Imperial College London

About: Imperial College London is a education organization based out in London, Westminster, United Kingdom. It is known for research contribution in the topics: Population & Medicine. The organization has 90019 authors who have published 209164 publications receiving 9337534 citations. The organization is also known as: Imperial College of Science, Technology and Medicine & Imperial College.

Carbon capture and storage update

Abstract: In recent years, Carbon Capture and Storage (Sequestration) (CCS) has been proposed as a potential method to allow the continued use of fossil-fuelled power stations whilst preventing emissions of CO2 from reaching the atmosphere. Gas, coal (and biomass)-fired power stations can respond to changes in demand more readily than many other sources of electricity production, hence the importance of retaining them as an option in the energy mix. Here, we review the leading CO2 capture technologies, available in the short and long term, and their technological maturity, before discussing CO2 transport and storage. Current pilot plants and demonstrations are highlighted, as is the importance of optimising the CCS system as a whole. Other topics briefly discussed include the viability of both the capture of CO2 from the air and CO2 reutilisation as climate change mitigation strategies. Finally, we discuss the economic and legal aspects of CCS.

Planck 2018 results: XI. Polarized dust foregrounds

Abstract: The study of polarized dust emission has become entwined with the analysis of the cosmic microwave background (CMB) polarization. We use new Planck maps to characterize Galactic dust emission as a foreground to the CMB polarization. We present Planck EE, BB, and TE power spectra of dust polarization at 353 GHz for six nested sky regions covering from 24 to 71 % of the sky. We present power-law fits to the angular power spectra, yielding evidence for statistically significant variations of the exponents over sky regions and a difference between the values for the EE and BB spectra. The TE correlation and E/B power asymmetry extend to low multipoles that were not included in earlier Planck polarization papers. We also report evidence for a positive TB dust signal. Combining data from Planck and WMAP, we determine the amplitudes and spectral energy distributions (SEDs) of polarized foregrounds, including the correlation between dust and synchrotron polarized emission, for the six sky regions as a function of multipole. This quantifies the challenge of the component separation procedure required for detecting the reionization and recombination peaks of primordial CMB B modes. The SED of polarized dust emission is fit well by a single-temperature modified blackbody emission law from 353 GHz to below 70 GHz. For a dust temperature of 19.6 K, the mean spectral index for dust polarization is $\beta_{\rm d}^{P} = 1.53\pm0.02 $. By fitting multi-frequency cross-spectra, we examine the correlation of the dust polarization maps across frequency. We find no evidence for decorrelation. If the Planck limit for the largest sky region applies to the smaller sky regions observed by sub-orbital experiments, then decorrelation might not be a problem for CMB experiments aiming at a primordial B-mode detection limit on the tensor-to-scalar ratio $r\simeq0.01$ at the recombination peak.

Deep Reinforcement Learning: A Brief Survey

Abstract: Deep reinforcement learning (DRL) is poised to revolutionize the field of artificial intelligence (AI) and represents a step toward building autonomous systems with a higherlevel understanding of the visual world. Currently, deep learning is enabling reinforcement learning (RL) to scale to problems that were previously intractable, such as learning to play video games directly from pixels. DRL algorithms are also applied to robotics, allowing control policies for robots to be learned directly from camera inputs in the real world. In this survey, we begin with an introduction to the general field of RL, then progress to the main streams of value-based and policy-based methods. Our survey will cover central algorithms in deep RL, including the deep Q-network (DQN), trust region policy optimization (TRPO), and asynchronous advantage actor critic. In parallel, we highlight the unique advantages of deep neural networks, focusing on visual understanding via RL. To conclude, we describe several current areas of research within the field.

Monoenergetic beams of relativistic electrons from intense laser–plasma interactions

Abstract: High-power lasers that fit into a university-scale laboratory can now reach focused intensities of more than 10(19) W cm(-2) at high repetition rates. Such lasers are capable of producing beams of energetic electrons, protons and gamma-rays. Relativistic electrons are generated through the breaking of large-amplitude relativistic plasma waves created in the wake of the laser pulse as it propagates through a plasma, or through a direct interaction between the laser field and the electrons in the plasma. However, the electron beams produced from previous laser-plasma experiments have a large energy spread, limiting their use for potential applications. Here we report high-resolution energy measurements of the electron beams produced from intense laser-plasma interactions, showing that--under particular plasma conditions--it is possible to generate beams of relativistic electrons with low divergence and a small energy spread (less than three per cent). The monoenergetic features were observed in the electron energy spectrum for plasma densities just above a threshold required for breaking of the plasma wave. These features were observed consistently in the electron spectrum, although the energy of the beam was observed to vary from shot to shot. If the issue of energy reproducibility can be addressed, it should be possible to generate ultrashort monoenergetic electron bunches of tunable energy, holding great promise for the future development of 'table-top' particle accelerators.