arXiv subjects

Helem Salinas

Publications and source records attributed to Helem Salinas.

5 recordsLinked to original sources

arXiv2026

PLATOSpec's first results: Three new transiting warm Jupiters from the WINE survey TIC 147027702, TIC 245076932 and TIC 87422071

We report the discovery and characterisation of three transiting warm Jupiters: TIC 147027702b, TIC 245076932b and TIC 87422071b. These systems were initially identified as transiting candidates using light curves generated from the full-frame images of the TESS mission. We confirmed the planetary nature of these objects with ground-based spectroscopic follow-up observations using FEROS and the new PLATOSpec spectrograph attached to the ESO 1.52 m telescope at the La Silla Observatory, and with ground-based photometric observations of the Observatoire Moana, Las Cumbres Observatory Global Telescope and ASTEP. From a global fit to the photometry and radial velocities, we determine that the planet TIC 147027702b has a low-eccentric orbit ($e = 0.13 \pm 0.05$) with a period of 44.4 days and has a mass of $1.09^{+0.07}_{-0.13}$ M$_J$ and a radius of $0.98 \pm 0.06$ R$_J$. TIC 245076932b has a moderately low mass of $0.51 \pm 0.05$ M$_J$, a radius of $0.97 \pm 0.05$ R$_J$, and an eccentric orbit ($e = 0.43 \pm 0.02$) with a period of 21.6 days. TIC 87422071b has a mass of $1.29 \pm 0.10$ M$_J$, a radius of $0.97 \pm 0.08$ R$_J$, and has a slightly eccentric orbit ($e = 0.12 \pm 0.07$) with a period of 11.3 days. These well-characterised warm Jupiters expand the currently limited sample of similar gas giants and provide valuable benchmarks for testing models of giant-planet formation, migration, and tidal evolution.

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arXiv2026

Unraveling the Brown Dwarf Desert: Four New Discoveries and a Unifying, Period-Coded Picture

We present four newly validated transiting brown dwarfs identified through TESS photometry and confirmed with high-precision radial velocity measurements obtained from the FEROS and PLATOSpec spectrographs. Notably, three of these companions exhibit orbital periods exceeding 100 days, thereby expanding the sample of long-period transiting brown dwarfs from two to five systems. The host stars of long-period brown dwarfs show mild subsolar metallicity. These discoveries highlight the expansion of the metal-poor, long-period distribution and help us better understand the brown dwarf desert. In our comparative analysis of eccentricity and metallicity demographics, we utilize catalogues of long-period giant planets, brown dwarfs, and low-mass stellar companions. After accounting for tidal influences, the eccentricity distribution aligns with that of low-mass stellar binaries, presenting a different profile than that observed within the giant planet population. Additionally, the metallicity of the host stars reveals a noteworthy trend: short-period transiting brown dwarfs are predominantly associated with metal-rich stars, whereas long-period brown dwarfs are more often found around metal-poor stars, demonstrating statistical similarities to low-mass stellar hosts. This trend has also been previously observed in studies of hot and cold Jupiters and points to a period-coded mixture of channels. A natural explanation is that most brown dwarfs originate from fragmentation at wider separations, with long-period systems retaining this stellar-like imprint, while only those embedded in massive, long-lived, metal-rich protoplanetary discs are efficiently delivered and stabilised to short orbits.

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arXiv2025

A Compact Multi-Planet System of Three Transiting Giant Planets Around TIC118798035

We report the discovery and characterization of three transiting giant planets in the TIC118798035 system. The three planets were identified as transiting candidates from data of the TESS mission, and confirmed with ground-based photometric transit observations along with radial velocity variations obtained with FEROS, HARPS and ESPRESSO. The three planets present transit timing variations (TTVs). We performed a N-body orbital fitting to the TTVs and radial velocities finding that TIC118798035 b is as warm low-density Neptune with a mass of 0.0250$\pm$0.0023 $M_J$, a radius of 0.655$\pm$0.018 $R_J$, and an orbital period of 11.507 d; TIC118798035 c is a warm Saturn with a mass of 0.403$\pm$0.024 $M_J$, a radius of 0.973$\pm$0.023 $R_J$, and an orbital period of 22.564 d; and TIC118798035 d is a warm Jupiter with a mass of 0.773$\pm$0.052 $M_J$, a radius of 0.923$\pm$0.044 $R_J$, and an orbital period of 48.925 d. The bulk metallicities of the three planets don't fully follow the mass-metallicity correlation found for the giant planets of the solar system, which hints at a somewhat different formation history for the planets of the TIC118798035 system. TIC118798035 is the only system having more than two transiting planets larger than 0.5 $R_J$ with a precise orbital and physical characterization, amenable for future atmospheric studies.

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arXiv2025

Exoplanet Transit Candidate Identification in TESS Full-Frame Images via a Transformer-Based Algorithm

The Transiting Exoplanet Survey Satellite (TESS) is surveying a large fraction of the sky, generating a vast database of photometric time series data that requires thorough analysis to identify exoplanetary transit signals. Automated learning approaches have been successfully applied to identify transit signals. However, most existing methods focus on the classification and validation of candidates, while few efforts have explored new techniques for the search of candidates. To search for new exoplanet transit candidates, we propose an approach to identify exoplanet transit signals without the need for phase folding or assuming periodicity in the transit signals, such as those observed in multi-transit light curves. To achieve this, we implement a new neural network inspired by Transformers to directly process Full Frame Image (FFI) light curves to detect exoplanet transits. Transformers, originally developed for natural language processing, have recently demonstrated significant success in capturing long-range dependencies compared to previous approaches focused on sequential data. This ability allows us to employ multi-head self-attention to identify exoplanet transit signals directly from the complete light curves, combined with background and centroid time series, without requiring prior transit parameters. The network is trained to learn characteristics of the transit signal, like the dip shape, which helps distinguish planetary transits from other variability sources. Our model successfully identified 214 new planetary system candidates, including 122 multi-transit light curves, 88 single-transit and 4 multi-planet systems from TESS sectors 1-26 with a radius > 0.27 $R_{\mathrm{Jupiter}}$, demonstrating its ability to detect transits regardless of their periodicity.

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arXiv2023

Distinguishing a planetary transit from false positives: a Transformer-based classification for planetary transit signals

Current space-based missions, such as the Transiting Exoplanet Survey Satellite (TESS), provide a large database of light curves that must be analysed efficiently and systematically. In recent years, deep learning (DL) methods, particularly convolutional neural networks (CNN), have been used to classify transit signals of candidate exoplanets automatically. However, CNNs have some drawbacks; for example, they require many layers to capture dependencies on sequential data, such as light curves, making the network so large that it eventually becomes impractical. The self-attention mechanism is a DL technique that attempts to mimic the action of selectively focusing on some relevant things while ignoring others. Models, such as the Transformer architecture, were recently proposed for sequential data with successful results. Based on these successful models, we present a new architecture for the automatic classification of transit signals. Our proposed architecture is designed to capture the most significant features of a transit signal and stellar parameters through the self-attention mechanism. In addition to model prediction, we take advantage of attention map inspection, obtaining a more interpretable DL approach. Thus, we can identify the relevance of each element to differentiate a transit signal from false positives, simplifying the manual examination of candidates. We show that our architecture achieves competitive results concerning the CNNs applied for recognizing exoplanetary transit signals in data from the TESS telescope. Based on these results, we demonstrate that applying this state-of-the-art DL model to light curves can be a powerful technique for transit signal detection while offering a level of interpretability.

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