A global cloud map of the nearest known brown dwarf

Brown dwarfs -- substellar bodies more massive than planets but not massive enough to initiate the sustained hydrogen fusion that powers self-luminous stars -- are born hot and slowly cool as they age. As they cool below about 2,300 K, liquid or crystalline particles composed of calcium aluminates, silicates and iron condense into atmospheric 'dust', which disappears at still cooler temperatures (around 1,300 K). Models to explain this dust dispersal include both an abrupt sinking of the entire cloud deck into the deep, unob- servable atmosphere and breakup of the cloud into scattered patches (as seen on Jupiter and Saturn). Thus far, observations of brown dwarfs have been limited to globally integrated measurements, which can reveal surface inhomogeneities but cannot unambiguously resolve surface features. Here we report a two-dimensional map of a brown dwarf's surface that allows identification of large-scale bright and dark features, indicative of patchy clouds.Comment: 17 pages, 8 figures. Spectra and map available upon reques

Observation and Modeling of Extrasolar Planets

The field of exoplanet research has currently yielded the discovery of 552 planets. This figure includes 132 transiting planets which can be studied in greater detail and have formed the cornerstone of research to characterise the exoplanet population. In particular, such studies seek to analyse the planetary atmospheres, but research has thus far yielded more questions than answers. Exoplanetary atmospheric studies have typically focussed on one planet apiece - complicating any comparative analysis as every result employs different methods and instruments. For a comprehensive, comparative study, a robust and reliable means of reducing and analysing such observations is required, along with a body of data from a single instrument. One such instrument is the Bubble Space Telescope (BST) whose NICMOS (Near Infrared Camera and Multi-Object Spectrometer) instrument has observed the transits of nine extrasolar planets across multiple wavelengths in the near-infrared. A robust pipeline has been developed to reduce all such observations using the fame techniques. This pipeline reduces grism images of an exoplanet host star across a transit event. These exposures are checked for bad pixels, flat fielded and background-subtracted before robust extraction of a transit light curve. This light curve is then detrended to remove systematic noise by application of a new technique developed in this study. Following detrending, the light curve is modelled using a be- spoke MCMC (Markov-Chain Monte-Carlo) algorithm to determine the planetary parameters. A continuum of wavelength-dependent transit light curves is also extracted, detrended and modelled to de- termine the variation in transit depth with wavelength; and .hereby infer the transmission spectrum of the planet's atmosphere. The finished pipeline has been applied to three sets of HST NIC- MOS observations covering the transits of WASP-2b, HD189733b and GJ436b. For each data set, a new set of planetary parameters has been derived and for WASP-2b and HD189733b an atmospheric transmission spectrum extracted. Both spectra show signs of atmospheric haze and molecular absorption, but also evidence of residual systematic noise, complicating analysis

A resonant sextuplet of sub-Neptunes transiting the bright star HD 110067

Funding: A.C.Ca. and T.G.Wi. acknowledge support from STFC consolidated grant numbers ST/R000824/1 and ST/V000861/1, and UKSA grant number ST/R003203/1. O.Ba. acknowledges that has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant agreement No. 865624). M.La. acknowledges funding from a UKRI Future Leader Fellowship, grant number MR/S035214/1. A.Br. was supported by the SNSA. Contributions at the Mullard Space Science Laboratory by E.M.Br. were supported by STFC through the consolidated grant ST/W001136/1. A.Br. was supported by the SNSA. Contributions at the Mullard Space Science Laboratory by E.M.Br. were supported by STFC through the consolidated grant ST/W001136/1. Ch.He. acknowledges support from the European Union H2020-MSCA-ITN-2019 under Grant Agreement no. 860470 (CHAMELEON).Planets with radii between that of the Earth and Neptune (hereafter referred to as 'sub-Neptunes') are found in close-in orbits around more than half of all Sun-like stars . However, their composition, formation and evolution remain poorly understood . The study of multiplanetary systems offers an opportunity to investigate the outcomes of planet formation and evolution while controlling for initial conditions and environment. Those in resonance (with their orbital periods related by a ratio of small integers) are particularly valuable because they imply a system architecture practically unchanged since its birth. Here we present the observations of six transiting planets around the bright nearby star HD 110067. We find that the planets follow a chain of resonant orbits. A dynamical study of the innermost planet triplet allowed the prediction and later confirmation of the orbits of the rest of the planets in the system. The six planets are found to be sub-Neptunes with radii ranging from 1.94R to 2.85R . Three of the planets have measured masses, yielding low bulk densities that suggest the presence of large hydrogen-dominated atmospheres.PostprintPeer reviewe

