Detection of 16 Gamma-Ray Pulsars Through Blind Frequency Searches Using the Fermi LAT

Abdo, A. A.; Ackermann, M.; Ajello, M.; Anderson, B.; Atwood, W. B.; Axelsson, M.; Baldini, Luca; Ballet, J.; Barbiellini, G.; Baring, Matthew G.; Bastieri, D.; Baughman, B. M.; Bechtol, K.; Bellazzini, R.; Berenji, B.; Bignami, G. F.; Blandford, R. D.; Bloom, E. D.; Bonamente, E.; Borgland, A. W.; Bregeon, J.; Brez, A.; Brigida, M.; Bruel, Pascal; Burnett, T. H.; Caliandro, G. A.; Cameron, R. A.; Caraveo, P. A.; Casandjian, J. M.; Cecchi, C.; Çelik, Ö.; Chekhtman, A.; Cheung, C. C.; Chiang, J.; Ciprini, S.; Claus, R.; Cohen-Tanugi, J.; Conrad, J.; Cutini, S.; Dermer, C. D.; Angelis, A. De; Luca, A. De; Palma, F. de; Digel, S. W.; Dormody, M.; Silva, E. Do Couto E; Drell, P. S.; Dubois, R.; Dumora, D.; Farnier, C.; Favuzzi, C.; Fegan, S. J.; Fukazawa, Yasushi; Funk, S.; Fusco, P.; Gargano, F.; Gasparrini, D.; Gehrels, N.; Germani, S.; Giebels, B.; Giglietto, N.; Giommi, P.; Giordano, F.; Glanzman, T.; Godfrey, G.; Grenier, I. A.; Grondin, M.-H.; Grove, J. E.; Guillemot, L.; Guiriec, S.; Gwon, C.; Hanabata, Y.; Harding, A. K.; Hayashida, M.; Hays, E.; Hughes, R. E.; Jóhannesson, G.; Johnson, R. P.; Johnson, T. J.; Johnson, W. N.; Kamae, T.; Katagiri, H.; Kataoka, J.; Kawai, N.; Kerr, M.; Knödlseder, J.; Kocian, M.; Kuss, M.; Landé, J.; Latronico, L.; Lemoine‐Goumard, M.; Longo, F.; Loparco, F.; Lott, B.; Lovellette, M. N.; Lubrano, P.; Madejski, G. M.; Makeev, A.; Marelli, M.; Mazziotta, M. N.; McConville, W.; McEnery, J. E.; Meurer, C.; Michelson, P. F.; Mitthumsiri, W.; Mizuno, T.; Monte, C.; Monzani, M. E.; Morselli, A.; Moskalenko, I. V.; Murgia, S.; Nolan, P. L.; Norris, J. P.; Nuss, E.; Ohsugi, T.; Omodei, N.; Orlando, E.; Ormes, J. F.; Paneque, D.; Parent, D.; Pelassa, V.; Pepé, M.; Pesce-Rollins, M.; Pierbattista, M.; Piron, F.; Porter, T. A.; Primack, Joel R.; Rainò, S.; Rando, R.; Ray, Paul S.; Razzano, M.; Rea, N.; Reimer, A.; Reposeur, T.; Ritz, S.; Rochester, Lynn; Rodriguez, A. Y.; Romani, Roger W.; Ryde, F.; Sadrozinski, H. F-W.; Sánchez, David; Sander, A.; Parkinson, P. M. Saz; Scargle, Jeffrey D.; Sgrò, C.; Siskind, E. J.; Smith, David Stanley; Smith, P. D.; Spandre, G.; Spinelli, P.; Starck, Jean-Luc; Strickman, M. S.; Suson, D. J.; Tajima, H.; Takahashi, H.; Takahashi, Tadayuki; Tanaka, Takaaki; Thayer, J. G.; Thompson, D. J.; Tibaldo, L.; Tibolla, O.; Torres, D. F.; Tosti, G.; Tramacere, A.; Uchiyama, Y.; Usher, T.; Etten, A. van; Vasileiou, V.; Vilchez, N.; Vitale, V.; Waite, A. P.; Wang, P.; Watters, K.; Winer, B. L.; Wolff, M. T.; Wood, K. S.; Ylinen, T.; Ziegler, M.

doi:10.1126/science.1175558

Cited by 312 publications

(295 citation statements)

References 39 publications

(56 reference statements)

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“…Among the earliest scientific results from the LAT was the discovery of a large population of radio-quiet gamma-ray pulsars (Abdo et al 2009b;Saz Parkinson et al 2010), using a new blind-search technique developed specifically for the very long time series expected with the LAT (Atwood et al 2006). These findings confirmed some predictions that many of the unidentified EGRET sources in the Galactic plane were pulsars(e.g., Yadigaroglu & Romani 1995), favoring outer gap pulsar models (Cheng et al 1986;Romani 1996Romani , 2014, where the gamma rays are generated in the outer magnetosphere, as opposed to polar cap models, where the emission comes from closer to the neutron star surface(e.g., Harding & Muslimov 1998).…”

Section: Introductionsupporting

confidence: 73%

Classification and Ranking of Fermi Lat Gamma-Ray Sources From the 3fgl Catalog Using Machine Learning Techniques

