A chemical survey of exoplanets with ARIEL

Thousands of exoplanets have now been discovered with a huge range of masses, sizes and orbits: from rocky Earth-like planets to large gas giants grazing the surface of their host star. However, the essential nature of these exoplanets remains largely mysterious: there is no known, discernible pattern linking the presence, size, or orbital parameters of a planet to the nature of its parent star. We have little idea whether the chemistry of a planet is linked to its formation environment, or whether the type of host star drives the physics and chemistry of the planet’s birth, and evolution. ARIEL was conceived to observe a large number (~1000) of transiting planets for statistical understanding, including gas giants, Neptunes, super-Earths and Earth-size planets around a range of host star types using transit spectroscopy in the 1.25–7.8 μm spectral range and multiple narrow-band photometry in the optical. ARIEL will focus on warm and hot planets to take advantage of their well-mixed atmospheres which should show minimal condensation and sequestration of high-Z materials compared to their colder Solar System siblings. Said warm and hot atmospheres are expected to be more representative of the planetary bulk composition. Observations of these warm/hot exoplanets, and in particular of their elemental composition (especially C, O, N, S, Si), will allow the understanding of the early stages of planetary and atmospheric formation during the nebular phase and the following few million years. ARIEL will thus provide a representative picture of the chemical nature of the exoplanets and relate this directly to the type and chemical environment of the host star. ARIEL is designed as a dedicated survey mission for combined-light spectroscopy, capable of observing a large and well-defined planet sample within its 4-year mission lifetime. Transit, eclipse and phase-curve spectroscopy methods, whereby the signal from the star and planet are differentiated using knowledge of the planetary ephemerides, allow us to measure atmospheric signals from the planet at levels of 10–100 part per million (ppm) relative to the star and, given the bright nature of targets, also allows more sophisticated techniques, such as eclipse mapping, to give a deeper insight into the nature of the atmosphere. These types of observations require a stable payload and satellite platform with broad, instantaneous wavelength coverage to detect many molecular species, probe the thermal structure, identify clouds and monitor the stellar activity. The wavelength range proposed covers all the expected major atmospheric gases from e.g. H2O, CO2, CH4 NH3, HCN, H2S through to the more exotic metallic compounds, such as TiO, VO, and condensed species. Simulations of ARIEL performance in conducting exoplanet surveys have been performed – using conservative estimates of mission performance and a full model of all significant noise sources in the measurement – using a list of potential ARIEL targets that incorporates the latest available exoplanet statistics. The conclusion at the end of the Phase A study, is that ARIEL – in line with the stated mission objectives – will be able to observe about 1000 exoplanets depending on the details of the adopted survey strategy, thus confirming the feasibility of the main science objectives.Peer reviewedFinal Published versio

Atmospheric retrieval of exoplanets

Exoplanetary atmospheric retrieval refers to the inference of atmospheric properties of an exoplanet given an observed spectrum. The atmospheric properties include the chemical compositions, temperature profiles, clouds/hazes, and energy circulation. These properties, in turn, can provide key insights into the atmospheric physicochemical processes of exoplanets as well as their formation mechanisms. Major advancements in atmospheric retrieval have been made in the last decade, thanks to a combination of state-of-the-art spectroscopic observations and advanced atmospheric modeling and statistical inference methods. These developments have already resulted in key constraints on the atmospheric H2O abundances, temperature profiles, and other properties for several exoplanets. Upcoming facilities such as the JWST will further advance this area. The present chapter is a pedagogical review of this exciting frontier of exoplanetary science. The principles of atmospheric retrievals of exoplanets are discussed in detail, including parametric models and statistical inference methods, along with a review of key results in the field. Some of the main challenges in retrievals with current observations are discussed along with new directions and the future landscape

Search for neutral heavy leptons produced in ZZ decays

Weak isosinglet Neutral Heavy Leptons (νm) have been searched for using data collected by the DELPHI detector corresponding to 3.3 × 106 hadronic Z0 decays at LEP1. Four separate searches have been performed, for short-lived νm production giving monojet or acollinear jet topologies, and for long-lived νm giving detectable secondary vertices or calorimeter clusters. No indication of the existence of these particles has been found, leading to an upper limit for the branching ratio BR(Z0 → νmν̄) of about 1.3 × 10-6 at 95% confidence level for νm masses between 3.5 and 50 GeV/c2. Outside this range the limit weakens rapidly with the νm mass. The results are also interpreted in terms of limits for the single production of excited neutrinos. © Springer-Verlag 1997

Neural Responses to Truth Telling and Risk Propensity under Asymmetric Information

This research was supported by the Laureate Institute for Brain Research and the William K. Warren Foundation. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.Trust is multi-dimensional because it can be characterized by subjective trust, trust antecedent, and behavioral trust. Previous research has investigated functional brain responses to subjective trust (e.g., a judgment of trustworthiness) or behavioral trust (e.g., decisions to trust) in perfect information, where all relevant information is available to all participants. In contrast, we conducted a novel examination of the patterns of functional brain activity to a trust antecedent, specifically truth telling, in asymmetric information, where one individual has more information than others, with the effect of varying risk propensity. We used functional magnetic resonance imaging (fMRI) and recruited 13 adults, who played the Communication Game, where they served as the “Sender” and chose either truth telling (true advice) or lie telling (false advice) regarding the best payment allocation for their partner. Our behavioral results revealed that subjects with recreational high risk tended to choose true advice. Moreover, fMRI results yielded that the choices of true advice were associated with increased cortical activation in the anterior rostral medial and frontopolar prefrontal cortices, middle frontal cortex, temporoparietal junction, and precuneus. Furthermore, when we specifically evaluated a role of the bilateral amygdala as the region of interest (ROI), decreased amygdala response was associated with high risk propensity, regardless of truth telling or lying. In conclusion, our results have implications for how differential functions of the cortical areas may contribute to the neural processing of truth telling.Yeshttp://www.plosone.org/static/editorial#pee

Recognizing sequences of sequences

The brain's decoding of fast sensory streams is currently impossible to emulate, even approximately, with artificial agents. For example, robust speech recognition is relatively easy for humans but exceptionally difficult for artificial speech recognition systems. In this paper, we propose that recognition can be simplified with an internal model of how sensory input is generated, when formulated in a Bayesian framework. We show that a plausible candidate for an internal or generative model is a hierarchy of 'stable heteroclinic channels'. This model describes continuous dynamics in the environment as a hierarchy of sequences, where slower sequences cause faster sequences. Under this model, online recognition corresponds to the dynamic decoding of causal sequences, giving a representation of the environment with predictive power on several timescales. We illustrate the ensuing decoding or recognition scheme using synthetic sequences of syllables, where syllables are sequences of phonemes and phonemes are sequences of sound-wave modulations. By presenting anomalous stimuli, we find that the resulting recognition dynamics disclose inference at multiple time scales and are reminiscent of neuronal dynamics seen in the real brain