Context. The direct detection and characterization of planetary and substellar companions at small angular separations is a rapidly advancing field. Dedicated high-contrast imaging instruments deliver unprecedented sensitivity, enabling detailed insights into the atmospheres of young low-mass companions. In addition, improvements in data reduction and point spread function(PSF)-subtraction algorithms are equally relevant for maximizing the scientific yield, both from new and archival data sets. Aims. We aim at developing a generic and modular data-reduction pipeline for processing and analysis of high-contrast imaging data obtained with pupil-stabilized observations. The package should be scalable and robust for future implementations and particularly suitable for the 3-5 µm wavelength range where typically thousands of frames have to be processed and an accurate subtraction of the thermal background emission is critical. Methods. PynPoint is written in Python 2.7 and applies various image-processing techniques, as well as statistical tools for analyzing the data, building on open-source Python packages. The current version of PynPoint has evolved from an earlier version that was developed as a PSF-subtraction tool based on principal component analysis (PCA).Results. The architecture of PynPoint has been redesigned with the core functionalities decoupled from the pipeline modules. Modules have been implemented for dedicated processing and analysis steps, including background subtraction, frame registration, PSF subtraction, photometric and astrometric measurements, and estimation of detection limits. The pipeline package enables end-to-end data reduction of pupil-stabilized data and supports classical dithering and coronagraphic data sets. As an example, we processed archival VLT/NACO L and M data of β Pic b and reassessed the brightness and position of the planet with a Markov chain Monte Carlo (MCMC) analysis; we also provide a derivation of the photometric error budget. Pipelinereading module writing module processing module processing module processing module front end back end data management computational resource management PynPoint toolbox input data output data Input Control of database connection DataIO Central database data sets (e.g. images, analysis results) attributes (e.g. pixel scale, dither position) Abstract interface for ports between pipeline modules and database Pipeline interface Processing Port types Pypeline PypelineModule ReadingModule WritingModule ProcessingModule Abstract interface for pipeline modules Abstract interfaces for different module types Front end Back end

Context. Planet formation is a frequent process, but little observational constraints exist about the mechanisms involved, especially for giant planets at large separation. The NaCo-ISPY large program is a 120 night L -band direct imaging survey aimed at investigating the giant planet population on wide orbits (a > 10 au) around stars hosting disks.Aims. Here we present the statistical analysis of a subsample of 45 young stars surrounded by protoplanetary disks (PPDs). This is the largest imaging survey uniquely focused on PPDs to date. Our goal is to search for young forming companions embedded in the disk material and to constrain their occurrence rate in relation to the formation mechanism. Methods. We used principal component analysis based point spread function subtraction techniques to reveal young companions forming in the disks. We calculated detection limits for our datasets and adopted a black-body model to derive temperature upper limits of potential forming planets. We then used Monte Carlo simulations to constrain the population of forming gas giant companions and compare our results to different types of formation scenarios. Results. Our data revealed a new binary system (HD38120) and a recently identified triple system with a brown dwarf companion orbiting a binary system (HD101412), in addition to 12 known companions. Furthermore, we detected signals from 17 disks, two of which (HD72106 and T CrA) were imaged for the first time. We reached median detection limits of L = 15.4 mag at 2 . 0, which were used to investigate the temperature of potentially embedded forming companions. We can constrain the occurrence of forming planets with semi-major axis a in [20 − 500] au and T eff in [600 − 3000] K to be 21.2 +24.3 −13.6 %, 14.8 +17.5 −9.6 %, and 10.8 +12.6 −7.0 % for R p = 2, 3, 5 R J , which is in line with the statistical results obtained for more evolved systems from other direct imaging surveys. These values are obtained under the assumption that extinction from circumstellar and circumplanetary material does not affect the companion signal, but we show the potential impact these factors might have on the detectability of forming objects. Conclusions. The NaCo-ISPY data confirm that massive bright planets accreting at high rates are rare. More powerful instruments with better sensitivity in the near-to mid-infrared (MIR) are likely required to unveil the wealth of forming planets sculpting the observed disk substructures.

Context. High-contrast imaging of exoplanets hinges on powerful post-processing methods to denoise the data and separate the signal of a companion from its host star, which is typically orders of magnitude brighter. Aims. Existing post-processing algorithms do not use all prior domain knowledge that is available about the problem. We propose a new method that builds on our understanding of the systematic noise and the causal structure of the data-generating process. Methods. Our algorithm is based on a modified version of half-sibling regression (HSR), a flexible denoising framework that combines ideas from the fields of machine learning and causality. We adapted the method to address the specific requirements of high-contrast exoplanet imaging data obtained in pupil tracking mode. The key idea is to estimate the systematic noise in a pixel by regressing the time series of this pixel onto a set of causally independent, signal-free predictor pixels. We use regularized linear models in this work; however, other (nonlinear) models are also possible. In a second step, we demonstrate how the HSR framework allows us to incorporate observing conditions such as wind speed or air temperature as additional predictors.Results. When we applied our method to four data sets from the VLT/NACO instrument, our algorithm provided a better false-positive fraction than a popular baseline method in the field. Additionally, we found that the HSR-based method provides direct and accurate estimates for the contrast of the exoplanets without the need to insert artificial companions for calibration in the data sets. Finally, we present a first piece of evidence that using the observing conditions as additional predictors can improve the results. Conclusions. Our HSR-based method provides an alternative, flexible, and promising approach to the challenge of modeling and subtracting the stellar PSF and systematic noise in exoplanet imaging data.

Direct imaging of exoplanets and circumstellar disks at optical and infrared wavelengths requires reaching high contrasts at short angular separations. This can only be achieved through the synergy of advanced instrumentation, such as adaptive optics and coronagraphy, with a relevant combination of observing strategy and post-processing algorithms to model and subtract residual starlight. In this context, VIP is a Python package providing the tools to reduce, post-process and analyze high-contrast imaging datasets, enabling the detection and characterization of directly imaged exoplanets, circumstellar disks, and stellar environments.

imaging data challenge: benchmarking the various image processing methods for exoplanet detection,"