Characterising Exoplanet Atmospheres using Traditional Methods and Supervised Machine Learning

Since the discovery of the first extrasolar planets over 25 years ago, the field of exoplanet research has exploded. Today we have over 4000 confirmed exoplanets, with a wide variety of sizes, orbital separations, and host stars. The characterisation of this diverse population of objects has led to exciting discoveries about the conditions of alien worlds. Future technological advances are expected to provide an abundance of exoplanet spectra with a higher precision and sensitivity than ever before. This calls for a parallel advancement in the accuracy and speed of atmospheric models to interpret this influx of data. In this thesis, my work on atmospheric retrievals is presented. Starting with traditional techniques, my first thesis paper applies a Bayesian retrieval in combination with an analytical atmospheric model to the Hubble transmission spectra of 38 different exoplanets. My second paper considers the theoretical model of the sodium doublet, and the effect of dropping the assumption of local-thermodynamic equilibrium. From here, I went on to develop a method that uses supervised machine learning to improve the speed and efficiency of the retrieval. This method was explained and tested in a collaborative paper with machine learning experts in Bern. The machine learning retrieval is then applied in several follow-up studies, covering a range of different scenarios. One of these was my final thesis paper, which further extends the new retrieval to high-resolution spectra using the cross-correlation function. In addition to my own papers, I have contributed to a number of studies led by collaborators by running retrievals, assisting other students, and participating in scientific discussions. I have also worked on several observing proposals, both for high-resolution ground-based observatories and for the upcoming James Webb Space Telescope. I plan to continue my work on exoplanet characterisation and machine learning in the future, using the technique to combine high- and low-resolution spectra to gain further insight into the atmospheres of these distant planets. The speed and efficiency of machine learning will also allow for statistical studies of exoplanets as the quantity of atmospheric spectra from new and upcoming telescopes escalates. Not only will these studies teach us about the conditions and potential habitability of exoplanets, but they will also answer questions about planet formation, diverse chemical processes, and the uniqueness of our solar system

Accounting for stellar activity signals in radial-velocity data by using change point detection techniques star

Context. Active regions on the photosphere of a star have been the major obstacle for detecting Earth-like exoplanets using the radial velocity (RV) method. A commonly employed solution for addressing stellar activity is to assume a linear relationship between the RV observations and the activity indicators along the entire time series, and then remove the estimated contribution of activity from the variation in RV data (overall correction method). However, since active regions evolve on the photosphere over time, correlations between the RV observations and the activity indicators will correspondingly be anisotropic. Aims. We present an approach that recognizes the RV locations where the correlations between the RV and the activity indicators significantly change in order to better account for variations in RV caused by stellar activity. Methods. The proposed approach uses a general family of statistical breakpoint methods, often referred to as change point detection (CPD) algorithms; several implementations of which are available in R and python. A thorough comparison is made between the breakpoint-based approach and the overall correction method. To ensure wide representativity, we use measurements from real stars that have different levels of stellar activity and whose spectra have different signal-to-noise ratios. Results. When the corrections for stellar activity are applied separately to each temporal segment identified by the breakpoint method, the corresponding residuals in the RV time series are typically much smaller than those obtained by the overall correction method. Consequently, the generalized Lomb-Scargle periodogram contains a smaller number of peaks caused by active regions. The CPD algorithm is particularly effective when focusing on active stars with long time series, such as alpha Cen B. In that case, we demonstrate that the breakpoint method improves the detection limit of exoplanets by 74% on average with respect to the overall correction method. Conclusions. CPD algorithms provide a useful statistical framework for estimating the presence of change points in a time series. Since the process underlying the RV measurements generates anisotropic data by its intrinsic properties, it is natural to use CPD to obtain cleaner signals from RV data. We anticipate that the improved exoplanet detection limit may lead to a widespread adoption of such an approach. Our test on the HD 192310 planetary system is encouraging, as we confirm the presence of the two hosted exoplanets and we determine orbital parameters consistent with the literature, also providing much more precise estimates for HD 192310 c.Peer reviewe