ALBERT

Description: Species tree reconstruction has been a subject of substantial research due to its central role across biology and medicine. A species tree is often reconstructed using a set of gene trees or by directly using sequence data. In either of these cases, one of the main confounding phenomena is the discordance between a species tree and a gene tree due to evolutionary events such as duplications and losses. Probabilistic methods can resolve the discordance by coestimating gene trees and the species tree but this approach poses a scalability problem for larger data sets. We present MixTreEM-DLRS: A two-phase approach for reconstructing a species tree in the presence of gene duplications and losses. In the first phase, MixTreEM, a novel structural expectation maximization algorithm based on a mixture model is used to reconstruct a set of candidate species trees, given sequence data for monocopy gene families from the genomes under study. In the second phase, PrIME-DLRS, a method based on the DLRS model (Åkerborg O, Sennblad B, Arvestad L, Lagergren J. 2009. Simultaneous Bayesian gene tree reconstruction and reconciliation analysis. Proc Natl Acad Sci U S A. 106(14):5714–5719), is used for selecting the best species tree. PrIME-DLRS can handle multicopy gene families since DLRS, apart from modeling sequence evolution, models gene duplication and loss using a gene evolution model (Arvestad L, Lagergren J, Sennblad B. 2009. The gene evolution model and computing its associated probabilities. J ACM. 56(2):1–44). We evaluate MixTreEM-DLRS using synthetic and biological data, and compare its performance with a recent genome-scale species tree reconstruction method PHYLDOG ( Boussau B, Szöllősi GJ, Duret L, Gouy M, Tannier E, Daubin V. 2013 . Genome-scale coestimation of species and gene trees. Genome Res. 23(2):323–330) as well as with a fast parsimony-based algorithm Duptree (Wehe A, Bansal MS, Burleigh JG, Eulenstein O. 2008. Duptree: a program for large-scale phylogenetic analyses using gene tree parsimony. Bioinformatics 24(13):1540–1541). Our method is competitive with PHYLDOG in terms of accuracy and runs significantly faster and our method outperforms Duptree in accuracy. The analysis constituted by MixTreEM without DLRS may also be used for selecting the target species tree, yielding a fast and yet accurate algorithm for larger data sets. MixTreEM is freely available at http://prime.scilifelab.se/mixtreem/ .

Description: We present a python package ldtk that automates the calculation of custom stellar limb-darkening (LD) profiles and model-specific limb-darkening coefficients using the library of phoenix -generated specific intensity spectra by Husser et al. The aim of the package is to facilitate analyses requiring custom generated LD profiles, such as the studies of exoplanet transits – especially transmission spectroscopy, where the transit modelling is carried out for custom narrow passbands – eclipsing binaries, interferometry, and microlensing events. First, ldtk can be used to compute custom LD profiles with uncertainties propagated from the uncertainties in the stellar parameter estimates. Secondly, ldtk can be used to estimate the LD-model-specific coefficients with uncertainties for the most common LD models. Thirdly, ldtk can be directly integrated into the log posterior computation of any pre-existing modelling code with minimal modifications. The last approach can be used to constrain the LD model parameter space directly by the LD profile, allowing for the marginalization over the LD parameter space without the need to approximate the constraint from the LD profile using a prior.

Description: KMOS (K-Band Multi Object Spectrograph) is a novel integral field spectrograph installed in the Very Large Telescope's (VLT's) ANTU unit. The instrument offers an ability to observe 24 2.8 arcsec  x  2.8 arcsec subfields positionable within a 7.2 arcmin patrol field, each subfield producing a spectrum with a 14  x  14-pixel spatial resolution. The main science drivers for KMOS are the study of galaxies, star formation, and molecular clouds, but its ability to simultaneously measure spectra of multiple stars makes KMOS an interesting instrument for exoplanet atmosphere characterization via transmission spectroscopy. We set to test whether transmission spectroscopy is practical with KMOS, and what are the conditions required to achieve the photometric precision needed, based on observations of a partial transit of WASP-19b, and full transits of GJ 1214b and HD 209458b. Our analysis uses the simultaneously observed comparison stars to reduce the effects from instrumental and atmospheric sources, and Gaussian processes to model the residual systematics. We show that KMOS can, in theory, deliver the photometric precision required for transmission spectroscopy. However, this is shown to require (a) pre-imaging to ensure accurate centring and (b) a very stable night with optimal observing conditions (seeing ~0.8 arcsec). Combining these two factors with the need to observe several transits, each with a sufficient out-of-transit baseline (and with the fact that similar or better precision can be reached with telescopes and instruments with smaller pressure), we conclude that transmission spectroscopy is not the optimal science case to take advantage of the abilities offered by KMOS and VLT.

Description: We present a python package ldtk that automates the calculation of custom stellar limb-darkening (LD) profiles and model-specific limb-darkening coefficients using the library of phoenix -generated specific intensity spectra by Husser et al. The aim of the package is to facilitate analyses requiring custom generated LD profiles, such as the studies of exoplanet transits – especially transmission spectroscopy, where the transit modelling is carried out for custom narrow passbands – eclipsing binaries, interferometry, and microlensing events. First, ldtk can be used to compute custom LD profiles with uncertainties propagated from the uncertainties in the stellar parameter estimates. Secondly, ldtk can be used to estimate the LD-model-specific coefficients with uncertainties for the most common LD models. Thirdly, ldtk can be directly integrated into the log posterior computation of any pre-existing modelling code with minimal modifications. The last approach can be used to constrain the LD model parameter space directly by the LD profile, allowing for the marginalization over the LD parameter space without the need to approximate the constraint from the LD profile using a prior.

