arXiv subjects

Iain McDonald

Publications and source records attributed to Iain McDonald.

At least 19 recordsLinked to original sources

arXiv2026

A catalogue of high angular resolution and contrast polarimetric maps of 45 nearby AGB stars with SPHERE/ZIMPOL

We present the largest catalogue of asymptotic giant branch (AGB) stars, 45 targets in total, observed in polarized light at high angular resolution (~ 20 milliarcsec). The main goal of the study is to detect and characterize dust shells in the close environment of nearby AGB stars. This work also aims to systematically classify the AGB star circumstellar morphologies obtained with the SPHERE instrument installed at the Very Large Telescope (VLT), thanks to its Zurich Imaging Polarimeter (ZIMPOL). We extracted and analyzed polarized intensity maps for 45 AGB stars, constructed from polarimetric observation data obtained with the SPHERE/ZIMPOL instrument. An ellipse fitting method was applied to characterize the circumstellar envelopes. Stellar parameters (luminosity, effective temperature, surface gravity, extinction, metallicity) were compiled and recalculated when necessary from spectral energy distribution (SED) fitting using the Python SED fitting tool (PySSED) software. These data were then used to train a random forest machine learning model to determine the most discriminating variables for a resolved envelope around a given star. We constructed polarization maps for all stars in the sample, revealing a wide diversity of circumstellar morphologies. We detected 16 dusty circumstellar envelopes, including three never observed before. They display a wide range of morphologies, all of them showing a clear departure from spherical symmetry, indicating interaction with a companion or asymmetric mass ejections. The random forest model identified optimal thresholds for several physical parameters, thus providing robust criteria to anticipate SPHERE's ability to resolve dust envelopes around AGB stars. These results facilitate the selection of targets for future observations and contribute to a better understanding of the evolution mechanisms of circumstellar envelopes.

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arXiv2026

Ages and masses of asymptotic giant branch stars from the period--luminosity diagram

A method of determining ages and masses of asymptotic giant branch (AGB) stars between 0.8 and $\sim$6 M$_\odot$ is demonstrated, based on comparing the star's position in the period--absolute-magnitude diagram to theoretical evolutionary models. For samples of Milky Way stars, the method provides errors (statistical and systematic, respectively) of order of $^{+29}_{-35} \pm 15$ per cent in age, $^{+14}_{-7} \pm 7$ per cent in initial mass and $^{+17}_{-11} \pm 27$ per cent in current mass. However, its applicability to individual stars depends strongly on both their position in the $P-L$ diagram and the uncertainty of that position. This method is applied to published samples of AGB stars from the \emph{Gaia}, NESS, DEATHSTAR and ATOMIUM surveys. These surveys' statistical ensembles are compared to expectations from stellar evolutionary models, finding that most AGB samples are biased towards stars of younger ages and higher masses. An average mass for Milky Way AGB stars is found to be $\sim$1.1 M$_\odot$, while mass returned to the interstellar medium by AGB stars typically comes from $\sim$1.2 M$_\odot$ stars with mass-loss rates of order $2-3 \times 10^{-6}$ M$_\odot$ yr$^{-1}$.

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arXiv2026

Investigation of Transit Timing and an Optical Transmission Spectrum of the Hot Jupiter WASP-11 b

WASP-11~b/HAT-P-10~b is an inflated hot Jupiter, which has a low density that makes it a good target for atmospheric studies using the transmission spectroscopy technique. In this work, we present 31 new transit light curves of WASP-11~b/HAT-P-10~b, obtained through the SPEARNET network. These data were analyzed along with previously published ground-based observations and space-based data from \texttt{TESS}. We refine the planetary parameters of WASP-11~b/HAT-P-10~b and perform a transit timing analysis using data spanning 16 years. The updated ($O-C$) diagram shows no significant evidence of orbital decay. The TTV analysis reveals no significant signals indicative of additional planets. Atmospheric analysis using multi-band optical observations indicates a strong Rayleigh scattering slope in the transmission spectra, which may originate from the planetary atmosphere itself or be influenced by contamination such as stellar activity or light from the companion star.

