The PALFA Survey: Going to great depths to find radio pulsars

The on-going PALFA survey is searching the Galactic plane (|b| < 5 deg., 32 < l < 77 deg. and 168 < l < 214 deg.) for radio pulsars at 1.4 GHz using ALFA, the 7-beam receiver installed at the Arecibo Observatory. By the end of August 2012, the PALFA survey has discovered 100 pulsars, including 17 millisecond pulsars (P < 30 ms). Many of these discoveries are among the pulsars with the largest DM/P ratios, proving that the PALFA survey is capable of probing the Galactic plane for millisecond pulsars to a much greater depth than any previous survey. This is due to the survey's high sensitivity, relatively high observing frequency, and its high time and frequency resolution. Recently the rate of discoveries has increased, due to a new more sensitive spectrometer, two updated complementary search pipelines, the development of online collaborative tools, and access to new computing resources. Looking forward, focus has shifted to the application of artificial intelligence systems to identify pulsar-like candidates, and the development of an improved full-resolution pipeline incorporating more sophisticated radio interference rejection. The new pipeline will be used in a complete second analysis of data already taken, and will be applied to future survey observations. An overview of recent developments, and highlights of exciting discoveries will be presented.Comment: Proceedings of IAUS 291 "Neutron Stars and Pulsars: Challenges and Opportunities after 80 years", J. van Leeuwen (ed.); 6 pages, 4 figure

Applying hybrid clustering in pulsar candidate sifting with multi-modality for FAST survey

Pulsar search is always the basis of pulsar navigation, gravitational wave detection and other research topics. Currently, the volume of pulsar candidates collected by Five-hundred-meter Aperture Spherical radio Telescope (FAST) shows an explosive growth rate that has brought challenges for its pulsar candidate filtering System. Particularly, the multi-view heterogeneous data and class imbalance between true pulsars and non-pulsar candidates have negative effects on traditional single-modal supervised classification methods. In this study, a multi-modal and semi-supervised learning based pulsar candidate sifting algorithm is presented, which adopts a hybrid ensemble clustering scheme of density-based and partition-based methods combined with a feature-level fusion strategy for input data and a data partition strategy for parallelization. Experiments on both HTRU (The High Time Resolution Universe Survey) 2 and FAST actual observation data demonstrate that the proposed algorithm could excellently identify the pulsars: On HTRU2, the precision and recall rates of its parallel mode reach 0.981 and 0.988. On FAST data, those of its parallel mode reach 0.891 and 0.961, meanwhile, the running time also significantly decrease with the increment of parallel nodes within limits. So, we can get the conclusion that our algorithm could be a feasible idea for large scale pulsar candidate sifting of FAST drift scan observation

Predicting Pulsars from Imbalanced Dataset with Hybrid Resampling Approach

Pulsar stars, usually neutron stars, are spherical and compact objects containing a large quantity of mass. Each pulsar star possesses a magnetic field and emits a slightly different pattern of electromagnetic radiation which is used to identify the potential candidates for a real pulsar star. Pulsar stars are considered an important cosmic phenomenon, and scientists use them to study nuclear physics, gravitational waves, and collisions between black holes. Defining the process of automatic detection of pulsar stars can accelerate the study of pulsar stars by scientists. This study contrives an accurate and efficient approach for true pulsar detection using supervised machine learning. For experiments, the high time-resolution (HTRU2) dataset is used in this study. To resolve the data imbalance problem and overcome model overfitting, a hybrid resampling approach is presented in this study. Experiments are performed with imbalanced and balanced datasets using well-known machine learning algorithms. Results demonstrate that the proposed hybrid resampling approach proves highly influential to avoid model overfitting and increase the prediction accuracy. With the proposed hybrid resampling approach, the extra tree classifier achieves a 0.993 accuracy score for true pulsar star prediction

SETI science working group report

This report covers the initial activities and deliberations of a continuing working group asked to assist the SETI Program Office at NASA. Seven chapters present the group's consensus on objectives, strategies, and plans for instrumental R&D and for a microwave search for extraterrestrial in intelligence (SETI) projected for the end of this decade. Thirteen appendixes reflect the views of their individual authors. Included are discussions of the 8-million-channel spectrum analyzer architecture and the proof-of-concept device under development; signal detection, recognition, and identification on-line in the presence of noise and radio interference; the 1-10 GHz sky survey and the 1-3 GHz targeted search envisaged; and the mutual interests of SETI and radio astronomy. The report ends with a selective, annotated SETI reading list of pro and contra SETI publications