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The increased brightness temperature of young rocky protoplanets during their magma ocean epoch makes them potentially amenable to atmospheric characterization to distances from the solar system far greater than thermally equilibrated terrestrial exoplanets, offering observational opportunities for unique insights into the origin of secondary atmospheres and the near surface conditions of prebiotic environments. The Large Interferometer For Exoplanets (LIFE) mission will employ a space-based mid-infrared nulling interferometer to directly measure the thermal emission of terrestrial exoplanets. Here, we seek to assess the capabilities of various instrumental design choices of the LIFE mission concept for the detection of cooling protoplanets with transient high-temperature magma ocean atmospheres, in young stellar associations in particular. Using the LIFE mission instrument simulator (LIFEsim) we assess how specific instrumental parameters and design choices, such as wavelength coverage, aperture diameter, and photon throughput, facilitate or disadvantage the detection of protoplanets. We focus on the observational sensitivities of distance to the observed planetary system, protoplanet brightness temperature using a blackbody assumption, and orbital distance of the potential protoplanets around both G- and M-dwarf stars. Our simulations suggest that LIFE will be able to detect (S/N $\geq$ 7) hot protoplanets in young stellar associations up to distances of $\approx$100 pc from the solar system for reasonable integration times (up to $\sim$hours). Detection of an Earth-sized protoplanet orbiting a solar-sized host star at 1 AU requires less than 30 minutes of integration time. M-dwarfs generally need shorter integration times. The contribution from wavelength regions $<$6 $\mu$m is important for decreasing the detection threshold and discriminating emission temperatures., Comment: 18 pages, 19 figures; accepted for publication in A&A

The transit technique has been very efficient in detecting planet candidate signals over the past decades. The so-called statistical validation approach has become a popular way of verifying a candidate's planetary nature. However, the incomplete consideration of false positive scenarios and data quality can lead to the misinterpretation of the results. In this work we revise the planetary status of K2-399\,b, a validated planet with an estimated false positive probability of 0.078% located in the middle of the so-called Neptunian desert, and hence a potential key target for atmospheric prospects. We use radial velocity data from the CARMENES, HARPS and TRES spectrographs, as well as ground-based multi-band transit photometry LCOGT MuSCAT3 and broad band photometry to test the planetary scenario. Our analysis of the available data does not support the existence of this (otherwise key) planet, and instead points to a scenario composed of an early G-dwarf orbited in a $846.62^{+0.22}_{-0.28}$~days period by a pair of eclipsing M-dwarfs (hence a hierarchical eclipsing binary) likely in the mid-type domain. We thus demote K2-399 b as a planet. We conclude that the validation process, while very useful to prioritise follow-up efforts, must always be conducted with careful attention to data quality while ensuring that all possible scenarios have been properly tested to get reliable results. We also encourage developers of validation algorithms to ensure the accuracy of a priori probabilities for different stellar scenarios that can lead to this kind of false validation. We further encourage the use of follow-up observations when possible (such as radial velocity and/or multi-band light curves) to confirm the planetary nature of detected transiting signals rather than only relying on validation tools., Comment: 14 pages, 12 figures, 5 tables, Accepted for publication in A&A Letters

Among the existing approaches for visual-based Recommender System (RS) explainability, utilizing user-uploaded item images as efficient, trustable explanations is a promising option. However, current models following this paradigm assume that, for any user, all images uploaded by other users can be considered negative training examples (i.e. bad explanatory images), an inadvertedly naive labelling assumption that contradicts the rationale of the approach. This work proposes a new explainer training pipeline by leveraging Positive-Unlabelled (PU) Learning techniques to train image-based explainer with refined subsets of reliable negative examples for each user selected through a novel user-personalized, two-step, similarity-based PU Learning algorithm. Computational experiments show this PU-based approach outperforms the state-of-the-art non-PU method in six popular real-world datasets, proving that an improvement of visual-based RS explainability can be achieved by maximizing training data quality rather than increasing model complexity.

