L. Whitehead

11.9k total citations
11 papers, 107 citations indexed

About

L. Whitehead is a scholar working on Nuclear and High Energy Physics, Computer Vision and Pattern Recognition and Ecology. According to data from OpenAlex, L. Whitehead has authored 11 papers receiving a total of 107 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Nuclear and High Energy Physics, 1 paper in Computer Vision and Pattern Recognition and 1 paper in Ecology. Recurrent topics in L. Whitehead's work include Particle physics theoretical and experimental studies (8 papers), Neutrino Physics Research (6 papers) and Astrophysics and Cosmic Phenomena (5 papers). L. Whitehead is often cited by papers focused on Particle physics theoretical and experimental studies (8 papers), Neutrino Physics Research (6 papers) and Astrophysics and Cosmic Phenomena (5 papers). L. Whitehead collaborates with scholars based in United Kingdom, United States and Switzerland. L. Whitehead's co-authors include S. Alonso Monsalve, Jong-Chul Park, Doojin Kim, Seodong Shin, Z. Ghorbanimoghaddam, A. De Roeck, J. S. Yu, A. Chatterjee, M. Bass and B. Lundberg and has published in prestigious journals such as Nuclear Physics B, Journal of High Energy Physics and IEEE Transactions on Neural Networks and Learning Systems.

In The Last Decade

L. Whitehead

10 papers receiving 104 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
L. Whitehead United Kingdom	7	90	16	8	5	5	11	107
T. Lohse Germany	4	102 1.1×	24 1.5×	7 0.9×	7 1.4×	3 0.6×	13	119
G. Schott Netherlands	3	61 0.7×	8 0.5×	14 1.8×	2 0.4×	3 0.6×	4	80
S. Söldner‐Rembold United Kingdom	6	119 1.3×	15 0.9×	7 0.9×	4 0.8×	5 1.0×	20	128
Pierluca Sangiorgi Italy	5	43 0.5×	16 1.0×	3 0.4×	3 0.6×	3 0.6×	24	61
D. P. Yallup United Kingdom	4	74 0.8×	28 1.8×	15 1.9×	4 0.8×	2 0.4×	8	96
A. Coccaro Italy	5	66 0.7×	5 0.3×	12 1.5×	5 1.0×	2 0.4×	7	74
M. Kado Italy	6	130 1.4×	27 1.7×	21 2.6×	5 1.0×	2 0.4×	9	137
E. Gross Israel	6	73 0.8×	10 0.6×	29 3.6×	5 1.0×	2 0.4×	19	93
J. Liao China	5	181 2.0×	27 1.7×	9 1.1×	2 0.4×	5 1.0×	8	185
B. Meirose Sweden	5	95 1.1×	13 0.8×	7 0.9×		4 0.8×	16	100

Countries citing papers authored by L. Whitehead

Since Specialization

Citations

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

Fields of papers citing papers by L. Whitehead

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of L. Whitehead

This figure shows the co-authorship network connecting the top 25 collaborators of L. Whitehead. A scholar is included among the top collaborators of L. Whitehead 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 L. Whitehead. L. Whitehead 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:

11 of 11 papers shown

1.

Whitehead, L., et al.. (2025). High-wavenumber Raman spectroscopy for the detection of Mycobacterium tuberculosis in saliva. Sensors & Diagnostics. 4(12). 1091–1102.

2.

Cesar, J. P., et al.. (2023). Neutrino characterisation using convolutional neural networks in CHIPS water Cherenkov detectors. Journal of Instrumentation. 18(6). P06032–P06032. 2 indexed citations

3.

Whitehead, L., et al.. (2022). Application of transfer learning to neutrino interaction classification. The European Physical Journal C. 82(12). 7 indexed citations

4.

Monsalve, S. Alonso, C. Jesús-Valls, T. Lux, et al.. (2021). Graph neural network for 3D classification of ambiguities and optical crosstalk in scintillator-based neutrino detectors. Physical review. D. 103(3). 6 indexed citations

5.

Roeck, A. De, Doojin Kim, Z. Ghorbanimoghaddam, et al.. (2020). Probing energetic light dark matter with multi-particle tracks signatures at DUNE. Journal of High Energy Physics. 2020(11). 13 indexed citations

6.

Monsalve, S. Alonso & L. Whitehead. (2020). Image-Based Model Parameter Optimization Using Model-Assisted Generative Adversarial Networks. IEEE Transactions on Neural Networks and Learning Systems. 31(12). 5645–5650. 19 indexed citations

7.

Chatterjee, A., A. De Roeck, Doojin Kim, et al.. (2018). Searching for boosted dark matter at ProtoDUNE. Physical review. D. 98(7). 22 indexed citations

8.

Chatterjee, A., Seodong Shin, Doojin Kim, et al.. (2018). Search for Boosted Dark Matter at ProtoDUNE : arXiv. 1 indexed citations

9.

Whitehead, L.. (2016). Neutrino oscillations with MINOS and MINOS+. Nuclear Physics B. 908. 130–150. 9 indexed citations

10.

Bass, M., M. Bishai, D. Cherdack, et al.. (2015). Baseline optimization for the measurement ofCPviolation, mass hierarchy, andθ23octant in a long-baseline neutrino oscillation experiment. Physical review. D. Particles, fields, gravitation, and cosmology. 91(5). 27 indexed citations

11.

Fortin, Ernest L., et al.. (1979). ROP volume 41 issue 3 Cover and Front matter. The Review of Politics. 41(3). f1–f4. 1 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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