arXiv subjects

Fatemeh Nikzat

Publications and source records attributed to Fatemeh Nikzat.

3 recordsLinked to original sources

arXiv2026

The UKIRT M33 Monitoring Project: A 15-Year Near-Infrared Variable Star Catalogue for the Central Kiloparsec, and Prospects for Star Formation History and Dust Return

We present results from the UKIRT M33 Monitoring Project, a near-infrared survey of variable red giants in the Local Group spiral galaxy M33. Combining four independent photometric datasets spanning UIST (2003), UFTI (2005), WFCAM (2005-2007), and a new UKIRT Hemisphere Survey epoch (2018), we construct a homogenised 15.11-year $K$-band light-curve catalogue for 847 stars in the central kiloparsec, of which 771 (91 per cent) are variable. Cross-matching with archival Spitzer photometry identifies 120 dust-enshrouded AGB candidates. We compare the spatial coverage of this central catalogue with the earlier UIST-only (Paper I) and disc-wide WFCAM (Paper IV) variable-star surveys. Building on this catalogue, two forthcoming papers will (i) measure individual pulsation periods to reconstruct the star formation history of the central kiloparsec, and (ii) refine dust and gas mass-loss rates across the disc using the extended time baseline.

astro-ph.GA↗

arXiv2020

Scalable End-to-end Recurrent Neural Network for Variable star classification

During the last decade, considerable effort has been made to perform automatic classification of variable stars using machine learning techniques. Traditionally, light curves are represented as a vector of descriptors or features used as input for many algorithms. Some features are computationally expensive, cannot be updated quickly and hence for large datasets such as the LSST cannot be applied. Previous work has been done to develop alternative unsupervised feature extraction algorithms for light curves, but the cost of doing so still remains high. In this work, we propose an end-to-end algorithm that automatically learns the representation of light curves that allows an accurate automatic classification. We study a series of deep learning architectures based on Recurrent Neural Networks and test them in automated classification scenarios. Our method uses minimal data preprocessing, can be updated with a low computational cost for new observations and light curves, and can scale up to massive datasets. We transform each light curve into an input matrix representation whose elements are the differences in time and magnitude, and the outputs are classification probabilities. We test our method in three surveys: OGLE-III, Gaia and WISE. We obtain accuracies of about $95\%$ in the main classes and $75\%$ in the majority of subclasses. We compare our results with the Random Forest classifier and obtain competitive accuracies while being faster and scalable. The analysis shows that the computational complexity of our approach grows up linearly with the light curve size, while the traditional approach cost grows as $N\log{(N)}$.

astro-ph.IM↗

arXiv2017

Monitoring survey of pulsating giant stars in the Local Group galaxies: survey description, science goals, target selection

The population of nearby dwarf galaxies in the Local Group constitutes a complete galactic environment, perfect suited for studying the connection between stellar populations and galaxy evolution. In this study, we are conducting an optical monitoring survey of the majority of dwarf galaxies in the Local Group, with the Isaac Newton Telescope (INT), to identify long period variable stars (LPVs). These stars are at the end points of their evolution and therefore their luminosity can be directly translated into their birth masses; this enables us to reconstruct the star formation history. By the end of the monitoring survey, we will have performed observations over ten epochs, spaced approximately three months apart, and identified long period, dust-producing AGB stars; five epochs of data have been obtained already. LPVs are also the main source of dust; in combination with Spitzer Space Telescope images at mid-IR wavelengths we will quantify the mass loss, and provide a detailed map of the mass feedback into the interstellar medium. We will also use the amplitudes in different optical passbands to determine the radius variations of the stars, and relate this to their mass loss.

astro-ph.GA↗