Search

With high equilibrium temperatures and tidally locked rotation, ultra-hot Jupiters (UHJs) are unique laboratories within which to probe extreme atmospheric physics and chemistry. In this paper, we present high-resolution dayside spectroscopy of the UHJ WASP-189b obtained with the new Gemini High-resolution Optical SpecTrograph (GHOST) at the Gemini South Observatory. The observations, which cover three hours of post-eclipse orbital phases, were obtained during the instrument's System Verification run. We detect the planet's atmosphere via the Doppler cross-correlation technique, and recover a detection of neutral iron in the planet's dayside atmosphere at a significance of 7.5$\sigma$ in the red-arm of the data, verifying the presence of a thermal inversion. We also investigate the presence of other species in the atmosphere and discuss the implications of model injection/recovery tests. These results represent the first atmospheric characterization of an exoplanet with GHOST's high-resolution mode, and demonstrate the potential of this new instrument in detecting and studying ultra-hot exoplanet atmospheres., Comment: 21 pages, 12 figures, 3 tables. Accepted for publication in The Astronomical Journal

The Gemini High-resolution Optical SpecTrograph (GHOST) is a new echelle spectrograph available on the Gemini-South telescope as of Semester 2024A. We present the first high resolution spectrum of the quasar J1449-1227 (redshift z_em=3.27) using data taken during the commissioning of GHOST. The observed quasar hosts an intervening iron-poor ([Fe/H] = -2.5) damped Lyman alpha (DLA) system at redshift z=2.904. Taking advantage of the high spectral resolving power of GHOST (R~55000), we are able to accurately model the metal absorption lines of the metal-poor DLA and find a supersolar [Si/Fe], suggesting the DLA gas is in an early stage of chemical enrichment. Using simple ionization models, we find that the large range in the C IV/Si IV column density ratio of individual components within the DLA's high ionization absorption profile can be reproduced by several metal-poor Lyman limit systems surrounding the low-ionization gas of the DLA. It is possible that this metal-poor DLA resides within a complex system of metal-poor galaxies or filaments with inflowing gas. The high spectral resolution, wavelength coverage and sensitivity of GHOST makes it an ideal spectrograph for characterizing the chemistry and kinematics of quasar absorption lines., Comment: Accepted for publication in MNRAS. 8 Pages, 5 figures

Current gait recognition research mainly focuses on identifying pedestrians captured by the same type of sensor, neglecting the fact that individuals may be captured by different sensors in order to adapt to various environments. A more practical approach should involve cross-modality matching across different sensors. Hence, this paper focuses on investigating the problem of cross-modality gait recognition, with the objective of accurately identifying pedestrians across diverse vision sensors. We present CrossGait inspired by the feature alignment strategy, capable of cross retrieving diverse data modalities. Specifically, we investigate the cross-modality recognition task by initially extracting features within each modality and subsequently aligning these features across modalities. To further enhance the cross-modality performance, we propose a Prototypical Modality-shared Attention Module that learns modality-shared features from two modality-specific features. Additionally, we design a Cross-modality Feature Adapter that transforms the learned modality-specific features into a unified feature space. Extensive experiments conducted on the SUSTech1K dataset demonstrate the effectiveness of CrossGait: (1) it exhibits promising cross-modality ability in retrieving pedestrians across various modalities from different sensors in diverse scenes, and (2) CrossGait not only learns modality-shared features for cross-modality gait recognition but also maintains modality-specific features for single-modality recognition.

This paper proposes a novel slacks-based interval DEA approach that computes interval targets, slacks, and crisp inefficiency scores. It uses interval arithmetic and requires solving a mixed-integer linear program. The corresponding super-efficiency formulation to discriminate among the efficient units is also presented. We also provide a case study of its application to sustainable tourism in the Mediterranean region, assessing the sustainable tourism efficiency of twelve Mediterranean regions to validate the proposed approach. The inputs and outputs cover the three sustainability dimensions and include GHG emissions as an undesirable output. Three regions were found inefficient, and the corresponding inputs and output improvements were computed. A total rank of the regions was also obtained using the super-efficiency model., Comment: 32 pages, 2 figures, 3 tables