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arXiv2026

The Transit Timing and Transmission Spectrum of Hot Jupiter WASP-43 b from a decade of Multi-band Transit Follow-up Observations

We present a new set of 35 transit light curves of the hot Jupiter WASP-43~b, obtained through the SPEARNET network. These datasets were analyzed together with previously published ground-based observations, as well as space-based data from \emph{TESS}, \emph{HST}, and \emph{JWST}, to refine the planetary parameters of WASP-43~b. A total of 188 mid-transit times, measured with \texttt{TransitFit}, were analyzed for potential timing variations. The transit timing variations do not show any significant evidence of orbital decay. Atmospheric retrievals using \emph{HST}/WFC3 G141 transmission spectra suggest that higher-temperature solutions are associated with higher water abundances. However, when these data are combined with observations from ground-based telescopes, \emph{TESS}, and \emph{JWST}, the increased modeling complexity across the broad wavelength baseline presents significant challenges for atmospheric characterization. These results highlight that high-precision, multi-instrument datasets will be necessary to break existing degeneracies in the atmospheric modeling of this target in the future.

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arXiv2026

The Isaac Newton Telescope Monitoring Survey of Local Group Dwarf Galaxies-VIII. A Census of Long-Period Variable Stars across the Andromeda Dwarf Satellite System

We present a comprehensive catalog, in the Sloan $i$ and Harris $V$ filters, of long-period variable (LPV) stars in the spheroidal dwarf satellites of the Andromeda galaxy, based on a dedicated survey for variable stars in Local Group dwarf systems. Using photometric time-series data obtained with the Wide Field Camera (WFC) on the 2.5 m Isaac Newton Telescope (INT), we identify approximately 2800 LPV candidates across 17 Andromeda satellites, spanning a broad range in luminosity and variability amplitude. This study is accompanied by a public data release that includes two comprehensive catalogs, a catalog of the complete stellar populations for each galaxy and a separate catalog listing all identified LPV candidates. Both are available through CDS/VizieR and provide a valuable resource for investigating quenching timescales, stellar mass distributions, and the effects of mass-loss and dust production in dwarf galaxies. We derive updated structural parameters, including newly measured half-light radii, and determine distance moduli using the Tip of the Red Giant Branch (TRGB) method with Sobel-filter edge detection, yielding values between $23.38\pm0.06$ and $25.35\pm0.06$ mag.

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arXiv2025

Metallicity Effects on Machine Learning Classification of Dusty Stellar Sources in the Magellanic Clouds

Differences in metallicity between the Large Magellanic Cloud (LMC) and the Small Magellanic Cloud (SMC) offer an opportunity to examine whether environmental metallicity affects the performance of machine learning models in classifying dusty stellar sources. The five stellar classes studied include young stellar objects (YSOs), red supergiants (RSGs), post-asymptotic giant branch stars (PAGBs), and oxygen- and carbon-rich asymptotic giant branch stars (OAGBs and CAGBs), which are key phases of stellar evolution involved in dust production. Using spectroscopically labeled data from the Surveying the Agents of Galaxy Evolution (SAGE) project, we trained and evaluated a probabilistic random forest (PRF) classifier with four approaches: (1) separate training on LMC and SMC, including all five classes, (2) excluding the underpopulated PAGB class, (3) combined LMC and SMC datasets, and (4) cross-galaxy training and testing. The model achieved 93\% accuracy on the SMC and 88\% on the LMC across all five classes. In the SMC, PAGB sources were misclassified as YSOs, mainly because of their small sample size (4 objects). When PAGB was excluded, both the LMC and the SMC reached 92\% accuracy. A combined dataset produced the same accuracy, and cross-galaxy training yielded similar results, indicating that metallicity does not significantly impact model performance. A comparison of absolute CMDs for the LMC and SMC confirms their similarity in stellar populations. These findings suggest that environmental metallicity has little effect on ML-based classification of dusty stellar sources, supporting the use of combined datasets and cross-galaxy models in low-metallicity environments.

