@article{ghosh_ghosh_chatterjee_2025, title={A Flexible Spherical Mixture Model for Gamma-Ray Burst Patterns Obtained from BATSE and FERMI Mission}, volume={137}, url={https://doi.org/10.1088/1538-3873/adb2c1}, DOI={10.1088/1538-3873/adb2c1}, abstractNote={Abstract The study encapsulates the investigation into the spherical distributional characteristics of parameters relevant to Gamma-Ray Bursts (GRBs), mainly focusing on their Galactic coordinates. The study utilized a mixture of von Mises Fisher spherical distributions to model the spatial distribution of GRBs in both BATSE and FERMI catalogs. Optimal numbers of mixture components were determined for different subsets of GRBs, including Long and Short GRBs. For the BATSE catalog, it turns out that a mixture of two spherical distributions provides a good fit for the whole data set and long and short GRBs. On the other hand, for the FERMI catalog, it turns out that a mixture of three spherical distributions provides a good fit for the whole data set, and a mixture of four distributions is adequate for both long and short GRBs. Additionally, an assessment was made to determine if the location parameter of GRBs follows any spherical distribution. Our flexible directional statistical modeling framework reveals that GRBs exhibit a non-uniform distribution on the celestial sphere, as evidenced by rejecting the null hypothesis of uniform distribution on a sphere using the Watson test. Our analysis statistically inquires the long-held assumption of their isotropic spread, especially in 2D projected spatial distributions of GRBs, suggesting that these cosmic events might not be uniformly scattered across the celestial sphere. The observed clumping of GRBs hints at the underlying cosmic scaffolding—the large-scale distribution of matter and star formation. Our results statistically asserts the explanation that the intrinsic GRB formation rate is typically tied to cosmic star formation rates with a delay time distribution, leading to a non-uniform rate as a function of redshift, demanding more nuanced calculations. However, this finding needs to consider potential biases introduced by the Milky Way’s obscuration and our heliocentric perspective.}, number={2}, journal={Publications of the Astronomical Society of the Pacific}, author={Ghosh, P. and Ghosh, S. and Chatterjee, D.}, year={2025}, pages={024503} }
 @article{chatterjee_ghosh_2025, title={Redshift‐Agnostic Machine Learning Classification: Unveiling Peak Performance in Galaxy, Star, and Quasar Classification (Using <scp>SDSS DR17</scp>)}, volume={2}, ISSN={0004-6337 1521-3994}, url={http://dx.doi.org/10.1002/asna.20240057}, DOI={10.1002/asna.20240057}, abstractNote={ABSTRACT Classification of galaxies, stars, and quasars using spectral data is fundamental to astronomy, but often relies heavily on redshift. This study evaluates the performance of 10 machine learning algorithms on SDSS data to classify these objects, with a particular focus on scenarios where redshift information is unavailable. Leveraging features such as “z,” “u,” “g,” “r,” “i,” and redshift, we assess the accuracy of various algorithms, including XGBoost, Random Forest, and recurrent neural networks (RNNs). Our analysis demonstrates the superior accuracy of the Random Forest classifier when redshift is included. The feature importance analysis reveals that “redshift” is the most critical feature, contributing 64.7% to the classification accuracy, followed by the “z” band (10.0%) and the “g” band (7.95%). However, even in the absence of redshift, XGBoost, Random Forest, and RNNs exhibit promising results, indicating the potential of photometric data for accurate classification. We systematically compare classification outcomes with and without redshift, revealing the relative importance of different features and identifying the most robust classifiers for redshift‐limited scenarios. This research not only highlights the power of machine learning for astronomical classification but also provides a framework for reliable classification when redshift data is lacking. By uncovering the distinguishing spectral characteristics of galaxies, stars, and quasars that are independent of redshift, we open new avenues for efficient and accurate classification in large‐scale photometric surveys and the study of faint, high‐redshift objects.}, journal={Astronomische Nachrichten}, publisher={Wiley}, author={Chatterjee, Debashis and Ghosh, Prithwish}, year={2025}, month={Feb} }
