Records |
Author |
Sanchis-Lozano, M.A.; Sanz, V. |
Title |
Observable imprints of primordial gravitational waves on the temperature anisotropies of the cosmic microwave background |
Type |
Journal Article |
Year |
2024 |
Publication |
Physical Review D |
Abbreviated Journal |
Phys. Rev. D |
Volume |
109 |
Issue |
6 |
Pages |
063529 - 11pp |
Keywords |
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Abstract |
We examine the contribution of tensor modes, in addition to the dominant scalar ones, on the temperature anisotropies of the cosmic microwave background (CMB). To this end, we analyze in detail the temperature two -point angular correlation function C(Theta) from the Planck 2018 dataset, focusing on large angles (Theta greater than or similar to 120 degrees) corresponding to small l multipoles. A hierarchical set of infrared cutoffs are naturally introduced to the scalar and tensor power spectra of the CMB by invoking an extra Kaluza-Klein spatial dimension compactifying at about the grand unified theory scale between the Planck epoch and the start of inflation. We associate this set of lower scalar and tensor cutoffs with the parity of the multipole expansion of the C(Theta) function. By fitting the Planck 2018 data we compute the multipole coefficients, thereby reproducing the well-known odd -parity preference in angular correlations seen by all three satellite missions: Cosmic Background Explorer, WMAP, and Planck. Our fits improve significantly once tensor modes are included in the analysis, hence providing a hint of the imprints of primordial gravitational waves on the temperature correlations observed in the CMB today. To conclude, we suggest a relationship between, on the one hand, the lack of (positive) large -angle correlations and the odd -parity dominance in the CMB and, on the other hand, the effect of primordial gravitational waves on the CMB temperature anisotropies. |
Address ![sorted by Address field, descending order (down)](img/sort_desc.gif) |
[Sanchis-Lozano, Miguel -Angel; Sanz, Veronica] Univ Valencia, Dept Fis Teor, CSIC, Valencia 46100, Spain, Email: miguel.angel.sanchis@ific.uv.es; |
Corporate Author |
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Thesis |
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Publisher |
Amer Physical Soc |
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 |
2470-0010 |
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:001195716600006 |
Approved |
no |
Is ISI |
yes |
International Collaboration |
no |
Call Number |
IFIC @ pastor @ |
Serial |
6038 |
Permanent link to this record |
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Author |
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 ![sorted by Address field, descending order (down)](img/sort_desc.gif) |
[Lessa, Andre] Univ Fed ABC, Ctr Ciencias Nat & Humanas, BR-09210580 Santo Andre, SP, Brazil, Email: andre.lessa@ufabc.edu.br |
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:001205498200004 |
Approved |
no |
Is ISI |
yes |
International Collaboration |
yes |
Call Number |
IFIC @ pastor @ |
Serial |
6108 |
Permanent link to this record |
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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 ![sorted by Address field, descending order (down)](img/sort_desc.gif) |
[Khosa, Charanjit Kaur] Univ Bristol, HH Wills Phys Lab, Tyndall Ave, Bristol BS8 1TL, Avon, England, Email: Charanjit.Kaur@bristol.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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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 |
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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 |
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 ![sorted by Address field, descending order (down)](img/sort_desc.gif) |
[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 |
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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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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 |
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 ![sorted by Address field, descending order (down)](img/sort_desc.gif) |
[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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Medium |
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Area |
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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 |
Permanent link to this record |