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Author (up) 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 (up) Hirn, J.; Sanz, V.; Garcia Navarro, J.E.; Goberna, M.; Montesinos-Navarro, A.; Navarro-Cano, J.A.; Sanchez-Martin, R.; Valiente-Banuet, A.; Verdu, M.
Title Transfer learning of species co-occurrence patterns between plant communities Type Journal Article
Year 2024 Publication Ecological Informatics Abbreviated Journal Ecol. Inform.
Volume 83 Issue Pages 102826 - 8pp
Keywords Generative artificial intelligence; Patchy vegetation; Plant communities; Restoration ecology; Species co-occurrence; Variational autoencoders
Abstract Aim: The use of neural networks (NNs) is spreading to all areas of life, and Ecology is no exception. However, the data-hungry nature of NNs can leave out many small, valuable datasets. Here we show how to apply transfer learning to rescue small datasets that can be invaluable in understanding patterns of species co-occurrence. Location: Semiarid plant communities in Spain and Me<acute accent>xico. Time period: 2016-2022. Major taxa studied: Angiosperms. Methods: Based on a large sample of plant species co-occurrence in vegetation patches in a semi-arid area of eastern Spain, we fit a generative artificial intelligence (AI) model that correctly reproduces which species live with which in these patches. Subsequently, we train the same type of model on two communities for which we only have smaller datasets (another semi-arid community in eastern Spain, and a tropical community in Mexico). Results: When we transfer the knowledge learnt from the large dataset directly to the other two, the predictions improve for the community more similar to our reference one. As for the more dissimilar community, improving the accuracy of the transfer requires a further tuning of the model to the local data. In particular, the knowledge transferred relates primarily to species frequency and, to a lesser extent, to their phylogenetic relationships, which are known to be determinants of species interaction patterns. Main conclusions: This AI-based approach can be performed for communities similar or not so similar to the reference community, opening the door to systematic transfer learning for accurate predictions on small datasets. Interestingly, this transfer operates by matching unrelated species between the origin and target datasets, implying that arbitrary datasets can then be transferred to, or even combined in order to augment each other, irrespective of the species involved, potentially allowing such models to be applied to a wide range of plant communities in different climates.
Address [Hirn, Johannes; Montesinos-Navarro, Alicia; Sanchez-Martin, Ricardo; Verdu, Miguel] Univ Valencia Generalitat Valenciana, Ctr Invest Desertificac CIDE, CSIC, Valencia, Spain
Corporate Author Thesis
Publisher Elsevier Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1574-9541 ISBN Medium
Area Expedition Conference
Notes WOS:001327519900001 Approved no
Is ISI yes International Collaboration yes
Call Number IFIC @ pastor @ Serial 6278
Permanent link to this record
 
 
Author (up) 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
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Author (up) 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 (up) 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
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Author (up) Khosa, C.K.; Sanz, V.
Title On the Impact of the LHC Run 2 Data on General Composite Higgs Scenarios Type Journal Article
Year 2022 Publication Advances in High Energy Physics Abbreviated Journal Adv. High. Energy Phys.
Volume 2022 Issue Pages 8970837 - 13pp
Keywords
Abstract We study the impact of Run 2 LHC data on general composite Higgs scenarios, where nonlinear effects, mixing with additional scalars, and new fermionic degrees of freedom could simultaneously contribute to the modification of Higgs properties. We obtain new experimental limits on the scale of compositeness, the mixing with singlets and doublets with the Higgs, and the mass and mixing angle of top-partners. We also show that for scenarios where new fermionic degrees of freedom are involved in electroweak symmetry breaking, there is an interesting interplay among Higgs coupling measurements, boosted Higgs properties, SMEFT global analyses, and direct searches for single and double production of vector-like quarks.
Address [Khosa, Charanjit K.] Univ Genoa, Dipartimento Fis, Via Dodecaneso 33, I-16146 Genoa, Italy, Email: khosacharanjit@gmail.com;
Corporate Author Thesis
Publisher Hindawi Ltd Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1687-7357 ISBN Medium
Area Expedition Conference
Notes WOS:000766325700001 Approved no
Is ISI yes International Collaboration yes
Call Number IFIC @ pastor @ Serial 5153
Permanent link to this record
 
 
Author (up) Khosa, C.K.; Sanz, V.
Title Anomaly Awareness Type Journal Article
Year 2023 Publication Scipost Physics Abbreviated Journal SciPost Phys.
Volume 15 Issue 2 Pages 053 - 24pp
Keywords
Abstract We present a new algorithm for anomaly detection called Anomaly Awareness. The algorithm learns about normal events while being made aware of the anomalies through a modification of the cost function. We show how this method works in different Particle Physics situations and in standard Computer Vision tasks. For example, we apply the method to images from a Fat Jet topology generated by Standard Model Top and QCD events, and test it against an array of new physics scenarios, including Higgs production with EFT effects and resonances decaying into two, three or four subjets. We find that the algorithm is effective identifying anomalies not seen before, and becomes robust as we make it aware of a varied-enough set of anomalies.
Address [Khosa, Charanjit K.] Univ Manchester, Dept Phys & Astron, Manchester M13 9PL, England
Corporate Author Thesis
Publisher Scipost Foundation Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2542-4653 ISBN Medium
Area Expedition Conference
Notes WOS:001048488200002 Approved no
Is ISI yes International Collaboration yes
Call Number IFIC @ pastor @ Serial 5610
Permanent link to this record
 
