The lens SW05 J143454.4+522850: a fossil group at redshift 0.6?
Monthly Notices of the Royal Astronomical Society Oxford University Press (OUP) 506:2 (2021) 1715-1722
Planet Hunters TESS III: two transiting planets around the bright G dwarf HD 152843
Monthly Notices of the Royal Astronomical Society, Volume 505, Issue 2, August 2021, Pages 1827–1840
Abstract:
We report on the discovery and validation of a two-planet system around a bright (V = 8.85 mag) early G dwarf (1.43 R⊙, 1.15 M⊙, TOI 2319) using data from NASA’s Transiting Exoplanet Survey Satellite (TESS). Three transit events from two planets were detected by citizen scientists in the month-long TESS light curve (sector 25), as part of the Planet Hunters TESS project. Modelling of the transits yields an orbital period of 11.6264+0.0022−0.0025 d and radius of 3.41+0.14−0.12 R⊕ for the inner planet, and a period in the range 19.26–35 d and a radius of 5.83+0.14−0.14 R⊕ for the outer planet, which was only seen to transit once. Each signal was independently statistically validated, taking into consideration the TESS light curve as well as the ground-based spectroscopic follow-up observations. Radial velocities from HARPS-N and EXPRES yield a tentative detection of planet b, whose mass we estimate to be 11.56+6.58−6.14 M⊕, and allow us to place an upper limit of 27.5 M⊕ (99 per cent confidence) on the mass of planet c. Due to the brightness of the host star and the strong likelihood of an extended H/He atmosphere on both planets, this system offers excellent prospects for atmospheric characterization and comparative planetology.
A low [CII]/[NII] ratio in the center of a massive galaxy at z = 3.7: Evidence for a transition to quiescence at high redshift? (Corrigendum)
Astronomy & Astrophysics EDP Sciences 650 (2021) c2
Deep learning for automatic segmentation of the nuclear envelope in electron microscopy data, trained with volunteer segmentations
Traffic Wiley 22:7 (2021) 240-253
Abstract:
Advancements in volume electron microscopy mean it is now possible to generate thousands of serial images at nanometre resolution overnight, yet the gold standard approach for data analysis remains manual segmentation by an expert microscopist, resulting in a critical research bottleneck. Although some machine learning approaches exist in this domain, we remain far from realizing the aspiration of a highly accurate, yet generic, automated analysis approach, with a major obstacle being lack of sufficient high-quality ground-truth data. To address this, we developed a novel citizen science project, Etch a Cell, to enable volunteers to manually segment the nuclear envelope (NE) of HeLa cells imaged with serial blockface scanning electron microscopy. We present our approach for aggregating multiple volunteer annotations to generate a high-quality consensus segmentation and demonstrate that data produced exclusively by volunteers can be used to train a highly accurate machine learning algorithm for automatic segmentation of the NE, which we share here, in addition to our archived benchmark data.An old stellar population or diffuse nebular continuum emission discovered in Green Pea galaxies
Astrophysical Journal Letters American Astronomical Society 912:2 (2021) L22