arXiv subjects

P. Ranaivomanana

Publications and source records attributed to P. Ranaivomanana.

5 recordsLinked to original sources

arXiv2026

BlackGEM observations of compact pulsating stars: Mode identification for the DOV PG 1159-035 using multi-colour photometry

While space-based telescopes offer unparalleled precision for asteroseismology, ground-based observations remain crucial for identifying compact pulsator candidates and enabling their pulsational study through multi-colour photometry. The BlackGEM telescope array significantly enhances the detection and characterisation possibilities for compact pulsators that tend to be much fainter than dwarf or giant pulsators. Using BlackGEM multi-colour photometry of the hot pre-white dwarf PG 1159-035, we demonstrate its capability to detect short, multi-periodic pulsations with amplitudes down to a few milli-magnitudes. The primary aim of this study is to establish the feasibility of pulsation mode identification in hot subdwarfs and white dwarfs via mode amplitude-ratio analysis derived from BlackGEM multi-colour observations. Pulsation frequencies were extracted from our target using iterative pre-whitening analysis. To validate our data-driven mode identification concept using multi-colour photometry, we used the well-studied hot pre-white dwarf PG 1159-035, with previously identified pulsation modes, as a prototypical object that served for validation. The pre-whitening analysis using BlackGEM's standard q-, u-, and i-band light curves of PG 1159-035 revealed pulsation frequencies of l=1 and l=2 modes, consistent with values obtained from the literature. Subsequent amplitude ratio calculations resulted in discernible distributions for the l=1 and l=2 modes. The future assembly of more BlackGEM amplitude ratios for well-known white dwarfs with already identified modes will lead to density estimators suitable for identifying newly detected modes in known or as-yet-undiscovered pulsators. Our proof-of-concept study paves the way for large-scale asteroseismic analyses of optically faint compact pulsating stars using ground-based facilities, such as BlackGEM.

astro-ph.SR↗

arXiv2025

Unsupervised learning for variability detection with Gaia DR3 photometry. The main sequence-white dwarf valley

The unprecedented volume and quality of data from space- and ground-based telescopes present an opportunity for machine learning to identify new classes of variable stars and peculiar systems that may have been overlooked by traditional methods. Extending prior methodological work, this study investigates the potential of an unsupervised learning approach to scale effectively to larger stellar populations, including objects in crowded fields, and without the need for pre-selected catalogues, specifically focusing on 13 405 sources selected from Gaia DR3 and lying in the selected region of the CMD. Our methodology incorporates unsupervised clustering techniques based primarily on statistical features extracted from Gaia DR3 epoch photometry. We used the t-distributed stochastic neighbour embedding (t-SNE) algorithm to identify variability classes, their subtypes, and spurious variability induced by instrumental effects. The clustering results revealed distinct groups, including hot subdwarfs, cataclysmic variables (CVs), eclipsing binaries, and objects in crowded fields, such as those in the Andromeda (M31) field. Several potential stellar subtypes also emerged within these clusters. Notably, objects previously labelled as RR Lyrae were found in an unexpected region of the CMD, potentially due to either unreliable astrometric measurements (e.g., due to binarity) or alternative evolutionary pathways. This study emphasises the robustness of the proposed method in finding variable objects in a large region of the Gaia CMD, including variable hot subdwarfs and CVs, while demonstrating its efficiency in detecting variability in extended stellar populations. The proposed unsupervised learning framework demonstrates scalability to large datasets and yields promising results in identifying stellar subclasses.

astro-ph.SR↗

arXiv2025

Variability in hot sub-luminous stars and binaries: Machine-learning analysis of Gaia DR3 multi-epoch photometry

