output
20052026
most citedThe Astropy Project: Sustaining and Growing a Community-oriented Open-source Project and the Latest Major Release (v5.0) of the Core Package

5k citations

Showing 2017Show all

118 papers · 1 filter

astro-ph.GA2017

Interferometry of class I methanol masers, statistics and the distance scale

M. A. Voronkov, S. L. Breen, S. P. Ellingsen +1

The Australia Telescope Compact Array (ATCA) participated in a number of survey programs to search for and image common class I methanol masers (at 36 and 44 GHz) with high angular…

astro-ph.GA2017★ 69 cited

The TOP-SCOPE survey of Planck Galactic Cold Clumps: Survey overview and results of an exemplar source, PGCC G26.53+0.17

Tie Liu, Kee-Tae Kim, Mika Juvela +11

The low dust temperatures (<14 K) of Planck Galactic Cold Clumps (PGCCs) make them ideal targets to probe the initial conditions and very early phase of star formation. TOP-SCOPE i…

astro-ph.HE2017★ 43 cited

An image-based search for pulsars among Fermi unassociated LAT sources

D. A. Frail, P. S. Ray, K. P. Mooley +9

We describe an image-based method that uses two radio criteria, compactness and spectral index, to identify promising pulsar candidates among Fermi Large Area Telescope (LAT) unass…

astro-ph.IM2017

Using low-frequency pulsar observations to study the 3-D structure of the Galactic magnetic field

C. Sobey, the LOFAR, MWA collaborations

The Galactic magnetic field (GMF) plays a role in many astrophysical processes and is a significant foreground to cosmological signals, such as the Epoch of Reionization (EoR), but…

astro-ph.HE2017

Studying Magnetic Fields using Low-frequency Pulsar Observations

C. Sobey, the LOFAR, MWA collaborations

Low-frequency polarisation observations of pulsars, facilitated by next-generation radio telescopes, provide powerful probes of astrophysical plasmas that span many orders of magni…

astro-ph.GA2017★ 28 cited

Eddington's Demon: Inferring Galaxy Mass Functions and other Distributions from Uncertain Data

Danail Obreschkow, Steven G. Murray, Aaron S. G. Robotham +1

We present a general modified maximum likelihood (MML) method for inferring generative distribution functions from uncertain and biased data. The MML estimator is identical to, but…