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The Ariel Space Mission will observe a large and diverse sample of exoplanetary atmospheres in the 0.5 to 7.8-micron range of the electromagnetic spectrum. As part of the Ariel observing programme, a shallow Reconnaissance survey (Tier 1) will provide transiting and eclipse spectroscopy on about 1000 targets, with low spectral resolution but sufficient SNR to identify the signature of molecular species. The wealth of information provided by this survey will be the basis for promoting targets for re-observation to reach sufficient SNR at higher spectral resolution. At the same time, these low spectral resolution observations are not suitable for estimating molecular abundances with an appropriate confidence level. Therefore, it is paramount to develop special data analysis techniques to extract their information content. This work investigates using the abundance posteriors from spectral retrieval as an unbiased metric to assess the presence of a molecule up to a certain threshold. The experimental dataset comprises simulated Tier 1 transmission spectra for about 300 targets from the Ariel Mission Reference Sample produced using the Alfnoor software. We use the TauRex 3 retrieval framework to run spectral retrievals on each “observed” spectrum, and we compute the probability that the spectra bear a molecule by integrating the posteriors above a specified threshold of molecular concentration. We find that the retrieved probabilities correlate with the abundances in the forward models and that this method is statistically reliable and has considerable predictive power and diagnostic ability. The predictive power is not significantly affected by adding molecules in the fitted composition that are not present in the forward models, while omitting molecules should be discouraged as it can lead to biased results.

ExoSim 2 is a time-domain simulator for exoplanet observations. The software can simulate exoplanetary transit, eclipse and phase curve observations from ground and space-based instruments. Such simulation can capture temporal effects, such as correlated noise and systematics on the light curve. The simulator will produce spectral images like those produced by an actual observation. ExoSim 2 has been developed for the Ariel Space Mission, to assess the impact of astronomical and instrumental systematic on astrophysical measurement, and to prepare the data reduction pipeline against realistic data sets. ExoSim 2 output can be utilised by different data reduction methods, not only to determine the best pipeline strategy to remove the systematics in the measurements but also to assess the confidence level of retrieved quantities. ExoSim 2 is a refactored version of ExoSim: an end-to-end simulator that models noise and systematics in a dynamical simulation. The first version of ExoSim (Sarkar et al. 2020) was developed for the Ariel Space Mission, then adapted to the James Webb Telescope and presented to the community as JexoSim (Sarkar et al. 2019 and Sarkar et al. 2021). ExoSim 2 is meant to be easier to use than its predecessor and largely customizable. It is completely written in Python, tested against Python 3.7+, and follows the object-oriented philosophy. It comes with an installer, documented examples, a comprehensive guide, and almost every part of the code can be replaced by a user-defined function, which allows the user to include new functionalities to the simulator. We believe that ExoSim 2 is a versatile tool, which can be used for the development of instruments other than Ariel, or to assess the impact of different astronomical or instrumental systematics.

The Ariel Space Mission is the M4 mission in ESA's Cosmic Vision program and will observe a large and diverse sample of exoplanetary atmospheres in the visible to the near-infrared range of the electromagnetic spectrum. Assessing the impact of diffraction, aberrations, and related systematics on the Ariel optical performance before having a system-level measurement is paramount to ensuring that the optical quality, complexity, costs, and risks are not too high. Several codes offer Physical Optics Propagation (POP) calculations, although generally, they are not easily customizable, e.g., for Monte Carlo simulations, are not free access and publicly available, or have technical limitations such as not providing support for refractive elements. PAOS, the Physical Ariel Optics Simulator, is an end-to-end Physical Optics Propagation (POP) model of the Ariel telescope and subsystems. PAOS implements Fresnel diffraction in the near and far fields to simulate the propagation of the complex electromagnetic wavefront through the Ariel optical chain and deliver the realistic PSFs vs. lambda at the intermediate and focal planes. PAOS is written with a full Python 3 stack and comes with an installer, documented examples, and an exhaustive guide. PAOS is meant to be easy to use, generic and versatile for POP simulations of optical systems other than Ariel’s, thanks to its generic input system and built-in GUI providing a seamless user interface and simulations.

