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Author |
Khosa, C.K.; Sanz, V.; Soughton, M. |
![goto web page (via DOI) doi](img/doi.gif)
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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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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 ![sorted by Publisher field, descending order (down)](img/sort_desc.gif) |
Scipost Foundation |
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English |
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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 |
LHCb Collaboration (Aaij, R. et al); Jashal, B.K.; Martinez-Vidal, F.; Oyanguren, A.; Remon Alepuz, C.; Ruiz Vidal, J. |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Search for the doubly charmed baryon Omega(+)(cc) |
Type |
Journal Article |
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Year |
2021 |
Publication |
Science China-Physics Mechanics & Astronomy |
Abbreviated Journal |
Sci. China-Phys. Mech. Astron. |
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Volume |
64 |
Issue |
10 |
Pages |
101062 - 12pp |
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Keywords |
charmed baryons; limits on production of particles; charmed quarks; experimental tests |
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Abstract |
A search for the doubly charmed baryon Omega(+)(cc) with the decay mode Omega(+)(cc) -> Xi K-+(c)-pi(+) is performed using proton-proton collision data at a centre-of-mass energy of 13 TeV collected by the LHCb experiment from 2016 to 2018, corresponding to an integrated luminosity of 5.4 fb(-1). No significant signal is observed within the invariant mass range of 3.6 to 4.0GeV/c(2). Upper limits are set on the ratio R of the production cross-section times the total branching fraction of the Omega(+)(cc) -> Xi K-+(c)-pi(+) decay with respect to the Xi(++)(cc) -> Lambda K-+(c)-pi(+)pi(+) decay. Upper limits at 95% credibility level for R in the range 0.005 to 0.11 are obtained for different hypotheses on the Omega(+)(cc) mass and lifetime in the rapidity range from 2.0 to 4.5 and transverse momentum range from 4 to 15 GeV/c. |
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Address |
[Baptista Leite, J.; Bediaga, I; Cruz Torres, M.; De Miranda, J. M.; dos Reis, A. C.; Gomes, A.; Massafferri, A.; Machado, D. Torres] Ctr Brasileiro Pesquisas Fis CBPF, Rio De Janeiro, Brazil |
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Publisher ![sorted by Publisher field, descending order (down)](img/sort_desc.gif) |
Science Press |
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English |
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ISSN |
1674-7348 |
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Notes |
WOS:000694853300003 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
4971 |
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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. |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Design of 1-year mortality forecast at hospital admission: A machine learning approach |
Type |
Journal Article |
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Year |
2021 |
Publication |
Health Informatics Journal |
Abbreviated Journal |
Health Inform. J. |
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Volume |
27 |
Issue |
1 |
Pages |
13pp |
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Keywords |
machine learning; palliative care; hospital admission data; mortality forecast |
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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. |
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Address |
[Blanes-Selva, Vicent; Benedi, Jose-Miguel; Garcia-Gomez, Juan M.] Univ Politecn Valencia, Valencia, Spain, Email: viblasel@upv.es |
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Publisher ![sorted by Publisher field, descending order (down)](img/sort_desc.gif) |
Sage Publications Inc |
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English |
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Edition |
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ISSN |
1460-4582 |
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Notes |
WOS:000645567000008 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
no |
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Call Number |
IFIC @ pastor @ |
Serial |
5182 |
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Permanent link to this record |
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Author |
Hernandez, P.; Pena, C.; Ramos, A.; Gomez-Cadenas, J.J. |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
A new formulation of compartmental epidemic modelling for arbitrary distributions of incubation and removal times |
Type |
Journal Article |
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Year |
2021 |
Publication |
Plos One |
Abbreviated Journal |
PLoS One |
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Volume |
16 |
Issue |
2 |
Pages |
e0244107 - 22pp |
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Keywords |
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Abstract |
The paradigm for compartment models in epidemiology assumes exponentially distributed incubation and removal times, which is not realistic in actual populations. Commonly used variations with multiple exponentially distributed variables are more flexible, yet do not allow for arbitrary distributions. We present a new formulation, focussing on the SEIR concept that allows to include general distributions of incubation and removal times. We compare the solution to two types of agent-based model simulations, a spatially homogeneous one where infection occurs by proximity, and a model on a scale-free network with varying clustering properties, where the infection between any two agents occurs via their link if it exists. We find good agreement in both cases. Furthermore a family of asymptotic solutions of the equations is found in terms of a logistic curve, which after a non-universal time shift, fits extremely well all the microdynamical simulations. The formulation allows for a simple numerical approach; software in Julia and Python is provided. |
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Address |
[Hernandez, Pilar] Univ Valencia, Dept Fis Teor, Valencia, Spain, Email: m.pilar.hernandez@uv.es |
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Publisher ![sorted by Publisher field, descending order (down)](img/sort_desc.gif) |
Public Library Science |
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English |
Summary Language |
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ISSN |
1932-6203 |
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Notes |
WOS:000616739700053 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
4750 |
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Permanent link to this record |
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Author |
n_TOF Collaboration (Gawlik, A. et al); Domingo-Pardo, C.; Tain, J.L.; Tarifeño-Saldivia, A. |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Radiative Neutron Capture Cross-Section Measurement of Ge Isotopes at n_TOF CERN Facility and Its Importance for Stellar Nucleosynthesis |
Type |
Journal Article |
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Year |
2021 |
Publication |
Acta Physica Polonica A |
Abbreviated Journal |
Acta Phys. Pol. A |
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Volume |
139 |
Issue |
4 |
Pages |
383-388 |
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Keywords |
n_TOF; neutron time-of-flight; neutron capture cross-section; MACS |
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Abstract |
This manuscript summarizes the results of radiative neutron capture cross-section measurements on two stable germanium isotopes, Ge-70 and Ge-73. Experiments were performed at the n_TOF facility at CERN via the time-of-flight technique, over a wide neutron energy range, for all stable germanium isotopes (70,72,73,74, and 76). Results for Ge-70 [Phys. Rev. C 100, 045804 (2019)] and Ge-73 [Phys. Lett. B 790, 458 (2019)] are already published. In the field of nuclear structure, such measurements allow to study excited levels close to the neutron binding energy and to obtain information on nuclear properties. In stellar nucleosynthesis research, neutron induced reactions on germanium are of importance for nucleosynthesis in the weak component of the slow neutron capture processes. |
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Address |
[Gawlik, A.; Andrzejewski, J.; Perkowski, J.] Univ Lodz, Lodz, Poland, Email: aleksandra.gawlik@uni.lodz.pl |
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Publisher ![sorted by Publisher field, descending order (down)](img/sort_desc.gif) |
Polish Acad Sciences Inst Physics |
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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 |
0587-4246 |
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Notes |
WOS:000653413700004 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
4840 |
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Permanent link to this record |