RT borsatonic@twitter
"These detections give insights into planet formation & age determination. A crucial 1st step in deepening our understanding! 🌌🌍🧪
Paper available on arXiv, soon in Astronomy & Astrophysics! Read it: https://arxiv.org/abs/2304.04285 📚🔭🎉 #exoplanets #astrochemistry #planetformation #discovery #AstronomyAndAstrophysics "
Cross-correlation spectroscopy is an invaluable tool in the study of exoplanets. However, aliasing between spectral lines makes it vulnerable to systematic biases. This work strives to constrain the aliases of the cross-correlation function to provide increased confidence in the detections of elements in the atmospheres of ultra-hot Jupiters (UHJs) observed with high-resolution spectrographs. We use a combination of archival transit observations of the UHJ KELT-9 b obtained with the HARPS-N and CARMENES spectrographs and show that it is possible to leverage each instrument's strengths to produce robust detections at substantially reduced signal-to-noise. Aliases that become present at low signal-to-noise regimes are constrained through a linear regression model. We confirm previous detections of H I, Na I, Mg I, Ca II, Sc II, Ti II, Cr II, Fe I, and Fe II, and detect eight new species Ca I, Cr I, Ni I, Sr II, Tb II at the 5$σ$ level and Ti I, V I, Ba II above the 3$σ$ level. Ionised terbium (Tb II) has never before been seen in an exoplanet atmosphere. We further conclude that a 5$σ$ threshold may not provide a reliable measure of confidence when used to claim detections, unless the systematics in the cross-correlation function caused by aliases are taken into account.
Yes, #MonthOfArXiv is A LOT OF WORK, but I do recommend it to everyone that needs a little extrinsic motivation to keep up with recent literature. 🔥
It made me so happy to talk to my supervisor today and point out something that I had read in the last couple of days while actually REALLY understanding what the paper was about (because I had to read it in detail for the thread, win-win 🙌🏻).
Do recommend! 🤭
Is this really me catching up to TODAY?
For the fifth paper of my #MonthOfArXiv (IT’S FROM TODAY!!), I chose a paper studying the planet MASCARA-4 b. If you are intrigued by that name, I recommend reading yesterday’s thread about MASCARA-1 b.
The title of the paper is “Detection of rubidium and samarium in the atmosphere of the ultra-hot Jupiter MASCARA-4 b” and was written by Jiang and collaborators.
Let’s check it out! 👇🏼🧵
Ultra-hot Jupiters (UHJs) possess the most extreme environments among various types of exoplanets, making them ideal laboratories to study the chemical composition and kinetics properties of exoplanet atmosphere with high-resolution spectroscopy (HRS). It has the advantage of resolving the tiny Doppler shift and weak signal from exoplanet atmosphere and has helped to detect dozens of heavy elements in UHJs including KELT-9b, WASP-76b, WASP-121b. MASCARA-4b is a 2.8-day UHJ with an equilibrium temperature of $\sim2250$ K, which is expected to contain heavy elements detectable with VLT. In this letter, we present a survey of atoms/ions in the atmosphere of the MASCARA-4b, using the two VLT/ESPRESSO transits data. Cross-correlation analyses are performed on the obtained transmission spectra at each exposure with the template spectra generated by petitRADTRANS for atoms/ions from element Li to U. We confirm the previous detection of Mg, Ca, Cr and Fe and report the detection of Rb, Sm, Ti+ and Ba+ with peak signal-to-noise ratios (SNRs) $>$ 5. We report a tentative detection of Sc+, with peak SNRs $\sim$6 but deviating from the estimated position. The most interesting discovery is the first-time detection of elements Rb and Sm in an exoplanet. Rb is an alkaline element like Na and K, while Sm is the first lanthanide series element and is by far the heaviest one detected in exoplanets. Detailed modeling and acquiring more data are required to yield abundance ratios of the heavy elements and to understand better the common presence of them in UHJ's atmospheres.
For the forth paper of my #MonthOfArXiv (catching up from yesterday again), I chose a paper studying the upper atmospheres of a couplet of planets.