The ARIEL Space Telescope will provide a large and diverse sample of exoplanet spectra, performing spectroscopic observations of about 1000 exoplanets in the wavelength range $0.5 \to 7.8 \; \mu m$. In this paper, we investigate the information content of ARIEL's Reconnaissance Survey low resolution transmission spectra. Among the goals of the ARIEL Reconnaissance Survey is also to identify planets without molecular features in their atmosphere. In this work, (1) we present a strategy that will allow to select candidate planets to be reobserved in a ARIEL's higher resolution Tier; (2) we propose a metric to preliminary classify exoplanets by their atmospheric composition without performing an atmospheric retrieval; (3) we introduce the possibility to find other methods to better exploit the data scientific content., Comment: 31 pages, 14 figures, submitted to ApJ

Ariel, the Atmospheric Remote-sensing Infrared Exoplanet Large-survey, is a medium-class space mission part of ESA's Cosmic Vision programme, due for launch in 2029. Ariel will survey a diverse sample of about 1000 extrasolar planets in the visible and infrared spectrum to answer questions about their composition, formation and evolution. Ariel mounts an off-axis Cassegrain telescope with a 1100 mm x 730 mm elliptical mirror and has two separate instruments (FGS and AIRS) that cover the 0.5-7.8 micron spectral range. To study the Ariel optical performance and related systematics, we developed PAOS, the Proper Ariel Optical Simulator, an End-to-End physical optics propagation model of the Ariel Telescope and subsystems based on PROPER, an optical propagation library for IDL, Python and Matlab. PAOS is a Python code that consists of a series of calls to PROPER library functions and procedures that reproduces the Ariel optical design, interleaved with additional code that can be specified according to the simulation. Using PAOS, we can investigate how diffraction affects the electromagnetic wavefront as it travels through the Ariel optical systems and the resulting PSFs in the photometric and spectroscopic channels of the mission. This enables to perform a large number of detailed analyses, both on the instrument side and on the optimisation of the Ariel mission. In particular, PAOS can be used to support the requirement on the maximum amplitude of the aberrations for the manufacturing of the Ariel primary mirror, as well as to develop strategies for in-flight calibration, e.g. focussing procedures for the FGS and AIRS focal planes, and to tackle systematics such as pointing jitter and vignetting. With the Ariel mission now in the process of finalizing the instrument design and the data analysis techniques, PAOS will greatly contribute in evaluating the Ariel payload performance with models to be included in the existing Ariel simulators such as ArielRad, the Ariel Radiometric model, and ExoSim, the Exoplanet Observation simulator, for the purpose of studying and optimising the science return from Ariel.

Ariel, the Atmospheric Remote-sensing Infrared Exoplanet Large-survey, is a medium-class space mission part of ESA's Cosmic Vision program, due for launch in 2029. Ariel is the first mission dedicated to the spectroscopic observation of a diverse, statistical sample of about 1000 transiting exoplanets, obtaining spectra in transit, eclipse, or both, to answer questions about their composition, formation and evolution. Ariel has adopted a four-tiered approach in which all targets are observed with different SNRs to optimise the science return from the mission. Ariel has two separate instruments (FGS and AIRS) that will perform simultaneous observations across the 0.5-7.8 micron spectral range, which encompasses both the peak emission of exoplanets and the spectral signatures of key molecules. This will enable Ariel to collect statistical information on the composition and the thermal structure of exo-atmospheres, allowing it to reveal underlying trends in exoplanetary populations. In particular, transit spectroscopy is expected to provide the bulk of information on the chemical composition of exo-atmospheres, while eclipses are necessary to constrain their thermodynamic state. In this framework, I report a preliminary study of Ariel targets observed in emission: at first, I investigate the information content from Tier 1 data, where spectra from the full population of Ariel targets are observed with low SNR, and binned as if Ariel were a multi-band photometer to increase the SNR. I then investigate the effectiveness of Ariel in detecting chemical-physical trends in exoplanetary populations observed in Tier 2, designed to reach SNR in excess of 7 on spectra binned to roughly half the spectral resolution of the focal planes, as specified by the mission requirements.

