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.Supernova neutrino burst detection with the Deep Underground Neutrino Experiment
The European Physical Journal C SpringerOpen 81:5 (2021) 423
Abstract:
We investigate the feasibility of using deep learning techniques, in the form of a one-dimensional convolutional neural network (1D-CNN), for the extraction of signals from the raw waveforms produced by the individual channels of liquid argon time projection chamber (LArTPC) detectors. A minimal generic LArTPC detector model is developed to generate realistic noise and signal waveforms used to train and test the 1D-CNN, and evaluate its performance on low-level signals. We demonstrate that our approach overcomes the inherent shortcomings of traditional cut-based methods by extending sensitivity to signals with ADC values below their imposed thresholds. This approach exhibits great promise in enhancing the capabilities of future generation neutrino experiments like DUNE to carry out their low-energy neutrino physics programsThe Simons Observatory: Bandpass and polarization-angle calibration requirements for B-mode searches
Journal of Cosmology and Astroparticle Physics IOP Publishing (2021)
Abstract:
We quantify the calibration requirements for systematic uncertainties on bandpasses and polarization angles for next-generation ground-based observatories targeting the large-angle $B$-mode polarization of the Cosmic Microwave Background, with a focus on the Simons Observatory (SO). We explore uncertainties on bandpass gain calibration, center frequencies, and polarization angles, including the frequency variation of the latter across the bandpass. We find that bandpass calibration factors and center frequencies must be known to percent levels or less to avoid biases on the tensor-to-scalar ratio $r$ on the order of $\Delta r\sim10^{-3}$, in line with previous findings. Polarization angles must be calibrated to the level of a few tenths of a degree, while their frequency variation between the edges of the band must be known to ${\cal O}(10)$ degrees. Given the tightness of these calibration requirements, we explore the level to which residual uncertainties on these systematics would affect the final constraints on $r$ if included in the data model and marginalized over. We find that the additional parameter freedom does not degrade the final constraints on $r$ significantly, broadening the error bar by ${\cal O}(10\%)$ at most. We validate these results by reanalyzing the latest publicly available data from the BICEP2 / Keck Array collaboration within an extended parameter space covering both cosmological, foreground and systematic parameters. Finally, our results are discussed in light of the instrument design and calibration studies carried out within SO.The hybrid radio/X-ray correlation of the black hole transient MAXI J1348-630
(2021)
Development, characterisation, and deployment of the SNO+ liquid scintillator
Journal of Instrumentation IOP Publishing 16 (2021) P05009