 @article{ghosh_chatterjee_2024, title={Comparative Analysis of Machine Learning Algorithms for Breast Cancer Classification: SVM Outperforms XGBoost, CNN, RNN, and Others}, url={https://doi.org/10.1101/2024.04.22.590658}, DOI={10.1101/2024.04.22.590658}, abstractNote={ABSTRACT This study evaluates ten machine learning algorithms for classifying breast cancer cases as malignant or benign based on physical attributes. Algorithms tested include XGBoost, CNN, RNN, AdaBoost, Adaptive Decision Learner, fLSTM, GRU, Random Forest, SVM, and Logistic Regression. Using a robust dataset from UCI machine learning Breast Cancer, SVM emerged as the most accurate, achieving 98.2456% accuracy. While AdaBoost, Logistic Regression, Neural Networks, and Random Forest showed promise, none matched SVM’s accuracy. These findings underscore the potential of machine learning, particularly SVMs, in cancer diagnosis and treatment by analyzing physical attributes for improved diagnostics and targeted therapies.}, journal={bioRxiv}, author={Ghosh, Prithwish and Chatterjee, Debashis}, year={2024}, month={Apr} }
 @article{ghosh_chatterjee_banerjee_das_saqr_2024, title={Do Magnetic murmurs guide birds? A directional statistical investigation for influence of Earth's Magnetic field on bird navigation}, volume={19}, ISSN={["1932-6203"]}, url={https://doi.org/10.1371/journal.pone.0304279}, DOI={10.1371/journal.pone.0304279}, abstractNote={This paper delves into the intricate relationship between changes in Magnetic inclination and declination at specific geographical locations and the navigational decisions of migratory birds. Leveraging a dataset sourced from a prominent bird path tracking web resource, encompassing six distinct bird species’ migratory trajectories, latitudes, longitudes, and observation timestamps, we meticulously analyzed the interplay between these avian movements and corresponding alterations in Magnetic inclination and declination. Employing a circular von Mises distribution assumption for the latitude and longitude distributions within each subdivision, we introduced a pioneering circular-circular regression model, accounting for von Mises error, to scrutinize our hypothesis. Our findings, predominantly supported by hypothesis tests conducted through circular-circular regression analysis, underscore the profound influence of Magnetic inclination and declination shifts on the dynamic adjustments observed in bird migration paths. Moreover, our meticulous examination revealed a consistent adherence to von Mises distribution across all bird directions. Notably, we unearthed compelling correlations between specific bird species, such as the Black Crowned Night Heron and Brown Pelican, exhibiting a noteworthy negative correlation with Magnetic inclination and a contrasting positive correlation with Magnetic declination. Similarly, the Pacific loon demonstrated a distinct negative correlation with Magnetic inclination and a positive association with Magnetic declination. Conversely, other avian counterparts showcased positive correlations with both Magnetic declination and inclination, further elucidating the nuanced dynamics between avian navigation and the Earth’s magnetic field parameters.}, number={6}, journal={PLOS ONE}, author={Ghosh, Prithwish and Chatterjee, Debashis and Banerjee, Amlan and Das, Shiladri Shekhar and Saqr, Ahmed M.}, editor={Saqr, Ahmed M.Editor}, year={2024}, month={Jun} }
 @article{chatterjee_ghosh_2024, title={From Deep Learning Maze to Neural Network Waltz: Unveiling Peak Performance in Stellar Classification (Using SDSS DR17)}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85194009546&partnerID=MN8TOARS}, DOI={10.21203/rs.3.rs-4264627/v1}, abstractNote={<title>Abstract</title> Stellar classification based on spectral characteristics plays a pivotal role in astronomy, facilitating the study of celestial bodies’ composition and evolution. In this research, we assess the performance of ten distinct machine learning algorithms in classifying stellar objects using data from the Sloan Digital Sky Survey (SDSS). Leveraging features such as ’u’, ’g’, ’r’, ’i’, ’z’, and ’redshift’, with ’class’ as the target variable, we evaluate the accuracy of algorithms including XGBoost, CNN, RNN, AdaBoost, Adaptive Decision Learner, LSTM Networks, GRU, Random Forest Classifier, SVM, and Logistic Regression. Our findings reveal that the Random Forest Classifier outperforms other algorithms with an accuracy of 97.805%, showcasing its efficacy in capturing the complex spectral patterns of stellar objects. Moreover, other algorithms such as XGBoost, RNN, Adaptive Decision Learner, and GRU demonstrate notable accuracies ranging from 96.609% to 97.395%. This study underscores the utility of machine learning in stellar classification, offering valuable insights for astronomical research and enhancing our comprehension of the cosmos.}, journal={Research Square}, author={Chatterjee, D. and Ghosh, P.}, year={2024} }
