| Records |
| Author |
Kasieczka, G. et al; Sanz, V. |
| Title |
The LHC Olympics 2020: a community challenge for anomaly detection in high energy physics |
Type |
Journal Article |
| Year |
2021 |
Publication |
Reports on Progress in Physics |
Abbreviated Journal |
Rep. Prog. Phys. |
| Volume |
84 |
Issue |
12 |
Pages |
124201 - 64pp |
| Keywords |
anomaly detection; machine learning; unsupervised learning; weakly supervised learning; semisupervised learning; beyond the standard model; model-agnostic methods |
Abstract  |
A new paradigm for data-driven, model-agnostic new physics searches at colliders is emerging, and aims to leverage recent breakthroughs in anomaly detection and machine learning. In order to develop and benchmark new anomaly detection methods within this framework, it is essential to have standard datasets. To this end, we have created the LHC Olympics 2020, a community challenge accompanied by a set of simulated collider events. Participants in these Olympics have developed their methods using an R&D dataset and then tested them on black boxes: datasets with an unknown anomaly (or not). Methods made use of modern machine learning tools and were based on unsupervised learning (autoencoders, generative adversarial networks, normalizing flows), weakly supervised learning, and semi-supervised learning. This paper will review the LHC Olympics 2020 challenge, including an overview of the competition, a description of methods deployed in the competition, lessons learned from the experience, and implications for data analyses with future datasets as well as future colliders. |
| Address |
[Kasieczka, Gregor] Univ Hamburg, Inst Expt Phys, Hamburg, Germany, Email: gregor.kasieczka@uni-hamburg.de; |
| Corporate Author |
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Thesis |
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| Publisher |
IOP Publishing Ltd |
Place of Publication |
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Editor |
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| Language |
English |
Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
0034-4885 |
ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
WOS:000727698500001 |
Approved |
no |
| Is ISI |
yes |
International Collaboration |
yes |
| Call Number |
IFIC @ pastor @ |
Serial |
5039 |
| Permanent link to this record |
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| Author |
Bonilla, J. et al; Vos, M. |
| Title |
Jets and Jet Substructure at Future Colliders |
Type |
Journal Article |
| Year |
2022 |
Publication |
Frontiers in Physics |
Abbreviated Journal |
Front. Physics |
| Volume |
10 |
Issue |
|
Pages |
897719 - 17pp |
| Keywords |
jets; jet substructure; collider; artificial intelligence; machine learning; snowmass; top quark; Higgs boson |
| Abstract |
Even though jet substructure was not an original design consideration for the Large Hadron Collider (LHC) experiments, it has emerged as an essential tool for the current physics program. We examine the role of jet substructure on the motivation for and design of future energy Frontier colliders. In particular, we discuss the need for a vibrant theory and experimental research and development program to extend jet substructure physics into the new regimes probed by future colliders. Jet substructure has organically evolved with a close connection between theorists and experimentalists and has catalyzed exciting innovations in both communities. We expect such developments will play an important role in the future energy Frontier physics program. |
| Address |
[Bonilla, Johan; Erbacher, Robin] Univ Calif, Dept Phys & Astron, Davis, CA USA, Email: bpnachman@lbl.gov; |
| Corporate Author |
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Thesis |
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| Publisher |
Frontiers Media Sa |
Place of Publication |
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Editor |
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| Language |
English |
Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
2296-424x |
ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
WOS:000822618100001 |
Approved |
no |
| Is ISI |
yes |
International Collaboration |
yes |
| Call Number |
IFIC @ pastor @ |
Serial |
5464 |
| Permanent link to this record |
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| Author |
Folgado, M.G.; Sanz, V. |
| Title |
Exploring the political pulse of a country using data science tools |
Type |
Journal Article |
| Year |
2022 |
Publication |
Journal of Computational Social Science |
Abbreviated Journal |
J. Comput. Soc. Sci. |
| Volume |
5 |
Issue |
|
Pages |
987-1000 |
| Keywords |
Politics; Spain; Sentiment analysis; Artificial Intelligence; Machine learning; Neural networks; Natural Language Processing (NLP) |
| Abstract |
In this paper we illustrate the use of Data Science techniques to analyse complex human communication. In particular, we consider tweets from leaders of political parties as a dynamical proxy to political programmes and ideas. We also study the temporal evolution of their contents as a reaction to specific events. We analyse levels of positive and negative sentiment in the tweets using new tools adapted to social media. We also train a Fully-Connected Neural Network (FCNN) to recognise the political affiliation of a tweet. The FCNN is able to predict the origin of the tweet with a precision in the range of 71-75%, and the political leaning (left or right) with a precision of around 90%. This study is meant to be viewed as an example of how to use Twitter data and different types of Data Science tools for a political analysis. |
| Address |
[Folgado, Miguel G.; Sanz, Veronica] Univ Valencia, Inst Fis Corpuscular IFIC, CSIC, Valencia 46980, Spain, Email: migarfol@upvnet.upv.es; |
| Corporate Author |
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Thesis |
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| Publisher |
Springernature |
Place of Publication |