Co-orbital objects, also known as trojans, are frequently found in simulations of planetary system formation. In these configurations, a planet shares its orbit with other massive bodies. It is still unclear why there have not been any co-orbitals discovered thus far in exoplanetary systems or even pairs of planets found in such a 1:1 mean motion resonance. Reconciling observations and theory is an open subject in the field. The main objective of the TROY project is to conduct an exhaustive search for exotrojans using diverse observational techniques. In this work, we analyze the radial velocity time series informed by transits, focusing the search around low-mass stars. We employed the alpha-test method on confirmed planets searching for shifts between spectral and photometric mid-transit times. This technique is sensitive to mass imbalances within the planetary orbit, allowing us to identify non-negligible co-orbital masses. Among the 95 transiting planets examined, we find one robust exotrojan candidate with a significant 3-sigma detection. Additionally, 25 exoplanets show compatibility with the presence of exotrojan companions at a 1-sigma level, requiring further observations to better constrain their presence. For two of those weak candidates, we find dimmings in their light curves within the predicted Lagrangian region. We established upper limits on the co-orbital masses for either the candidates and null detections. Our analysis reveals that current high-resolution spectrographs effectively rule out co-orbitals more massive than Saturn around low-mass stars. This work points out to dozens of targets that have the potential to better constraint their exotrojan upper mass limit with dedicated radial velocity observations. We also explored the potential of observing the secondary eclipses of the confirmed exoplanets to enhance the exotrojan search., Comment: 41 pages, 9 figures, 6 tables. Accepted in Astronomy & Astrophysics

Dietary Restriction (DR) is one of the most popular anti-ageing interventions, prompting exhaustive research into genes associated with its mechanisms. Recently, Machine Learning (ML) has been explored to identify potential DR-related genes among ageing-related genes, aiming to minimize costly wet lab experiments needed to expand our knowledge on DR. However, to train a model from positive (DR-related) and negative (non-DR-related) examples, existing ML methods naively label genes without known DR relation as negative examples, assuming that lack of DR-related annotation for a gene represents evidence of absence of DR-relatedness, rather than absence of evidence; this hinders the reliability of the negative examples (non-DR-related genes) and the method's ability to identify novel DR-related genes. This work introduces a novel gene prioritization method based on the two-step Positive-Unlabelled (PU) Learning paradigm: using a similarity-based, KNN-inspired approach, our method first selects reliable negative examples among the genes without known DR associations. Then, these reliable negatives and all known positives are used to train a classifier that effectively differentiates DR-related and non-DR-related genes, which is finally employed to generate a more reliable ranking of promising genes for novel DR-relatedness. Our method significantly outperforms the existing state-of-the-art non-PU approach for DR-relatedness prediction in three relevant performance metrics. In addition, curation of existing literature finds support for the top-ranked candidate DR-related genes identified by our model.

Chance-aligned sources or blended companions can cause false positives in planetary transit detections or simply bias the determination of the candidate properties. In the era of high-precision space-based photometers, the need for high-spatial resolution images has demonstrated to be critical for validating and confirming transit signals. This already applied to the Kepler mission, it is now applicable to the TESS survey and will be critical for PLATO. We present the results of the AstraLux-TESS survey, a catalog of high-spatial resolution images obtained with the AstraLux instrument (Calar Alto) in the context of the TESS Follow-up Observing Program. We use the lucky-imaging technique to obtain high-spatial resolution images from planet candidate hosts included mostly in two relevant regimes: exoplanet candidates belonging to the level-one requirement of the TESS mission (planets with radii $R<4~R_{\oplus}$), and candidates around intermediate-mass stars. Among the 185 planet host candidate stars observed, we found 13 (7%) to be accompanied by additional sources within 2.2 arcsec separation. Among them, six are not associated to sources in the Gaia DR3 catalog, thus contaminating the TESS light curve. We provide upper limits and probabilities to the possible existence of field contaminants through the sensitivity limits of our images. Among the isolated hosts, we can discard hazardous companions (bright enough to mimic a planetary transit signals) for all their planets. The results from this catalog are key for the statistical validation of small planets (prime targets of the TESS mission) and planets around intermediate-mass stars in the main-sequence. These two populations of planets are hard to confirm with the radial velocity technique. Our results also demonstrate the importance of this type of follow-up observations for future transit missions like PLATO, even in the Gaia era., Comment: Accepted for publication in A&A. 10 pages, 5 figures, 4 tables. Language revision included