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arXiv2025

Evidence for the Keplerian orbit of a close companion around a giant star

Close companions influence stellar evolution through tidal interactions, mass transfer, and mass loss effects. While such companions are detected around young stellar objects, main-sequence stars, red giants, and compact objects, direct observational evidence of close-in companions around asymptotic giant branch (AGB) stars has remained elusive. Here, we present (sub)millimeter time-domain imaging spectroscopy revealing the Keplerian motion of a close-in companion around the AGB star pi1 Gruis. The companion, slightly more massive than the AGB star, is likely a main-sequence star. Unlike more evolved stars with companions at comparable distances, pi1 Gru's companion follows a circular orbit, suggesting an eccentricity-generating mechanism late- or post-AGB. Our analysis suggests that model-predicted circularization rates may be underestimated. Our results highlight the potential of multi-epoch (sub)millimeter interferometry in detecting the Keplerian motion of close companions to giant stars and open avenues for our understanding of tidal interaction physics and binary evolution.

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arXiv2025

Central-star extinctions towards planetary nebulae

Planetary nebulae trace the hottest and most luminous phase of evolution of solar-type stars. We use these hot, bright stars to investigate extinctions towards a complete sample of 262 confirmed PNe with large angular diameters, which have the most reliable photometry and hottest central stars. For 162 of these PNe, we identify central stars, produce spectral energy distributions from survey data using PySSED, then fit reddened model spectra to the observed photometry to obtain extinctions accurate down to $E(B-V)$ of $\pm 0.02$ mag. The fitting is performed by Nelder-Mead $\chi^2$ minimisation, with uncertainties evaluated through MCMC. The catalogue of stellar temperatures is updated for our sample for the calculation of luminosities. The extinctions agree well with interstellar extinction. We find evidence of circumnebular extinction for one PN, and evaluate its effect on the planetary nebulae luminosity function. Four new close binaries are identified from the spectral energy distributions. The binary fraction in the full sample is between 23% and 36%. We use our compiled data to evaluate the quality of the central star identifications in the literature. Three objects in our sample have previously been classified as post-RGB systems but we find that their parameters may also be consistent with post-AGB evolution.

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arXiv2025

Comparison of Photometric and Spectroscopic Labels in Classifying Dusty Stellar Sources Using Machine Learning in the Magellanic Clouds

Dusty stellar sources, including young stellar objects (YSOs) and evolved stars such as oxygen- and carbon-rich AGBs (OAGBs, CAGBs), red supergiants (RSGs), and post-AGB stars (PAGBs), play a key role in the chemical enrichment of galaxies. Photometric surveys in the Magellanic Clouds have cataloged many such objects, but their classifications are often uncertain due to overlaps between populations. We trained machine learning models on spectroscopically labeled data from the SAGE project and applied them to photometric catalogs. The spectroscopic model achieves about 89\% accuracy. Applied to photometric labels, nearly all OAGBs are correctly identified, and YSOs have a 95\% confirmation rate. In contrast, 16\% of CAGBs are reclassified as OAGBs, only 8\% of RSGs retain their labels, and fewer than half of PAGBs are confirmed. Photometry is thus reliable for abundant populations with distinct signatures, but spectroscopic confirmation remains essential for rare or overlapping stellar classes.

