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ABSTRACT We evaluate the performance of the Lyman α forest weak gravitational lensing estimator of Metcalf et al. on forest data from hydrodynamic simulations and ray-trace simulated lensing potentials. We compare the results to those obtained from the Gaussian random field simulated Lyα forest data and lensing potentials used in previous work. We find that the estimator is able to reconstruct the lensing potentials from the more realistic data and investigate dependence on spectrum signal to noise. The non-linearity and non-Gaussianity in this forest data arising from gravitational instability and hydrodynamics causes a reduction in signal to noise by a factor of ∼2.7 for noise free data and a factor of ∼1.5 for spectra with signal to noise of order unity (comparable to current observational data). Compared to Gaussian field lensing potentials, using ray-traced potentials from N-body simulations incurs a further signal-to-noise reduction of a factor of ∼1.3 at all noise levels. The non-linearity in the forest data is also observed to increase bias in the reconstructed potentials by $$5-25{{\ \rm per\ cent}}$$, and the ray-traced lensing potential further increases the bias by $$20-30{{\ \rm per\ cent}}$$. We demonstrate methods for mitigating these issues including Gaussianization and bias correction which could be used in real observations. 

ABSTRACT We explore the use of deep learning to infer the temperature of the intergalactic medium from the transmitted flux in the high-redshift Ly α forest. We train neural networks on sets of simulated spectra from redshift z = 2–3 outputs of cosmological hydrodynamic simulations, including high-temperature regions added in post-processing to approximate bubbles heated by He ii reionization. We evaluate how well the trained networks are able to reconstruct the temperature from the effect of Doppler broadening in the simulated input Ly α forest absorption spectra. We find that for spectra with high resolution (10 $$\, {\rm km}\, {\rm s}^{-1}$$ pixel) and moderate signal-to-noise ratio (20–50), the neural network is able to reconstruct the intergalactic medium temperature smoothed on scales of $$\sim 6 \, h^{-1}\, {\rm Mpc}$$ quite well. Concentrating on discontinuities, we find that high-temperature regions of width $$25 \, h^{-1}\, {\rm Mpc}$$ and temperature $$20\, 000$$ K can be fairly easily detected and characterized. We show an example where multiple sightlines are combined to yield tomographic images of hot bubbles. Deep learning techniques may be useful in this way to help us understand the complex temperature structure of the intergalactic medium around the time of helium reionization. 

Online professional networking platforms are widely used and may help workers to search for and obtain jobs. We run the first randomized evaluation of training work seekers to join and use one of the largest platforms, LinkedIn. Training increases the end-of-program employment rate by 10 percent (7 percentage points), and this effect persists for at least 12 months. The available employment, platform use, and job search data suggest that employment effects are explained by work seekers using the platform to acquire information about prospective employers and perhaps by work seekers accessing referrals and conveying information to prospective employers on the platform. (JEL J22, J23, J24, J64, M53, O15)