arXiv subjects

L. A. Nieto

Publications and source records attributed to L. A. Nieto.

3 recordsLinked to original sources

arXiv2026

Automatic search for transiting planets in TESS-SPOC FFIs with RAVEN: over 100 newly validated planets and over 2000 vetted candidates

Space-based missions such as TESS are identifying a wealth of short-period ($\lesssim30$ d) transiting planets. Despite the growing number of confirmed and candidate planets, the sample is still incomplete and highly biased, challenging demographic studies. Moreover, there are still a large number of unconfirmed candidates that can end up being false positives. We use the new pipeline RAVEN to perform a uniform search and validation of transiting planet candidates in TESS data. We focus on a magnitude-limited sample of over 2.2 million main sequence stars well characterised by Gaia and observed by TESS in its Full Frame Images during its first 4 years of operations (sectors 1 to 55). We aim to detect candidates with periods within $0.5-16$ days. RAVEN detects candidates with a box least squares algorithm, classifies them into transiting planets and false positives using machine learning models trained with realistic simulations, and performs statistical validation. We present several samples of candidates with different levels of vetting and validation. We newly validate 118 planets, including 31 newly detected here. We also present a sample of over 2000 candidates not validated but with high probability of being planets, including $\sim1000$ new candidates, a small sample of newly identified mono- and duo-transiting candidates, and a sample of large radii ($>8~\mathrm{R_{\oplus}}$) candidates with high planet probability suited for further follow-up. Our samples of vetted and validated transiting planet candidates represent a major effort towards improving the candidate sample from TESS.

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arXiv2023

ExoplANNET: A deep learning algorithm to detect and identify planetary signals in radial velocity data

The detection of exoplanets with the radial velocity method consists in detecting variations of the stellar velocity caused by an unseen sub-stellar companion. Instrumental errors, irregular time sampling, and different noise sources originating in the intrinsic variability of the star can hinder the interpretation of the data, and even lead to spurious detections. In recent times, work began to emerge in the field of extrasolar planets that use Machine Learning algorithms, some with results that exceed those obtained with the traditional techniques in the field. We seek to explore the scope of the neural networks in the radial velocity method, in particular for exoplanet detection in the presence of correlated noise of stellar origin. In this work, a neural network is proposed to replace the computation of the significance of the signal detected with the radial velocity method and to classify it as of planetary origin or not. The algorithm is trained using synthetic data of systems with and without planetary companions. We injected realistic correlated noise in the simulations, based on previous studies of the behaviour of stellar activity. The performance of the network is compared to the traditional method based on null hypothesis significance testing. The network achieves 28 % fewer false positives. The improvement is observed mainly in the detection of small-amplitude signals associated with low-mass planets. In addition, its execution time is five orders of magnitude faster than the traditional method. The superior performance exhibited by the algorithm has only been tested on simulated radial velocity data so far. Although in principle it should be straightforward to adapt it for use in real time series, its performance has to be tested thoroughly. Future work should permit evaluating its potential for adoption as a valuable tool for exoplanet detection.

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arXiv2022

Null transit detections of 68 radial velocity exoplanets observed by TESS

In recent years the number of exoplanets has grown considerably. The most successful techniques in these detections are the radial velocity (RV) and planetary transits techniques, the latter significantly advanced by the Kepler, K2 and, more recently, the TESS missions. The detection of exoplanets both by means of transit and by RVs is of importance, because this would allows characterizing their bulk densities, and internal compositions. The Transiting Exoplanet Survey Satellite (TESS) survey offers a unique possibility to search for transits of extrasolar planets detected by RV. In this work, we present the results of the search for transits of planets detected with the radial velocity technique, using the photometry of the TESS space mission. We focus on systems with super-Earths and Neptunes planets on orbits with periods shorter than 30 days. This cut is intended to keep objects with a relatively high transit probability, and is also consistent with duration of TESS observations on a single sector. Given the summed geometric transit probabilities, the expected number of transiting planets is $3.4 \pm 1.8$. The sample contains two known transiting planets. We report null results for the remaining 66 out of 68 planets studied, and we exclude in all cases planets larger than 2.4 R$_{\oplus}$, under the assumption of central transits. The remaining two planets orbit HD~136352 and have been recently been announced.

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