Records |
Author |
Caron, S.; Eckner, C.; Hendriks, L.; Johannesson, G.; Ruiz de Austri, R.; Zaharijas, G. |
Title |
Mind the gap: the discrepancy between simulation and reality drives interpretations of the Galactic Center Excess |
Type |
Journal Article |
Year |
2023 |
Publication |
Journal of Cosmology and Astroparticle Physics |
Abbreviated Journal |
J. Cosmol. Astropart. Phys. |
Volume |
06 |
Issue |
6 |
Pages |
013 - 56pp |
Keywords |
dark matter simulations; gamma ray experiments; Machine learning; millisecond pulsars |
Abstract |
The Galactic Center Excess (GCE) in GeV gamma rays has been debated for over a decade, with the possibility that it might be due to dark matter annihilation or undetected point sources such as millisecond pulsars (MSPs). This study investigates how the gamma-ray emission model (-yEM) used in Galactic center analyses affects the interpretation of the GCE's nature. To address this issue, we construct an ultra-fast and powerful inference pipeline based on convolutional Deep Ensemble Networks. We explore the two main competing hypotheses for the GCE using a set of-yEMs with increasing parametric freedom. We calculate the fractional contribution (fsrc) of a dim population of MSPs to the total luminosity of the GCE and analyze its dependence on the complexity of the ryEM. For the simplest ryEM, we obtain fsrc = 0.10 f 0.07, while the most complex model yields fsrc = 0.79 f 0.24. In conclusion, we find that the statement about the nature of the GCE (dark matter or not) strongly depends on the assumed ryEM. The quoted results for fsrc do not account for the additional uncertainty arising from the fact that the observed gamma-ray sky is out-of-distribution concerning the investigated ryEM iterations. We quantify the reality gap between our ryEMs using deep-learning-based One-Class Deep Support Vector Data Description networks, revealing that all employed ryEMs have gaps to reality. Our study casts doubt on the validity of previous conclusions regarding the GCE and dark matter, and underscores the urgent need to account for the reality gap and consider previously overlooked “out of domain” uncertainties in future interpretations. |
Address ![sorted by Address field, ascending order (up)](img/sort_asc.gif) |
[Caron, Sascha; Hendriks, Luc] Radboud Univ Nijmegen, Theoret High Energy Phys, Heyendaalseweg 135, NL-6525 AJ Nijmegen, Netherlands, Email: scaron@nikhef.nl; |
Corporate Author |
|
Thesis |
|
Publisher |
IOP Publishing Ltd |
Place of Publication |
|
Editor |
|
Language |
English |
Summary Language |
|
Original Title |
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
Series Volume |
|
Series Issue |
|
Edition |
|
ISSN |
1475-7516 |
ISBN |
|
Medium |
|
Area |
|
Expedition |
|
Conference |
|
Notes |
WOS:001025516000009 |
Approved |
no |
Is ISI |
yes |
International Collaboration |
yes |
Call Number |
IFIC @ pastor @ |
Serial |
5576 |
Permanent link to this record |
|
|
|
Author |
Ferrer-Sanchez, A.; Martin-Guerrero, J.; Ruiz de Austri, R.; Torres-Forne, A.; Font, J.A. |
Title |
Gradient-annihilated PINNs for solving Riemann problems: Application to relativistic hydrodynamics |
Type |
Journal Article |
Year |
2024 |
Publication |
Computer Methods in Applied Mechanics and Engineering |
Abbreviated Journal |
Comput. Meth. Appl. Mech. Eng. |
Volume |
424 |
Issue |
|
Pages |
116906 - 18pp |
Keywords |
Riemann problem; Euler equations; Machine learning; Neural networks; Relativistic hydrodynamics |
Abstract |
We present a novel methodology based on Physics-Informed Neural Networks (PINNs) for solving systems of partial differential equations admitting discontinuous solutions. Our method, called Gradient-Annihilated PINNs (GA-PINNs), introduces a modified loss function that forces the model to partially ignore high-gradients in the physical variables, achieved by introducing a suitable weighting function. The method relies on a set of hyperparameters that control how gradients are treated in the physical loss. The performance of our methodology is demonstrated by solving Riemann problems in special relativistic hydrodynamics, extending earlier studies with PINNs in the context of the classical Euler equations. The solutions obtained with the GA-PINN model correctly describe the propagation speeds of discontinuities and sharply capture the associated jumps. We use the relative l(2) error to compare our results with the exact solution of special relativistic Riemann problems, used as the reference ''ground truth'', and with the corresponding error obtained with a second-order, central, shock-capturing scheme. In all problems investigated, the accuracy reached by the GA-PINN model is comparable to that obtained with a shock-capturing scheme, achieving a performance superior to that of the baseline PINN algorithm in general. An additional benefit worth stressing is that our PINN-based approach sidesteps the costly recovery of the primitive variables from the state vector of conserved variables, a well-known drawback of grid-based solutions of the relativistic hydrodynamics equations. Due to its inherent generality and its ability to handle steep gradients, the GA-PINN methodology discussed in this paper could be a valuable tool to model relativistic flows in astrophysics and particle physics, characterized by the prevalence of discontinuous solutions. |
