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Register a new userStreaming distributed Fourier transform for large-scale interferometry imaging
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Imaging data from upcoming radio telescopes requires distributing processing at large scales. This paper presents a distributed Fourier transform algorithm for radio interferometry processing. It generates arbitrary grid chunks with full non-coplanarity corrections while minimising memory residency, data transfer and compute work. We utilise window functions to isolate the influence between regions of grid and image space. This allows us to distribute image data between nodes and construct parts of grid space exactly when and where needed. The developed prototype easily handles image data terabytes in size, while generating visibilities at great throughput and accuracy. Scaling is demonstrated to be better than cubic in baseline length, reducing the risk involved in growing radio astronomy processing to the Square Kilometre Array and similar telescopes.
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In this work, we present two parallel algorithms for the large-scale discrete Fourier transform (DFT) on Tensor Processing Unit (TPU) clusters. The two parallel algorithms are associated with two formulations of DFT: one is based on the Kronecker product, to be specific, dense matrix multiplications between the input data and the Vandermonde matrix, denoted as KDFT in this work; the other is based on the famous Cooley-Tukey algorithm and phase adjustment, denoted as FFT in this work. Both KDFT and FFT formulations take full advantage of TPUs strength in matrix multiplications. The KDFT formulation allows direct use of nonuniform inputs without additional step. In the two parallel algorithms, the same strategy of data decomposition is applied to the input data. Through the data decomposition, the dense matrix multiplications in KDFT and FFT are kept local within TPU cores, which can be performed completely in parallel. The communication among TPU cores is achieved through the one-shuffle scheme in both parallel algorithms, with which sending and receiving data takes place simultaneously between two neighboring cores and along the same direction on the interconnect network. The one-shuffle scheme is designed for the interconnect topology of TPU clusters, minimizing the time required by the communication among TPU cores. Both KDFT and FFT are implemented in TensorFlow. The three-dimensional complex DFT is performed on an example of dimension $8192 times 8192 times 8192$ with a full TPU Pod: the run time of KDFT is 12.66 seconds and that of FFT is 8.3 seconds. Scaling analysis is provided to demonstrate the high parallel efficiency of the two DFT implementations on TPUs.
We present an overview of SITELLE, an Imaging Fourier Transform Spectrometer (iFTS) available at the 3.6-meter Canada-France-Hawaii Telescope. SITELLE is a Michelson-type interferometer able to reconstruct the spectrum of every light source within its 11 field of view in filter-selected bands of the visible (350 to 900 nm). The spectral resolution can be adjusted up to R = 10 000 and the spatial resolution is seeing-limited and sampled at 0.32 arcsec per pixel. We describe the design of the instrument as well as the data reduction and analysis process. To illustrate SITELLEs capabilities, we present some of the data obtained during and since the August 2015 commissioning run. In particular, we demonstrate its ability to separate the components of the [OII] $lambdalambda$ 3726,29 doublet in Orion and to reach R = 9500 around H-alpha; to detect diffuse emission at a level of 4 x 10e-17 erg/cm2/s/arcsec2; to obtain integrated spectra of stellar absorption lines in galaxies despite the well-known multiplex disadvantage of the iFTS; and to detect emission-line galaxies at different redshifts.
The Far-Infrared Surveyor (FIS) onboard the AKARI satellite has a spectroscopic capability provided by a Fourier transform spectrometer (FIS-FTS). FIS-FTS is the first space-borne imaging FTS dedicated to far-infrared astronomical observations. We describe the calibration process of the FIS-FTS and discuss its accuracy and reliability. The calibration is based on the observational data of bright astronomical sources as well as two instrumental sources. We have compared the FIS-FTS spectra with the spectra obtained from the Long Wavelength Spectrometer (LWS) of the Infrared Space Observatory (ISO) having a similar spectral coverage. The present calibration method accurately reproduces the spectra of several solar system objects having a reliable spectral model. Under this condition the relative uncertainty of the calibration of the continuum is estimated to be $pm$ 15% for SW, $pm$ 10% for 70-85 cm^(-1) of LW, and $pm$ 20% for 60-70 cm^(-1) of LW; and the absolute uncertainty is estimated to be +35/-55% for SW, +35/-55% for 70-85 cm^(-1) of LW, and +40/-60% for 60-70 cm^(-1) of LW. These values are confirmed by comparison with theoretical models and previous observations by the ISO/LWS.
We present new data obtained with SpIOMM, the imaging Fourier transform spectrometer attached to the 1.6-m telescope of the Observatoire du Mont-Megantic in Quebec. Recent technical and data reduction improvements have significantly increased SpIOMMs capabilities to observe fainter objects or weaker nebular lines, as well as continuum sources and absorption lines, and to increase its modulation efficiency in the near ultraviolet. To illustrate these improvements, we present data on the supernova remnant Cas A, planetary nebulae M27 and M97, the Wolf-Rayet ring nebula M1-67, spiral galaxies M63 and NGC 3344, as well as the interacting pair of galaxies Arp 84.
Astronomical optical interferometers (OI) sample the Fourier transform of the intensity distribution of a source at the observation wavelength. Because of rapid atmospheric perturbations, the phases of the complex Fourier samples (visibilities) cannot be directly exploited , and instead linear relationships between the phases are used (phase closures and differential phases). Consequently, specific image reconstruction methods have been devised in the last few decades. Modern polychromatic OI instruments are now paving the way to multiwavelength imaging. This paper presents the derivation of a spatio-spectral (3D) image reconstruction algorithm called PAINTER (Polychromatic opticAl INTErferometric Reconstruction software). The algorithm is able to solve large scale problems. It relies on an iterative process, which alternates estimation of polychromatic images and of complex visibilities. The complex visibilities are not only estimated from squared moduli and closure phases, but also from differential phases, which help to better constrain the polychromatic reconstruction. Simulations on synthetic data illustrate the efficiency of the algorithm.
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