The title of the paper is “Characterisation of the upper atmospheres of HAT-P-32 b, WASP-69 b, GJ 1214 b, and WASP-76 b through their He I triplet absorption” and was written by Lampón and collaborators.
Let’s dive right into it! 👇🏼🧵
Characterisation of atmospheres undergoing photo-evaporation is key to understanding the formation, evolution, and diversity of planets. However, only a few upper atmospheres that experience this kind of hydrodynamic escape have been characterised. Our aim is to characterise the upper atmospheres of the hot Jupiters HAT-P-32 b and WASP-69 b, the warm sub-Neptune GJ 1214 b, and the ultra-hot Jupiter WASP-76 b through high-resolution observations of their HeI triplet absorption. In addition, we also reanalyse the warm Neptune GJ 3470 b and the hot Jupiter HD 189733 b. We used a spherically symmetric 1D hydrodynamic model coupled with a non-local thermodynamic equilibrium model. Comparing synthetic absorption spectra with observations, we constrained the main parameters of the upper atmosphere of these planets and classify them according to their hydrodynamic regime. Our results show that HAT-P-32 b photo-evaporates at (130$\pm$70)$\times$10$^{11}$ gs$^{-1}$ with a hot (12 400$\pm$2900 K) upper atmosphere; WASP-69 b loses its atmosphere at (0.9$\pm$0.5)$\times$10$^{11}$ gs$^{-1}$ and 5250$\pm$750 K; and GJ 1214 b, with a relatively cold outflow of 3750$\pm$750 K, photo-evaporates at (1.3$\pm$1.1)$\times$10$^{11}$ gs$^{-1}$. For WASP-76 b, its weak absorption prevents us from constraining its temperature and mass-loss rate significantly; we obtained ranges of 6000-17 000\,K and 23.5$\pm$21.5$\times$10$^{11}$ gs$^{-1}$. Our reanalysis of GJ 3470 b yields colder temperatures, 3400$\pm$350 K, but practically the same mass-loss rate as in our previous results. Our reanalysis of HD 189733 b yields a slightly higher mass-loss rate, (1.4$\pm$0.5)$\times$10$^{11}$ gs$^{-1}$, and temperature, 12 700$\pm$900 K compared to previous estimates. Our results support that photo-evaporated outflows tend to be very light.
Paper No. 2 for today!
For the third paper of my #MonthOfArXiv I have chosen a paper from my own field. I hope that I can give you a personal flavour, because it is about an important mechanism. Hooked? Follow me! 👇🏼🧵
The title of the paper is “The PEPSI Exoplanet Transit Survey. III: The detection of Fe I, Cr I and Ti I in the atmosphere of MASCARA-1 b through high-resolution emission spectroscopy” and was written by Scandariato et al.
Hot giant planets like MASCARA-1 b are expected to have thermally inverted atmospheres, that makes them perfect laboratory for the atmospheric characterization through high-resolution spectroscopy. Nonetheless, previous attempts of detecting the atmosphere of MASCARA-1 b in transmission have led to negative results. In this paper we aim at the detection of the optical emission spectrum of MASCARA-1 b. We used the high-resolution spectrograph PEPSI to observe MASCARA-1 (spectral type A8) near the secondary eclipse of the planet. We cross-correlated the spectra with synthetic templates computed for several atomic and molecular species. We obtained the detection of FeI, CrI and TiI in the atmosphere of MASCARA-1 b with a S/N ~7, 4 and 5 respectively, and confirmed the expected systemic velocity of ~13 km/s and the radial velocity semi-amplitude of MASCARA-1 b of ~200 km/s. The detection of Ti is of particular importance in the context of the recently proposed Ti cold-trapping below a certain planetary equilibrium temperature. We confirm the presence of an the atmosphere around MASCARA-1 b through emission spectroscopy. We conclude that the atmospheric non detection in transmission spectroscopy is due to the high gravity of the planet and/or to the overlap between the planetary track and its Doppler shadow.
