| Records |
| Author |
Otten, S.; Caron, S.; de Swart, W.; van Beekveld, M.; Hendriks, L.; van Leeuwen, C.; Podareanu, D.; Ruiz de Austri, R.; Verheyen, R. |
| Title |
Event generation and statistical sampling for physics with deep generative models and a density information buffer |
Type |
Journal Article |
| Year |
2021 |
Publication |
Nature Communications |
Abbreviated Journal  |
Nat. Commun. |
| Volume |
12 |
Issue |
1 |
Pages |
2985 - 16pp |
| Keywords |
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| Abstract |
Simulating nature and in particular processes in particle physics require expensive computations and sometimes would take much longer than scientists can afford. Here, we explore ways to a solution for this problem by investigating recent advances in generative modeling and present a study for the generation of events from a physical process with deep generative models. The simulation of physical processes requires not only the production of physical events, but to also ensure that these events occur with the correct frequencies. We investigate the feasibility of learning the event generation and the frequency of occurrence with several generative machine learning models to produce events like Monte Carlo generators. We study three processes: a simple two-body decay, the processes e(+)e(-)-> Z -> l(+)l(-) and pp -> tt<mml:mo><overbar></mml:mover> including the decay of the top quarks and a simulation of the detector response. By buffering density information of encoded Monte Carlo events given the encoder of a Variational Autoencoder we are able to construct a prior for the sampling of new events from the decoder that yields distributions that are in very good agreement with real Monte Carlo events and are generated several orders of magnitude faster. Applications of this work include generic density estimation and sampling, targeted event generation via a principal component analysis of encoded ground truth data, anomaly detection and more efficient importance sampling, e.g., for the phase space integration of matrix elements in quantum field theories. Here, the authors report buffered-density variational autoencoders for the generation of physical events. This method is computationally less expensive over other traditional methods and beyond accelerating the data generation process, it can help to steer the generation and to detect anomalies. |
| Address |
[Otten, Sydney; Caron, Sascha; de Swart, Wieske; van Beekveld, Melissa; Hendriks, Luc; Verheyen, Rob] Radboud Univ Nijmegen, Inst Math Astro & Particle Phys IMAPP, Nijmegen, Netherlands, Email: Sydney.Otten@ru.nl |
| Corporate Author |
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Thesis |
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| Publisher |
Nature Research |
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 |
2041-1723 |
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:000658761600003 |
Approved |
no |
| Is ISI |
yes |
International Collaboration |
yes |
| Call Number |
IFIC @ pastor @ |
Serial |
4862 |
| Permanent link to this record |
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| Author |
Cabrera, M.E.; Casas, J.A.; Ruiz de Austri, R. |
| Title |
MSSM forecast for the LHC |
Type |
Journal Article |
| Year |
2010 |
Publication |
Journal of High Energy Physics |
Abbreviated Journal |
J. High Energy Phys. |
| Volume |
05 |
Issue |
5 |
Pages |
043 - 48pp |
| Keywords |
Beyond Standard Model; Supersymmetric Effective Theories |
| Abstract |
We perform a forecast of the MSSM with universal soft terms (CMSSM) for the LHC, based on an improved Bayesian analysis. We do not incorporate ad hoc measures of the fine-tuning to penalize unnatural possibilities: such penalization arises from the Bayesian analysis itself when the experimental value of M-Z is considered. This allows to scan the whole parameter space, allowing arbitrarily large soft terms. Still the low-energy region is statistically favoured (even before including dark matter or g-2 constraints). Contrary to other studies, the results are almost unaffected by changing the upper limits taken for the soft terms. The results are also remarkable stable when using flat or logarithmic priors, a fact that arises from the larger statistical weight of the low-energy region in both cases. Then we incorporate all the important experimental constrains to the analysis, obtaining a map of the probability density of the MSSM parameter space, i.e. the forecast of the MSSM. Since not all the experimental information is equally robust, we perform separate analyses depending on the group of observables used. When only the most robust ones are used, the favoured region of the parameter space contains a significant portion outside the LHC reach. This effect gets reinforced if the Higgs mass is not close to its present experimental limit and persits when dark matter constraints are included. Only when the g-2 constraint (based on e(+)e(-) data) is considered, the preferred region (for μ> 0) is well inside the LHC scope. We also perform a Bayesian comparison of the positive- and negative-mu possibilities. |
| Address |
[Eugenia Cabrera, Maria; Alberto Casas, J.] UAM, IFT UAM CSIC, Inst Fis Teor, Madrid 28049, Spain, Email: maria.cabrera@uam.es |
| 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 |
1126-6708 |
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 |
ISI:000278251300005 |
Approved |
no |
| Is ISI |
yes |
International Collaboration |
no |
| Call Number |
IFIC @ elepoucu @ |
Serial |
435 |
| Permanent link to this record |
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| Author |
Bridges, M.; Cranmer, K.; Feroz, F.; Hobson, M.; Ruiz de Austri, R.; Trotta, R. |
| Title |