 
Author (up) Khosa, C.K.; Sanz, V.; Soughton, M.
Title Using machine learning to disentangle LHC signatures of Dark Matter candidates Type Journal Article
Year 2021 Publication Scipost Physics Abbreviated Journal SciPost Phys.
Volume 10 Issue 6 Pages 151 - 26pp
Keywords
Abstract We study the prospects of characterising Dark Matter at colliders using Machine Learning (ML) techniques. We focus on the monojet and missing transverse energy (MET) channel and propose a set of benchmark models for the study: a typical WIMP Dark Matter candidate in the form of a SUSY neutralino, a pseudo-Goldstone impostor in the shape of an Axion-Like Particle, and a light Dark Matter impostor whose interactions are mediated by a heavy particle. All these benchmarks are tensioned against each other, and against the main SM background (Z+jets). Our analysis uses both the leading-order kinematic features as well as the information of an additional hard jet. We explore different representations of the data, from a simple event data sample with values of kinematic variables fed into a Logistic Regression algorithm or a Fully Connected Neural Network, to a transformation of the data into images related to probability distributions, fed to Deep and Convolutional Neural Networks. We also study the robustness of our method against including detector effects, dropping kinematic variables, or changing the number of events per image. In the case of signals with more combinatorial possibilities (events with more than one hard jet), the most crucial data features are selected by performing a Principal Component Analysis. We compare the performance of all these methods, and find that using the 2D images of the combined information of multiple events significantly improves the discrimination performance.
Address [Khosa, Charanjit Kaur; Sanz, Veronica; Soughton, Michael] Univ Sussex, Dept Phys & Astron, Brighton BN1 9QH, E Sussex, England, Email: Charanjit.Kaur@sussex.ac.uk;
Corporate Author Thesis
Publisher Scipost Foundation Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2542-4653 ISBN Medium
Area Expedition Conference
Notes WOS:000680038800002 Approved no
Is ISI yes International Collaboration yes
Call Number IFIC @ pastor @ Serial 4927
Permanent link to this record
 
 
Author (up) Khosa, C.K.; Sanz, V.; Soughton, M.
Title A simple guide from machine learning outputs to statistical criteria in particle physics Type Journal Article
Year 2022 Publication Scipost Physics Core Abbreviated Journal SciPost Phys. Core
Volume 5 Issue 4 Pages 050 - 31pp
Keywords
Abstract In this paper we propose ways to incorporate Machine Learning training outputs into a study of statistical significance. We describe these methods in supervised classification tasks using a CNN and a DNN output, and unsupervised learning based on a VAE. As use cases, we consider two physical situations where Machine Learning are often used: high-pT hadronic activity, and boosted Higgs in association with a massive vector boson.
Address [Khosa, Charanjit Kaur] Univ Bristol, HH Wills Phys Lab, Tyndall Ave, Bristol BS8 1TL, Avon, England, Email: Charanjit.Kaur@bristol.ac.uk;
Corporate Author Thesis
Publisher Scipost Foundation Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes WOS:000929724800002 Approved no
Is ISI yes International Collaboration yes
Call Number IFIC @ pastor @ Serial 5475
Permanent link to this record
 
 
Author (up) Lessa, A.; Sanz, V.
Title Going beyond Top EFT Type Journal Article
Year 2024 Publication Journal of High Energy Physics Abbreviated Journal J. High Energy Phys.
Volume 04 Issue 4 Pages 107 - 29pp
Keywords SMEFT; Dark Matter at Colliders; Supersymmetry
Abstract We present a new way to interpret Top Standard Model measurements going beyond the SMEFT framework. Instead of the usual paradigm in Top EFT, where the main effects come from tails in momenta distributions, we propose an interpretation in terms of new physics which only shows up at loop-level. The effects of these new states, which can be lighter than required within the SMEFT, appear as distinctive structures at high momenta, but may be suppressed at the tails of distributions. As an illustration of this phenomena, we present the explicit case of a UV model with a Z \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ \mathcal{Z} $$\end{document} 2 symmetry, including a Dark Matter candidate and a top-partner. This simple UV model reproduces the main features of this class of signatures, particularly a momentum-dependent form factor with more structure than the SMEFT. As the new states can be lighter than in SMEFT, we explore the interplay between the reinterpretation of direct searches for colored states and Dark Matter, and Top measurements, made by ATLAS and CMS in the differential t t over bar \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ t\overline{t} $$\end{document} final state. We also compare our method with what one would expect using the SMEFT reinterpretation, finding that using the full loop information provides a better discriminating power.
Address [Lessa, Andre] Univ Fed ABC, Ctr Ciencias Nat & Humanas, BR-09210580 Santo Andre, SP, Brazil, Email: andre.lessa@ufabc.edu.br
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:001205498200004 Approved no
Is ISI yes International Collaboration yes
Call Number IFIC @ pastor @ Serial 6108
Permanent link to this record