Dyadic regression models, which predict real-valued outcomes for pairs of entities, are fundamental in many domains (e.g. predicting the rating of a user to a product in Recommender Systems) and promising and under exploration in many others (e.g. approximating the adequate dosage of a drug for a patient in personalized pharmacology). In this work, we demonstrate that non-uniformity in the observed value distributions of individual entities leads to severely biased predictions in state-of-the-art models, skewing predictions towards the average of observed past values for the entity and providing worse-than-random predictive power in eccentric yet equally important cases. We show that the usage of global error metrics like Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) is insufficient to capture this phenomenon, which we name eccentricity bias, and we introduce Eccentricity-Area Under the Curve (EAUC) as a new complementary metric that can quantify it in all studied models and datasets. We also prove the adequateness of EAUC by using naive de-biasing corrections to demonstrate that a lower model bias correlates with a lower EAUC and vice-versa. This work contributes a bias-aware evaluation of dyadic regression models to avoid potential unfairness and risks in critical real-world applications of such systems.

Recommender Systems have become crucial in the modern world, commonly guiding users towards relevant content or products, and having a large influence over the decisions of users and citizens. However, ensuring transparency and user trust in these systems remains a challenge; personalized explanations have emerged as a solution, offering justifications for recommendations. Among the existing approaches for generating personalized explanations, using existing visual content created by users is a promising option to maximize transparency and user trust. State-of-the-art models that follow this approach, despite leveraging highly optimized architectures, employ surrogate learning tasks that do not efficiently model the objective of ranking images as explanations for a given recommendation; this leads to a suboptimal training process with high computational costs that may not be reduced without affecting model performance. This work presents BRIE, a novel model where we leverage Bayesian Pairwise Ranking to enhance the training process, allowing us to consistently outperform state-of-the-art models in six real-world datasets while reducing its model size by up to 64 times and its CO${_2}$ emissions by up to 75% in training and inference.

Context: High-spatial resolution Atacama Large Millimeter/submillimeter Array (ALMA) data have revealed a plethora of substructures in protoplanetary disks. Some of those features are thought to trace the formation of embedded planets. One example is the gas and dust that accumulated in the co-orbital Lagrangian regions $L_4$/$L_5$, which were tentatively detected in recent years and might be the pristine material for the formation of Trojan bodies. Aims: This work is part of the TROY project, whose ultimate goal is to find robust evidence of exotrojan bodies and study their implications in the exoplanet field. Here, we focus on the early stages of the formation of these bodies by inspecting the iconic system PDS 70, the only confirmed planetary system in formation. Methods: We reanalyzed archival high-angular resolution Band 7 ALMA observations from PDS 70 by doing an independent imaging process to look for emission in the Lagrangian regions of the two detected gas giant protoplanets, PDS 70 b and c. We then projected the orbital paths and visually inspected emission features at the regions around the $L_4$/$L_5$ locations as defined by $\pm$ 60$^{\circ}$ in azimuth from the planet position. Results: We found emission at a $\sim$4-$\sigma$ level ($\sim$6-$\sigma$ when correcting from a cleaning effect) at the position of the $L_{5}$ region of PDS 70 b. This emission corresponds to a dust mass in a range of 0.03- 2 M$_{Moon}$, which potentially accumulated in this gravitational well. Conclusions: The tentative detection of the co-orbital dust trap that we report requires additional observations to be confirmed. We predict that we could detect the co-orbital motion of PDS 70 b and the dust presumably associated with $L_5$ by observing again with the same sensitivity and angular resolution as early as February 2026., Comment: 8 pages, 5 figures, 2 tables. Published in Astronomy & Astrophysics

