M. Paganini

10.0k total citations · 1 hit paper
9 papers, 424 citations indexed

About

M. Paganini is a scholar working on Nuclear and High Energy Physics, Computer Vision and Pattern Recognition and Artificial Intelligence. According to data from OpenAlex, M. Paganini has authored 9 papers receiving a total of 424 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Nuclear and High Energy Physics, 2 papers in Computer Vision and Pattern Recognition and 2 papers in Artificial Intelligence. Recurrent topics in M. Paganini's work include Particle physics theoretical and experimental studies (3 papers), High-Energy Particle Collisions Research (3 papers) and Astrophysics and Cosmic Phenomena (3 papers). M. Paganini is often cited by papers focused on Particle physics theoretical and experimental studies (3 papers), High-Energy Particle Collisions Research (3 papers) and Astrophysics and Cosmic Phenomena (3 papers). M. Paganini collaborates with scholars based in United States, Switzerland and Belgium. M. Paganini's co-authors include Luke de Oliveira, Benjamin Nachman, Yuandong Tian, Ari S. Morcos, Haonan Yu, W. Legros, André Nicolet, Daniel Guest, J. W. Smith and M. Kagan and has published in prestigious journals such as Physical Review Letters, IEEE Transactions on Magnetics and Physical review. D.

In The Last Decade

M. Paganini

7 papers receiving 419 citations

Hit Papers

align trajectories sort by overperformance

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

CaloGAN: Simulating 3D high energy particle showers in multilayer electromagnetic calorimeters with generative adversarial networks

2018 195 citations M. Paganini, Luke de Oliveira et al. Physical review. D profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
M. Paganini United States	5	269	140	82	41	31	9	424
Luke de Oliveira United States	6	353 1.3×	157 1.1×	70 0.9×	45 1.1×	42 1.4×	7	501
V. M. Mikuni United States	13	325 1.2×	166 1.2×	35 0.4×	28 0.7×	25 0.8×	27	420
S. Vallecorsa Switzerland	15	148 0.6×	380 2.7×	59 0.7×	48 1.2×	17 0.5×	58	569
Frank Gaede Germany	12	428 1.6×	99 0.7×	45 0.5×	31 0.8×	17 0.5×	39	526
Johann Brehmer United States	12	454 1.7×	149 1.1×	24 0.3×	24 0.6×	19 0.6×	23	600
J. Seixas Switzerland	14	356 1.3×	50 0.4×	21 0.3×	28 0.7×	14 0.5×	66	556
R. Frühwirth Austria	6	284 1.1×	73 0.5×	28 0.3×	26 0.6×	50 1.6×	12	419
Patrick Komiske United States	13	568 2.1×	179 1.3×	31 0.4×	25 0.6×	50 1.6×	18	655
Anja Butter Germany	14	445 1.7×	149 1.1×	20 0.2×	29 0.7×	16 0.5×	24	517
Eric Metodiev United States	10	436 1.6×	248 1.8×	17 0.2×	18 0.4×	39 1.3×	18	606

Countries citing papers authored by M. Paganini

Since Specialization

Citations

This map shows the geographic impact of M. Paganini'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 M. Paganini with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites M. Paganini more than expected).

Fields of papers citing papers by M. Paganini

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by M. Paganini. 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 M. Paganini. The network helps show where M. Paganini may publish in the future.

Co-authorship network of co-authors of M. Paganini

This figure shows the co-authorship network connecting the top 25 collaborators of M. Paganini. A scholar is included among the top collaborators of M. Paganini 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 M. Paganini. M. Paganini is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

9 of 9 papers shown

1.

Morcos, Ari S., Haonan Yu, M. Paganini, & Yuandong Tian. (2019). One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers. Neural Information Processing Systems. 32. 4932–4942. 17 indexed citations

2.

Guest, Daniel, et al.. (2019). lwtnn/lwtnn: Version 2.8.1. Figshare.

3.

Oliveira, Luke de, Benjamin Nachman, & M. Paganini. (2019). Electromagnetic showers beyond shower shapes. Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment. 951. 162879–162879. 21 indexed citations

4.

Paganini, M., Luke de Oliveira, & Benjamin Nachman. (2018). Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multilayer Calorimeters. Physical Review Letters. 120(4). 42003–42003. 142 indexed citations

5.

Oliveira, Luke de, M. Paganini, & Benjamin Nachman. (2018). Controlling Physical Attributes in GAN-Accelerated Simulation of Electromagnetic Calorimeters. Journal of Physics Conference Series. 1085. 42017–42017. 44 indexed citations

6.

Paganini, M., Luke de Oliveira, & Benjamin Nachman. (2018). CaloGAN: Simulating 3D high energy particle showers in multilayer electromagnetic calorimeters with generative adversarial networks. Physical review. D. 97(1). 195 indexed citations breakdown →

7.

Oliveira, Luke de & M. Paganini. (2017). lukedeo/adversarial-jets: Initial Release. Zenodo (CERN European Organization for Nuclear Research).

8.

Paganini, M.. (2017). Electromagnetic Calorimeter Shower Images with Variable Incidence Angle and Position. Data Archiving and Networked Services (DANS). 2. 2 indexed citations

9.

Legros, W., André Nicolet, & M. Paganini. (1989). Numerical modelization of transient current in relay. IEEE Transactions on Magnetics. 25(5). 3593–3595. 3 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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