Parkinson

et al. 2016

ApJ

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135

174

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We apply a number of statistical and machine learning techniques to classify and rank gamma-ray sources from the Third Fermi Large Area Telescope Source Catalog (3FGL), according to their likelihood of falling into the two major classes of gamma-ray emitters: pulsars (PSR) or active galactic nuclei (AGNs). Using 1904 3FGL sources that have been identified/associated with AGNs (1738) and PSR (166), we train (using 70% of our sample) and test (using 30%) our algorithms and find that the best overall accuracy (>96%) is obtained with the Random Forest (RF) technique, while using a logistic regression (LR) algorithm results in only marginally lower accuracy. We apply the same techniques on a subsample of 142 known gamma-ray pulsars to classify them into two major subcategories: young (YNG) and millisecond pulsars (MSP). Once more, the RF algorithm has the best overall accuracy (∼90%), while a boosted LR analysis comes a close second. We apply our two best models (RF and LR) to the entire 3FGL catalog, providing predictions on the likely nature of unassociated sources, including the likely type of pulsar (YNG or MSP). We also use our predictions to shed light on the possible nature of some gamma-ray sources with known associations (e.g., binaries, supernova remnants/pulsar wind nebulae). Finally, we provide a list of plausible X-ray counterparts for some pulsar candidates, obtained using Swift, Chandra, and XMM. The results of our study will be of interest both for in-depth follow-up searches (e.g., pulsar) at various wavelengths and for broader population studies.

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Section: Introductionsupporting

confidence: 73%

Classification and Ranking of Fermi Lat Gamma-Ray Sources From the 3fgl Catalog Using Machine Learning Techniques

Parkinson

et al. 2016

ApJ

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135

174

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“…Technical specifications of pulsar surveys conducted between 2000-present, and projected specifications for instruments under development. X-ray pulsar searches undertaken during this period (Abdo et al 2009;Ransom et al 2011) are omitted. Here F c (MHz) is the central observing frequency, B (MHz) is the bandwidth, ∆v (kHz) is the channel width (to 3.d.p), n chans indicates the number of frequency channels, t samp (µs) is the sample frequency (to 3.d.p), and t obs (s) the length of the observation (to 1.d.p).…”

Section: Feature Evaluation Issuesmentioning

confidence: 99%

Fifty years of pulsar candidate selection: from simple filters to a new principled real-time classification approach

Lyon

Stappers

Cooper

et al. 2016

Mon. Not. R. Astron. Soc.

193

174

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Improving survey specifications are causing an exponential rise in pulsar candidate numbers and data volumes. We study the candidate filters used to mitigate these problems during the past fifty years. We find that some existing methods such as applying constraints on the total number of candidates collected per observation, may have detrimental effects on the success of pulsar searches. Those methods immune to such effects are found to be ill-equipped to deal with the problems associated with increasing data volumes and candidate numbers, motivating the development of new approaches. We therefore present a new method designed for on-line operation. It selects promising candidates using a purpose-built tree-based machine learning classifier, the Gaussian Hellinger Very Fast Decision Tree (GH-VFDT), and a new set of features for describing candidates. The features have been chosen so as to i) maximise the separation between candidates arising from noise and those of probable astrophysical origin, and ii) be as survey-independent as possible. Using these features our new approach can process millions of candidates in seconds (∼1 million every 15 seconds), with high levels of pulsar recall (90%+). This technique is therefore applicable to the large volumes of data expected to be produced by the Square Kilometre Array (SKA). Use of this approach has assisted in the discovery of 20 new pulsars in data obtained during the LOFAR Tied-Array All-Sky Survey (LOTAAS).

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“…1. The figure also gives lines of constant magnetic field (computed assuming dipole braking, B = 3.2 10 19 (PṖ) 1/2 G), characteristic age (τ c = 1 2 P/Ṗ), and spin-down luminosity (L SD = I ΩΩ = 4 10 46Ṗ / P 3 erg s −1 ). …”

Section: Introductionmentioning

confidence: 99%

“…Accounting for the selection effects of radio observations, a total population of ∼ 10 6 RPP is estimated for the whole Galaxy [40]. Observations at gamma-ray energy, where pulsar beaming angles are larger, are now contributing to increase the number of known RPP [1]. About one hundred RPP have been detected also at X-ray energies [7]: they include the youngest and more energetic pulsars (like the Crab), a few older neutron stars at small distances (e.g.…”

Section: Introductionmentioning

confidence: 99%

X-ray emission from isolated neutron stars

Mereghetti

2010

High-Energy Emission From Pulsars and Their Systems

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X-ray emission is a common feature of all varieties of isolated neutron stars (INS) and, thanks to the advent of sensitive instruments with good spectroscopic, timing, and imaging capabilities, X-ray observations have become an essential tool in the study of these objects. Non-thermal X-rays from young, energetic radio pulsars have been detected since the beginning of X-ray astronomy, and the long-sought thermal emission from cooling neutron star's surfaces can now be studied in detail in many pulsars spanning different ages, magnetic fields, and, possibly, surface compositions. In addition, other different manifestations of INS have been discovered with X-ray observations. These new classes of high-energy sources, comprising the nearby X-ray Dim Isolated Neutron Stars, the Central Compact Objects in supernova remnants, the Anomalous X-ray Pulsars, and the Soft Gamma-ray Repeaters, now add up to several tens of confirmed members, plus many candidates, and allow us to study a variety of phenomena unobservable in "standard" radio pulsars.

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Detection of 16 Gamma-Ray Pulsars Through Blind Frequency Searches Using the Fermi LAT

Cited by 312 publications

References 39 publications

Classification and Ranking of Fermi Lat Gamma-Ray Sources From the 3fgl Catalog Using Machine Learning Techniques

Classification and Ranking of Fermi Lat Gamma-Ray Sources From the 3fgl Catalog Using Machine Learning Techniques

Fifty years of pulsar candidate selection: from simple filters to a new principled real-time classification approach

X-ray emission from isolated neutron stars

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