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Author |
Barenboim, G.; Hirn, J.; Sanz, V. |
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Title |
Symmetry meets AI |
Type |
Journal Article |
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Year |
2021 |
Publication |
Scipost Physics |
Abbreviated Journal |
SciPost Phys. |
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Volume |
11 |
Issue |
1 |
Pages |
014 - 11pp |
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Keywords |
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Abstract |
We explore whether Neural Networks (NNs) can discover the presence of symmetries as they learn to perform a task. For this, we train hundreds of NNs on a decoy task based on well-controlled Physics templates, where no information on symmetry is provided. We use the output from the last hidden layer of all these NNs, projected to fewer dimensions, as the input for a symmetry classification task, and show that information on symmetry had indeed been identified by the original NN without guidance. As an interdisciplinary application of this procedure, we identify the presence and level of symmetry in artistic paintings from different styles such as those of Picasso, Pollock and Van Gogh. |
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Address |
[Barenboim, Gabriela; Hirn, Johannes; Sanz, Veronica] Univ Valencia, CSIC, Dept Fis Teor, E-46100 Burjassot, Spain |
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Scipost Foundation |
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English |
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2542-4653 |
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Notes |
WOS:000680039500002 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
4920 |
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Author |
Khosa, C.K.; Sanz, V.; Soughton, M. |
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Title |
Using machine learning to disentangle LHC signatures of Dark Matter candidates |
Type |
Journal Article |
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Year |
2021 |
Publication |
Scipost Physics |
Abbreviated Journal |
SciPost Phys. |
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Volume |
10 |
Issue |
6 |
Pages |
151 - 26pp |
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Keywords |
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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. |
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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; |
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Publisher |
Scipost Foundation |
Place of Publication |
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Language |
English |
Summary Language |
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Abbreviated Series Title |
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Series Volume |
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ISSN |
2542-4653 |
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Notes |
WOS:000680038800002 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
4927 |
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Author |
Doring, C.; Centelles Chulia, S.; Lindner, M.; Schaefer, B.M.; Bartelmann, M. |
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Title |
Gravitational wave induced baryon acoustic oscillations |
Type |
Journal Article |
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Year |
2022 |
Publication |
Scipost Physics |
Abbreviated Journal |
SciPost Phys. |
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Volume |
12 |
Issue |
3 |
Pages |
114 - 47pp |
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Abstract |
We study the impact of gravitational waves originating from a first order phase transition on structure formation. To do so, we perform a second order perturbation analysis in the 1 + 3 covariant framework and derive a wave equation in which second order, adiabatic density perturbations of the photon-baryon fluid are sourced by the gravitational wave energy density during radiation domination and on sub-horizon scales. The scale on which such waves affect the energy density perturbation spectrum is found to be proportional to the horizon size at the time of the phase transition times its inverse duration. Consequently, structure of the size of galaxies and bigger can only be affected in this way by relatively late phase transitions at >= 10(6) s. Using cosmic variance as a bound we derive limits on the strength a and the relative duration (beta/H-*)(-1) of phase transitions as functions of the time of their occurrence which results in a new exclusion region for the energy density in gravitational waves today. We find that the cosmic variance bound forbids only relative long lasting phase transitions, e.g. beta/H-* less than or similar to 6.8 for t(*) approximate to 5 x 10(11 )s, which exhibit a substantial amount of supercooling alpha > 20 to affect the matter power spectrum. |
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Address |
[Doering, Christian; Lindner, Manfred] Max Planck Inst Kernphys, Saupfercheckweg 1, D-69117 Heidelberg, Germany, Email: cdoering@mpi-hd.mpg.de; |
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Scipost Foundation |
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English |
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2542-4653 |
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Notes |
WOS:000782238100035 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
5201 |
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Author |
Cranmer, K. et al; Sanz, V. |
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Title |
Publishing statistical models: Getting the most out of particle physics experiments |
Type |
Journal Article |
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Year |
2022 |
Publication |
Scipost Physics |
Abbreviated Journal |
SciPost Phys. |
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Volume |
12 |
Issue |
1 |
Pages |
037 - 55pp |
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Keywords |
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Abstract |
The statistical models used to derive the results of experimental analyses are of incredible scientific value and are essential information for analysis preservation and reuse. In this paper, we make the scientific case for systematically publishing the full statistical models and discuss the technical developments that make this practical. By means of a variety of physics cases – including parton distribution functions, Higgs boson measurements, effective field theory interpretations, direct searches for new physics, heavy flavor physics, direct dark matter detection, world averages, and beyond the Standard Model global fits – we illustrate how detailed information on the statistical modelling can enhance the short- and long-term impact of experimental results. |
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Address |
[Cranmer, Kyle; Held, Alexander] NYU, New York, NY 10003 USA, Email: kyle.cranmer@nyu.edu; |
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Scipost Foundation |
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English |
Summary Language |
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Original Title |
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Series Editor |
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Abbreviated Series Title |
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Series Volume |
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ISSN |
2542-4653 |
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Notes |
WOS:000807448000032 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
5255 |
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Author |
Aarrestad, T. et al; Mamuzic, J.; Ruiz de Austri, R. |
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Title |
Benchmark data and model independent event classification for the large hadron collider |
Type |
Journal Article |
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Year |
2022 |
Publication |
Scipost Physics |
Abbreviated Journal |
SciPost Phys. |
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Volume |
12 |
Issue |
1 |
Pages |
043 - 57pp |
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Keywords |
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Abstract |
We describe the outcome of a data challenge conducted as part of the Dark Machines (https://www.darkmachines.org) initiative and the Les Houches 2019 workshop on Physics at TeV colliders. The challenged aims to detect signals of new physics at the Large Hadron Collider (LHC) using unsupervised machine learning algorithms. First, we propose how an anomaly score could be implemented to define model-independent signal regions in LHC searches. We define and describe a large benchmark dataset, consisting of > 1 billion simulated LHC events corresponding to 10 fb(-1) of proton-proton collisions at a center-of-mass energy of 13 TeV. We then review a wide range of anomaly detection and density estimation algorithms, developed in the context of the data challenge, and we measure their performance in a set of realistic analysis environments. We draw a number of useful conclusions that will aid the development of unsupervised new physics searches during the third run of the LHC, and provide our benchmark dataset for future studies at https://www.phenoMLdata.org. Code to reproduce the analysis is provided at https://github.com/bostdiek/DarkMachines-UnsupervisedChallenge. |
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Address |
[Aarrestad, Thea; Heinrich, Lukas A.; Jawahar, Pratik; Pierini, Maurizio; Touranakou, Mary; Wozniak, Kinga A.] European Org Nucl Res CERN, CH-1211 Geneva 23, Switzerland |
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Publisher |
Scipost Foundation |
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 |
2542-4653 |
ISBN |
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Medium |
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Expedition |
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Conference |
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Notes |
WOS:000807448000038 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
5256 |
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| Permanent link to this record |
