
Updated firmware was eventually provided but too late to be used for these occultation campaigns. These cameras also do not appear to save the almanac information between uses; therefore, we ran the cameras for at least 20 minutes so that we were certain to get the correct leap second information. Before the update, the system time was off by 2 seconds. There were no direct indications that this update happened without watching the clock very carefully. The observing protocol for these systems naturally led to a period of operation before data collection well in excess of the almanac update interval. We collected data with an inexpensive laptop with a spinning hard disk. This system was not quite capable of 5 Hz read-out speeds for full frames, but laptops with solid-state hard drives showed much higher readout speeds, closer to 12 Hz.
2018 Summary All of the systems once again used the QHY cameras with embedded GPS receivers. SharpCap was again used for data collection after upgrading to version 3. 1. 5219. 0. With this version, the error in recording the position was fixed. Most operations were the same as before but the gain control values were a factor of 10 higher in the new software. Thus, our previous "standard" gain value of 30 was now 300. Additionally, the GPS receiver status was now visible all the time on the main control screen to help monitor its state more closely. Buie et al.
Prediction The prediction for this event was finalized very close to deployment. We should have had new astrometry from HST in March 2017 but those observations were lost to an HST schedule interruption due to an unrelated technical anomaly. The earliest we were able to reschedule HST was 2017 May 1. Until we obtained the 2017 May 1 data, the most recent observation data was from 2016 Oct 24. The new data provided a substantial increase in the total arc-length of the Arrokoth astrometry, from 2. 3 years to 2. 9 years with a corresponding decrease in the extrapolation to the time of the event from 7 months down to just one month. Also on May 1, we obtained the HST observations of the three 2017 target stars. We used these target star observations for astrometry and to search for stellar duplicity.
We did not find any stellar companions or duplicity in the HST images down to the resolution limit of the data (∼40 mas). We were able to significantly improve the orbit estimate with this new astrometry; however, we still needed to resolve fundamental questions about the position of the occultation star and the associated uncertainty. We began working on the HST data to improve our constraints on the star positions. However, on 2017 May 6 we were provided access to preliminary data from the planned Gaia DR2  (Gaia Collaboration et al. 2016). Those data included the occultation stars among the stars in the region around Arrokoth back to 2014. We reprocessed all of the HST data with updated reference stars to improve the orbit estimate. Within a week we had refined the event prediction enough so that we could determine where to deploy the observing teams and begin the process of shipping equipment and setting up travel logistics.
(2013). The AC is a shutter-less 1k×1k frame transfer CCD camera with pixel scale of 0. 12 /pixel that supports sub-frame windowing. We acquired a nearly 60-minute sequence centered on the nominal overhead passage time. We positioned the CCD so that the target star and a nearby reference star were fully included in the window. The CCD was read out with 2×2 on-chip binning with a window of 88×65 binned pixels. We used an exposure time of 0. 1 s. Nominally, the Gemini header creation system creates image timestamps. However, this system was never intended to operate at the high cadences of our sequence. Because of this limitation, we disabled the header creation system to maximize cadence and minimize inter-exposure deadtime. We created image timestamps by monitoring file creation times which were produced by GPS time within the Linux system.
This imaging configuration resulted in a 0. 107 s median deadtime due to image readout and file writing. The resulting image cadence was 4. 8 Hz. We de-biased and flattened science frames in the usual manner using sky-flats. We extracted photometry using the SExtractor software package, and calibrated the relative flux of the target star using the brighter reference star. The resulting photometry had a mean signal-to-noise ratio (SNR) of 48. No occulting structures, dust, rings, or solid bodies were seen. The cross-track offset for these data was 384 km, too far away to be relevant for the solid-body occultation.
We performed photometry on the target star and the one nearby brighter comparison. We carefully selected a background region to avoid other stars in the field. The optimal aperture was 6 binned pixels or 3. 65 arcsec. Figure  4  shows the resulting differential light curve, normalized to one, with a signal-to-noise ratio (mean over standard deviation) of 21. These data are the closest to the shadow centerline and have significantly higher SNR and time resolution compared to the mobile stations. We saw no evidence for any solid body event. The data have a cadence of roughly 2 km per sample and grazing events as short as 200 m can be ruled out. In the subsequent analysis, we simply treat this as a non-detection and do not consider potential grazing chord constraints. We will return to the constraints provided by these data when discussing the data from all occultation events together (see §7.
2017 JULY 10 EVENT We originally considered this event for a large mobile deployment. Because of a number of logistical difficulties, we focused on large aperture observations to search for or constrain the presence of rings or diffuse dust structures. The global view of the ground track in Figure  5  shows some of the difficulties. The shadow only crossed land in regions of South America that were unlikely to be clear. More importantly, this event occurred just 17 • from a 99% illuminated moon and the occulted star was the faintest star of the four. The largest effort went into supporting an observation with SOFIA  (Temi et al. 2014). Data were collected at other fixed sites but in the end were less constraining than the Gemini data from 2017-06-03.
The goals of the lightcurve investigation dictated the timespan and spacing of the observations, while we set the end time of those observations to allow all of these data to be included in the SOFIA prediction, with the smallest temporal gap between the end of data and the time of the occultation opportunity. The new dataset doubled the number of astrometric measurements between this and the previous occultation. We once again reduced all images against the pre-release version of the Gaia DR2 catalog  (Gaia Collaboration et al. 2018). The vastly improved prediction uncertainty for the cross-track position was 14 km. The in-track error (timing) was 21 km (0. 86 sec).