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The detection of a low-mass exoplanet on a relatively wide orbit has implications for models of planetary formation and evolution, and could open the door to a new era of exoplanet characterization. The detection of a low-mass exoplanet on a relatively wide orbit.

Knowledge of the 10 B microdistribution is of great relevance in BNCT studies. Since 10 B concentration assesment through neutron autoradiography depends on the correct quantification of tracks in a nuclear track detector, image acquisition and processing conditions should be controlled and verified, in order to obtain accurate results to be applied in the frame of BNCT. With this aim, an image verification process was proposed, based on parameters extracted from the quantified nuclear tracks. Track characterization was performed by selecting a set of morphological and pixel-intensity uniformity parameters from the quantified objects (area, diameter, roundness, aspect ratio, heterogeneity and clumpiness). Their distributions were studied, leading to the observation of varying behaviours in images generated by different samples and acquisition conditions. The distributions corresponding to samples coming from the BNC reaction showed similar attributes in each analyzed parameter, proving to be robust to the experimental process, but sensitive to light and focus conditions. Considering those observations, a manual feature extraction was performed as a pre-processing step. A Support Vector Machine (SVM) and a fully dense Neural Network (NN) were optimized, trained, and tested. The final performance metrics were similar for both models: 93%-93% for the SVM, vs 94%-95% for the NN in accuracy and precision respectively. Based on the distribution of the predicted class probabilities, the latter had a better capacity to reject inadequate images, so the NN was selected to perform the image verification step prior to quantification. The trained NN was able to correctly classify the images regardless of their track density. The exhaustive characterization of the nuclear tracks provided new knowledge related to the autoradiographic images generation. The inclusion of machine learning in the analysis workflow proves to optimize the boron determination process and paves the way for further applications in the field of boron imaging.

Transiting candidate validation is essentially a Bayesian model comparison problem: different models, all explaining the observations comparably well, compete for the support of the available data. The basic characteristics of the planet validation problem are discussed and the different approaches taken to tackle its difficulties are reviewed.

The interiors of giant planets remain poorly understood. Even for the planets in the Solar System, difficulties in observation lead to large uncertainties in the properties of planetary cores. Exoplanets that have undergone rare evolutionary processes provide a route to understanding planetary interiors. Planets found in and near the typically barren hot-Neptune ‘desert’ (a region in mass–radius space that contains few planets) have proved to be particularly valuable in this regard. These planets include HD149026b, which is thought to have an unusually massive core, and recent discoveries such as LTT9779b and NGTS-4b, on which photoevaporation has removed a substantial part of their outer atmospheres. Here we report observations of the planet TOI-849b, which has a radius smaller than Neptune’s but an anomalously large mass of 39.1(+2.7−2.6) Earth masses and a density of 5.2(+0.7−0.8) grams per cubic centimetre, similar to Earth’s. Interior-structure models suggest that any gaseous envelope of pure hydrogen and helium consists of no more than 3.9(+0.8−0.9) per cent of the total planetary mass. The planet could have been a gas giant before undergoing extreme mass loss via thermal self-disruption or giant planet collisions, or it could have avoided substantial gas accretion, perhaps through gap opening or late formation. Although photoevaporation rates cannot account for the mass loss required to reduce a Jupiter-like gas giant, they can remove a small (a few Earth masses) hydrogen and helium envelope on timescales of several billion years, implying that any remaining atmosphere on TOI-849b is likely to be enriched by water or other volatiles from the planetary interior. We conclude that TOI-849b is the remnant core of a giant planet.

Two consecutive transits of planetary companion OGLE-TR-111b were observed in the I band. Combining these observations with data from the literature, we find that the timing of the transits cannot be explained by a constant period, and that the observed variations cannot be originated by the presence of a satellite. However, a perturbing planet with the mass of the Earth in an exterior orbit could explain the observations if the orbit of OGLE-TR-111b is eccentric. We also show that the eccentricity needed to explain the observations is not ruled out by the radial velocity data found in the literature.

Research in extrasolar-planet science is data-driven. With the advent of radial-velocity instruments like HARPS and HARPS-N, and transit space missions like Kepler, our ability to discover and characterise extrasolar planets is no longer limited by instrumental precision but by our ability to model the data accurately. This chapter presents the models that describe radial-velocity measurements and transit light curves. I begin by deriving the solution of the two-body problem and from there, the equations describing the radial velocity of a planet-host star and the distance between star and planet centres, necessary to model transit light curves. Stochastic models are then presented and I delineate how they are used to model complex physical phenomena affecting the exoplanet data sets, such as stellar activity. Finally, I give a brief overview of the processes of Bayesian inference, focussing on the construction of likelihood functions and prior probability distributions. In particular, I describe different methods to specify ignorance priors.

