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H. Thuruthipilly

Publications and source records attributed to H. Thuruthipilly.

4 recordsLinked to original sources

Dissecting ultra-diffuse galaxies in the field

Context. Ultra-diffuse galaxies (UDGs) in the field are faint, diffuse systems that remain poorly represented in the literature due to the need for spectroscopic confirmation and the difficulty of obtaining high S/N emission line measurements. Aims. We present a spectroscopic study of 17 blue UDG candidates in the field using optical emission lines to confirm their diffuse nature and properties. Methods. We derived spectroscopic redshifts($z_{\rm spec}$) for our field UDG candidates. We then computed their effective radii ($r_{\rm eff}$) and central surface brightnesses ($μ_{0,g}$). We measured the H$α$ and H$β$ emission line fluxes in the 17 spectra and derived star-formation rates (SFR) from the line-luminosity relation. We performed forced photometry on our sample to obtain photometric fluxes and applied an aperture correction on the H$α$ integrated fluxes, propagating the correction to the derived SFRs. We then computed stellar masses ($M_*$) using colour relations and estimated dust attenuation and corrected the SFRs accordingly. Two sources were further examined as potential giant low surface-brightness galaxies(GLSBGs). Results. We identify nine confirmed UDGs, eight other low surface-brightness galaxies (LSBGs), including one GLSBG. The $z_{\rm spec}$ of our field UDGs span a range of $0.015-0.037$, their $r_{\rm eff}$ spans $1.69-4.99$ kpc and $μ_{0,g}$ between $24.05-24.98~{\rm mag~arcsec}^{-2}$. Galaxies exhibit low to moderate dust content, with an average V-band attenuation of 0.29 mag. The spectroscopically confirmed UDGs presented in this work, after the aperture correction performed, lie along the star-forming main sequence. Conclusions. Our results indicate that blue field UDGs are characterised by heterogeneous dust attenuation and occupy the same region of the star formation-stellar mass plane as dwarf LSBGs.

astro-ph.GA

Impact of stochastic star-formation histories and dust on selecting quiescent galaxies with JWST photometry

While the James Webb Space Telescope (JWST) now allows identifying quiescent galaxies (QGs) out to early epochs, the photometric selection of quiescent galaxy candidates (QGCs) and the derivation of key physical quantities are highly sensitive to the assumed star-formation histories (SFHs). We aim to quantify how the inclusion of JWST/MIRI data and different SFH models impacts the selection and characterisation of QGCs. We test the robustness of the physical properties inferred from the spectral energy distribution (SED) fitting, such as M*, age, star formation rate (SFR), and AV, and study how they impact the quiescence criteria of the galaxies across cosmic time. We perform SED fitting for ~13000 galaxies at z<6 from the CEERS/MIRI fields with up to 20 optical-mid infrared (MIR) broadband coverage. We implement three SFH prescriptions: flexible delayed, NonParametric, and extended Regulator. For each model, we compare results obtained with and without MIRI photometry and dust emission models. We evaluate the impact of these configurations on the number of candidate QGCs, selected based on rest UVJ colours, sSFR and main-sequence offset, and on their key physical properties such as M*, AV, and stellar ages. The number of QGCs selected varies significantly with the choice of SFH from 171 to 224 out of 13000 galaxies, depending on the model. This number increases to 222-327 when MIRI data are used (up to ~45% more QGCs). This enhancement is driven by improved constraints on dust attenuation and M*. We find a strong correlation between AV and M*, with massive galaxies (M*~10^11 M\odot) being 1.5-4.2 times more attenuated in magnitude than low-mass systems (M*~10^9 M\odot), depending on SFH. Regardless of the SFH assumption, ~13% of QGCs exhibit significant attenuation (AV > 0.5) in support of recent JWST studies challenging the notion that quiescent galaxies are uniformly dust-free.

astro-ph.GA

DES to HSC: Detecting low surface brightness galaxies in the Abell 194 cluster using transfer learning

Low surface brightness galaxies (LSBGs) are important for understanding galaxy evolution and cosmological models. The upcoming large-scale surveys are expected to uncover a large number of LSBGs, requiring accurate automated or machine learning-based methods for their detection. We study the scope of transfer learning for the identification of LSBGs. We use transformer models divided into two categories: LSBG Detection Transformer (LSBG DETR) and LSBG Vision Transformer (LSBG ViT), trained on Dark Energy Survey (DES) data, to identify LSBGs from dedicated Hyper Suprime-Cam (HSC) observations of the Abell 194 cluster, which are two magnitudes deeper than DES. The data from DES and HSC were standardized based on pixel-level surface brightness. We used two transformer ensembles to detect LSBGs. This was followed by a single-component Sérsic model fit and a final visual inspection to filter out potential false positives and improve sample purity. We present a sample of 171 low surface brightness galaxies (LSBGs) in the Abell 194 cluster using HSC data, including 87 new discoveries. Of these, 159 were identified using transformer models, and 12 additional LSBGs were found through visual inspection. The transformer model achieved a true positive rate (TPR) of 93% in HSC data without any fine-tuning. Among the LSBGs, 28 were classified as ultra-diffuse galaxies (UDGs). The number of UDGs and the radial UDG number density suggest a linear relationship between UDG numbers and cluster mass on a log scale. UDGs share similar Sérsic parameters with dwarf galaxies and occupy the extended end of the $R_{\mathrm{eff}}-M_g$ plane, suggesting they might be an extended subpopulation of dwarf galaxies. We have demonstrated that transformer models trained on shallower surveys can be successfully applied to deeper surveys with appropriate data normalization.

astro-ph.GA

Shedding Light on Low Surface Brightness Galaxies in Dark Energy Survey with Transformers

Low surface brightness galaxies (LSBGs) which are defined as galaxies that are fainter than the night sky, play a crucial role in understanding galaxy evolution and cosmological models. Upcoming large-scale surveys like Rubin Observatory Legacy Survey of Space and Time (LSST) and Euclid are expected to observe billions of astronomical objects. In this context, using semi-automatic methods to identify LSBGs would be a highly challenging and time-consuming process and demand automated or machine learning-based methods to overcome this challenge. We study the use of transformer models in separating LSBGs from artefacts in the data from the Dark Energy Survey (DES) data release 1. Using the transformer models, we then search for new LSBGs from the DES that the previous searches may have missed. Properties of the newly found LSBGs are investigated, along with an analysis of the properties of the total LSBG sample in DES. We identified 4,083 new LSBGs in DES, adding an additional $\sim17\% $ to the LSBGs already known in DES. This also increased the number density of LSBGs in DES to 5.5 deg$^{-2}$. We performed a clustering analysis of the LSBGs in DES using an angular two-point auto-correlation function and found that LSBGs cluster more strongly than their high surface brightness counterparts. We associated 1310 LSBGs with galaxy clusters and identified 317 among them as ultra-diffuse galaxies (UDGs). We found that these cluster LSBGs are getting bluer and larger in size towards the edge of the clusters when compared with those in the centre. Transformer models have the potential to be on par with convolutional neural networks as state-of-the-art algorithms in analysing astronomical data.

astro-ph.GA