As the largest radio telescope in the world, the Square Kilometre Array (SKA) will lead the next generation of radio astronomy. The feats of engineering required to construct the telescope array will be matched only by the techniques developed to exploit the rich scientific value of the data. To drive forward the development of efficient and accurate analysis methods, we are designing a series of data challenges that will provide the scientific community with high-quality datasets for testing and evaluating new techniques. In this paper we present a description and results from the first such Science Data Challenge (SDC1). Based on SKA MID continuum simulated observations and covering three frequencies (560 MHz, 1400MHz and 9200 MHz) at three depths (8 h, 100 h and 1000 h), SDC1 asked participants to apply source detection, characterization and classification methods to simulated data. The challenge opened in November 2018, with nine teams submitting results by the deadline of April 2019. In this work we analyse the results for 8 of those teams, showcasing the variety of approaches that can be successfully used to find, characterise and classify sources in a deep, crowded field. The results also demonstrate the importance of building domain knowledge and expertise on this kind of analysis to obtain the best performance. As high-resolution observations begin revealing the true complexity of the sky, one of the outstanding challenges emerging from this analysis is the ability to deal with highly resolved and complex sources as effectively as the unresolved source population.

We present a novel approach for analysing radial velocity data that combines two features: all the planets are searched at once and the algorithm is fast. This is achieved by utilizing compressed sensing techniques,which aremodified to be compatible with theGaussian process framework. The resulting tool can be used like a Lomb-Scargle periodogram and has the same aspect but with much fewer peaks due to aliasing. The method is applied to five systems with published radial velocity data sets: HD 69830, HD 10180, 55 Cnc, GJ 876 and a simulated very active star. The results are fully compatible with previous analysis, though obtained more straightforwardly. We further show that 55 Cnc e and f could have been respectively detected and suspected in early measurements from the Lick Observatory and Hobby-Eberly Telescope available in 2004, and that frequencies due to dynamical interactions in GJ 876 can be seen. © 2016 The Authors. Published by Oxford University Press on behalf of the Royal Astronomical Society.

Current synoptic sky surveys monitor large areas of the sky to find variable and transient astronomical sources. As the number of detections per night at a single telescope easily exceeds several thousand, current detection pipelines make intensive use of machine learning algorithms to classify the detected objects and to filter out the most interesting candidates. A number of upcoming surveys will produce up to three orders of magnitude more data, which renders high-precision classification systems essential to reduce the manual and, hence, expensive vetting by human experts. We present an approach based on convolutional neural networks to discriminate between true astrophysical sources and artefacts in reference-subtracted optical images. We show that relatively simple networks are already competitive with state-of-the-art systems and that their quality can further be improved via slightly deeper networks and additional pre-processing steps – eventually yielding models outperforming state-of-the-art systems. In particular, our best model correctly classifies about 97.3 per cent of all ‘real’ and 99.7 per cent of all ‘bogus’ instances on a test set containing 1942 ‘bogus’ and 227 ‘real’ instances in total. Furthermore, the networks considered in this work can also successfully classify these objects at hand without relying on difference images, which might pave the way for future detection pipelines not containing image subtraction steps at all.

Aims: Several studies suggest that the activity level of a planet-host star can be influenced by the presence of a close-by orbiting planet. Moreover, the interaction mechanisms that have been proposed, magnetic interaction and tidal interaction, exhibit a very different dependence on the orbital separation between the star and the planet. A detection of activity enhancement and characterization of its dependence on planetary orbital distance can, in principle, allow us to characterize the physical mechanism behind the activity enhancement. Methods: We used the HARPS-N spectrograph to measure the stellar activity level of HD 80606 during the planetary periastron passage and compared the activity measured to that close to apastron. Being characterized by an eccentricity of 0.93 and an orbital period of 111 days, the system's extreme variation in orbital separation makes it a perfect target to test our hypothesis. Results: We find no evidence for a variation in the activity level of the star as a function of planetary orbital distance, as measured by all activity indicators employed: log(R'HK), Hα, NaI, and HeI. None of the models employed, whether magnetic interaction or tidal interaction, provides a good description of the data. The photometry revealed no variation either, but it was strongly affected by poor weather conditions. Conclusions: We find no evidence for star-planet interaction in HD 80606 at the moment of the periastron passage of its very eccentric planet. The straightforward explanation for the non-detection is the absence of interaction as a result of a low magnetic field strength on either the planet or the star and of the low level of tidal interaction between the two. However, we cannot exclude two scenarios: i) the interaction can be instantaneous and of magnetic origin, being concentrated on the substellar point and its surrounding area; and ii) the interaction can lead to a delayed activity enhancement. In either scenario, a star-planet interaction would not be detectable with the dataset described in this paper.

