Context. PDS 70 is a young (5.4 Myr), nearby (∼113 pc) star hosting a known transition disk with a large gap. Recent observations with SPHERE and NACO in the near-infrared (NIR) allowed us to detect a planetary mass companion, PDS 70 b, within the disk cavity. Moreover, observations in H α with MagAO and MUSE revealed emission associated to PDS 70 b and to another new companion candidate, PDS 70 c, at a larger separation from the star. PDS 70 is the only multiple planetary system at its formation stage detected so far through direct imaging. Aims. Our aim is to confirm the discovery of the second planet PDS 70 c using SPHERE at VLT, to further characterize its physical properties, and search for additional point sources in this young planetary system. Methods. We re-analyzed archival SPHERE NIR observations and obtained new data in Y, J, H and K spectral bands for a total of four different epochs. The data were reduced using the data reduction and handling pipeline and the SPHERE data center. We then applied custom routines (e.g. ANDROMEDA and PACO) to subtract the starlight. Results. We re-detect both PDS 70 b and c and confirm that PDS 70 c is gravitationally bound to the star. We estimate this second planet to be less massive than 5 M Jup and with a T eff around 900 K. Also, it has a low gravity with log g between 3.0 and 3.5 dex. In addition, a third object has been identified at short separation (∼0.12 ) from the star and gravitationally bound to the star. Its spectrum is however very blue, so that we are probably seeing stellar light reflected by dust and our analysis seems to demonstrate that it is a feature of the inner disk. We, however, cannot completely exclude the possibility that it is a planetary mass object enshrouded by a dust envelope. In this latter case, its mass should be of the order of few tens of M ⊕ . Moreover, we propose a possible structure for the planetary system based on our data that, however, cannot be stable on a long timescale.

Context. The detection of exoplanets by direct imaging is an active research topic in astronomy. Even with the coupling of an extreme adaptive-optics system with a coronagraph, it remains challenging due to the very high contrast between the host star and the exoplanets. Aims. The purpose of this paper is to describe a method, named PACO, dedicated to source detection from angular differential imaging data. Given the complexity of the fluctuations of the background in the datasets, involving spatially variant correlations, we aim to show the potential of a processing method that learns the statistical model of the background from the data. Methods. In contrast to existing approaches, the proposed method accounts for spatial correlations in the data. Those correlations and the average stellar speckles are learned locally and jointly to estimate the flux of the (potential) exoplanets. By preventing from subtracting images including the stellar speckles residuals, the photometry is intrinsically preserved. A nonstationary multi-variate Gaussian model of the background is learned. The decision in favor of the presence or the absence of an exoplanet is performed by a binary hypothesis test. Results. The statistical accuracy of the model is assessed using VLT/SPHERE-IRDIS datasets. It is shown to capture the nonstationarity in the data so that a unique threshold can be applied to the detection maps to obtain consistent detection performance at all angular separations. This statistical model makes it possible to directly assess the false alarm rate, probability of detection, photometric and astrometric accuracies without resorting to Monte-Carlo methods. Conclusions. PACO offers appealing characteristics: it is parameter-free and photometrically unbiased. The statistical performance in terms of detection capability, photometric and astrometric accuracies can be straightforwardly assessed. A fast approximate version of the method is also described that can be used to process large amounts of data from exoplanets search surveys.