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Author |
van Beekveld, M.; Caron, S.; Ruiz de Austri, R. |
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Title |
The current status of fine-tuning in supersymmetry |
Type |
Journal Article |
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Year |
2020 |
Publication |
Journal of High Energy Physics |
Abbreviated Journal |
J. High Energy Phys. |
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Volume |
01 |
Issue |
1 |
Pages |
147 - 41pp |
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Keywords |
Supersymmetry Phenomenology |
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Abstract |
In this paper, we minimize and compare two different fine-tuning measures in four high-scale supersymmetric models that are embedded in the MSSM. In addition, we determine the impact of current and future dark matter direct detection and collider experiments on the fine-tuning. We then compare the low-scale electroweak measure with the high-scale Barbieri-Giudice measure. We find that they reduce to the same value when the higgsino parameter drives the degree of fine-tuning. We also find spectra where the high-scale measure turns out to be lower than the low-scale measure. Depending on the high-scale model and fine-tuning definition, we find a minimal fine-tuning of 3-38 (corresponding to O(10-1)%) for the low-scale measure, and 63-571 (corresponding to O(1-0.1)%) for the high-scale measure. We stress that it is too early to conclude on the fate of supersymmetry, based only on the fine-tuning paradigm. |
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Address |
[van Beekveld, Melissa; Caron, Sascha] Radboud Univ Nijmegen, Theoret High Energy Phys, Heyendaalseweg 135, NL-6525 AJ Nijmegen, Netherlands, Email: mcbeekveld@gmail.com; |
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Springer |
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English |
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1029-8479 |
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Notes |
WOS:000512011100001 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
4275 |
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Author |
Otten, S.; Rolbiecki, K.; Caron, S.; Kim, J.S.; Ruiz de Austri, R.; Tattersall, J. |
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Title |
DeepXS: fast approximation of MSSM electroweak cross sections at NLO |
Type |
Journal Article |
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Year |
2020 |
Publication |
European Physical Journal C |
Abbreviated Journal |
Eur. Phys. J. C |
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Volume |
80 |
Issue |
1 |
Pages |
12 - 9pp |
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Keywords |
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Abstract |
We present a deep learning solution to the prediction of particle production cross sections over a complicated, high-dimensional parameter space. We demonstrate the applicability by providing state-of-the-art predictions for the production of charginos and neutralinos at the Large Hadron Collider (LHC) at the next-to-leading order in the phenomenological MSSM-19 and explicitly demonstrate the performance for pp ->(chi) over tilde (+)(1)(chi) over tilde (-)(1), (chi) over tilde (0)(2)(chi) over tilde (0)(2) and (chi) over tilde (0)(2)(chi) over tilde (+/-)(1) as a proof of concept which will be extended to all SUSY electroweak pairs. We obtain errors that are lower than the uncertainty from scale and parton distribution functions with mean absolute percentage errors of well below 0.5% allowing a safe inference at the next-to-leading order with inference times that improve the Monte Carlo integration procedures that have been available so far by a factor of O(10(7)) from O(min) to O(mu s) per evaluation. |
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Address |
[Otten, Sydney; Caron, Sascha] Radboud Univ Nijmegen, IMAPP, Nijmegen, Netherlands, Email: Sydney.Otten@ru.nl |
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Publisher |
Springer |
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English |
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ISSN |
1434-6044 |
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Notes |
WOS:000513271500001 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
4279 |
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Author |
Otten, S.; Caron, S.; de Swart, W.; van Beekveld, M.; Hendriks, L.; van Leeuwen, C.; Podareanu, D.; Ruiz de Austri, R.; Verheyen, R. |
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Title |
Event generation and statistical sampling for physics with deep generative models and a density information buffer |
Type |
Journal Article |
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Year |
2021 |
Publication |
Nature Communications |
Abbreviated Journal |
Nat. Commun. |
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Volume |
12 |
Issue |
1 |
Pages |
2985 - 16pp |
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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. |
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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 |
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Publisher |
Nature Research |
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English |
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ISSN |
2041-1723 |
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Notes |
WOS:000658761600003 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
4862 |
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Author |
van Beekveld, M.; Caron, S.; Hendriks, L.; Jackson, P.; Leinweber, A.; Otten, S.; Patrick, R.; Ruiz de Austri, R.; Santoni, M.; White, M. |
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Title |
Combining outlier analysis algorithms to identify new physics at the LHC |
Type |
Journal Article |
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Year |
