Fast folding algorithm

Abstract: This paper presents a method to detect gamma-ray pulsars using a fast folding algorithm (Staelin, 1969) mapped onto reconfigurable hardware. In contrast, existing techniques require gigapoint complex FFTs. the algorithm has been written in Streams-C and compiled with the sc2 compiler to the target Annapolis Micro Systems (AMS) Firebird board (Xilinx Virtex E processor). To accelerate detection of new gamma-ray pulsars, the sc2 compiler generates a hardware implementation of the algorithm for finding periodicities in data sets. The data to be analyzed comes from a high-energy gamma-ray telescope onboard a spacecraft. This astrophysics application poses a "good example" of the use of a high level reconfigurable computing tool such as sc2 to accelerate an algorithm because it uses real satellite data, the algorithm can be parallelized, and was originally validated using a high level scientific language, IDL. By recasting the algorithm into Streams-C, the scientific software developer can create a hardware implementation on a reconfigurable computing platform. We describe the fast folding algorithm, the Streams-C implementation, and discuss techniques to optimize performance within the Streams-C framework. The compiler-generated hardware delivers approximately 3X to 6X speed up over a comparable 800MHz general-purpose processor doing the software-only algorithm.

Abstract: Many pulsar folding algorithms are currently deployed to generate strong SNRs for the total intensity profiles. But they require large observation times to effectively improve the SNR. Over the years, new approaches to de-noise the pulsar total intensity data have sprung up, powered by Machine learning and Deep learning algorithms. Efforts are made to implement the currently proposed supervised machine learning models, such as ensembling techniques like Decision Tree Regressor, Random Forest Regressor, Adaboost Regressor, Gradient Boosting Regressor (GBR), K-Nearest Neighbours(KNN), and Support Vector Regressor (SVR) to find out the best possible algorithm which can work over a variety of pulsars from the EPN database of pulsars. All the data used in this work is extracted from the European Pulsar Network (EPN) database of pulsar profiles. The training dataset is obtained by post-processing of pulsar profile data from the EPN database and testing is performed on a preselected portion of the original data. The results are obtained by testing the above algorithms for 10 different pulsars including some historically significant ones, and the predicted profiles are plotted. We find that Gradient boosting regressor works the best in denoising pulsar data. Through this work, we will try to emphasize that there is a reduction in the number of periods of folding by 35-40% when a combination of machine learning models with the existing pulsar folding techniques like Fast Folding Algorithm(FFA) is employed, which can further reduce the pulsar observation times for the telescopes hunting for pulsars today.

Abstract: The PALFA survey, the most sensitive blind search for radio pulsars, has now discovered 180 pulsars in the Galactic Plane, the vast of which have periods shorter than 2 seconds. One reason that pulsar surveys may miss long-period radio pulsars is the strong effect of red noise at low modulation frequencies. It is possible to address this reduction in sensitivity by using a Fast-Folding Algorithm (FFA). We have adapted this algorithm for radio pulsar searching and applied it to PALFA observations. A sensitivity analysis of the algorithm has been conducted using synthetic pulsar signals injected in real observational data and this study shows that the FFA improves the PALFA survey sensitivity, as reported in Lazarus et al.(2015), by at least a factor of two at periods of ~6 sec, implying that the PALFA survey should discover more long-period radio pulsars in the future.

Abstract: Pulsar searches are computationally demanding efforts to discover dispersed periodic signals in time- and frequency-resolved data from radio telescopes. The complexity and computational expense of simultaneously determining the frequency-dependent delay (dispersion) and the periodicity of the signal is further exacerbated by the presence of various types of radio-frequency interference (RFI) and observing-system effects. New observing systems with wider bandwidths, higher bit rates and greater overall sensitivity (also to RFI) further enhance these challenges. We present a novel approach to the analysis of pulsar search data. Specifically, we present a neural-network-based pipeline that efficiently suppresses a wide range of RFI signals and instrumental instabilities and furthermore corrects for (a priori unknown) interstellar dispersion. After initial training of the network, our analysis can be run in real time on a standard desktop computer with a commonly available, consumer-grade GPU. We complement our neural network with standard algorithms for periodicity searches. In particular with the Fast Fourier Transform (FFT) and the Fast Folding Algorithm (FFA) and demonstrate that with these straightforward extensions, our method is capable of identifying even faint pulsars, while maintaining an extremely low number of false positives. We furthermore apply our analysis to a subset of the PALFA survey and demonstrate that in most cases the automated dispersion removal of our network produces a time series of similar quality as dedispersing using the actual dispersion measure of the pulsar in question. On our test data we are able to make predictions whether a pulsar is present in the data or not 200 times faster than real time.