 @article{chatterjee_ghosh_2024, title={Mercury's Meteorite Mysteries: A Directional Statistical Guide to Mercury's North Pole, Hidden Hazards and Roadmap to Safe Landing Havens Based on Solar Elevation, Ice Stability, Temperature}, volume={136}, ISSN={["1538-3873"]}, url={https://doi.org/10.1088/1538-3873/ad851b}, DOI={10.1088/1538-3873/ad851b}, abstractNote={Abstract Studying meteoroid impact patterns on planetary surfaces is critical for understanding surface dynamics and selecting safe landing sites. Mercury, one of the least explored rocky planets, presents unique challenges due to its extreme temperatures and the angular nature of its surface data. Traditional linear statistical methods are often inadequate for analyzing such directional data. This study introduces a novel approach using directional data analysis techniques to interpret the cyclical nature of meteoroid impact locations on Mercury. We employed Watson’s test, Bayesian Information Criterion scores, and a mixture of von Mises–Fisher distributions to model the distribution of impact craters and solar elevations on Mercury’s surface. Our findings indicate that while location parameters adhere to the von Mises distribution, solar elevations do not exhibit directional distribution characteristics. Additionally, by filtering datasets based on temperature thresholds and crater diameters, we pinpointed areas with a high probability of providing suitable landing surfaces. These insights enhance our understanding of the surface conditions on Mercury and provide valuable groundwork for future exploration missions.}, number={11}, journal={PUBLICATIONS OF THE ASTRONOMICAL SOCIETY OF THE PACIFIC}, author={Chatterjee, Debashis and Ghosh, Prithwish}, year={2024}, month={Nov} }
 @article{dutta_ghosh_chakraborty_2024, title={Modeling informative dropout in longitudinal data: A joint model approach}, volume={58}, ISSN={0256-422X}, url={http://dx.doi.org/10.3329/jsr.v58i1.75415}, DOI={10.3329/jsr.v58i1.75415}, abstractNote={In medical studies, a disease’s progress is often monitored through indicators that signify the improved or worsened condition of the patient. These are known as longitudinal biomarkers, which are observed along with an event of importance. Together, they form the framework of joint modeling, which has a longitudinal process and an inherently associated time-to-event process. In clinical studies, the change in biomarkers is often monitored in the form of a change in the patient’s plasma level after a drug is administered to the patient. Again, in such studies, patients also withdraw from the trials prematurely or at a later phase, thus giving rise to dropouts. In most cases, this dropout is not random (Missing Not at Random). A joint model has been considered to incorporate this informative dropout in longitudinal response. To demonstrate this approach, a one-compartmental pharmacokinetic (PK) nonlinear mixed-effects model consisting of time-dependent parameters has been used in this work. The dropout mechanism has been introduced using a proportional hazard model. A Bayesian model framework is adopted to study the model’s performance through detailed simulation. A PK study on the drug Divalproex subject to an informative dropout model has been discussed. Journal of Statistical Research 2024, Vol. 58, No. 1, pp. 97-110.}, number={1}, journal={Journal of Statistical Research}, publisher={Bangladesh Academy of Sciences}, author={Dutta, Srimanti and Ghosh, Prithwish and Chakraborty, Arindom}, year={2024}, month={Aug}, pages={97–110} }
 @article{chatterjee_ghosh_2024, title={Navigating Martian Terrain: A Directional Probabilistic Model for Crater Formation and Landing Site Detection}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85193635014&partnerID=MN8TOARS}, DOI={10.2139/ssrn.4826419}, abstractNote={This paper delves into the directional distribution of extraterrestrial objects impacting Mars, presenting a novel directional statistical mixture model validated through rigorous examination. Building upon previous research on non-random meteoroid impacts on Earth, our study statistically validates this notion using extensive datasets of Martian surface impacts. We observe a non-uniform distribution of Martian impacts, with the von Mises distribution favored for crater data. Despite deviations from circular uniformity, an optimal 6-mixture von Mises-Fisher distribution effectively models the Martian dataset. Statistical tests confirm significant differences between parameters, highlighting underlying patterns and directional tendencies in meteor strikes. Moreover, the integration of advanced data analytics, visual representations, and historical insights offers a robust framework for enhancing Mars landing efficiency. Comparative analysis with Earth reveals significant differences in impact environments driven by atmospheric, geological, and astronomical factors. While Earth exhibits non-uniform impact patterns, Mars demonstrates a greater tendency towards uniformity despite environmental complexities. These findings provide crucial insights for interpreting meteorite data and refining extraterrestrial object trajectory models, advancing our understanding and exploration of the Red Planet.}, journal={SSRN}, author={Chatterjee, D. and Ghosh, P.}, year={2024} }
