| Records |
| Author |
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 |
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| 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 |
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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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| 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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Expedition |
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Conference |
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| Notes |
WOS:001048488200002 |
Approved |
no |
| Is ISI |
yes |
International Collaboration |
yes |
| Call Number |
IFIC @ pastor @ |
Serial  |
5610 |
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| 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; |
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Thesis |
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| Publisher |
Springer |
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 |
1029-8479 |
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:000946004000003 |
Approved |
no |
| Is ISI |
yes |
International Collaboration |
yes |
| Call Number |
IFIC @ pastor @ |
Serial |
5501 |
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| Author |
Garcia Navarro, J.E.; Fernandez-Prieto, L.M.; Villaseñor, A.; Sanz, V.; Ammirati, J.B.; Diaz Suarez, E.A.; Garcia, C. |
| Title |
Performance of Deep Learning Pickers in Routine Network Processing Applications |
Type |
Journal Article |
| Year |
2022 |
Publication |
Seismological Research Letters |
Abbreviated Journal |
Seismol. Res. Lett. |
| Volume |
93 |
Issue |
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Pages |
2529-2542 |
| Keywords |
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| Abstract |
Picking arrival times of P and S phases is a fundamental and time‐consuming task for the routine processing of seismic data acquired by permanent and temporary networks. A large number of automatic pickers have been developed, but to perform well they often require the tuning of multiple parameters to adapt them to each dataset. Despite the great advance in techniques, some problems remain, such as the difficulty to accurately pick S waves and earthquake recordings with a low signal‐to‐noise ratio. Recently, phase pickers based on deep learning (DL) have shown great potential for event identification and arrival‐time picking. However, the general adoption of these methods for the routine processing of monitoring networks has been held back by factors such as the availability of well‐documented software, computational resources, and a gap in knowledge of these methods. In this study, we evaluate recent available DL pickers for earthquake data, comparing the performance of several neural network architectures. We test the selected pickers using three datasets with different characteristics. We found that the analyzed DL pickers (generalized phase detection, PhaseNet, and EQTransformer) perform well in the three tested cases. They are very efficient at ignoring large‐amplitude transient noise and at picking S waves, a task that is often difficult even for experienced analysts. Nevertheless, the performance of the analyzed DL pickers varies widely in terms of sensitivity and false discovery rate, with some pickers missing a significant percentage of true picks and others producing a large number of false positives. There are also variations in run time between DL pickers, with some of them requiring significant resources to process large datasets. In spite of these drawbacks, we show that DL pickers can be used efficiently to process large seismic datasets and obtain results comparable or better than current standard procedures. |
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Expedition |
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Conference |
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| Notes |
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Approved |
no |
| Is ISI |
yes |
International Collaboration |
yes |
| Call Number |
IFIC @ pastor @ |
Serial |
5500 |
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| 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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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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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: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 |
Cepedello, R.; Esser, F.; Hirsch, M.; Sanz, V. |
| Title |
Mapping the SMEFT to discoverable models |
Type |
Journal Article |
| Year |
2022 |
Publication |
Journal of High Energy Physics |
Abbreviated Journal |
J. High Energy Phys. |
| Volume |
09 |
Issue |
9 |
Pages |
229 - 34pp |
| Keywords |
SMEFT; Other Weak Scale BSM Models |
| Abstract |
The matching of specific new physics scenarios onto the SMEFT framework is a well-understood procedure. The inverse problem, the matching of the SMEFT to UV scenarios, is more difficult and requires the development of new methods to perform a systematic exploration of models. In this paper we use a diagrammatic technique to construct in an automated way a complete set of possible UV models (given certain, well specified assumptions) that can produce specific groups of SMEFT operators, and illustrate its use by generating models with no tree-level contributions to four-fermion (4F) operators. Those scenarios, which only contribute to 4F at one-loop order, can contain relatively light particles that could be discovered at the LHC in direct searches. For this class of models, we find an interesting interplay between indirect SMEFT and direct searches. We discuss some examples on how this interplay would look like when combining low-energy observables with the SMEFT Higgs-fermion analyses and searches for resonance at the LHC. |
| Address |
[Cepedello, Ricardo] Univ Wurzburg, Inst Theoret Phys & Astrophys, Emil Hilb Weg 22, D-97074 Wurzburg, Germany, Email: ricardo.cepedello@physik.uni-wuerzburg.de; |
| Corporate Author |
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Thesis |
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| Publisher |
Springer |
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 |
1029-8479 |
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:000861474500009 |
Approved |
no |
| Is ISI |
yes |
International Collaboration |
yes |
| Call Number |
IFIC @ pastor @ |
Serial |
5361 |
| Permanent link to this record |
