| |
Records |
Links |
|
Author  |
Gomez Ambrosio, R.; ter Hoeve, J.; Madigan, M.; Rojo, J.; Sanz, V. |
|
| |
Title |
Unbinned multivariate observables for global SMEFT analyses from machine learning |
Type |
Journal Article |
| |
Year |
2023 |
Publication |
Journal of High Energy Physics |
Abbreviated Journal |
J. High Energy Phys. |
|
| |
Volume |
03 |
Issue |
3 |
Pages |
033 - 66pp |
|
| |
Keywords |
SMEFT; Higgs Properties |
|
| |
Abstract |
Theoretical interpretations of particle physics data, such as the determination of the Wilson coefficients of the Standard Model Effective Field Theory (SMEFT), often involve the inference of multiple parameters from a global dataset. Optimizing such interpretations requires the identification of observables that exhibit the highest possible sensitivity to the underlying theory parameters. In this work we develop a flexible open source frame-work, ML4EFT, enabling the integration of unbinned multivariate observables into global SMEFT fits. As compared to traditional measurements, such observables enhance the sensitivity to the theory parameters by preventing the information loss incurred when binning in a subset of final-state kinematic variables. Our strategy combines machine learning regression and classification techniques to parameterize high-dimensional likelihood ratios, using the Monte Carlo replica method to estimate and propagate methodological uncertainties. As a proof of concept we construct unbinned multivariate observables for top-quark pair and Higgs+Z production at the LHC, demonstrate their impact on the SMEFT parameter space as compared to binned measurements, and study the improved constraints associated to multivariate inputs. Since the number of neural networks to be trained scales quadratically with the number of parameters and can be fully parallelized, the ML4EFT framework is well-suited to construct unbinned multivariate observables which depend on up to tens of EFT coefficients, as required in global fits. |
|
| |
Address |
[Ambrosio, Raquel Gomez] Univ Milano Bicocca, Dipartimento Fis G Occhialini, Piazza Sci 3, I-20126 Milan, Italy, Email: raquel.gomezambrosio@unito.it; |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
Springer |
Place of Publication |
|
Editor |
|
|
| |
Language |
English |
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
1029-8479 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
WOS:000946004000003 |
Approved |
no |
|
| |
Is ISI |
yes |
International Collaboration |
yes |
|
| |
Call Number |
IFIC @ pastor @ |
Serial |
5501 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Hirn, J.; Garcia, J.E.; Montesinos-Navarro, A.; Sanchez-Martin, R.; Sanz, V.; Verdu, M. |
|
| |
Title |
A deep Generative Artificial Intelligence system to predict species coexistence patterns |
Type |
Journal Article |
| |
Year |
2022 |
Publication |
Methods in Ecology and Evolution |
Abbreviated Journal |
Methods Ecol. Evol. |
|
| |
Volume |
13 |
Issue |
|
Pages |
1052-1061 |
|
| |
Keywords |
artificial intelligence; direct interactions; generative adversarial networks; indirect interactions; species coexistence; variational AutoEncoders |
|
| |
Abstract |
Predicting coexistence patterns is a current challenge to understand diversity maintenance, especially in rich communities where these patterns' complexity is magnified through indirect interactions that prevent their approximation with classical experimental approaches. We explore cutting-edge Machine Learning techniques called Generative Artificial Intelligence (GenAI) to predict species coexistence patterns in vegetation patches, training generative adversarial networks (GAN) and variational AutoEncoders (VAE) that are then used to unravel some of the mechanisms behind community assemblage. The GAN accurately reproduces real patches' species composition and plant species' affinity to different soil types, and the VAE also reaches a high level of accuracy, above 99%. Using the artificially generated patches, we found that high-order interactions tend to suppress the positive effects of low-order interactions. Finally, by reconstructing successional trajectories, we could identify the pioneer species with larger potential to generate a high diversity of distinct patches in terms of species composition. Understanding the complexity of species coexistence patterns in diverse ecological communities requires new approaches beyond heuristic rules. Generative Artificial Intelligence can be a powerful tool to this end as it allows to overcome the inherent dimensionality of this challenge. |
|
| |
Address |
[Hirn, Johannes; Enrique Garcia, Jose; Sanz, Veronica] Univ Valencia, CSIC, Inst Fis Corpuscular IFIC, Valencia, Spain, Email: miguel.verdu@ext.uv.es |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
