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 ![sorted by Address field, ascending order (up)](img/sort_asc.gif) |
[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 |
|
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 |
Otten, S.; Rolbiecki, K.; Caron, S.; Kim, J.S.; Ruiz de Austri, R.; Tattersall, J. |
Title |
DeepXS: fast approximation of MSSM electroweak cross sections at NLO |
Type |
Journal Article |
Year |
2020 |
Publication |
European Physical Journal C |
Abbreviated Journal |
Eur. Phys. J. C |
Volume |
80 |
Issue |
1 |
Pages |
12 - 9pp |
Keywords |
|
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. |
Address ![sorted by Address field, ascending order (up)](img/sort_asc.gif) |
[Otten, Sydney; Caron, Sascha] Radboud Univ Nijmegen, IMAPP, Nijmegen, Netherlands, Email: Sydney.Otten@ru.nl |
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 |
1434-6044 |
ISBN |
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Medium |
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Area |
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Expedition |
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Conference |
|
Notes |
WOS:000513271500001 |
Approved |
no |
Is ISI |
yes |
International Collaboration |
yes |
Call Number |
IFIC @ pastor @ |
Serial |
4279 |
Permanent link to this record |
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Author |
Panes, B.; Eckner, C.; Hendriks, L.; Caron, S.; Dijkstra, K.; Johannesson, G.; Ruiz de Austri, R.; Zaharijas, G. |
Title |
Identification of point sources in gamma rays using U-shaped convolutional neural networks and a data challenge |
Type |
Journal Article |
Year |
2021 |
Publication |
Astronomy & Astrophysics |
Abbreviated Journal |
Astron. Astrophys. |
Volume |
656 |
Issue |
|
Pages |
A62 - 18pp |
Keywords |
catalogs; gamma rays: general; astroparticle physics; methods: numerical; methods: data analysis; techniques: image processing |
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. |
Address ![sorted by Address field, ascending order (up)](img/sort_asc.gif) |
[Panes, Boris] Pontificia Univ Catolica Chile, Ave Vicuna Mackenna 4860, Macul, Region Metropol, Chile, Email: bapanes@gmail.com |
Corporate Author |
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Thesis |
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Publisher |
Edp Sciences S A |
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 |
0004-6361 |
ISBN |
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Medium |
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Area |
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Expedition |
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Conference |
|
Notes |
WOS:000725877600001 |
Approved |
no |
Is ISI |
yes |
International Collaboration |
yes |
Call Number |
IFIC @ pastor @ |
Serial |
5053 |
Permanent link to this record |
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Author |
Stoppa, F.; Bhattacharyya, S.; Ruiz de Austri, R.; Vreeswijk, P.; Caron, S.; Zaharijas, G.; Bloemen, S.; Principe, G.; Malyshev, D.; Vodeb, V.; Groot, P.J.; Cator, E.; Nelemans, G. |
Title |
AutoSourceID-Classifier Star-galaxy classification using a convolutional neural network with spatial information |
Type |
Journal Article |
Year |
2023 |
Publication |
Astronomy & Astrophysics |
Abbreviated Journal |
Astron. Astrophys. |
Volume |
680 |
Issue |
|
Pages |
A109 - 16pp |
Keywords |
methods: data analysis; techniques: image processing; astronomical databases: miscellaneous; stars: imaging; Galaxies: statistics |
Abstract |
Aims. Traditional star-galaxy classification techniques often rely on feature estimation from catalogs, a process susceptible to introducing inaccuracies, thereby potentially jeopardizing the classification's reliability. Certain galaxies, especially those not manifesting as extended sources, can be misclassified when their shape parameters and flux solely drive the inference. We aim to create a robust and accurate classification network for identifying stars and galaxies directly from astronomical images.Methods. The AutoSourceID-Classifier (ASID-C) algorithm developed for this work uses 32x32 pixel single filter band source cutouts generated by the previously developed AutoSourceID-Light (ASID-L) code. By leveraging convolutional neural networks (CNN) and additional information about the source position within the full-field image, ASID-C aims to accurately classify all stars and galaxies within