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arXiv2025

Long-period variable stars in NGC 147 and NGC 185-II. Their dust production

This study presents a comparative analysis of mass-loss and dust-production rates in the dwarf galaxies NGC 147 and NGC 185, focusing on long-period variables (LPVs) and pulsating asymptotic giant branch (AGB) stars as primary indicators of dust feedback into the interstellar medium. For NGC 147, the total mass-loss rate is calculated as $(9.44 \pm 3.78) \times 10^{-4} M_{sun} yr^{-1}$, with LPV luminosities ranging from $(6.20 \pm 0.25) \times 10^{2} L_\odot$ to $( 7.87 \pm 0.32) \times 10^{3} L_\odot $. In NGC 185, the total mass-loss rate is higher, at $(1.58 \pm 0.63) \times 10^{-3} M_{sun} yr^{-1}$, with LPV luminosities spanning $ (5.68 \pm 0.23) \times 10^{2} L_\odot $ to $(1.54 \pm 0.66) \times 10^{4} L_\odot$. A positive correlation is observed between stellar luminosity, intrinsic reddening due to circumstellar dust self-extinction, and elevated mass-loss rates. Additionally, comparisons of calculated dust injection rates, two-dimensional dust distribution maps, and observed dust masses provide evidence for a gravitational interaction between NGC 147 and the Andromeda galaxy, which influences the dust distribution within the system.

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arXiv2025

Exploration of groups and outliers in Gaia RVS stellar spectra with metric learning

The Gaia mission is transforming our view of the Milky Way by providing distances towards a billion stars, and much more. The third data release includes nearly a million spectra from its Radial Velocity Spectrometer (RVS). Identifying unexpected features in such vast datasets presents a significant challenge. It is impossible to visually inspect all of the spectra and difficult to analyze them in a comprehensive way. In order to supplement traditional analysis approaches, and in order to facilitate deeper insights from these spectra, we present a new dataset together with an interactive portal that applies established self-supervised metric learning techniques, dimensionality reduction, and anomaly detection, to allow researchers to visualize, analyze, and interact with the Gaia RVS spectra in straightforward but under-utilized manner. We demonstrate a few example interactions with the dataset, examining groupings and the most unusual RVS spectra, according to our metric. This combination of methodology and public availability enables broader exploration, and may reveal yet-to-be-discovered stellar phenomena.

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arXiv2025

Radio Emission from a Nearby M dwarf Binary

We present the detection of the binary system 2MASS J02132062+3648506 AB using the Karl G. Jansky Very Large Array (VLA) archive data observed at 4-8 GHz. The system is a triple consisting of a tight binary ($\sim0.2"$) of two M dwarfs of spectral class M4.5 and M6.5 and a wide T3 brown dwarf companion ($\sim$16.4"). The binary displays coronal and chromospheric activity as traced by previously measured X-ray flux and H$\alpha$ emission. We detect the unresolved binary at a peak flux density of $\sim356\ \mu \mathrm{Jybeam}^{-1}$ at a signal-to-noise ratio (SNR) of $\sim36$ and determine a radio luminosity of $\mathrm{log}L_R/\mathrm{log}L_\mathrm{bol}\approx-7.76$. The radio emission is quiescent, polarised at a mean circular polarisation fraction $f_\mathrm{c}=45.20 \pm 1.58$ % and exhibits a spectral index $\alpha=-0.44\pm0.07$ . We probe the binary using the Enhanced Multi-Element Remotely Linked Interferometer Network (e-MERLIN) with an angular resolution of $\sim40$ mas at 5 GHz and detect a component at a peak flux density of $\sim90\ \mu$Jy $\mathrm{beam}^{-1}$ at a SNR $\sim5$ . We propose a gyrosynchrotron origin for the radio emission and estimate a magnetic field strength $B<174.86$ G, an emitting region of size $L<1.54$ times the radius of the M4.5 primary and a plasma number density $n_\mathrm{e}<2.91\times10^5\ \mathrm{cm}^{-3}$. The brown dwarf companion is not detected. Additionally, we have analysed observations of 2MASS J04183483+213127, a chromospherically active L5 brown dwarf which is also not detected. Accordingly, we place $3\sigma$ flux density upper limits at $36.9\ \mu$Jy $\mathrm{beam}^{-1}$ and $42.3\ \mu$Jy $\mathrm{beam}^{-1}$ for Stokes I and V respectively.