Wiley |
Place of Publication |
|
Editor |
|
|
| |
Language |
English |
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
2041-210x |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
WOS:000765239700001 |
Approved |
no |
|
| |
Is ISI |
yes |
International Collaboration |
yes |
|
| |
Call Number |
IFIC @ pastor @ |
Serial |
5155 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Huang, F.; Sanz, V.; Shu, J.; Xue, X. |
|
| |
Title |
LIGO as a probe of dark sectors |
Type |
Journal Article |
| |
Year |
2021 |
Publication |
Physical Review D |
Abbreviated Journal |
Phys. Rev. D |
|
| |
Volume |
104 |
Issue |
10 |
Pages |
095001 - 9pp |
|
| |
Keywords |
|
|
| |
Abstract |
We show how current LIGO data is able to probe interesting theories beyond the Standard Model, particularly dark sectors where a dark Higgs boson triggers symmetry breaking via a first-order phase transition. We use publicly available LIGO O2 data to illustrate how these sectors, even if disconnected from the Standard Model, can be probed by gravitational wave detectors. We link the LIGO measurements with the model content and mass scale of the dark sector, finding that current O2 data are testing a broad set of scenarios that can be mapped into many different types of dark-sector models where the breaking of SU(N) theories with Nf fermions is triggered by a dark Higgs boson at scales ? similar or equal to 108-109 GeV with reasonable parameters for the scalar potential. |
|
| |
Address |
[Huang, Fei; Shu, Jing; Xue, Xiao] Chinese Acad Sci, Inst Theoret Phys, CAS Key Lab Theoret Phys, Beijing 100190, Peoples R China, Email: huangf4@uci.edu; |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
Amer Physical Soc |
Place of Publication |
|
Editor |
|
|
| |
Language |
English |
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
2470-0010 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
WOS:000716446500001 |
Approved |
no |
|
| |
Is ISI |
yes |
International Collaboration |
yes |
|
| |
Call Number |
IFIC @ pastor @ |
Serial |
5021 |
|
| Permanent link to this record |
| |
|
| |
|
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 |
|
Thesis |
|
|
| |
Publisher |
IOP Publishing Ltd |
Place of Publication |
|
Editor |
|
|
| |
Language |
English |
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
0034-4885 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
WOS:000727698500001 |
Approved |
no |
|
| |
Is ISI |
yes |
International Collaboration |
yes |
|
| |
Call Number |
IFIC @ pastor @ |
Serial |
5039 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Khosa, C.K.; Mars, L.; Richards, J.; Sanz, V. |
|
| |
Title |
Convolutional neural networks for direct detection of dark matter |
Type |
Journal Article |
| |
Year |
2020 |
Publication |
Journal of Physics G |
Abbreviated Journal |
J. Phys. G |
|
| |
Volume |
47 |
Issue |
9 |
Pages |
095201 - 20pp |
|
| |
Keywords |
dark matter; dark matter detection; neural networks; xenon1T; WIMPs |
|
| |
Abstract |
The XENON1T experiment uses a time projection chamber (TPC) with liquid xenon to search for weakly interacting massive particles (WIMPs), a proposed dark matter particle, via direct detection. As this experiment relies on capturing rare events, the focus is on achieving a high recall of WIMP events. Hence the ability to distinguish between WIMP and the background is extremely important. To accomplish this, we suggest using convolutional neural networks (CNNs); a machine learning procedure mainly used in image recognition tasks. To explore this technique we use XENON collaboration open-source software to simulate the TPC graphical output of dark matter signals and main backgrounds. A CNN turns out to be a suitable tool for this purpose, as it can identify features in the images that differentiate the two types of events without the need to manipulate or remove data in order to focus on a particular region of the detector. We find that the CNN can distinguish between the dominant background events (ER) and 500 GeV WIMP events with a recall of 93.4%, precision of 81.2% and an accuracy of 87.2%. |
|
| |
Address |
[Khosa, Charanjit K.; Mars, Lucy; Richards, Joel; Sanz, Veronica] Univ Sussex, Dept Phys & Astron, Brighton BN1 9QH, E Sussex, England, Email: charanjit.kaur@sussex.ac.uk; |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
Iop Publishing Ltd |
Place of Publication |
|
Editor |
|
|
| |
Language |
English |
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
0954-3899 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
WOS:000555607800001 |
Approved |
no |
|
| |
Is ISI |
yes |
International Collaboration |
yes |
|
| |
Call Number |
IFIC @ pastor @ |
Serial |
4485 |
|
| Permanent link to this record |