 @article{chatterjee_ghosh_2024, title={On Spatio-temporal Directional Association of Blue and Humpback Whale’s Migratory Path Navigation with Sun, Moon, Ocean Current & Earth’s Magnetic Field}, url={https://doi.org/10.22541/au.172139840.08896939/v1}, DOI={10.22541/au.172139840.08896939/v1}, author={Chatterjee, Debashis and Ghosh, Prithwish}, year={2024}, month={Jul} }
 @article{ghosh_chatterjee_banerjee_2024, title={On the directional nature of celestial object's fall on the earth (Part 1: distribution of fireball shower, meteor fall, and crater on earth's surface)}, volume={531}, ISSN={["1365-2966"]}, url={http://dx.doi.org/10.1093/mnras/stae1066}, DOI={10.1093/mnras/stae1066}, abstractNote={ABSTRACT This paper investigates the directional distribution of extraterrestrial objects (meteors, fireballs) impacting Earth’s surface and forming craters. It also introduces a novel directional statistical mixture model to analyze their falls, validated through rigorous testing. First, we address whether these falls follow non-uniform directional patterns by explicitly employing directional statistical tools for analysing such data. Using projection techniques for longitude and latitude and more importantly, a general spherical statistical approach, we statistically investigate the suitability of the von Mises distribution and its spherical version, the von Mises–Fisher distribution, (a maximum entropy distribution for directional data). Moreover, leveraging extensive data sets encompassing meteor falls, fireball showers, and craters, we propose and validate a novel mixture von Mises–Fisher model for comprehensively analysing extraterrestrial object falls. Our study reveals distinct statistical characteristics across data sets: fireball falls exhibit non-uniformity, while meteor craters suggest a potential for both uniform and von Mises distributions with a preference for the latter after further refinement. Meteor landings deviate from a single-directional maximum entropic distribution; we demonstrate the effectiveness of an optimal 13-component mixture von Mises–Fisher distribution for accurate modelling. Similar analyses resulted in 3- and 6-component partitions for fireball and crater data sets. This research presents valuable insights into the spatial patterns and directional statistical distribution models governing extraterrestrial objects’ fall on Earth, useful for various future works.}, number={1}, journal={MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY}, author={Ghosh, Prithwish and Chatterjee, Debashis and Banerjee, Amlan}, year={2024}, month={May}, pages={1294–1307} }
 @inproceedings{ghosh_banerjee_2024, title={Optimizing Breast Cancer Classification: A Comparative Analysis of Supervised and Unsupervised Machine Learning Techniques}, url={https://doi.org/10.3390/proceedings2024103040}, DOI={10.3390/proceedings2024103040}, abstractNote={This study focuses on the comprehensive analysis of machine learning algorithms for the classification of breast cancer into benign and malignant categories using the Wisconsin breast cancer dataset [...]}, author={Ghosh, Prithwish and Banerjee, Debolina}, year={2024}, month={Apr} }
 @article{chatterjee_ghosh_banerjee_das_2024, title={Optimizing machine learning for water safety: A comparative analysis with dimensionality reduction and classifier performance in potability prediction}, volume={3}, ISSN={["2767-3219"]}, url={https://doi.org/10.1371/journal.pwat.0000259}, DOI={10.1371/journal.pwat.0000259}, abstractNote={In this study, we investigated the effectiveness of machine learning techniques in predicting water potability based on water quality attributes. Initially, we applied seven classification-based methods directly to the original dataset, yielding varying accuracy scores. Notably, the Support Vector Machine (SVM) achieved the highest accuracy of 69%, while other methods such as XGBoost, k-Nearest Neighbors, Gaussian Naive Bayes, and Random Forest demonstrated competitive performance with scores ranging from 62% to 68%. Subsequently, we employed Principal Component Analysis (PCA) to reduce the dataset’s dimensionality to six principal components, followed by reapplication of the machine learning techniques. The results showed an increase in accuracy across all classifiers, increasing to nearly 100%. This study provides insights into the impact of dimensionality reduction on predictive accuracy and underscores the importance of selecting appropriate techniques for water potability prediction.}, number={8}, journal={PLOS WATER}, author={Chatterjee, Debashis and Ghosh, Prithwish and Banerjee, Amlan and Das, Shiladri Shekhar}, editor={Reddythota, DanielEditor}, year={2024}, month={Aug} }