Address ![sorted by Address field, ascending order (up)](img/sort_asc.gif) |
[Ferrer-Sanchez, Antonio; Martin-Guerrero, JoseD.] ETSE UV, Elect Engn Dept, IDAL, Avgda Univ S-N, Valencia 46100, Spain, Email: Antonio.Ferrer-Sanchez@uv.es |
Corporate Author |
|
Thesis |
|
Publisher |
Elsevier Science Sa |
Place of Publication |
|
Editor |
|
Language |
English |
Summary Language |
|
Original Title |
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
Series Volume |
|
Series Issue |
|
Edition |
|
ISSN |
0045-7825 |
ISBN |
|
Medium |
|
Area |
|
Expedition |
|
Conference |
|
Notes |
WOS:001221797400001 |
Approved |
no |
Is ISI |
yes |
International Collaboration |
no |
Call Number |
IFIC @ pastor @ |
Serial |
6126 |
Permanent link to this record |
|
|
|
Author |
Folgado, M.G.; Sanz, V. |
Title |
Exploring the political pulse of a country using data science tools |
Type |
Journal Article |
Year |
2022 |
Publication |
Journal of Computational Social Science |
Abbreviated Journal |
J. Comput. Soc. Sci. |
Volume |
5 |
Issue |
|
Pages |
987-1000 |
Keywords |
Politics; Spain; Sentiment analysis; Artificial Intelligence; Machine learning; Neural networks; Natural Language Processing (NLP) |
Abstract |
In this paper we illustrate the use of Data Science techniques to analyse complex human communication. In particular, we consider tweets from leaders of political parties as a dynamical proxy to political programmes and ideas. We also study the temporal evolution of their contents as a reaction to specific events. We analyse levels of positive and negative sentiment in the tweets using new tools adapted to social media. We also train a Fully-Connected Neural Network (FCNN) to recognise the political affiliation of a tweet. The FCNN is able to predict the origin of the tweet with a precision in the range of 71-75%, and the political leaning (left or right) with a precision of around 90%. This study is meant to be viewed as an example of how to use Twitter data and different types of Data Science tools for a political analysis. |
Address ![sorted by Address field, ascending order (up)](img/sort_asc.gif) |
[Folgado, Miguel G.; Sanz, Veronica] Univ Valencia, Inst Fis Corpuscular IFIC, CSIC, Valencia 46980, Spain, Email: migarfol@upvnet.upv.es; |
Corporate Author |
|
Thesis |
|
Publisher |
Springernature |
Place of Publication |
|
Editor |
|
Language |
English |
Summary Language |
|
Original Title |
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
Series Volume |
|
Series Issue |
|
Edition |
|
ISSN |
2432-2717 |
ISBN |
|
Medium |
|
Area |
|
Expedition |
|
Conference |
|
Notes |
WOS:000742263500002 |
Approved |
no |
Is ISI |
yes |
International Collaboration |
yes |
Call Number |
IFIC @ pastor @ |
Serial |
5077 |
Permanent link to this record |
|
|
|
Author |
Kasieczka, G. et al; Sanz, V. |
Title |
The LHC Olympics 2020: a community challenge for anomaly detection in high energy physics |
Type |
Journal Article |
Year |
2021 |
Publication |
Reports on Progress in Physics |
Abbreviated Journal |
Rep. Prog. Phys. |
Volume |
84 |
Issue |
12 |
Pages |
124201 - 64pp |
Keywords |
anomaly detection; machine learning; unsupervised learning; weakly supervised learning; semisupervised learning; beyond the standard model; model-agnostic methods |
Abstract |
A new paradigm for data-driven, model-agnostic new physics searches at colliders is emerging, and aims to leverage recent breakthroughs in anomaly detection and machine learning. In order to develop and benchmark new anomaly detection methods within this framework, it is essential to have standard datasets. To this end, we have created the LHC Olympics 2020, a community challenge accompanied by a set of simulated collider events. Participants in these Olympics have developed their methods using an R&D dataset and then tested them on black boxes: datasets with an unknown anomaly (or not). Methods made use of modern machine learning tools and were based on unsupervised learning (autoencoders, generative adversarial networks, normalizing flows), weakly supervised learning, and semi-supervised learning. This paper will review the LHC Olympics 2020 challenge, including an overview of the competition, a description of methods deployed in the competition, lessons learned from the experience, and implications for data analyses with future datasets as well as future colliders. |
Address ![sorted by Address field, ascending order (up)](img/sort_asc.gif) |
[Kasieczka, Gregor] Univ Hamburg, Inst Expt Phys, Hamburg, Germany, Email: gregor.kasieczka@uni-hamburg.de; |
Corporate Author |
|
Thesis |
|
Publisher |
IOP Publishing Ltd |
Place of Publication |
|
Editor |
|
Language |
English |
Summary Language |
|
Original Title |
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
Series Volume |
|
Series Issue |
|
Edition |
|
ISSN |
0034-4885 |
ISBN |
|
Medium |
|
Area |
|
Expedition |
|
Conference |
|
Notes |
WOS:000727698500001 |
Approved |
no |
Is ISI |
yes |
International Collaboration |
yes |
Call Number |
IFIC @ pastor @ |
Serial |
5039 |
Permanent link to this record |