| Records |
Author  |
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 |
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| 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; |
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| Publisher |
Scipost Foundation |
Place of Publication |
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| Language |
English |
Summary Language |
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Expedition |
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Conference |
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| Notes |
WOS:000929724800002 |
Approved |
no |
| Is ISI |
yes |
International Collaboration |
yes |
| Call Number |
IFIC @ pastor @ |
Serial |
5475 |
| Permanent link to this record |
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| Author |
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 |
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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. |
| 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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Editor |
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| Language |
English |
Summary Language |
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Original Title |
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Series Title |
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Abbreviated Series Title |
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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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| Area |
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Expedition |
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Conference |
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| Notes |
WOS:000680038800002 |
Approved |
no |
| Is ISI |
yes |
International Collaboration |
yes |
| Call Number |
IFIC @ pastor @ |
Serial |
4927 |
| Permanent link to this record |
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| Author |
LHC BSM Reinterpretation Forum (Abdallah, W. et al); Mitsou, V.A.; Sanz, V. |
| Title |
Reinterpretation of LHC results for new physics: status and recommendations after run 2 |
Type |
Journal Article |
| Year |
2020 |
Publication |
Scipost Physics |
Abbreviated Journal |
SciPost Phys. |
| Volume |
9 |
Issue |
2 |
Pages |
022 - 45pp |
| Keywords |
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| Abstract |
We report on the status of efforts to improve the reinterpretation of searches and measurements at the LHC in terms of models for new physics, in the context of the LHC Reinterpretation Forum. We detail current experimental offerings in direct searches for new particles, measurements, technical implementations and Open Data, and provide a set of recommendations for further improving the presentation of LHC results in order to better enable reinterpretation in the future. We also provide a brief description of existing software reinterpretation frameworks and recent global analyses of new physics that make use of the current data. |
| Address |
[Abdallah, Waleed; Dutta, Juhi] Harish Chandra Res Inst HBNI, Allahabad 211019, Uttar Pradesh, India, Email: Andy.Buckley@glasgow.ac.uk; |
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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 Issue |
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Edition |
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| ISSN |
2542-4653 |
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:000573102600007 |
Approved |
no |
| Is ISI |
yes |
International Collaboration |
yes |
| Call Number |
IFIC @ pastor @ |
Serial |
4547 |
| Permanent link to this record |
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| Author |
van Beekveld, M.; Beenakker, W.; Caron, S.; Kip, J.; Ruiz de Austri, R.; Zhang, Z.Y. |
| Title |
Non-standard neutrino spectra from annihilating neutralino dark matter |
Type |
Journal Article |
| Year |
2023 |
Publication |
Scipost Physics Core |
Abbreviated Journal |
SciPost Phys. Core |
| Volume |
6 |
Issue |
1 |
Pages |
006 - 23pp |
| Keywords |
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| Abstract |
Neutrino telescope experiments are rapidly becoming more competitive in indirect de-tection searches for dark matter. Neutrino signals arising from dark matter annihilations are typically assumed to originate from the hadronisation and decay of Standard Model particles. Here we showcase a supersymmetric model, the BLSSMIS, that can simulta-neously obey current experimental limits while still providing a potentially observable non-standard neutrino spectrum from dark matter annihilation. |
| Address |
[van Beekveld, Melissa] Univ Oxford, Rudolf Peierls Ctr Theoret Phys, Clarendon Lab, Parks Rd, Oxford OX1 3PU, England, Email: melissa.vanbeekveld@physics.ox.ac.uk; |
| Corporate Author |
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Thesis |
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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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Expedition |
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Conference |
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| Notes |
WOS:000928492200001 |
Approved |
no |
| Is ISI |
yes |
International Collaboration |
yes |
| Call Number |
IFIC @ pastor @ |
Serial |
5480 |
| Permanent link to this record |