 @article{chatterjee_ghosh_2024, title={Statistical Machine Learning Evidence Supports Five as the Optimal Number of Clusters for Gamma-Ray Burst Classification Over Three}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85192851003&partnerID=MN8TOARS}, DOI={10.2139/ssrn.4822664}, abstractNote={Using statistical machine learning methods, this study provides evidence suggesting that five clusters may be the optimal number for classifying Gamma-Ray bursts (GRBs), compared to the previously proposed classification of three clusters. This paper explores the classification of Gamma-Ray Bursts (GRBs) into distinct types using statistical methods, explicitly employing k-means and Gaussian Mixture Model-based clustering using Euclidean distance and Mahalanabis distance. The primary aim of this investigation is to statistically validate and compare the existence of two GRB types through the cluster analysis techniques.Our findings revealed that K Means achieved an accuracy of approximately 31.14% with the Euclidean distance metric, while Mahalanobis distance improved the accuracy to 47.09%. Interestingly, the optimal number of GRB partitions determined by K Means was 3. On the other hand, GMM-based clustering demonstrated significant improvements. GMM with the Euclidean distance gave us an accuracy of 96.93%, and GMM with Mahalanobis distance gave us an accuracy of 94.30%. The optimal number of clusters identified in this approach was 5. The five types of GRBs were characterized as intermediate/faint/intermediate, long/intermediate/soft, intermediate/intermediate/intermediate, short/faint/hard, and long/bright/intermediate. Hence, the GMMBC clustering algorithm proves to be more powerful than the conventional k means clustering technique, so we may convey that 5 partitioned GRB is more accurate than 3-way classification.}, journal={SSRN}, author={Chatterjee, D. and Ghosh, P.}, year={2024} }
 @inproceedings{ghosh_chakraborty_2023, title={A Heuristic Evaluation of Partitioning Techniques Considering Early-Type Galaxy Databases}, volume={56}, url={https://doi.org/10.3390/ASEC2023-15287}, DOI={10.3390/ASEC2023-15287}, abstractNote={Galaxies are one of the most interesting and complex astronomical objects statistically due to their continuous diversification caused mainly due to incidents such as accretion, action, or mergers. Multivariate studies are one of the most useful tools to analyze these type of data and to understand various components of them. We study a sample of the local universe of Orlando 509 galaxies, imputed with a Predictive Mean Matching (PMM) multiple imputation algorithm, with the aim of classifying the galaxies into distinct clusters through k-medoids and k-mean algorithms and, in turn, performing a heuristic evaluation of the two partitioning algorithms through the percentage of misclassification observed. From the clustering algorithms, it was observed that there were four distinct clusters of the galaxies with misclassification of about 1.96%. Also, comparing the percentage of misclassification heuristically k-means is a superior algorithm to k-medoids under fixed optimal sizes when the said category of galaxy datasets is concerned. By considering that galaxies are continuously evolving complex objects and using appropriate statistical tools, we are able to derive an explanatory classification of galaxies, based on the physical diverse properties of galaxies, and also establish a better method of partitioning when working on the galaxies.}, number={1}, booktitle={Engineering Proceedings}, author={Ghosh, Prithwish and Chakraborty, Shinjon}, year={2023}, month={Oct} }
 @phdthesis{ghosh_2023, place={HARVARD}, title={A Novel Spherical Statistics-based Spatio-Temporal Analysis to Unveil Distributional Properties of Meteor Strike on Earth}, url={http://dx.doi.org/10.13140/RG.2.2.20434.32963}, DOI={10.13140/RG.2.2.20434.32963}, journal={Visva Bharati}, author={Ghosh, Prithwish}, editor={Ghosh, PrithwishEditor}, year={2023}, month={May} }
 @phdthesis{ghosh_2023, title={A Novel Spherical Statistics-based Spatio-Temporal Analysis to Unveil Distributional Properties of Meteor Strike on Earth}, school={Department of Statistics Visva Bharati}, author={Ghosh, P.}, year={2023}, month={Jan} }