Context. The Centaur (10199) Chariklo has the first ring system discovered around a small object. It was first observed using stellar occultation in 2013. Stellar occultations allow sizes and shapes to be determined with kilometre accuracy, and provide the characteristics of the occulting object and its vicinity. Aims. Using stellar occultations observed between 2017 and 2020, our aim is to constrain the physical parameters of Chariklo and its rings. We also determine the structure of the rings, and obtain precise astrometrical positions of Chariklo. Methods. We predicted and organised several observational campaigns of stellar occultations by Chariklo. Occultation light curves were measured from the datasets, from which ingress and egress times, and the ring widths and opacity values were obtained. These measurements, combined with results from previous works, allow us to obtain significant constraints on Chariklo's shape and ring structure. Results. We characterise Chariklo's ring system (C1R and C2R), and obtain radii and pole orientations that are consistent with, but more accurate than, results from previous occultations. We confirm the detection of W-shaped structures within C1R and an evident variation in radial width. The observed width ranges between 4.8 and 9.1 km with a mean value of 6.5 km. One dual observation (visible and red) does not reveal any differences in the C1R opacity profiles, indicating a ring particle size larger than a few microns. The C1R ring eccentricity is found to be smaller than 0.022 (3σ), and its width variations may indicate an eccentricity higher than ~0.005. We fit a tri-axial shape to Chariklo's detections over 11 occultations, and determine that Chariklo is consistent with an ellipsoid with semi-axes of 143.8-1.5+1.4, 135.2-2.8+1.4, and 99.1-2.7+5.4 km. Ultimately, we provided seven astrometric positions at a milliarcsecond accuracy level, based on Gaia EDR3, and use it to improve Chariklo's ephemeris.

Context. A stellar occultation by Neptune's main satellite, Triton, was observed on 5 October 2017 from Europe, North Africa, and the USA. We derived 90 light curves from this event, 42 of which yielded a central flash detection. Aims. We aimed at constraining Triton's atmospheric structure and the seasonal variations of its atmospheric pressure since the Voyager 2 epoch (1989). We also derived the shape of the lower atmosphere from central flash analysis. Methods. We used Abel inversions and direct ray-tracing code to provide the density, pressure, and temperature profiles in the altitude range ∼8 km to ∼190 km, corresponding to pressure levels from 9 μbar down to a few nanobars. Results. (i) A pressure of 1.18 ± 0.03 μbar is found at a reference radius of 1400 km (47 km altitude). (ii) A new analysis of the Voyager 2 radio science occultation shows that this is consistent with an extrapolation of pressure down to the surface pressure obtained in 1989. (iii) A survey of occultations obtained between 1989 and 2017 suggests that an enhancement in surface pressure as reported during the 1990s might be real, but debatable, due to very few high S/N light curves and data accessible for reanalysis. The volatile transport model analysed supports a moderate increase in surface pressure, with a maximum value around 2005-2015 no higher than 23 μbar. The pressures observed in 1995-1997 and 2017 appear mutually inconsistent with the volatile transport model presented here. (iv) The central flash structure does not show evidence of an atmospheric distortion. We find an upper limit of 0.0011 for the apparent oblateness of the atmosphere near the 8 km altitude.

Context. Quaoar is a classical trans-Neptunian object (TNO) with an area-equivalent diameter of 1100 km and an orbital semi-major axis of 43.3 astronomical units. Based on stellar occultations observed between 2018 and 2021, an inhomogeneous ring (Q1R, i.e., Quaoar's first ring) has been detected around this body. Aims. A new stellar occultation by Quaoar was observed on August 9, 2022, with the aim of improving Quaoar's shape models and the physical parameters of Q1R, while searching for additional material around the body. Methods. The occultation provided nine effective chords across Quaoar, pinning down its size, shape, and astrometric position. Large facilities, such as Gemini North and the Canada-France-Hawaii Telescope (CFHT), were used to obtain high acquisition rates and signal-to-noise ratios. The light curves were also used to characterize the Q1R ring (radial profiles and orbital elements). Results. Quaoar's elliptical fit to the occultation chords yields the limb with an apparent semi-major axis of 579.5 ± 4.0 km, apparent oblateness of 0.12 ± 0.01, and area-equivalent radius of 543 ± 2 km. Quaoar's limb orientation is consistent with Q1R and Weywot orbiting in Quaoar's equatorial plane. The orbital radius of Q1R is refined to a value of 4057 ± 6 km. The radial opacity profile of the more opaque ring profile follows a Lorentzian shape that extends over 60 km, with a full width at half maximum (FWHM) of ?5 km and a peak normal optical depth of 0.4. Besides the secondary events related to the already reported rings, new secondary events detected during the August 2022 occultation in three different data sets are consistent with another ring around Quaoar with a radius of 2520 ± 20 km, assuming the ring is circular and co-planar with Q1R. This new ring has a typical width of 10 km and a normal optical depth of ?0.004. Just as Q1R, it also lies outside Quaoar's classical Roche limit.

In this work, we present the results of the study of the cosmic microwave background temperatureerature power spectrum through auto-encoders in which the latent variables are the cosmological parameters. This method was trained and calibrated using a data set composed of 80 000 power spectra from random cosmologies computed numerically with the camb code. Due to the specific architecture of the auto-encoder, the encoder part is a model that estimates the maximum-likelihood parameters from a given power spectrum. On the other hand, the decoder part is a model that computes the power spectrum from the cosmological parameters and can be used as a forward model in a fully Bayesian analysis. We show that the encoder is able to estimate the true cosmological parameters with a precision varying from ≈ 0.004 per cent to ≈ 0.2 per cent (depending on the cosmological parameter), while the decoder computes the power spectra with a mean percentage error of ≈ 0.0018 per cent for all the multipole range. We also demonstrate that the decoder recovers the expected trends when varying the cosmological parameters one by one, and that it does not introduce any significant bias on the estimation of cosmological parameters through a Bayesian analysis. These studies gave place to the cosmic-kite python software, which is publicly available and can be downloaded and installed from https://github.com/Martindelosrios/cosmic-kite. Although this algorithm does not improve the precision of the measurements compared with the traditional methods, it reduces significantly the computation time and represents the first attempt towards forcing the latent variables to have a physical interpretation.