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Editor |
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| Language |
English |
Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
2432-2717 |
ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
WOS:000742263500002 |
Approved |
no |
| Is ISI |
yes |
International Collaboration |
yes |
| Call Number |
IFIC @ pastor @ |
Serial |
5077 |
| Permanent link to this record |
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| Author |
Blanes-Selva, V.; Ruiz-Garcia, V.; Tortajada, S.; Benedi, J.M.; Valdivieso, B.; Garcia-Gomez, J.M. |
| Title |
Design of 1-year mortality forecast at hospital admission: A machine learning approach |
Type |
Journal Article |
| Year |
2021 |
Publication |
Health Informatics Journal |
Abbreviated Journal |
Health Inform. J. |
| Volume |
27 |
Issue |
1 |
Pages |
13pp |
| Keywords |
machine learning; palliative care; hospital admission data; mortality forecast |
| Abstract |
Palliative care is referred to a set of programs for patients that suffer life-limiting illnesses. These programs aim to maximize the quality of life (QoL) for the last stage of life. They are currently based on clinical evaluation of the risk of 1-year mortality. The main aim of this work is to develop and validate machine-learning-based models to predict the exitus of a patient within the next year using data gathered at hospital admission. Five machine-learning techniques were applied using a retrospective dataset. The evaluation was performed with five metrics computed by a resampling strategy: Accuracy, the area under the ROC curve, Specificity, Sensitivity, and the Balanced Error Rate. All models reported an AUC ROC from 0.857 to 0.91. Specifically, Gradient Boosting Classifier was the best model, producing an AUC ROC of 0.91, a sensitivity of 0.858, a specificity of 0.808, and a BER of 0.1687. Information from standard procedures at hospital admission combined with machine learning techniques produced models with competitive discriminative power. Our models reach the best results reported in the state of the art. These results demonstrate that they can be used as an accurate data-driven palliative care criteria inclusion. |
| Address |
[Blanes-Selva, Vicent; Benedi, Jose-Miguel; Garcia-Gomez, Juan M.] Univ Politecn Valencia, Valencia, Spain, Email: viblasel@upv.es |
| Corporate Author |
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Thesis |
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| Publisher |
Sage Publications Inc |
Place of Publication |
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Editor |
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| Language |
English |
Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
1460-4582 |
ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
WOS:000645567000008 |
Approved |
no |
| Is ISI |
yes |
International Collaboration |
no |
| Call Number |
IFIC @ pastor @ |
Serial |
5182 |
| Permanent link to this record |
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| Author |
Caron, S.; Eckner, C.; Hendriks, L.; Johannesson, G.; Ruiz de Austri, R.; Zaharijas, G. |
| Title |
Mind the gap: the discrepancy between simulation and reality drives interpretations of the Galactic Center Excess |
Type |
Journal Article |
| Year |
2023 |
Publication |
Journal of Cosmology and Astroparticle Physics |
Abbreviated Journal |
J. Cosmol. Astropart. Phys. |
| Volume |
06 |
Issue |
6 |
Pages |
013 - 56pp |
| Keywords |
dark matter simulations; gamma ray experiments; Machine learning; millisecond pulsars |
| Abstract |
The Galactic Center Excess (GCE) in GeV gamma rays has been debated for over a decade, with the possibility that it might be due to dark matter annihilation or undetected point sources such as millisecond pulsars (MSPs). This study investigates how the gamma-ray emission model (-yEM) used in Galactic center analyses affects the interpretation of the GCE's nature. To address this issue, we construct an ultra-fast and powerful inference pipeline based on convolutional Deep Ensemble Networks. We explore the two main competing hypotheses for the GCE using a set of-yEMs with increasing parametric freedom. We calculate the fractional contribution (fsrc) of a dim population of MSPs to the total luminosity of the GCE and analyze its dependence on the complexity of the ryEM. For the simplest ryEM, we obtain fsrc = 0.10 f 0.07, while the most complex model yields fsrc = 0.79 f 0.24. In conclusion, we find that the statement about the nature of the GCE (dark matter or not) strongly depends on the assumed ryEM. The quoted results for fsrc do not account for the additional uncertainty arising from the fact that the observed gamma-ray sky is out-of-distribution concerning the investigated ryEM iterations. We quantify the reality gap between our ryEMs using deep-learning-based One-Class Deep Support Vector Data Description networks, revealing that all employed ryEMs have gaps to reality. Our study casts doubt on the validity of previous conclusions regarding the GCE and dark matter, and underscores the urgent need to account for the reality gap and consider previously overlooked “out of domain” uncertainties in future interpretations. |
| Address |
[Caron, Sascha; Hendriks, Luc] Radboud Univ Nijmegen, Theoret High Energy Phys, Heyendaalseweg 135, NL-6525 AJ Nijmegen, Netherlands, Email: scaron@nikhef.nl; |
| Corporate Author |
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Thesis |
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| Publisher |
IOP Publishing Ltd |
Place of Publication |
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Editor |
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| Language |
English |
Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
1475-7516 |
ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
WOS:001025516000009 |
Approved |
no |
| Is ISI |
yes |
International Collaboration |
yes |
| Call Number |
IFIC @ pastor @ |
Serial |
5576 |
| Permanent link to this record |