We report on the chemo-dynamical analysis of SPLUS J142445.34-254247.1, an extremely metal-poor halo star enhanced in elements formed by the rapid neutron-capture process. This star was first selected as a metal-poor candidate from its narrow-band S-PLUS photometry and followed up spectroscopically in medium-resolution with Gemini South/GMOS, which confirmed its low-metallicity status. High-resolution spectroscopy was gathered with GHOST at Gemini South, allowing for the determination of chemical abundances for 36 elements, from carbon to thorium. At [Fe/H]=-3.39, SPLUS J1424-2542 is one of the lowest metallicity stars with measured Th and has the highest logeps(Th/Eu) observed to date, making it part of the "actinide-boost" category of r-process enhanced stars. The analysis presented here suggests that the gas cloud from which SPLUS J1424-2542 was formed must have been enriched by at least two progenitor populations. The light-element (Z<=30) abundance pattern is consistent with the yields from a supernova explosion of metal-free stars with 11.3-13.4 Msun, and the heavy-element (Z>=38) abundance pattern can be reproduced by the yields from a neutron star merger (1.66Msun and 1.27Msun) event. A kinematical analysis also reveals that SPLUS J1424-2542 is a low-mass, old halo star with a likely in-situ origin, not associated with any known early merger events in the Milky Way., Comment: 26 pages, 11 figures, accepted for publication on ApJ

This study focuses on Pristine$\_180956.78$$-$$294759.8$ (hereafter P180956, $[Fe/H] =-1.95\pm0.02$), a star selected from the Pristine Inner Galaxy Survey (PIGS), and followed-up with the recently commissioned Gemini High-resolution Optical SpecTrograph (GHOST) at the Gemini South telescope. The GHOST spectrograph's high efficiency in the blue spectral region ($3700-4800$~\AA) enables the detection of elemental tracers of early supernovae (\eg Al, Mn, Sr, Eu). The star exhibits chemical signatures resembling those found in ultra-faint dwarf systems, characterised by very low abundances of neutron-capture elements (Sr, Ba, Eu), which are uncommon among stars in the Milky Way halo. Our analysis suggests that P180956 bears the chemical imprints of a small number (2 or 4) of low-mass hypernovae ($\sim10-15 M_{\odot}$), which are needed to mostly reproduce the abundance pattern of the light-elements (\eg [Si, Ti/Mg, Ca] $\sim0.6$), and one fast-rotating intermediate-mass supernova ($\sim300\kms$, $\sim80-120 M_{\odot}$), which is the main channel contributing to the high [Sr/Ba] ($\sim +1.2$). The small pericentric ($\sim0.7$ kpc) and apocentric ($\sim13$ kpc) distances and its orbit confined to the plane ($\lesssim 2$ kpc), indicate that this star was likely accreted during the early Galactic assembly phase. Its chemo-dynamical properties suggest that P180956 formed in a system similar to an ultra-faint dwarf galaxy accreted either alone, as one of the low-mass building blocks of the proto-Galaxy, or as a satellite of Gaia-Sausage-Enceladus. The combination of Gemini's large aperture with GHOST's high efficiency and broad spectral coverage makes this new spectrograph one of the leading instruments for near-field cosmology investigations., Comment: Accepted version, minor editing. New figure showing Sr and Ba lines. Section 4.7 revised

The Gemini High-resolution Optical SpecTrograph (GHOST) is the newest high resolution spectrograph to be developed for a large aperture telescope, recently deployed and commissioned at the Gemini-South telescope. In this paper, we present the first science results from the GHOST spectrograph taking during its commissioning runs. We have observed the bright metal-poor benchmark star HD 122563, along with two stars in the ultra faint dwarf galaxy, Ret II, one of which was previously identified as a candidate member, but did not have a previous detailed chemical abundance analysis. This star (GDR3 0928) is found to be a bona fide member of Ret II, and from a spectral synthesis analysis, it is also revealed to be a CEMP-r star, with significant enhancements in the several light elements (C, N, O, Na, Mg, and Si), in addition to featuring an r-process enhancement like many other Ret II stars. The light-element enhancements in this star resemble the abundance patterns seen in the CEMP-no stars of other ultra faint dwarf galaxies, and are thought to have been produced by an independent source from the r-process. These unusual abundance patterns are thought to be produced by faint supernovae, which may be produced by some of the earliest generations of stars., Comment: 23 pages, 9 figures, 7 tables, submitted to the AAS Journals