The field of analytic combinatorics is dedicated to the creation of effective techniques to study the large-scale behaviour of combinatorial objects. Although classical results in analytic combinatorics are mainly concerned with univariate generating functions, over the last two decades a theory of analytic combinatorics in several variables (ACSV) has been developed to study the asymptotic behaviour of multivariate sequences. In this work we survey ACSV from a probabilistic perspective, illustrating how its most advanced methods provide efficient algorithms to derive limit theorems, and comparing the results to past work deriving combinatorial limit theorems. Using the results of ACSV, we provide a SageMath package that can automatically compute (and rigorously verify) limit theorems for a large variety of combinatorial generating functions. To illustrate the techniques involved, we also establish explicit local central limit theorems for a family of combinatorial classes whose generating functions are linear in the variables tracking each parameter. Applications covered by this result include the distribution of cycles in certain restricted permutations (proving a limit theorem stated as a conjecture in recent work of Chung et al.), integer compositions, and $n$-colour compositions with varying restrictions and values tracked. Key to establishing these explicit results in arbitrary dimension is an interesting symbolic determinant, which we compute by conjecturing and then proving an appropriate $LU$-factorization. It is our hope that this work provides readers a blueprint to apply the powerful tools of ACSV to prove central limit theorems in their own work, making them more accessible to combinatorialists, probabilists, and those in adjacent fields., Comment: 26 pages with new structure. Companion Sage notebooks on GitHub. Added examples

Ground-based observing time is precious in the era of exoplanet follow-up and characterization, especially in high-precision radial velocity instruments. Blind-search radial velocity surveys thus require a dedicated observational strategy in order to optimize the observing time, which is particularly crucial for the detection of small rocky worlds at large orbital periods. We develop an algorithm with the purpose of improving the efficiency of radial velocity observations in the context of exoplanet searches, and we apply it to the K-dwarfs Orbited By habitable Exoplanets (KOBE) experiment. We aim at accelerating exoplanet confirmations or, alternatively, rejecting false signals as early as possible in order to save telescope time and increase the efficiency of both blind-search surveys and follow-up of transiting candidates. Once a minimum initial number of radial velocity datapoints is reached in such a way that a periodicity starts to emerge according to generalized Lomb-Scargle (GLS) periodograms, that period is targeted with the proposed algorithm, named $\texttt{KOBEsim}$. The algorithm selects the next observing date that maximizes the Bayesian evidence for such periodicity in comparison with a model with no Keplerian orbits. By means of simulated data, we prove that the algorithm accelerates the exoplanet detection, needing $29 - 33\,\%$ less observations and $41 - 47\,\%$ less timespan of the full dataset for low-mass planets ($m_{\rm p}\,<\,10\,M_{\oplus}$) in comparison with a conventional monotonic cadence strategy. The enhancement in the number of datapoints for $20\,M_{\oplus}$ planets is also appreciable, $16\,\%$. We also test $\texttt{KOBEsim}$ with real data for a particular KOBE target, and for the confirmed planet $HD~102365\,b$. Both of them demonstrate that the strategy is capable of speeding up the detection up to a factor of $2$., Comment: Accepted for publication in A$\&$A: 14 pages, 10 figures, 5 tables. Language corrected

