Toward an X-ray inventory of nearby neutron stars

Astronomy & Astrophysics EDP Sciences 658 (2022) a95

Authors:

A Vahdat, B Posselt, A Santangelo, GG Pavlov

A Late-Time Radio Flare following a Possible Transition in Accretion State in the Tidal Disruption Event AT 2019azh

(2022)

Authors:

I Sfaradi, A Horesh, R Fender, DA Green, DRA Williams, J Bright, S Schulze

Relevance of photon-photon dispersion within the jet for blazar axionlike particle searches

Physical Review D American Physical Society 105:2 (2022) 23017

Authors:

James Davies, Garret Cotter, Manuel Meyer

Abstract:

Axionlike particles (ALPs) could mix with photons in the presence of astrophysical magnetic fields. Searching for this effect in gamma-ray observations of blazars has provided some of the strongest constraints on ALP parameter space so far. Previously, photon-photon dispersion of gamma rays off of the cosmic microwave background has been shown to be important for these calculations and is universally included in ALP-photon mixing models. Here, we assess the effects of dispersion off of other photon fields within the blazar (produced by the accretion disk, the broad line region, the dust torus, starlight, and the synchrotron field) by modeling the jet and fields of the flat spectrum radio quasar 3C454.3 and propagating ALPs through the model both with and without the full dispersion calculation. We find that the full dispersion calculation can strongly affect the mixing, particularly at energies above 100 GeV—often reducing the ALP-photon conversion probability. This could have implications for future searches planned with, e.g., the Cherenkov Telescope Array, particularly those looking for a reduced opacity of the Universe at the highest energies.

Quantifying Uncertainty in Deep Learning Approaches to Radio Galaxy Classification

ArXiv 2201.01203 (2022)

Authors:

Devina Mohan, Anna MM Scaife, Fiona Porter, Mike Walmsley, Micah Bowles

Building high accuracy emulators for scientific simulations with deep neural architecture search.

Mach. Learn. Sci. Technol. 3 (2022) 1

Authors:

Muhammad Firmansyah Kasim, Duncan Watson-Parris, Lucia Deaconu, Sophy Oliver, Peter W Hatfield, Dustin H Froula, Gianluca Gregori, Matt Jarvis, Samar Khatiwala, Jun Korenaga, Jacob Topp-Mugglestone, Eleonora Viezzer, Sam M Vinko