Citizen science is gaining popularity as a valuable tool for labelling large collections of astronomical images by the general public. This is often achieved at the cost of poorer quality classifications made by amateur participants, which are usually verified by employing smaller data sets labelled by professional astronomers. Despite its success, citizen science alone will not be able to handle the classification of current and upcoming surveys. To alleviate this issue, citizen science projects have been coupled with machine learning techniques in pursuit of a more robust automated classification. However, existing approaches have neglected the fact that, apart from the data labelled by amateurs, (limited) expert knowledge of the problem is also available along with vast amounts of unlabelled data that have not yet been exploited within a unified learning framework. This paper presents an innovative learning methodology for citizen science capable of taking advantage of expert- and amateur-labelled data, featuring a transfer of labels between experts and amateurs. The proposed approach first learns from unlabelled data with a convolutional autoencoder and then exploits amateur and expert labels via the pre-training and fine-tuning of a convolutional neural network, respectively. We focus on the classification of galaxy images from the Galaxy Zoo project, from which we test binary, multi-class, and imbalanced classification scenarios. The results demonstrate that our solution is able to improve classification performance compared to a set of baseline approaches, deploying a promising methodology for learning from different confidence levels in data labelling.

Vision Transformers (ViTs) have become a dominant paradigm for visual representation learning with self-attention operators. Although these operators provide flexibility to the model with their adjustable attention kernels, they suffer from inherent limitations: (1) the attention kernel is not discriminative enough, resulting in high redundancy of the ViT layers, and (2) the complexity in computation and memory is quadratic in the sequence length. In this paper, we propose a novel attention operator, called lightweight structure-aware attention (LiSA), which has a better representation power with log-linear complexity. Our operator learns structural patterns by using a set of relative position embeddings (RPEs). To achieve log-linear complexity, the RPEs are approximated with fast Fourier transforms. Our experiments and ablation studies demonstrate that ViTs based on the proposed operator outperform self-attention and other existing operators, achieving state-of-the-art results on ImageNet, and competitive results on other visual understanding benchmarks such as COCO and Something-Something-V2. The source code of our approach will be released online., Comment: 8 pages, 5 figures

The accuracy in determining the spatial-kinematical parameters of open clusters makes them ideal tracers of the Galactic structure. Young open clusters (YOCs) are the main representative of the clustered star formation mode, which identifies how most of the stars in the Galaxy form. We apply the Kriging technique to a sample of Gaia YOCs within a 3.5 kpc radius around the Sun and log(age) $\leq$ 7.5, age in years, to obtain the $Z(X,Y)$ and $V_Z(X,Y)$ maps. The previous work by Alfaro et al. (1991) showed that Kriging can provide reliable results even with small data samples ($N \sim 100$). We approach the 3D spatial and vertical velocity field structure of the Galactic disk defined by YOCs and analyze the hierarchy of the stellar cluster formation, which shows a rich hierarchical structure, displaying complexes embedded within each other. We discuss the fundamental characteristics of the methodology used to perform the mapping and point out the main results obtained in phenomenological terms. Both the 3D spatial distribution and the vertical velocity field reveal a complex disk structure with a high degree of substructures. Their analysis provides clues about the main physical mechanisms that shape the phase space of the clustered star formation in this Galactic area. Warp, corrugations, and high local deviations in $Z$ and $V_Z$, appear intimately connected in a single but intricate scenario.

In this article, a new concept of LR-type interval-valued intuitionistic fuzzy numbers (LR-type IVIFN) has been introduced. The theory has also been enriched by demonstrating diagrammatic representations of LR-type IVIFNs and establishing arithmetic operations among these fuzzy numbers. The total order properties of lexicographic criteria have been used for ranking LR-type IVIFNs. Further, a linear programming problem having both equality as well as inequality type constraints with all the parameters as LR-type IVIFNs and unrestricted decision variables has been formulated. An algorithm to find a unique optimal solution to the problem using the lexicographic ranking method has been developed. In the proposed methodology, the given linear programming problem is converted to an equivalent mixed 0-1 lexicographic non-linear programming problem. Various theorems have been proved to show the equivalence of the proposed problem and its different constructions. The model formulation, algorithm and discussed results have not only developed a new idea but also generalized various well-known related works existing in the literature. A numerical problem has also been exemplified to show the steps involved in the approach. Finally, a practical application in production planning is framed, solved and analyzed to establish the applicability of the study., Comment: 29 pages, 4 figures