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arXiv2025

The Isaac Newton Telescope Monitoring Survey of Local Group Dwarf Galaxies. VII. Long-Period Variable Stars in the Nearest Starburst Dwarf Galaxy, IC 10

To identify long-period variable (LPV) stars in IC10 - the nearest starburst galaxy of the Local Group (LG) - we conducted an optical monitoring survey using the 2.5-m Isaac Newton Telescope (INT) with the wide-field camera (WFC) in the i-band and V-band from 2015 to 2017. We created a photometric catalog for 53,579 stars within the area of CCD4 of WFC ($\sim$ 0.07 deg$^2$ corresponding to 13.5 kpc$^2$ at the distance of IC10), of which we classified 536 and 380 stars as long-period variable candidates (LPVs), mostly asymptotic giant branch stars (AGBs) and red supergiants (RSGs), within CCD4 and two half-light radii of IC10, respectively. By comparing our output catalog to the catalogs from Pan-STARRS, Spitzer Space Telescope, Hubble Space Telescope (HST), and carbon stars from the Canada-France-Hawai'i Telescope (CFHT) survey, we determined the success of our detection method. We recovered $\sim$ 73% of Spitzer's sources in our catalog and demonstrated that our survey successfully identified 43% of the variable stars found with Spitzer, and also retrieved 40% of the extremely dusty AGB stars among the Spitzer variables. In addition, we successfully identified $\sim$ 70% of HST variables in our catalog. Furthermore, we found all the confirmed LPVs that Gaia DR3 detected in IC10 among our identified LPVs. This paper is the first in a series on IC10, presenting the variable star survey methodology and the photometric catalog, available to the public through the Centre de Données Astronomiques de Strasbourg.

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arXiv2025

Dusty stellar sources classification by implementing machine learning methods based on spectroscopic observations in the Magellanic Clouds

Dusty stellar point sources are a significant stage in stellar evolution and contribute to the metal enrichment of galaxies. These objects can be classified using photometric and spectroscopic observations with color-magnitude diagrams (CMD) and infrared excesses in spectral energy distributions (SED). We employed supervised machine learning spectral classification to categorize dusty stellar sources, including young stellar objects (YSOs) and evolved stars (oxygen- and carbon-rich asymptotic giant branch stars, AGBs), red supergiants (RSGs), and post-AGB (PAGB) stars in the Large and Small Magellanic Clouds, based on spectroscopic labeled data from the Surveying the Agents of Galaxy Evolution (SAGE) project, which used 12 multiwavelength filters and 618 stellar objects. Despite missing values and uncertainties in the SAGE spectral datasets, we achieved accurate classifications. To address small and imbalanced spectral catalogs, we used the Synthetic Minority Oversampling Technique (SMOTE) to generate synthetic data points. Among models applied before and after data augmentation, the Probabilistic Random Forest (PRF), a tuned Random Forest (RF), achieved the highest total accuracy, reaching $\mathbf{89\%}$ based on recall in categorizing dusty stellar sources. Using SMOTE does not improve the best model's accuracy for the CAGB, PAGB, and RSG classes; it remains $\mathbf{100\%}$, $\mathbf{100\%}$, and $\mathbf{88\%}$, respectively, but shows variations for OAGB and YSO classes. We also collected photometric labeled data similar to the training dataset, classifying them using the top four PRF models with over $\mathbf{87\%}$ accuracy. Multiwavelength data from several studies were classified using a consensus model integrating four top models to present common labels as final predictions.