In the next decade, the Ariel Space Telescope will provide the first statistical data set of exoplanet spectra, performing spectroscopic observations of about 1000 exoplanets in the wavelength range 0.5 - 7.8 micron during its Reconnaissance Survey. The Ariel Reconnaissance Survey has been designed specifically to identify planets without molecular features in their atmosphere, and select targets (about 500) for accurate chemical characterisation with higher SNR spectroscopic observations. In this work, we investigate the information content of Ariel's Reconnaissance Survey low resolution transmission spectra. We produce different planetary populations using the Ariel candidate target list, randomizing the planetary atmospheres, and simulating the Ariel observations using the Alfnoor software. Then we analyse the dataset, getting three different results: (1) We present a solid strategy that will allow selecting candidate planets to be reobserved in an Ariel's higher resolution, using a chi-squared based metric to identify the flat spectra. (2) Because the reconnaissance survey is not optimised for spectral retrieval, we propose a novel model-independent metric to preliminary classify exoplanets by their atmospheric composition. Without any other planetary information than the spectrum, our metric proves capable of indicating the presence of a molecule when its abundance in the atmosphere is in excess of 10-4 in mixing ratio. (3) We introduce the possibility of finding other methods to better exploit the data scientific content. We report as an example of possible strategies, a preliminary study involving Deep and Machine Learning algorithms. We show that their performance in identifying the presence of a certain molecule in the spectra is marginally better than our metric for some of these algorithms, while others outperform the metric. We conclude that the the Ariel reconnaissance survey is effective in detecting exoplanets manifesting featureless spectra, and we further show that the data collected in this observing mode have a rich scientific content, allowing for a first chemical classification of the observed targets.

Objectives This study aimed to determine whether the intracoronary electrograms (IC-EGMs) recorded using a standard percutaneous coronary intervention guidewire could provide myocardial viability information. Background The revascularization of dysfunctional but viable myocardium may confer prognostic benefits compared with medical therapy in patients with post-ischemic heart failure. However, knowledge of myocardial viability is often unavailable at the time of the procedure. Methods The peak-to-peak voltage of 317 IC-EGMs recordings from 25 patients with a previous myocardial infarction and systolic dysfunction were matched with corresponding delayed-enhancement magnetic resonance imaging sites using a 17-segment model of the left ventricle. Results Sixty-seven recordings were obtained from segments classified as complete scar on delayed-enhancement magnetic resonance imaging (group A), 162 from partially viable segments (group B), and 88 from fully viable segments (group C). Three high-pass (HP) filters (0.5, 30, and 100 Hz) were applied to the signals to modulate their spatial resolution. For all filters, the peak-to-peak voltage significantly decreased from group C to group B to group A (p Conclusions The amplitude of the IC-EGMs discriminates viable from nonviable left ventricular segments. Because this technique is simple and inexpensive and provides real-time results, it is potentially useful to aid decision making in the catheterization laboratory.

This study aimed to determine whether the intracoronary electrograms (IC-EGMs) recorded using a standard percutaneous coronary intervention guidewire could provide myocardial viability information.The revascularization of dysfunctional but viable myocardium may confer prognostic benefits compared with medical therapy in patients with post-ischemic heart failure. However, knowledge of myocardial viability is often unavailable at the time of the procedure.The peak-to-peak voltage of 317 IC-EGMs recordings from 25 patients with a previous myocardial infarction and systolic dysfunction were matched with corresponding delayed-enhancement magnetic resonance imaging sites using a 17-segment model of the left ventricle.Sixty-seven recordings were obtained from segments classified as complete scar on delayed-enhancement magnetic resonance imaging (group A), 162 from partially viable segments (group B), and 88 from fully viable segments (group C). Three high-pass (HP) filters (0.5, 30, and 100 Hz) were applied to the signals to modulate their spatial resolution. For all filters, the peak-to-peak voltage significantly decreased from group C to group B to group A (p 0.001 for all comparisons). When receiver-operating characteristic analysis was used to compare nonviable (group A) with viable (group B + C) segments, the optimal discriminating voltages were 4.6, 2.2, and 0.78 mV for, respectively, HP-0.5, HP-30, and HP-100 filters, with a sensitivity of 92%, 94%, and 99% and a specificity of 70%, 79%, and 69%.The amplitude of the IC-EGMs discriminates viable from nonviable left ventricular segments. Because this technique is simple and inexpensive and provides real-time results, it is potentially useful to aid decision making in the catheterization laboratory.