Description: We introduce a new transit search and vetting pipeline for observations from the K2 mission, and present the candidate transiting planets identified by this pipeline out of the targets in Campaigns 5 and 6. Our pipeline uses the Gaussian process-based k2sc code to correct for the K2 pointing systematics and simultaneously model stellar variability. The systematics-corrected, variability-detrended light curves are searched for transits with the box-least-squares method, and a period-dependent detection threshold is used to generate a preliminary candidate list. Two or three individuals vet each candidate manually to produce the final candidate list, using a set of automatically generated transit fits and assorted diagnostic tests to inform the vetting. We detect 145 single-planet system candidates and 5 multi-planet systems, independently recovering the previously published hot Jupiters EPIC 212110888b, WASP-55b (EPIC 212300977b) and Qatar-2b (EPIC 212756297b). We also report the outcome of reconnaissance spectroscopy carried out for all candidates with Kepler magnitude Kp ≤ 13, identifying 12 targets as likely false positives. We compare our results to those of other K2 transit search pipelines, noting that ours performs particularly well for variable and/or active stars, but that the results are very similar overall. All the light curves and code used in the transit search and vetting process are publicly available, as are the follow-up spectra.

Description: We present a fast and user friendly exoplanet transit light-curve modelling package pytransit , implementing optimized versions of the Giménez and Mandel & Agol transit models. The package offers an object-oriented python interface to access the two models implemented natively in fortran with OpenMP parallelization. A partial OpenCL version of the quadratic Mandel–Agol model is also included for GPU-accelerated computations. The aim of pytransit is to facilitate the analysis of photometric time series of exoplanet transits consisting of hundreds of thousands of data points, and of multipassband transit light curves from spectrophotometric observations, as a part of a researcher's programming toolkit for building complex, problem-specific analyses.

Description: KMOS (K-Band Multi Object Spectrograph) is a novel integral field spectrograph installed in the Very Large Telescope's (VLT's) ANTU unit. The instrument offers an ability to observe 24 2.8 arcsec  x  2.8 arcsec subfields positionable within a 7.2 arcmin patrol field, each subfield producing a spectrum with a 14  x  14-pixel spatial resolution. The main science drivers for KMOS are the study of galaxies, star formation, and molecular clouds, but its ability to simultaneously measure spectra of multiple stars makes KMOS an interesting instrument for exoplanet atmosphere characterization via transmission spectroscopy. We set to test whether transmission spectroscopy is practical with KMOS, and what are the conditions required to achieve the photometric precision needed, based on observations of a partial transit of WASP-19b, and full transits of GJ 1214b and HD 209458b. Our analysis uses the simultaneously observed comparison stars to reduce the effects from instrumental and atmospheric sources, and Gaussian processes to model the residual systematics. We show that KMOS can, in theory, deliver the photometric precision required for transmission spectroscopy. However, this is shown to require (a) pre-imaging to ensure accurate centring and (b) a very stable night with optimal observing conditions (seeing ~0.8 arcsec). Combining these two factors with the need to observe several transits, each with a sufficient out-of-transit baseline (and with the fact that similar or better precision can be reached with telescopes and instruments with smaller pressure), we conclude that transmission spectroscopy is not the optimal science case to take advantage of the abilities offered by KMOS and VLT.

Description: We present k2sc ( K2 Systematics Correction), a python pipeline to model instrumental systematics and astrophysical variability in light curves from the K2 mission. k2sc uses Gaussian Process regression to model position-dependent systematics and time-dependent variability simultaneously, enabling the user to remove both (e.g. for transit searches) or to remove systematics while preserving variability (for variability studies). For periodic variables, k2sc automatically computes estimates of the period, amplitude and evolution time-scale of the variability. We apply k2sc to publicly available K2 data from Campaigns 3–5 showing that we obtain photometric precision approaching that of the original Kepler mission. We compare our results to other publicly available K2 pipelines, showing that we obtain similar or better results, on average. We use transit injection and recovery tests to evaluate the impact of k2sc on planetary transit searches in K2 Pre-search Data Conditioning data, for planet-to-star radius ratios down to R p / R * = 0.01 and periods up to P = 40 d, and show that k2sc significantly improves the ability to distinguish between true and false detections, particularly for small planets. k2sc can be run automatically on many light curves, or manually tailored for specific objects such as pulsating stars or large amplitude eclipsing binaries. It can be run on ASCII and FITS light-curve files, regardless of their origin. Both the code and the processed light curves are publicly available, and we provide instructions for downloading and using them. The methodology used by k2sc will be applicable to future transit search missions such as TESS and PLATO .

Description: A significant fraction of an exoplanet transit model evaluation time is spent calculating projected distances between the planet and its host star. This is a relatively fast operation for a circular orbit, but slower for an eccentric one. However, because the planet’s position and its time derivatives are constant for any specific point in orbital phase, the projected distance can be calculated rapidly and accurately in the vicinity of the transit by expanding the planet’s x and y positions in the sky plane into a Taylor series at mid-transit. Calculating the projected distance for an elliptical orbit using the four first time derivatives of the position vector (velocity, acceleration, jerk, and snap) is ∼100 times faster than calculating it using the Newton’s method, and also significantly faster than calculating z for a circular orbit because the approach does not use numerically expensive trigonometric functions. The speed gain in the projected distance calculation leads to 2–25 times faster transit model evaluation speed, depending on the transit model complexity and orbital eccentricity. Calculation of the four position derivatives using numerical differentiation takes $sim 1, mu$s with a modern laptop and needs to be done only once for a given orbit, and the maximum error the approximation introduces to a transit light curve is below 1 ppm for the major part of the physically plausible orbital parameter space.