Probing cosmic dawn with emission lines: predicting infrared and nebular line emission for ALMA and JWST

Infrared and nebular lines provide some of our best probes of the physics regulating the properties of the interstellar medium (ISM) at high-redshift. However, interpreting the physical conditions of high-redshift galaxies directly from emission lines remains complicated due to inhomogeneities in temperature, density, metallicity, ionisation parameter, and spectral hardness. We present a new suite of cosmological, radiation-hydrodynamics simulations, each centred on a massive Lyman-break galaxy that resolves such properties in an inhomogeneous ISM. Many of the simulated systems exhibit transient but well defined gaseous disks that appear as velocity gradients in [CII]~158.6$\mu$m emission. Spatial and spectral offsets between [CII]~158.6$\mu$m and [OIII]~88.33$\mu$m are common, but not ubiquitous, as each line probes a different phase of the ISM. These systems fall on the local [CII]-SFR relation, consistent with newer observations that question previously observed [CII]~158.6$\mu$m deficits. Our galaxies are consistent with the nebular line properties of observed $z\sim2-3$ galaxies and reproduce offsets on the BPT and mass-excitation diagrams compared to local galaxies due to higher star formation rate (SFR), excitation, and specific-SFR, as well as harder spectra from young, metal-poor binaries. We predict that local calibrations between H$\alpha$ and [OII]~3727$\AA$ luminosity and galaxy SFR apply up to $z>10$, as do the local relations between certain strong line diagnostics (R23 and [OIII]~5007$\AA$/H$\beta$) and galaxy metallicity. Our new simulations are well suited to interpret the observations of line emission from current (ALMA and HST) and upcoming facilities (JWST and ngVLA)

Non-nociceptive roles of opioids in the CNS: opioids' effects on neurogenesis, learning, memory and affect.

Mortality due to opioid use has grown to the point where, for the first time in history, opioid-related deaths exceed those caused by car accidents in many states in the United States. Changes in the prescribing of opioids for pain and the illicit use of fentanyl (and derivatives) have contributed to the current epidemic. Less known is the impact of opioids on hippocampal neurogenesis, the functional manipulation of which may improve the deleterious effects of opioid use. We provide new insights into how the dysregulation of neurogenesis by opioids can modify learning and affect, mood and emotions, processes that have been well accepted to motivate addictive behaviours

Inverse problem instabilities in large-scale modelling of matter in extreme conditions

Our understanding of physical systems often depends on our ability to match complex computational modeling with the measured experimental outcomes. However, simulations with large parameter spaces suffer from inverse problem instabilities, where similar simulated outputs can map back to very different sets of input parameters. While of fundamental importance, such instabilities are seldom resolved due to the intractably large number of simulations required to comprehensively explore parameter space. Here, we show how Bayesian inference can be used to address inverse problem instabilities in the interpretation of x-ray emission spectroscopy and inelastic x-ray scattering diagnostics. We find that the extraction of information from measurements on the basis of agreement with simulations alone is unreliable and leads to a significant underestimation of uncertainties. We describe how to statistically quantify the effect of unstable inverse models and describe an approach to experimental design that mitigates its impact

Inverse problem instabilities in large-scale modelling of matter in extreme conditions

Our understanding of physical systems often depends on our ability to match complex computational modeling with the measured experimental outcomes. However, simulations with large parameter spaces suffer from inverse problem instabilities, where similar simulated outputs can map back to very different sets of input parameters. While of fundamental importance, such instabilities are seldom resolved due to the intractably large number of simulations required to comprehensively explore parameter space. Here, we show how Bayesian inference can be used to address inverse problem instabilities in the interpretation of x-ray emission spectroscopy and inelastic x-ray scattering diagnostics. We find that the extraction of information from measurements on the basis of agreement with simulations alone is unreliable and leads to a significant underestimation of uncertainties. We describe how to statistically quantify the effect of unstable inverse models and describe an approach to experimental design that mitigates its impact