List of Publications & Metrics
[ Last update: Apr. ‘25]
Google Scholar Stats
Metrics | All Time | Last 5 Years | |
Publications | 17 | 13 | |
Citations | 227 | 211 | |
h-index | 9 | 9 | |
i10-index | 8 | 8 |
† Denotes publications with mentees as first authors
✽ Co-First Authors
First-Author & Major Contribution
10.† Environmental Sculpting of Galaxy Structure at Fixed Stellar Mass: A Multi-Scale Analysis Across Cosmic Time using 3 Million HSC Galaxies | C. Igel✽, A. Ghosh✽, A. J. Connolly, B. Robertson, C. M. Urry et al. (submitted to ApJ)
9. Morphological Parameters of z<1 AGN Host Galaxies in Stripe 82X: Evidence for a Significant Dependence of AGN-Galaxy Co-evolution on X-ray Luminosity | C. Tian, C. M. Urry, A. Ghosh, D. Nagai, T. T. Ananna, et al. (submitted to ApJ)
8. Automatic Machine Learning Framework to Study Morphological Parameters of AGN Host Galaxies within z < 1.4 in the Hyper Suprime-Cam Wide Survey | C. Tian, C. M. Urry, A. Ghosh, D. Nagai, T. T. Ananna, et al. ApJ, 981, 5 (Mar. 2025)
7. Denser Environments Cultivate Larger Galaxies: A Comprehensive Study beyond the Local Universe with 3 Million Hyper Suprime-Cam Galaxies | A. Ghosh, C.M. Urry, M. C. Powell, R. Shimakawa, F. C. van den Bosch, et al. ApJ, 971(2), 142 (Aug. 2024)
6. Morphological Parameters and Associated Uncertainties for 8 Million Galaxies in the Hyper Suprime-Cam Wide Survey | A. Ghosh, C.M. Urry, L.P. Levasseur, A. Mishra, C. Tian, et al. ApJ, 953(2), 134 (Aug. 2023)
5. Using Machine Learning to Determine Morphologies of z < 1 AGN Host Galaxies in the Hyper Suprime-Cam Wide Survey | C. Tian, C. M. Urry, A. Ghosh, R. Ofman, T. T. Ananna, et al. ApJ, 944(2), 124 (Feb. 2023)
4. GaMPEN: A Machine Learning Framework for Estimating Bayesian Posteriors of Galaxy Morphological Parameters | A. Ghosh, C.M. Urry, A. Rau, L.P. Levasseur, M. Cranmer et. al. ApJ, 935(2), 138 (Aug. 2022)
3. Galaxy Morphology Network (GaMorNet): A Convolutional Neural Network to study morphology and quenching in ~100,000 SDSS & ~20,000 CANDELS galaxies | A. Ghosh, C.M.Urry, Z.Wang, D. Turp, K.Schawinski, M.C.Powell ApJ, 895(2), 112 (Jun. 2020)
2. Acceleration in Astrophysical Environments with CR Propa | A. Ghosh, S. Buitink, O.Scholten, T. Winchen, PoS(ICRC2017) 582
1. An extended fractal growth regime in the diffusion limited aggregation including edge diffusion. | A. Ghosh, R. Batabyal, G. P. Das, B. N. Dev, AIP Advances 6, 015301 (Jan. 2016)
Secondary Contribution
7. Obscured AGN at z < 1.5: X-ray to Far-Infrared SEDs and Host Galaxy Morphologies in the GOODS Fields | W. H. Jarvis, et al. incl. A. Ghosh (submitted to ApJ)
6. Morphological Classification of Galaxies Through Structural and Star Formation Parameters Using Machine Learning | Aguilar-Argüello, et al. incl. A. Ghosh MNRAS 537, 876–896 (Jan. 2025)
5. Stripe 82-XL: the ~54.8 deg2 and ~18.8 Ms Chandra and XMM-Newton point source catalog and number of counts | A. Peca, et al. incl. A. Ghosh ApJ, 974(2), 156 (Oct. 2024)
4. Stripe 82X Data Release 3: Multiwavelength Catalog with New Spectroscopic Redshifts and Black Hole Masses | S. LaMassa, et al. incl. A. Ghosh ApJ, 974(2), 235 (Oct. 2024)
3. The Accretion History of AGN: The Spectral Energy Distributions of X-ray Luminous AGN | C. Auge, et al. incl. A. Ghosh ApJ, 957, 19 (Oct. 2023)
2. GALFIT-ing AGN Host Galaxies in COSMOS: HST vs. Subaru | C. Dewsnap, P. Barmby, S. C. Gallagher, C. M. Urry, A. Ghosh, M. C. Powell. ApJ, 944, 137 (Feb. 2023)
1. Probing the Jets of Blazars Using the Temporal Symmetry of their Multiwavelength Outbursts | N.Roy, R.Chatterjee, M. Joshi, A. Ghosh MNRAS 482, 1, 743-757 (Jan. 2019)
Theses
2. PhD Thesis – Investigating Galaxy Morphology in Large Surveys Using Novel Machine Learning Frameworks
1. Masters Thesis – Diffusive Shock Acceleration with CR Propa
Some Older Project Reports/Posters which are not on arXiv / Zeonodo
APS April Meeting ‘16 as a part of APS-FIP Distinguished Student Program
2. Project Report -- Feasibility Study of Tensor Interaction in Leptonic Tau Decays at Belle and Belle II
3. Project Report -- Gamma-Ray Variability of Fermi Blazars