2021 |
Publication |
Journal of High Energy Physics |
Abbreviated Journal |
J. High Energy Phys. |
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Volume |
09 |
Issue |
9 |
Pages |
024 - 33pp |
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Keywords |
Phenomenological Models; Supersymmetry Phenomenology |
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Abstract |
The lack of evidence for new physics at the Large Hadron Collider so far has prompted the development of model-independent search techniques. In this study, we compare the anomaly scores of a variety of anomaly detection techniques: an isolation forest, a Gaussian mixture model, a static autoencoder, and a beta-variational autoencoder (VAE), where we define the reconstruction loss of the latter as a weighted combination of regression and classification terms. We apply these algorithms to the 4-vectors of simulated LHC data, but also investigate the performance when the non-VAE algorithms are applied to the latent space variables created by the VAE. In addition, we assess the performance when the anomaly scores of these algorithms are combined in various ways. Using supersymmetric benchmark points, we find that the logical AND combination of the anomaly scores yielded from algorithms trained in the latent space of the VAE is the most effective discriminator of all methods tested. |
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Address |
[van Beekveld, Melissa] Clarendon Lab, Rudolf Peierls Ctr Theoret Phys, 20 Pks Rd, Oxford OX1 3PU, England, Email: mcbeekveld@gmail.com; |
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Corporate Author |
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Publisher |
Springer |
Place of Publication |
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Language |
English |
Summary Language |
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Original Title |
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Series Editor |
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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:000695421600003 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
4973 |
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Author |
Panes, B.; Eckner, C.; Hendriks, L.; Caron, S.; Dijkstra, K.; Johannesson, G.; Ruiz de Austri, R.; Zaharijas, G. |
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Title |
Identification of point sources in gamma rays using U-shaped convolutional neural networks and a data challenge |
Type |
Journal Article |
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Year |
2021 |
Publication |
Astronomy & Astrophysics |
Abbreviated Journal |
Astron. Astrophys. |
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Volume |
656 |
Issue |
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Pages |
A62 - 18pp |
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Keywords |
catalogs; gamma rays: general; astroparticle physics; methods: numerical; methods: data analysis; techniques: image processing |
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Abstract |
Context. At GeV energies, the sky is dominated by the interstellar emission from the Galaxy. With limited statistics and spatial resolution, accurately separating point sources is therefore challenging. Aims. Here we present the first application of deep learning based algorithms to automatically detect and classify point sources from gamma-ray data. For concreteness we refer to this approach as AutoSourceID. Methods. To detect point sources, we utilized U-shaped convolutional networks for image segmentation and k-means for source clustering and localization. We also explored the Centroid-Net algorithm, which is designed to find and count objects. Using two algorithms allows for a cross check of the results, while a combination of their results can be used to improve performance. The training data are based on 9.5 years of exposure from The Fermi Large Area Telescope (Fermi-LAT) and we used source properties of active galactic nuclei (AGNs) and pulsars (PSRs) from the fourth Fermi-LAT source catalog in addition to several models of background interstellar emission. The results of the localization algorithm are fed into a classification neural network that is trained to separate the three general source classes (AGNs, PSRs, and FAKE sources). Results. We compared our localization algorithms qualitatively with traditional methods and find them to have similar detection thresholds. We also demonstrate the robustness of our source localization algorithms to modifications in the interstellar emission models, which presents a clear advantage over traditional methods. The classification network is able to discriminate between the three classes with typical accuracy of similar to 70%, as long as balanced data sets are used in classification training. We published online our training data sets and analysis scripts and invite the community to join the data challenge aimed to improve the localization and classification of gamma-ray point sources. |
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Address |
[Panes, Boris] Pontificia Univ Catolica Chile, Ave Vicuna Mackenna 4860, Macul, Region Metropol, Chile, Email: bapanes@gmail.com |
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Corporate Author |
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Thesis |
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Publisher |
Edp Sciences S A |
Place of Publication |
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Language |
English |
Summary Language |
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Original Title |
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Abbreviated Series Title |
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ISSN |
0004-6361 |
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Notes |
WOS:000725877600001 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
5053 |
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