Recent evidence of extremely metal-rich stars found in the Sombrero galaxy (M104) halo suggests that this galaxy has undergone a recent major merger with a relatively massive galaxy. In this paper, we present wide-field deep images of the M104 outskirts obtained with a 18-cm amateur telescope with the purpose of detecting any coherent tidal features from this possible major merger. Our new data, together with a model of the M104 inner halo and scattered light from stars around the field, allow us to trace for the first time the full path of the stream on both sides of the disk of the galaxy. We fully characterize the ring-like tidal structure and we confirm that this is the only observable coherent substructure in the inner halo region. This result is in agreement with the hypothesis that M104 was created by a wet major merger more than 3.5 Gyr ago that heated up the stellar population, blurring all old substructure. We generated a set of numerical models that reproduce the formation of the observed tidal structure. Our best fit model suggests the formation of this stream in the last 3 Gyr is independent of the wet major merger that created the M104 system. Therefore, the formation of the tidal stream can put a constraint on the time when the major merger occurred., Comment: 10 pages, 5 figures, submitted to MNRAS

Capturing the 'mutual gaze' of people is essential for understanding and interpreting the social interactions between them. To this end, this paper addresses the problem of detecting people Looking At Each Other (LAEO) in video sequences. For this purpose, we propose LAEO-Net++, a new deep CNN for determining LAEO in videos. In contrast to previous works, LAEO-Net++ takes spatio-temporal tracks as input and reasons about the whole track. It consists of three branches, one for each character's tracked head and one for their relative position. Moreover, we introduce two new LAEO datasets: UCO-LAEO and AVA-LAEO. A thorough experimental evaluation demonstrates the ability of LAEO-Net++ to successfully determine if two people are LAEO and the temporal window where it happens. Our model achieves state-of-the-art results on the existing TVHID-LAEO video dataset, significantly outperforming previous approaches. Finally, we apply LAEO-Net++ to a social network, where we automatically infer the social relationship between pairs of people based on the frequency and duration that they LAEO, and show that LAEO can be a useful tool for guided search of human interactions in videos. The code is available at https://github.com/AVAuco/laeonetplus., Comment: 16 pages, 16 Figures. arXiv admin note: substantial text overlap with arXiv:1906.05261

Human head pose estimation in images has applications in many fields such as human-computer interaction or video surveillance tasks. In this work, we address this problem, defined here as the estimation of both vertical (tilt/pitch) and horizontal (pan/yaw) angles, through the use of a single Convolutional Neural Network (ConvNet) model, trying to balance precision and inference speed in order to maximize its usability in real-world applications. Our model is trained over the combination of two datasets: 'Pointing'04' (aiming at covering a wide range of poses) and 'Annotated Facial Landmarks in the Wild' (in order to improve robustness of our model for its use on real-world images). Three different partitions of the combined dataset are defined and used for training, validation and testing purposes. As a result of this work, we have obtained a trained ConvNet model, coined RealHePoNet, that given a low-resolution grayscale input image, and without the need of using facial landmarks, is able to estimate with low error both tilt and pan angles (~4.4{\deg} average error on the test partition). Also, given its low inference time (~6 ms per head), we consider our model usable even when paired with medium-spec hardware (i.e. GTX 1060 GPU). * Code available at: https://github.com/rafabs97/headpose_final * Demo video at: https://www.youtube.com/watch?v=2UeuXh5DjAE, Comment: Accepted for publication at Neural Computing and Applications

Gait is a popular biometric pattern used for identifying people based on their way of walking. Traditionally, gait recognition approaches based on deep learning are trained using the whole training dataset. In fact, if new data (classes, view-points, walking conditions, etc.) need to be included, it is necessary to re-train again the model with old and new data samples. In this paper, we propose iLGaCo, the first incremental learning approach of covariate factors for gait recognition, where the deep model can be updated with new information without re-training it from scratch by using the whole dataset. Instead, our approach performs a shorter training process with the new data and a small subset of previous samples. This way, our model learns new information while retaining previous knowledge. We evaluate iLGaCo on CASIA-B dataset in two incremental ways: adding new view-points and adding new walking conditions. In both cases, our results are close to the classical `training-from-scratch' approach, obtaining a marginal drop in accuracy ranging from 0.2% to 1.2%, what shows the efficacy of our approach. In addition, the comparison of iLGaCo with other incremental learning methods, such as LwF and iCarl, shows a significant improvement in accuracy, between 6% and 15% depending on the experiment., Comment: Accepted for presentation at IJCB'2020