Hot sub-luminous stars represent a population of stripped and evolved red giants that is located on the extreme horizontal branch. Since they exhibit a wide range of variability due to pulsations or binary interactions, it is crucial to unveil their intrinsic and extrinsic variability to understand the physical processes of their formation. In the Hertzsprung-Russell diagram, they overlap with interacting binaries such as cataclysmic variables (CVs). By leveraging the most recent clustering algorithm tools, we investigate the variability of 1,576 candidate hot subdwarf variables using comprehensive data from Gaia DR3 multi-epoch photometry and Transiting Exoplanet Survey Satellite (TESS) observations. We present a novel approach that uses the t-distributed stochastic neighbour embedding and the uniform manifold approximation and projection dimensionality reduction algorithms to facilitate the identification and classification of different populations of variable hot subdwarfs and CVs in a large dataset. In addition to the publicly available Gaia time-series statistics table, we adopted additional statistical features that enhanced the performance of the algorithms. The clustering results led to the identification of 85 new hot subdwarf variables based on Gaia and TESS light curves and of 108 new variables based on Gaia light curves alone, including reflection-effect systems, HW Vir, ellipsoidal variables, and high-amplitude pulsating variables. A significant number of known CVs (140) distinctively cluster in the 2D feature space among an additional 152 objects that we consider candidates for new CVs. This study paves the way for more efficient and comprehensive analyses of stellar variability from ground- and space-based observations, and for the application of machine-learning classifications of candidate variable stars in large surveys.

astro-ph.SR↗

arXiv2023

Identifying and characterising the population of hot sub-luminous stars with multi-colour MeerLICHT data

Colour-magnitude diagrams reveal a population of blue (hot) sub-luminous objects with respect to the main sequence. These hot sub-luminous stars are the result of evolutionary processes that require stars to expel their obscuring, hydrogen-rich envelopes to reveal the hot helium core. As such, these objects offer a direct window into the hearts of stars that are otherwise inaccessible to direct observation. We showcase MeerLICHT's capabilities of detecting faint hot subdwarfs and identifying the dominant frequency in the photometric variability of these compact hot stars, in comparison to their $Gaia$ DR3 data. We hunt for oscillations, which will be an essential ingredient for accurately probing stellar interiors in future asteroseismology. Comparative MeerLICHT and $Gaia$ colour-magnitude diagrams are presented as a way to select hot subdwarfs from our sample. A dedicated frequency determination technique is developed and applied to the selected candidates to determine their dominant variability using time-series data from MeerLICHT and $Gaia$ DR3. We explore the power of both datasets in determining the dominant frequency. Using the $g-i$ colour, MeerLICHT offers a colour-magnitude diagram that is comparable in quality to that of $Gaia$ DR3. The MeerLICHT colour-colour diagrams allow for the study of different stellar populations. The frequency analysis of MeerLICHT and $Gaia$ DR3 data demonstrates the superiority of our MeerLICHT multi-colour photometry in estimating the dominant frequency compared to the sparse $Gaia$ DR3 data. MeerLICHT's multi-band photometry leads to the discovery of high-frequency faint subdwarfs. Our MeerLICHT results are a proof-of-concept of the capacity of the BlackGEM instrument currently in the commissioning stage at ESO's La Silla Observatory in Chile.

astro-ph.SR↗

arXiv2022

The SUNBIRD survey: the K-band luminosity functions of young massive clusters in intensely star-forming galaxies

Strongly star-forming galaxies are prolific in producing the young and most massive star clusters (YMCs) still forming today. This work investigates the star cluster luminosity functions (CLFs, dN/dL ~ L^{-alpha}) of 26 starburst and luminous infrared galaxies (LIRGs) taken from the SUNBIRD survey. The targets were imaged using near-infrared (NIR) K-band adaptive optics systems. Single power-law fits of the derived CLFs result in a slope ranging between 1.53 and 2.41, with the median and average of 1.87 +/- 0.23 and 1.93 +/- 0.23, respectively. Possible biases such as blending effects and the choice of binning should only flatten the slope by no more than ~0.15, especially for cases where the luminosity distance of the host galaxy is below 100 Mpc. Results from this follow-up study strengthen the conclusion from our previous work: the CLF slopes are shallower for strongly star-forming galaxies in comparison to those with less intense star formation activity. There is also a (mild) correlation between the slope and both the host galaxy's star formation rate (SFR) and SFR density Sigma_SFR, i.e. the CLF flattens with an increasing SFR and Sigma_SFR. Finally, we also find that CLFs on sub-galactic scales associated with the nuclear regions of cluster-rich targets (N ~ 300) have typically shallower slopes than the ones of the outer field by ~0.5. Our analyses suggest that the extreme environments of strongly star-forming galaxies are likely to influence the cluster formation mechanisms and ultimately their physical properties.

astro-ph.GA↗