The detection of habitable worlds is one of humanity's greatest endeavors. So far, astrobiological studies show that one of the most critical components for life development is liquid water. Its chemical properties and its capacity to dissolve and hence transport other substances makes this constituent a key piece in the development of life. As a consequence, looking for life as we know it is directly related to the search for liquid water. For a remote detection of life in distant planetary systems, this means looking for planets in the so-called habitable zone. In this sense, K-dwarf stars are the perfect hosts. Contrary to G-dwarfs, the habitable zone is closer, thus making planet detection easier using transit or radial velocity techniques. Contrary to M-dwarfs, the stellar activity is much smaller, hence having a smaller impact in both the detectability and in the true habitability of the planet. Also, K-dwarfs are the quietest in terms of oscillations, and granulation noise. Despite this, there is a dearth of planets in the habitable zone of K-dwarfs due to a lack of observing programs devoted to this parameter space. In response to a call for Legacy Programs of the Calar Alto observatory, we have started the first dedicated and systematic search for habitable planets around K-dwarfs, the K-dwarfs Orbited By habitable Exoplanets (KOBE). This survey is monitoring the radial velocity of 50 carefully pre-selected K-dwarfs with the CARMENES instrument along 5 semesters with an average of 90 data points per target. Based on planet occurrence rates convolved with our detectability limits, we expect to find $1.68\pm 0.25$ planets per star in the KOBE sample and in half of the sample we expect to find one of those planets within the habitable zone. In this paper, we describe the project motivation, goals and target selection and preliminary stellar characterization., Comment: Accepted for publication in A&A. 12 pages, 9 figures, 1 table. Language corrected

We present a strategy for computing asymptotics of coefficients of $d$-variate algebraic generating functions. Using known constructions, we embed the coefficient array into an array represented by a rational generating functions in $d+1$ variables, and then apply ACSV theory to analyse the latter. This method allows us to give systematic results in the multivariate case, seems more promising than trying to derive analogs of the rational ACSV theory for algebraic GFs, and gives the prospect of further improvements as embedding methods are studied in more detail., Comment: 9 pages

Any experiment with climate models relies on a potentially large set of spatio-temporal boundary conditions. These can represent both the initial state of the system and/or forcings driving the model output throughout the experiment. Whilst these boundary conditions are typically fixed using available reconstructions in climate modelling studies, they are highly uncertain, that uncertainty is unquantified, and the effect on the output of the experiment can be considerable. We develop efficient quantification of these uncertainties that combines relevant data from multiple models and observations. Starting from the coexchangeability model, we develop a coexchangable process model to capture multiple correlated spatio-temporal fields of variables. We demonstrate that further exchangeability judgements over the parameters within this representation lead to a Bayes linear analogy of a hierarchical model. We use the framework to provide a joint reconstruction of sea-surface temperature and sea-ice concentration boundary conditions at the last glacial maximum (19-23 ka) and use it to force an ensemble of ice-sheet simulations using the FAMOUS-Ice coupled atmosphere and ice-sheet model. We demonstrate that existing boundary conditions typically used in these experiments are implausible given our uncertainties and demonstrate the impact of using more plausible boundary conditions on ice-sheet simulation., Comment: Accepted with major revisions in the Journal of the American Statistical Association, resubmission is in review

Computer simulations can provide mechanistic insight into ionic liquids (ILs) and predict the properties of experimentally unrealized ion combinations. However, ILs suffer from a particularly large disparity in the time scales of atomistic and ensemble motion. Coarse-grained models are therefore used in place of costly atomistic simulations, allowing simulation of longer time scales and larger systems. Nevertheless, constructing the many-body potential of mean force that defines the structure and dynamics of a coarse-grained system can be complicated and computationally intensive. Machine learning shows great promise for the key coupled challenges of dimensionality reduction and learning the potential of mean force. To improve the coarse-graining of ILs, we present a neural network model trained on all-atom classical molecular dynamics simulations. The potential of mean force is expressed as two jointly-trained neural network interatomic potentials that learn the coupled short-range and the many-body long range molecular interactions. These interatomic potentials treat temperature as an explicit input variable to capture the temperature dependence of the potential of mean force. The model reproduces structural quantities with high fidelity, outperforms the temperature-independent baseline at capturing dynamics, generalizes to unseen temperatures, and incurs low simulation cost., Comment: 9 pages, 6 figures, 2 supplementary material pages