Understanding the demographics of close-in planets is crucial for insights into exoplanet formation and evolution. We present a detailed analysis of occurrence rates for close-in (0.5-16 day) planets with radii between 2 and 20\\(\\,R_{\\oplus}\\) around FGK main-sequence stars. Our study uses a comprehensive sample from four years of TESS Science Processing Operations Center full-frame image data cross-matched with Gaia, analysed through our rigorous detection, vetting, and validation pipeline. Using high-confidence planet candidates, we apply a hierarchical Bayesian model to determine occurrence rates in the two-dimensional orbital period-radius plane. Our results are presented using 10-by-10 bins across the period-radius parameter space, offering unprecedented resolution and statistical precision. We find an overall occurrence rate of \\(9.4^{+0.7}_{-0.6}\\%\\). When using identical binning, our occurrence rate posteriors distributions align with Kepler's but have a magnitude smaller uncertainties on average. For hot Jupiters, we estimate the overall occurrence rate of \\(0.39^{+0.03}_{-0.02}\\%\\). This value is consistent with the previous Kepler FGK-type result within \\(1\\sigma\\). We find an overall occurrence rate of Neptunian desert planets of \\(0.08\\pm0.01\\%\\), to our knowledge the first such determination. Additionally, in a volume-limited Gaia subsample within 100 pc in the same parameter region, we measure an overall planet occurrence rate of \\(15.4^{+1.6}_{-1.5}\\%\\) and a hot Jupiter occurrence rate of \\(0.42^{+0.16}_{-0.12}\\%\\). Our results establishes an improved foundation for constraining theoretical models of exoplanet populations.

We present RAVEN, a newly developed vetting and validation pipeline for TESS exoplanet candidates. The pipeline employs a Bayesian framework to derive the posterior probability of a candidate being a planet against a set of False Positive (FP) scenarios, through the use of a Gradient Boosted Decision Tree and a Gaussian Process classifier, trained on comprehensive synthetic training sets of simulated planets and 8 astrophysical FP scenarios injected into TESS lightcurves. These training sets allow large scale candidate vetting and performance verification against individual FP scenarios. A Non-Simulated FP training set consisting of real TESS candidates caused primarily by stellar variability and systematic noise is also included. The machine learning derived probabilities are combined with scenario specific prior probabilities, including the candidates' positional probabilities, to compute the final posterior probabilities. Candidates with a planetary posterior probability greater than 99% against each FP scenario and whose implied planetary radius is less than 8\\(R_{\\oplus}\\) are considered to be statistically validated by the pipeline. In this first version, the pipeline has been developed for candidates with a lightcurve released from the TESS Science Processing Operations Centre, an orbital period between 0.5 and 16 days and a transit depth greater than 300ppm. The pipeline obtained area-under-curve (AUC) scores > 97% on all FP scenarios and > 99% on all but one. Testing on an independent external sample of 1361 pre-classified TOIs, the pipeline achieved an overall accuracy of 91%, demonstrating its effectiveness for automated ranking of TESS candidates. For a probability threshold of 0.9 the pipeline reached a precision of 97% with a recall score of 66% on these TOIs. The RAVEN pipeline is publicly released as a cloud-hosted app, making it easily accessible to the community.

Context: LHS 3844 b (TOI-136 b) is a ultra short-period, Earth-size exoplanet detected by TESS. It is one of the most favourable object for atmospheric characterisation and the study of its surface with the James Webb Space Telescope. However, the dynamical mass of this planet has not been measured yet. Aims: We aim to determine the mass of LHS 3844 b using high-precision radial velocity (RV) measurements and assess the robustness of the inferred signal across different noise and orbital modelling assumptions. Methods: We analyse 25 ESPRESSO RV observations within a fully Bayesian framework. We explore 15 competing RV models that differ in their treatment of correlated stellar variability (through different Gaussian Process kernels) and long-term drifts. Marginal likelihoods are computed for all models and used for Bayesian model comparison and evidence-weighted parameter estimation. Results: The RV planetary signal is robustly detected across all models, and the inferred semi-amplitude remains stable under all tested noise and drift prescriptions. From the evidence-weighted posterior samples we derive a planetary mass of \\(2.27 \\pm 0.23\\) M\\(_\\oplus\\) and a bulk density of \\(5.67 \\pm 0.65\\) gcm\\(^{-3}\\), consistent with a predominantly rocky composition. Model comparison favours GP kernels including periodic or quasi-periodic components associated with stellar rotation and disfavors models with additional long-term drifts. Using interior-structure inference, we find that the core mass fraction is comparable to (or slightly smaller than) Earth's and only trace amounts of water are permitted, supporting a dry, terrestrial interior. We also investigate a tentative additional signal near \\(\\sim 6.9\\) days, but Bayesian model comparison does not provide conclusive support for its planetary interpretation.

We present the discovery of an exoplanet transiting TOI-908 (TIC-350153977) using data from TESS sectors 1, 12, 13, 27, 28 and 39. TOI-908 is a T = 10.7 mag G-dwarf (\\(T_{eff}\\) = 5626 \\(\\pm\\) 61 K) solar-like star with a mass of 0.950 \\(\\pm\\) 0.010 \\(M_{\\odot}\\) and a radius of 1.028 \\(\\pm\\) 0.030 \\(R_{\\odot}\\). The planet, TOI-908 b, is a 3.18 \\(\\pm\\) 0.16 \\(R_{\\oplus}\\) planet in a 3.18 day orbit. Radial velocity measurements from HARPS reveal TOI-908 b has a mass of approximately 16.1 \\(\\pm\\) 4.1 \\(M_{\\oplus}\\) , resulting in a bulk planetary density of 2.7+0.2-0.4 g cm-3. TOI-908 b lies in a sparsely-populated region of parameter space known as the Neptune desert. The planet likely began its life as a sub-Saturn planet before it experienced significant photoevaporation due to X-rays and extreme ultraviolet radiation from its host star, and is likely to continue evaporating, losing a significant fraction of its residual envelope mass.