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arXiv2025

Machine Learning Classification of Young Stellar Objects and Evolved Stars in the Magellanic Clouds Using the Probabilistic Random Forest Classifier

The Magellanic Clouds (MCs) are excellent locations to study stellar dust emission and its contribution to galaxy evolution. Through spectral and photometric classification, MCs can serve as a unique environment for studying stellar evolution and galaxies enriched by dusty stellar point sources. We applied machine learning classifiers to spectroscopically labeled data from the Surveying the Agents of Galaxy Evolution (SAGE) project, which involved 12 multiwavelength filters and 618 stellar objects at the MCs. We classified stars into five categories: young stellar objects (YSOs), carbon-rich asymptotic giant branch (CAGB) stars, oxygen-rich AGB (OAGB) stars, red supergiants (RSG), and post-AGB (PAGB) stars. Following this, we augmented the distribution of imbalanced classes using the Synthetic Minority Oversampling Technique (SMOTE). Therefore, the Probabilistic Random Forest (PRF) classifier achieved the highest overall accuracy, reaching ${89\%}$ based on the recall metric, in categorizing dusty stellar sources before and after data augmentation. In this study, SMOTE did not impact the classification accuracy for the CAGB, PAGB, and RSG categories but led to changes in the performance of the OAGB and YSO classes.

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arXiv2024

Detection of the Long Period Variable Stars of And II Dwarf Satellite galaxy

We conducted an extensive study of the spheroidal dwarf satellite galaxies around the Andromeda galaxy to produce an extensive catalog of LPV stars. The optical monitoring project consists of 55 dwarf galaxies and four globular clusters that are members of the Local Group. We have made observations of these galaxies using the WFC mounted on the 2.5 m INT in nine different periods, both in the i-band filter Sloan and in the filter V-band Harris. We aim to select AGB stars with brightness variations larger than 0.2 mag to investigate the evolutionary processes in these dwarf galaxies. The resulting catalog of LPV stars in Andromeda's satellite galaxies offers updated information on features like half-light radii, TRGB magnitudes, and distance moduli. This manuscript will review the results obtained for And II galaxy. Using the Sobel filter, we have calculated the distance modulus for this satellite galaxy, which ranges from 23.90 to 24.11 mag.

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arXiv2024

Machine learning based stellar classification with highly sparse photometry data

Identifying stars belonging to different classes is vital in order to build up statistical samples of different phases and pathways of stellar evolution. In the era of surveys covering billions of stars, an automated method of identifying these classes becomes necessary. Many classes of stars are identified based on their emitted spectra. In this paper, we use a combination of the multi-class multi-label Machine Learning (ML) method XGBoost and the PySSED spectral-energy-distribution fitting algorithm to classify stars into nine different classes, based on their photometric data. The classifier is trained on subsets of the SIMBAD database. Particular challenges are the very high sparsity (large fraction of missing values) of the underlying data as well as the high class imbalance. We discuss the different variables available, such as photometric measurements on the one hand, and indirect predictors such as Galactic position on the other hand. We show the difference in performance when excluding certain variables, and discuss in which contexts which of the variables should be used. Finally, we show that increasing the number of samples of a particular type of star significantly increases the performance of the model for that particular type, while having little to no impact on other types. The accuracy of the main classifier is ~0.7 with a macro F1 score of 0.61. While the current accuracy of the classifier is not high enough to be reliably used in stellar classification, this work is an initial proof of feasibility for using ML to classify stars based on photometry.

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arXiv2024

SMC-Last Extracted Photometry

We present point-source photometry from the Spitzer Space Telescope's final survey of the Small Magellanic Cloud (SMC). We mapped 30 square degrees in two epochs in 2017, with the second extending to early 2018 at 3.6 and 4.5 microns using the Infrared Array Camera. This survey duplicates the footprint from the SAGE-SMC program in 2008. Together, these surveys cover a nearly 10 yr temporal baseline in the SMC. We performed aperture photometry on the mosaicked maps produced from the new data. We did not use any prior catalogs as inputs for the extractor in order to be sensitive to any moving objects (e.g., foreground brown dwarfs) and other transient phenomena (e.g., cataclysmic variables or FU Ori-type eruptions). We produced a point-source catalog with high-confidence sources for each epoch as well as combined-epoch catalog. For each epoch and the combined-epoch data, we also produced a more complete archive with lower-confidence sources. All of these data products will be available to the community at the Infrared Science Archive.

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