a survey. Subsequently, we employed a modified Platt scaling calibration for the output of the CNN, ensuring that the derived probabilities were effectively calibrated, delivering precise and reliable results.Results. We show that ASID-C, trained on MeerLICHT telescope images and using the Dark Energy Camera Legacy Survey (DECaLS) morphological classification, is a robust classifier and outperforms similar codes such as SourceExtractor. To facilitate a rigorous comparison, we also trained an eXtreme Gradient Boosting (XGBoost) model on tabular features extracted by SourceExtractor. While this XGBoost model approaches ASID-C in performance metrics, it does not offer the computational efficiency and reduced error propagation inherent in ASID-C's direct image-based classification approach. ASID-C excels in low signal-to-noise ratio and crowded scenarios, potentially aiding in transient host identification and advancing deep-sky astronomy. |
Address ![sorted by Address field, ascending order (up)](img/sort_asc.gif) |
[Stoppa, F.; Vreeswijk, P.; Bloemen, S.; Groot, P. J.; Nelemans, G.] Radboud Univ Nijmegen, Dept Astrophys IMAPP, POB 9010, NL-6500 GL Nijmegen, Netherlands, Email: f.stoppa@astro.ru.nl |
Corporate Author |
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Thesis |
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Publisher |
Edp Sciences S A |
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 |
0004-6361 |
ISBN |
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Medium |
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Area |
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Expedition |
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Conference |
|
Notes |
WOS:001131898100001 |
Approved |
no |
Is ISI |
yes |
International Collaboration |
yes |
Call Number |
IFIC @ pastor @ |
Serial |
5888 |
Permanent link to this record |
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Author |
Stoppa, F.; Vreeswijk, P.; Bloemen, S.; Bhattacharyya, S.; Caron, S.; Johannesson, G.; Ruiz de Austri, R.; van den Oetelaar, C.; Zaharijas, G.; Groot, P.J.; Cator, E.; Nelemans, G. |
Title |
AutoSourceID-Light Fast optical source localization via U-Net and Laplacian of Gaussian |
Type |
Journal Article |
Year |
2022 |
Publication |
Astronomy & Astrophysics |
Abbreviated Journal |
Astron. Astrophys. |
Volume |
662 |
Issue |
|
Pages |
A109 - 8pp |
Keywords |
astronomical databases; miscellaneous; methods; data analysis; stars; imaging; techniques; image processing |
Abstract |
Aims. With the ever-increasing survey speed of optical wide-field telescopes and the importance of discovering transients when they are still young, rapid and reliable source localization is paramount. We present AutoSourceID-Light (ASID-L), an innovative framework that uses computer vision techniques that can naturally deal with large amounts of data and rapidly localize sources in optical images. Methods. We show that the ASID-L algorithm based on U-shaped networks and enhanced with a Laplacian of Gaussian filter provides outstanding performance in the localization of sources. A U-Net network discerns the sources in the images from many different artifacts and passes the result to a Laplacian of Gaussian filter that then estimates the exact location. Results. Using ASID-L on the optical images of the MeerLICHT telescope demonstrates the great speed and localization power of the method. We compare the results with SExtractor and show that our method outperforms this more widely used method. ASID-L rapidly detects more sources not only in low- and mid-density fields, but particularly in areas with more than 150 sources per square arcminute. The training set and code used in this paper are publicly available. |
Address ![sorted by Address field, ascending order (up)](img/sort_asc.gif) |
[Stoppa, F.; Vreeswijk, P.; Bloemen, S.; Groot, P. J.; Nelemans, G.] Radboud Univ Nijmegen, Dept Astrophys, IMAPP, POB 9010, NL-6500 GL Nijmegen, Netherlands, Email: f.stoppa@astro.ru.nl |
Corporate Author |
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Thesis |
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Publisher |
Edp Sciences S A |
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 |
0004-6361 |
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:000818665600009 |
Approved |
no |
Is ISI |
yes |
International Collaboration |
yes |
Call Number |
IFIC @ pastor @ |
Serial |
5291 |
Permanent link to this record |