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We have designed, fabricated, and measured a 5-channel prototype spectrometer pixel operating in the WR10 band to demonstrate a novel moderate-resolution (R=f/{Delta}f~100), multi-pixel, broadband, spectrometer concept for mm and submm-wave astronomy. Our design implements a transmission line filter bank using waveguide resonant cavities as a series of narrow-band filters, each coupled to an aluminum kinetic inductance detector (KID). This technology has the potential to perform the next generation of spectroscopic observations needed to drastically improve our understanding of the epoch of reionization (EoR), star formation, and large-scale structure of the universe. We present our design concept, results from measurements on our prototype device, and the latest progress on our efforts to develop a 4-pixel demonstrator instrument operating in the 130-250 GHz band.
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Imaging and spectroscopy at (sub-)millimeter wavelengths are key frontiers in astronomy and cosmology. Large area spectral surveys with moderate spectral resolution (R=50-200) will be used to characterize large scale structure and star formation through intensity mapping surveys in emission lines such as the CO rotational transitions. Such surveys will also be used to study the SZ effect, and will detect the emission lines and continuum spectrum of individual objects. WSPEC is an instrument proposed to target these science goals. It is a channelizing spectrometer realized in rectangular waveguide, fabricated using conventional high-precision metal machining. Each spectrometer is coupled to free space with a machined feed horn, and the devices are tiled into a 2D array to fill the focal plane of the telescope. The detectors will be aluminum Lumped-Element Kinetic Inductance Detectors (LEKIDs). To target the CO lines and SZ effect, we will have bands at 135-175 GHz and 190-250 GHz, each Nyquist-sampled at R~200 resolution. Here we discuss the instrument concept and design, and successful initial testing of a WR10 (i.e. 90 GHz) prototype spectrometer. We recently tested a WR5 (180 GHz) prototype to verify that the concept works at higher frequencies, and also designed a resonant backshort structure that may further increase the optical efficiency. We are making progress towards integrating a spectrometer with a LEKID array and deploying a prototype device to a telescope for first light.
SuperSpec is an ultra-compact spectrometer-on-a-chip for millimeter and submillimeter wavelength astronomy. Its very small size, wide spectral bandwidth, and highly multiplexed readout will enable construction of powerful multibeam spectrometers for high-redshift observations. The spectrometer consists of a horn-coupled microstrip feedline, a bank of narrow-band superconducting resonator filters that provide spectral selectivity, and Kinetic Inductance Detectors (KIDs) that detect the power admitted by each filter resonator. The design is realized using thin-film lithographic structures on a silicon wafer. The mm-wave microstrip feedline and spectral filters of the first prototype are designed to operate in the band from 195-310 GHz and are fabricated from niobium with at Tc of 9.2K. The KIDs are designed to operate at hundreds of MHz and are fabricated from titanium nitride with a Tc of 2K. Radiation incident on the horn travels along the mm-wave microstrip, passes through the frequency-selective filter, and is finally absorbed by the corresponding KID where it causes a measurable shift in the resonant frequency. In this proceedings, we present the design of the KIDs employed in SuperSpec and the results of initial laboratory testing of a prototype device. We will also briefly describe the ongoing development of a demonstration instrument that will consist of two 500-channel, R=700 spectrometers, one operating in the 1-mm atmospheric window and the other covering the 650 and 850 micron bands.
In this paper we characterize and construct novel oversampled filter banks implementing fusion frames. A fusion frame is a sequence of orthogonal projection operators whose sum can be inverted in a numerically stable way. When properly designed, fusion frames can provide redundant encodings of signals which are optimally robust against certain types of noise and erasures. However, up to this point, few implementable constructions of such frames were known; we show how to construct them using oversampled filter banks. In this work, we first provide polyphase domain characterizations of filter bank fusion frames. We then use these characterizations to construct filter bank fusion fra
We experimentally demonstrate the principle of an on-chip submillimeter wave filter bank spectrometer, using superconducting microresonators as narrow band-separation filters. The filters are made of NbTiN/SiNx/NbTiN microstrip line resonators, which have a resonance frequency in the range of 614-685 GHz---two orders of magnitude higher in frequency than what is currently studied for use in circuit quantum electrodynamics and photodetectors. The frequency resolution of the filters decreases from 350 to 140 with increasing frequency, most likely limited by dissipation of the resonators.
Data is said to follow the transform (or analysis) sparsity model if it becomes sparse when acted on by a linear operator called a sparsifying transform. Several algorithms have been designed to learn such a transform directly from data, and data-adaptive sparsifying transforms have demonstrated excellent performance in signal restoration tasks. Sparsifying transforms are typically learned using small sub-regions of data called patches, but these algorithms often ignore redundant information shared between neighboring patches. We show that many existing transform and analysis sparse representations can be viewed as filter banks, thus linking the local properties of patch-based model to the global properties of a convolutional model. We propose a new transform learning framework where the sparsifying transform is an undecimated perfect reconstruction filter bank. Unlike previous transform learning algorithms, the filter length can be chosen independently of the number of filter bank channels. Numerical results indicate filter bank sparsifying transforms outperform existing patch-based transform learning for image denoising while benefiting from additional flexibility in the design process.
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