For the second paper in my #MonthOfArXiv, I read a paper that is again not really in my field, but I just love reading about Hycean Worlds, maybe because I can somehow picture them? 🤔
The title of today’s first paper (originally from Friday) is “The Runaway Greenhouse Effect on Hycean Worlds” and it is written by Hamish Innes, Shang-Min Tsai and Raymond T. Pierrehumbert.
What is a Hycean world, you wonder? Well, let’s find out together! 👇🏼🧵
Hycean worlds are a proposed subset of sub-Neptune exoplanets with substantial water inventories, liquid surface oceans and extended hydrogen-dominated atmospheres that could be favourable for habitability. In this work, we aim to quantitatively define the inner edge of the Hycean habitable zone using a 1D radiative-convective model. As a limiting case, we model a dry hydrogen-helium envelope above a surface ocean. We find that 10 to 20 bars of atmosphere produces enough greenhouse effect to drive a liquid surface ocean supercritical when forced with current Earth-like instellation. Introducing water vapour into the atmosphere, we show the runaway greenhouse instellation limit is greatly reduced due to the presence of superadiabatic layers where convection is inhibited. This moves the inner edge of the habitable zone from $\approx$ 1 AU for a G-star to 1.6 AU (3.85 AU) for a Hycean world with a H$_2$-He inventory of 1 bar (10 bar). For an M-star, the inner edge is equivalently moved from 0.17 AU to 0.28 AU (0.54 AU). Our results suggest that most of the current Hycean world observational targets are not likely to sustain a liquid water ocean. We present an analytical framework for interpreting our results, finding that the maximum possible OLR scales approximately inversely with the dry mass inventory of the atmosphere. We discuss the possible limitations of our 1D modelling and recommend the use of 3D convection-resolving models to explore the robustness of superadiabatic layers.
The main takeaway is that causal definitions of algorithmic fairness lead to Pareto-dominated policies.
In plain English, this means: whatever your preference (more efficiency or more equity) there will always be another strategy to is more satisfying to everyone involved.
Or, even simpler: this is bad for everyone involved.
How to fix this? The consequentialist framework (CF) to algorithmic fairness foregrounds the results of decisions, rather than properties of the prediction.
One starts by identifying the utility of different possible outcomes, eg efficiency and equity. Optimal decision policies can be derived with Linear Programming that uses stakeholder preferences.
This approach has advantages over static experimental designs (eg randomized trials)
"Learning to Be Fair: A Consequentialist Approach to Equitable Decision-Making"
While designing fair machine learning systems an approach could be to ensure parity in error rates across race, gender etc. It turns out to really matter how we define this statistically: some strategies might sound fair but ignore downstream effects and can cause unexpected harm to the very groups we try to protect.
In an attempt to make algorithms fair, the machine learning literature has largely focused on equalizing decisions, outcomes, or error rates across race or gender groups. To illustrate, consider a hypothetical government rideshare program that provides transportation assistance to low-income people with upcoming court dates. Following this literature, one might allocate rides to those with the highest estimated treatment effect per dollar, while constraining spending to be equal across race groups. That approach, however, ignores the downstream consequences of such constraints, and, as a result, can induce unexpected harms. For instance, if one demographic group lives farther from court, enforcing equal spending would necessarily mean fewer total rides provided, and potentially more people penalized for missing court. Here we present an alternative framework for designing equitable algorithms that foregrounds the consequences of decisions. In our approach, one first elicits stakeholder preferences over the space of possible decisions and the resulting outcomes--such as preferences for balancing spending parity against court appearance rates. We then optimize over the space of decision policies, making trade-offs in a way that maximizes the elicited utility. To do so, we develop an algorithm for efficiently learning these optimal policies from data for a large family of expressive utility functions. In particular, we use a contextual bandit algorithm to explore the space of policies while solving a convex optimization problem at each step to estimate the best policy based on the available information. This consequentialist paradigm facilitates a more holistic approach to equitable decision-making.
Bibiana Prinoth (moved!)
rhgrouls 