M. Bachtis
Impact in
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- Particle Detector Development and Performance
- Particle physics theoretical and experimental studies
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- Embedded Systems Design Techniques
Papers in
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- Particle Detector Development and Performance 4
- Particle physics theoretical and experimental studies 4
- Dark Matter and Cosmic Phenomena 1
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- Distributed and Parallel Computing Systems 1
- Co-authors
- T. Alexopoulos (1 shared paper)G. Tsipolitis (1 shared paper)E. N. Gazis (1 shared paper)Katherine Compton (2 shared papers)W. H. Smith (3 shared papers)Michael Schulte (2 shared papers)Amin Farmahini-Farahani (2 shared papers)Sridhara Dasu (3 shared papers)
- Journals
- Journal of Instrumentation (1 paper)Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment (1 paper)CERN Bulletin (1 paper)CERN Document Server (European Organization for Nuclear Research) (1 paper)
- Partner nations
- United StatesGreeceSwitzerland
In The Last Decade
M. Bachtis
5 papers receiving 18 citations
Peers
Comparison fields: 5 of 12
- Nuclear and High Energy Physics 9
- Hardware and Architecture 4
- Media Technology 2
- Radiation 2
- Computer Vision and Pattern Recognition 3
Countries citing papers authored by M. Bachtis
This map shows the geographic impact of M. Bachtis'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. Bachtis with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites M. Bachtis more than expected).
Fields of papers citing papers by M. Bachtis
This network shows the impact of papers produced by M. Bachtis. 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. Bachtis. The network helps show where M. Bachtis may publish in the future.
Co-authors
The 21 scholars most cited alongside M. Bachtis, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2008 | 8 | |
| 2 | 2009 | 7 | |
| 3 | 2019 | 1 | |
| 4 | 2009 | 1 | |
| 5 | 2012 | 1 |
About M. Bachtis
M. Bachtis is a scholar working on Nuclear and High Energy Physics, Computer Networks and Communications, Computer Vision and Pattern Recognition, Radiation and Artificial Intelligence, having authored 5 papers that have together received 18 indexed citations. Recurring topics across this work include Particle Detector Development and Performance (4 papers), Particle physics theoretical and experimental studies (4 papers), Dark Matter and Cosmic Phenomena (1 paper), Radiation Detection and Scintillator Technologies (1 paper), Computational Physics and Python Applications (1 paper), Image and Object Detection Techniques (1 paper), Image Processing and 3D Reconstruction (1 paper) and Distributed and Parallel Computing Systems (1 paper). The work is most often cited by research in Nuclear and High Energy Physics (9 citations), Hardware and Architecture (4 citations), Media Technology (2 citations), Radiation (2 citations) and Computer Vision and Pattern Recognition (3 citations). M. Bachtis has collaborated with scholars based in United States, Greece and Switzerland. Frequent co-authors include T. Alexopoulos, G. Tsipolitis, E. N. Gazis, Katherine Compton, W. H. Smith, Michael Schulte, Amin Farmahini-Farahani, Sridhara Dasu, M. Grothe and M. Weinberg. Their work appears in journals such as Journal of Instrumentation, Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment, CERN Bulletin and CERN Document Server (European Organization for Nuclear Research).
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.