 @book{ghosh_dey_2023, place={Kolkata}, title={Classification of Households by Non-Expenditure Approach: A Soft Check and Study Based of Household Data of 78th Round Report dataset July 2023}, institution={Ministry of Statistics and Programme Implementation, Government of India}, author={Ghosh, P. and Dey, S.}, year={2023}, month={Jul} }
 @inproceedings{revisit to a prominent example of the curse of dimensionality: re-investigating anomaly behaviour of monte carlo estimate of π in higher  dimension_2023, url={https://drive.google.com/file/d/1cBTBhRYi4TeRCMImsisZA499-7FD6Xpq/view}, booktitle={Conference: 16th International Conference MSAST 2022}, year={2023}, month={Dec} }
 @article{ghosh_chakraborty_chatterjee_2023, title={Revisit to a Prominent Example Of The Curse of Dimensionality: Re-Investigating Anomaly Behaviour of Monte Carlo Estimate of Π In Higher Dimension}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85163576210&partnerID=MN8TOARS}, DOI={10.2139/ssrn.4496375}, abstractNote={The Curse of Dimensionality is responsible for gradually decreasing the probability of obtaining random points generated inside the hyper-sphere relative to the hyper-cube in a higher dimension. This paper presents a rigorous statistical theory behind a popular, nontrivial example of the so-called curse of dimensionality, namely, the anomaly behavior of the Monte Carlo estimation of π using pth dimensional sphere and pth dimensional cube as p → ∞. We illustrate the theory in dimensions p = 2,3,4,5,6,7,8,9,10 by simulation.}, journal={SSRN}, author={Ghosh, P. and Chakraborty, S. and Chatterjee, D.}, year={2023} }
 @inproceedings{ghosh_chakraborty_2023, title={Spectral Classification of Quasar Subject to Redshift: A Statistical Study}, url={https://doi.org/10.3390/IOCMA2023-14418}, DOI={10.3390/IOCMA2023-14418}, abstractNote={Quasars are astronomical star-like objects having a large ultraviolet flux of radiation accompanied by generally broad emission lines and absorption lines in some cases found at large redshift. The used data is extracted from the Veron Cetti Catalogue of AGN and Quasar. The objective of this work is to partition the quasar based on their spectral properties using multivariate techniques and classify them with respect to the obtained clusters. Performing the K-means partitioning method, two robust clusters were obtained with cluster sizes 39,581 and 129,377. The percentage of misclassification observed based on the obtained clusters considering a multivariate classification technique and machine learning classification algorithm, i.e., linear discriminant analysis and XG-Boost, respectively. The linear discriminant analysis and XG-Boost evaluated a misclassification of around 0.84 and 0.15%, respectively. Additionally, a heuristic literature-based categorization subject to redshift yielded an accuracy of around 96 %. This gives us cross-validating arguments about the astronomical data, that machine learning algorithms might perform on par with conventional multivariate techniques, if not better.}, author={Ghosh, Prithwish and Chakraborty, Shinjon}, year={2023}, month={Apr} }
 @inproceedings{ghosh_2023, title={”Revisit to A Prominent Example of The Curse of Dimensionality: Re-investigating Anomaly Behaviour of Monte Carlo Estimate of π in Higher Dimension}, booktitle={17th International Conference MSTAST}, author={Ghosh, P.}, year={2023}, month={Dec} }
 @article{ghosh_2022, title={Breast Cancer Wisconsin (Diagnostic) Prediction}, volume={11}, ISSN={2319-7064}, url={http://dx.doi.org/10.21275/sr22501213650}, DOI={10.21275/sr22501213650}, number={5}, journal={International Journal of Science and Research (IJSR)}, publisher={International Journal of Science and Research}, author={Ghosh, Prithwish}, editor={Ghosh, PrithwishEditor}, year={2022}, month={May}, pages={178–185} }
 @inproceedings{ghosh_chakraboty_2022, title={Classification and Distributional properties of Gamma Ray Bursts}, volume={11}, booktitle={16th International Conference MSAST}, author={Ghosh, P. and Chakraboty, S.}, year={2022}, month={Dec}, pages={148–156} }
 @inproceedings{ghosh_chakraborty_2022, title={Classification and Distributional properties of Gamma Ray Bursts}, url={https://drive.google.com/file/d/1xSQR00CTKG5hfPMDaTbNEwjtpH62r2h4/view?usp=sharing}, booktitle={Conference: 16th International Conference MSAST 2022}, author={Ghosh, Prithwish and Chakraborty, Shinjon}, editor={Ghosh, Prithwish and Chakraborty, ShinjonEditors}, year={2022}, month={Dec} }
 @phdthesis{ghosh_2021, title={A detailed Production scenario on Rice and Wheat in major Crop Production Districts of West Bengal}, school={Indian Statistical Institute, & Sister Nivedita University}, author={Ghosh, P.}, year={2021}, month={Jul} }