MODE documents are assigned an inmutable MODE Note Number, under the scheme MODE_(TYP)_XXX.
XXXis a progressive inmutable global identifierTYPis the category, which can change in time according to the publication status. The available categories are (BOO: book, PUB: published in journal, PRE: preprint, PRO: published proceedings, THE: thesis, INT: internal, OTH: other) forbook, publication on journal, preprint, proceeding, thesis, internal, other
Documents are added to the table below, in reverse chronological order (more recent on top)
For documents with no available link, you can upload the document to our material area and link it here using the syntax [pdf](./material/blah.pdf)
For INT (internal) documents, you can upload the document to our internal area and link it here using the syntax [pdf](https://github.com/mode-collaboration/documents/internal/blah.pdf)
| Note ID | Author(s) | Title | Type | Date | Reference (if available) | Link to document (optional) |
|---|---|---|---|---|---|---|
| MODE_THE_008 | Lukas Layer | Inference Aware Neural Optimization for Top Pair Cross-Section Measurements with CMS Open Data | THE (PhD) | t.b.d. | - | - |
| MODE_PRE_007 | T. Dorigo, S. Guglielmini, J. Kieseler, L. Layer, G.C. Strong | Deep Regression of Muon Energy with a K-Nearest Neighbor Algorithm | PRE | 2022-03-06 | arXiv:2203.02841 | arXiv:2203.02841 |
| MODE_THE_006 | Benedetta dal Sasso | Classificazione robusta agli errori sistematici in applicazioni alla fisica delle particelle | THE (master's) | 2022-02 | - | |
| MODE_PUB_005 | C. Neubüser, Jan Kieseler, Paul Lujan | Optimising longitudinal and lateral calorimeter granularity for software compensation in hadronic showers using deep neural networks | PUB | 2022-01-29 | Eur. Phys. J. C (2022) 82: 92 (2022) | doi:10.1140/epjc/s10052-022-10031-7 |
| MODE_PUB_004 | J. Kieseler, G.C. Strong, F. Chiandotto, T. Dorigo, L. Layer | Calorimetric Measurement of Multi-TeV Muons via Deep Regression | PUB | 2022-01-27 | Eur. Phys. J. C (2022) 82: 79 | doi:10.1140/epjc/s10052-022-09993-5 |
| MODE_PUB_003 | AMVA4NewPhysics authors (A. Stakia, T. Dorigo, G. Banelli, D. Bortoletto, A. Casa, P. de Castro Manzano, C. Delaere, J. Donini, L. Finos, M. Gallinaro, A. Giammanco, A. Held, F. Jiménez Morales, G. Kotkowski, S-P Liew, F. Maltoni, G. Menardi, I. Papavergou, A. Saggio, B. Scarpa, G.C. Strong, C. Tosciri, J. Varela, P. Vischia, A. Weiler) | Advances in Multi-Variate Analysis Methods for New Physics Searches at the Large Hadron Collider | PUB | 2021-12 | Rev. Phys. 7 (2021) 100063 | arXiv:2105.07530 |
| MODE_PRE_002 | T. Dorigo, P. de Castro Manzano | Dealing with Nuisance Parameters using Machine Learning in High Energy Physics: a Review | BOO | 2020-07-17 | arXiv:2007.09121 | arXiv:2007.09121 |
| MODE_PUB_001 | The MODE Collaboration (A.G. Baydin, K. Cranmer, P. de Castro Manzano, C. Delaere, D. Derkach, J. Donini, T. Dorigo, A. Giammanco, J. Kieseler, L. Layer, G. Louppe, F. Ratnikov, G.C. Strong, M. Tosi, A. Ustyuzhanin, P. Vischia, H. Yarar) | Toward Machine Learning Optimization of Experimental Design | PUB | 2021-03-30 | Nuclear Physics News Internationa 31, 1 (2021) | doi:10.1080/10619127.2021.1881364 |