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Register a new userLenstool-HPC: A High Performance Computing based mass modelling tool for cluster-scale gravitational lenses
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With the upcoming generation of telescopes, cluster scale strong gravitational lenses will act as an increasingly relevant probe of cosmology and dark matter. The better resolved data produced by current and future facilities requires faster and more efficient lens modeling software. Consequently, we present Lenstool-HPC, a strong gravitational lens modeling and map generation tool based on High Performance Computing (HPC) techniques and the renowned Lenstool software. We also showcase the HPC concepts needed for astronomers to increase computation speed through massively parallel execution on supercomputers. Lenstool-HPC was developed using lens modelling algorithms with high amounts of parallelism. Each algorithm was implemented as a highly optimised CPU, GPU and Hybrid CPU-GPU version. The software was deployed and tested on the Piz Daint cluster of the Swiss National Supercomputing Centre (CSCS). Lenstool-HPC perfectly parallel lens map generation and derivative computation achieves a factor 30 speed-up using only 1 GPUs compared to Lenstool. Lenstool-HPC hybrid Lens-model fit generation tested at Hubble Space Telescope precision is scalable up to 200 CPU-GPU nodes and is faster than Lenstool using only 4 CPU-GPU nodes.
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Lets HPC (www.letshpc.org) is an open-access online platform to supplement conventional classroom oriented High Performance Computing (HPC) and Parallel & Distributed Computing (PDC) education. The web based platform provides online plotting and analysis tools which allow users to learn, evaluate, teach and see the performance of parallel algorithms from a systems viewpoint. The user can quantitatively compare and understand the importance of numerous deterministic as well as non-deterministic factors of both the software and the hardware that impact the performance of parallel programs. At the heart of this platform is a database archiving the performance and execution environment related data of standard parallel algorithms executed on different computing architectures using different programming environments, this data is contributed by various stakeholders in the HPC community. The plotting and analysis tools of our platform can be combined seamlessly with the database to aid self-learning, teaching, evaluation and discussion of different HPC related topics. Instructors of HPC/PDC related courses can use the platforms tools to illustrate the importance of proper analysis in understanding factors impacting performance, to encourage peer learning among students, as well as to allow students to prepare a standard lab/project report aiding the instructor in uniform evaluation. The platforms modular design enables easy inclusion of performance related data from contributors as well as addition of new features in the future.
We present an algorithm using Principal Component Analysis (PCA) to subtract galaxies from imaging data, and also two algorithms to find strong, galaxy-scale gravitational lenses in the resulting residual image. The combined method is optimized to find full or partial Einstein rings. Starting from a pre-selection of potential massive galaxies, we first perform a PCA to build a set of basis vectors. The galaxy images are reconstructed using the PCA basis and subtracted from the data. We then filter the residual image with two different methods. The first uses a curvelet (curved wavelets) filter of the residual images to enhance any curved/ring feature. The resulting image is transformed in polar coordinates, centered on the lens galaxy center. In these coordinates, a ring is turned into a line, allowing us to detect very faint rings by taking advantage of the integrated signal-to-noise in the ring (a line in polar coordinates). The second way of analysing the PCA-subtracted images identifies structures in the residual images and assesses whether they are lensed images according to their orientation, multiplicity and elongation. We apply the two methods to a sample of simulated Einstein rings, as they would be observed with the ESA Euclid satellite in the VIS band. The polar coordinates transform allows us to reach a completeness of 90% and a purity of 86%, as soon as the signal-to-noise integrated in the ring is higher than 30, and almost independent of the size of the Einstein ring. Finally, we show with real data that our PCA-based galaxy subtraction scheme performs better than traditional subtraction based on model fitting to the data. Our algorithm can be developed and improved further using machine learning and dictionary learning methods, which would extend the capabilities of the method to more complex and diverse galaxy shapes.
Power-spectrum analysis is an important tool providing critical information about a signal. The range of applications includes communication-systems to DNA-sequencing. If there is interference present on a transmitted signal, it could be due to a natural cause or superimposed forcefully. In the latter case, its early detection and analysis becomes important. In such situations having a small observation window, a quick look at power-spectrum can reveal a great deal of information, including frequency and source of interference. In this paper, we present our design of a FPGA based reconfigurable platform for high performance power-spectrum analysis. This allows for the real-time data-acquisition and processing of samples of the incoming signal in a small time frame. The processing consists of computation of power, its average and peak, over a set of input values. This platform sustains simultaneous data streams on each of the four input channels.
We present a procedure for efficiently compressing astronomical radio data for high performance applications. Integrated, post-correlation data are first passed through a nearly lossless rounding step which compares the precision of the data to a generalized and calibration-independent form of the radiometer equation. This allows the precision of the data to be reduced in a way that has an insignificant impact on the data. The newly developed Bitshuffle lossless compression algorithm is subsequently applied. When the algorithm is used in conjunction with the HDF5 library and data format, data produced by the CHIME Pathfinder telescope is compressed to 28% of its original size and decompression throughputs in excess of 1 GB/s are obtained on a single core.
A fundamental prediction of the cold dark matter (CDM) model of structure formation is the existence of a vast population of dark matter haloes extending to subsolar masses. By contrast, other possibilities for the nature of the dark matter, such as a warm thermal relic or a sterile neutrino (WDM) predict a cutoff in the mass function at a mass of $sim 10^8~{rm M}_odot$. We use mock observations to demonstrate the viability of a forward modelling approach to extract information on the cosmological number density of low-mass dark matter haloes along the line-of-sight to galaxy-galaxy strong lenses. This can be used to constrain the mass of a thermal relic dark matter particle, $m_mathrm{DM}$. With 50 strong lenses at Hubble Space Telescope resolution and signal-to-noise (similar to the existing SLACS survey), the expected 2$sigma$ constraint for CDM is $m_mathrm{DM} > 3.7 , mathrm{keV}$. If, however, the dark matter is a warm particle of $m_mathrm{DM}=2.2 , mathrm{keV}$, one could rule out $m_mathrm{DM} > 3.2 , mathrm{keV}$. Our [Approximate Bayesian Computation] method can be extended to the large samples of strong lenses that will be observed by future space telescopes, potentially to rule out the standard CDM model of cosmogony. To aid future survey design, we quantify how these constraints will depend on data quality (spatial resolution and integration time) as well as on the lensing geometry (source and lens redshifts).
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