Purba Mukherjee

604 total citations
22 papers, 267 citations indexed

About

Purba Mukherjee is a scholar working on Astronomy and Astrophysics, Nuclear and High Energy Physics and Oceanography. According to data from OpenAlex, Purba Mukherjee has authored 22 papers receiving a total of 267 indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Astronomy and Astrophysics, 8 papers in Nuclear and High Energy Physics and 4 papers in Oceanography. Recurrent topics in Purba Mukherjee's work include Cosmology and Gravitation Theories (19 papers), Galaxies: Formation, Evolution, Phenomena (13 papers) and Gamma-ray bursts and supernovae (7 papers). Purba Mukherjee is often cited by papers focused on Cosmology and Gravitation Theories (19 papers), Galaxies: Formation, Evolution, Phenomena (13 papers) and Gamma-ray bursts and supernovae (7 papers). Purba Mukherjee collaborates with scholars based in India, Malta and Romania. Purba Mukherjee's co-authors include Narayan Banerjee, Anjan A. Sen, Ankan Mukherjee, Jurgen Mifsud, Rahul Shah, Jackson Levi Said, Supratik Pal, Konstantinos F. Dialektopoulos, Diego Pavón and H. K. Jassal and has published in prestigious journals such as The Astrophysical Journal, Monthly Notices of the Royal Astronomical Society and Reports on Progress in Physics.

In The Last Decade

Purba Mukherjee

21 papers receiving 253 citations

Peers

Purba Mukherjee
Luis A. Escamilla United Kingdom
C. Danielle Leonard United Kingdom
Alex Krolewski Canada
Kazuyuki Akitsu Japan
Carlos A. P. Bengaly Brazil
K. Migkas Germany
Wilmar Cardona Colombia
Bill S. Wright United Kingdom
E. Keihänen Finland
Z. Sakr Lebanon
Luis A. Escamilla United Kingdom
Purba Mukherjee
Citations per year, relative to Purba Mukherjee Purba Mukherjee (= 1×) peers Luis A. Escamilla

Countries citing papers authored by Purba Mukherjee

Since Specialization
Citations

This map shows the geographic impact of Purba Mukherjee's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Purba Mukherjee with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Purba Mukherjee more than expected).

Fields of papers citing papers by Purba Mukherjee

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Purba Mukherjee. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Purba Mukherjee. The network helps show where Purba Mukherjee may publish in the future.

Co-authorship network of co-authors of Purba Mukherjee

This figure shows the co-authorship network connecting the top 25 collaborators of Purba Mukherjee. A scholar is included among the top collaborators of Purba Mukherjee based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Purba Mukherjee. Purba Mukherjee is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Mukherjee, Purba, Maria Giovanna Dainotti, Konstantinos F. Dialektopoulos, Jackson Levi Said, & Jurgen Mifsud. (2025). Model-independent calibration of Gamma-Ray Bursts with neural networks. Journal of High Energy Astrophysics. 49. 100439–100439.
2.
Shah, Rahul, Purba Mukherjee, & Supratik Pal. (2025). Interacting dark sectors in light of DESI DR2. Monthly Notices of the Royal Astronomical Society. 542(4). 2936–2942. 14 indexed citations
3.
Mukherjee, Purba & Anjan A. Sen. (2025). New expansion rate anomalies at characteristic redshifts geometrically determined using DESI-DR2 BAO and DES-SN5YR observations. Reports on Progress in Physics. 88(9). 98401–98401. 4 indexed citations
4.
Shah, Rahul, Purba Mukherjee, & Supratik Pal. (2024). Reconciling S8: insights from interacting dark sectors. Monthly Notices of the Royal Astronomical Society. 536(3). 2404–2420. 9 indexed citations
5.
Shah, Rahul, et al.. (2024). Role of future SNIa data from Rubin LSST in reinvestigating cosmological models. Monthly Notices of the Royal Astronomical Society. 530(3). 2627–2636. 2 indexed citations
6.
Mukherjee, Purba, Konstantinos F. Dialektopoulos, Jackson Levi Said, & Jurgen Mifsud. (2024). A possible late-time transition of M B inferred via neural networks. Journal of Cosmology and Astroparticle Physics. 2024(9). 60–60. 5 indexed citations
7.
Mukherjee, Purba & Anjan A. Sen. (2024). Model-independent cosmological inference post DESI DR1 BAO measurements. Physical review. D. 110(12). 37 indexed citations
8.
Velázquez, Juan J. L., Luis A. Escamilla, Purba Mukherjee, & J. Alberto Vázquez. (2024). Non-Parametric Reconstruction of Cosmological Observables Using Gaussian Processes Regression. Universe. 10(12). 464–464. 6 indexed citations
9.
Mukherjee, Purba, et al.. (2023). Examining the validity of the minimal varying speed of light model through cosmological observations: Relaxing the null curvature constraint. Physics of the Dark Universe. 43. 101380–101380. 4 indexed citations
10.
Mukherjee, Purba, et al.. (2023). Reconstructing the Hubble Parameter with Future Gravitational-wave Missions Using Machine Learning. The Astrophysical Journal. 960(1). 61–61. 4 indexed citations
11.
Banerjee, Narayan, Purba Mukherjee, & Diego Pavón. (2023). Checking the second law at cosmic scales. Journal of Cosmology and Astroparticle Physics. 2023(11). 92–92. 5 indexed citations
12.
Dialektopoulos, Konstantinos F., Purba Mukherjee, Jackson Levi Said, & Jurgen Mifsud. (2023). Neural network reconstruction of cosmology using the Pantheon compilation. The European Physical Journal C. 83(10). 11 indexed citations
13.
Shah, Rahul, et al.. (2023). A thorough investigation of the prospects of eLISA in addressing the Hubble tension: Fisher forecast, MCMC and Machine Learning. Journal of Cosmology and Astroparticle Physics. 2023(6). 38–38. 12 indexed citations
14.
Banerjee, Narayan, Purba Mukherjee, & Diego Pavón. (2023). Spatial curvature and thermodynamics. Monthly Notices of the Royal Astronomical Society. 521(4). 5473–5482. 7 indexed citations
15.
Dialektopoulos, Konstantinos F., Purba Mukherjee, Jackson Levi Said, & Jurgen Mifsud. (2023). Neural network reconstruction of scalar-tensor cosmology. Physics of the Dark Universe. 43. 101383–101383. 8 indexed citations
16.
Mukherjee, Purba & Narayan Banerjee. (2022). Constraining the curvature density parameter in cosmology. Physical review. D. 105(6). 24 indexed citations
17.
Mukherjee, Purba, Jackson Levi Said, & Jurgen Mifsud. (2022). Neural network reconstruction of H'(z) and its application in teleparallel gravity. Journal of Cosmology and Astroparticle Physics. 2022(12). 29–29. 18 indexed citations
18.
Mukherjee, Purba & Narayan Banerjee. (2021). Nonparametric reconstruction of interaction in the cosmic dark sector. Physical review. D. 103(12). 23 indexed citations
19.
Mukherjee, Purba & Ankan Mukherjee. (2021). Assessment of the cosmic distance duality relation using Gaussian process. Monthly Notices of the Royal Astronomical Society. 504(3). 3938–3946. 29 indexed citations
20.
Mukherjee, Purba, et al.. (2019). Holographic dark energy: constraints on the interaction from diverse observational data sets. The European Physical Journal Plus. 134(4). 16 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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