A coverage study of the CMSSM based on ATLAS sensitivity using fast neural networks techniques |
Type |
Journal Article |
| Year |
2011 |
Publication |
Journal of High Energy Physics |
Abbreviated Journal |
J. High Energy Phys. |
| Volume |
03 |
Issue |
3 |
Pages |
012 - 23pp |
| Keywords |
Supersymmetry; Phenomenology |
| Abstract |
We assess the coverage properties of confidence and credible intervals on the CMSSM parameter space inferred from a Bayesian posterior and the profile likelihood based on an ATLAS sensitivity study. In order to make those calculations feasible, we introduce a new method based on neural networks to approximate the mapping between CMSSM parameters and weak-scale particle masses. Our method reduces the computational effort needed to sample the CMSSM parameter space by a factor of similar to 10(4) with respect to conventional techniques. We find that both the Bayesian posterior and the profile likelihood intervals can significantly over-cover and identify the origin of this effect to physical boundaries in the parameter space. Finally, we point out that the effects intrinsic to the statistical procedure are conflated with simplifications to the likelihood functions from the experiments themselves. |
| Address |
[Bridges, Michael; Feroz, Farhan; Hobson, Mike] Univ Cambridge, Cavendish Lab, Astrophys Grp, Cambridge CB3 0HE, England, Email: mb435@mrao.cam.ac.uk |
| 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 |
1126-6708 |
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 |
ISI:000289295200012 |
Approved |
no |
| Is ISI |
yes |
International Collaboration |
yes |
| Call Number |
IFIC @ pastor @ |
Serial |
610 |
| Permanent link to this record |
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| Author |
Casas, J.A.; Moreno, J.M.; Rius, N.; Ruiz de Austri, R.; Zaldivar, B. |
| Title |
Fair scans of the seesaw. Consequences for predictions on LFV processes |
Type |
Journal Article |
| Year |
2011 |
Publication |
Journal of High Energy Physics |
Abbreviated Journal |
J. High Energy Phys. |
| Volume |
03 |
Issue |
3 |
Pages |
034 - 22pp |
| Keywords |
Neutrino Physics; Supersymmetric Standard Model |
| Abstract |
We give a straightforward procedure to scan the seesaw parameter-space, using the common “R-parametrization”, in a complete way. This includes a very simple rule to incorporate the perturbativity requirement as a condition for the entries of the R-matrix. As a relevant application, we show that the somewhat propagated belief that BR(mu -> e, gamma) in supersymmetric seesaw models depends strongly on the value of theta(13) is an “optical effect” produced by incomplete scans, and does not hold after a careful analytical and numerical study. When the complete scan is done, BR(mu -> e, gamma) gets very insensitive to theta(13). This holds even if the right-handed neutrino masses are kept constant or under control (as is required for succesful leptogenesis). In most cases the values of BR(mu -> e, gamma) are larger than the experimental upper bound. Including (unflavoured) leptogenesis does not introduce any further dependence on theta(13), although decreases the typical value of BR(mu -> e, gamma). |
| Address |
[Alberto Casas, J.; Moreno, Jesus M.; Zaldivar, Bryam] UAM, IFT UAM CSIC, Inst Fis Teor, Madrid 28049, Spain, Email: alberto.casas@uam.es |
| 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 |
1126-6708 |
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 |
ISI:000289295200034 |
Approved |
no |
| Is ISI |
yes |
International Collaboration |
no |
| Call Number |
IFIC @ pastor @ |
Serial |
612 |
| Permanent link to this record |
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| Author |
Feroz, F.; Cranmer, K.; Hobson, M.; Ruiz de Austri, R.; Trotta, R. |
| Title |
Challenges of profile likelihood evaluation in multi-dimensional SUSY scans |
Type |
Journal Article |
| Year |
2011 |
Publication |
Journal of High Energy Physics |
Abbreviated Journal |
J. High Energy Phys. |
| Volume |
06 |
Issue |
6 |
Pages |
042 - 23pp |
| Keywords |
Supersymmetry Phenomenology |
| Abstract |
Statistical inference of the fundamental parameters of supersymmetric theories is a challenging and active endeavor. Several sophisticated algorithms have been employed to this end. While Markov-Chain Monte Carlo (MCMC) and nested sampling techniques are geared towards Bayesian inference, they have also been used to estimate frequentist confidence intervals based on the profile likelihood ratio. We investigate the performance and appropriate configuration of MULTINEST, a nested sampling based algorithm, when used for profile likelihood-based analyses both on toy models and on the parameter space of the Constrained MSSM. We find that while the standard configuration previously used in the literarture is appropriate for an accurate reconstruction of the Bayesian posterior, the profile likelihood is poorly approximated. We identify a more appropriate MULTINEST configuration for profile likelihood analyses, which gives an excellent exploration of the profile likelihood (albeit at a larger computational cost), including the identification of the global maximum likelihood value. We conclude that with the appropriate configuration MULTINEST is a suitable tool for profile likelihood studies, indicating previous claims to the contrary are not well founded. |
| Address |
[Feroz, F; Hobson, M] Univ Cambridge, Cavendish Lab, Cambridge CB3 0HE, England, Email: f.feroz@mrao.cam.ac.uk |
| 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 |
1126-6708 |
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:000293136500042 |
Approved |
no |
| Is ISI |
yes |
International Collaboration |
yes |
| Call Number |
IFIC @ elepoucu @ |
Serial |
745 |
| Permanent link to this record |
