| Records |
| Author |
Bonilla, J. et al; Vos, M. |
| Title |
Jets and Jet Substructure at Future Colliders |
Type |
Journal Article |
| Year |
2022 |
Publication |
Frontiers in Physics |
Abbreviated Journal |
Front. Physics |
| Volume |
10 |
Issue |
|
Pages |
897719 - 17pp |
| Keywords |
jets; jet substructure; collider; artificial intelligence; machine learning; snowmass; top quark; Higgs boson |
| Abstract |
Even though jet substructure was not an original design consideration for the Large Hadron Collider (LHC) experiments, it has emerged as an essential tool for the current physics program. We examine the role of jet substructure on the motivation for and design of future energy Frontier colliders. In particular, we discuss the need for a vibrant theory and experimental research and development program to extend jet substructure physics into the new regimes probed by future colliders. Jet substructure has organically evolved with a close connection between theorists and experimentalists and has catalyzed exciting innovations in both communities. We expect such developments will play an important role in the future energy Frontier physics program. |
| Address |
[Bonilla, Johan; Erbacher, Robin] Univ Calif, Dept Phys & Astron, Davis, CA USA, Email: bpnachman@lbl.gov; |
| Corporate Author |
|
Thesis |
|
| Publisher |
Frontiers Media Sa |
Place of Publication |
|
Editor |
|
| Language |
English |
Summary Language  |
|
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
2296-424x |
ISBN |
|
Medium |
|
| Area |
|
Expedition |
|
Conference |
|
| Notes |
WOS:000822618100001 |
Approved |
no |
| Is ISI |
yes |
International Collaboration |
yes |
| Call Number |
IFIC @ pastor @ |
Serial |
5464 |
| Permanent link to this record |
| |
|
| |
| 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 |
[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 |
Amerio, A.; Cuoco, A.; Fornengo, N. |
| Title |
Extracting the gamma-ray source-count distribution below the Fermi-LAT detection limit with deep learning |
Type |
Journal Article |
| Year |
2023 |
Publication |
Journal of Cosmology and Astroparticle Physics |
Abbreviated Journal |
J. Cosmol. Astropart. Phys. |
| Volume |
09 |
Issue |
9 |
Pages |
029 - 39pp |
| Keywords |
gamma ray theory; Machine learning |
| Abstract |
We reconstruct the extra-galactic gamma-ray source-count distribution, or dN/dS, of resolved and unresolved sources by adopting machine learning techniques. Specifically, we train a convolutional neural network on synthetic 2-dimensional sky-maps, which are built by varying parameters of underlying source-counts models and incorporate the FermiLAT instrumental response functions. The trained neural network is then applied to the Fermi-LAT data, from which we estimate the source count distribution down to flux levels a factor of 50 below the Fermi-LAT threshold. We perform our analysis using 14 years of data collected in the (1, 10) GeV energy range. The results we obtain show a source count distribution which, in the resolved regime, is in excellent agreement with the one derived from cataloged sources, and then extends as dN/dS " S-2 in the unresolved regime, down to fluxes of 5 center dot 10-12 cm-2 s-1. The neural network architecture and the devised methodology have the flexibility to enable future analyses to study the energy dependence of the source-count distribution. |
| Address |
[Amerio, A.] Univ Valencia, Inst Fis Corpuscular IFIC, Calle Catedrat Jose Beltran 2, Paterna 46980, Spain, Email: aurelio.amerio@ific.uv.es; |
| 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:001097055700001 |
Approved |
no |
| Is ISI |
yes |
International Collaboration |
yes |
| Call Number |
IFIC @ pastor @ |
Serial |
5785 |
| 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 |
[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 |
