zfit: Scalable pythonic fitting

Eschle, J.; Navarro, A. Puig; Coutinho, R. Silva; Serra, N.

doi:10.1016/j.softx.2020.100508

Cited by 7 publications

(4 citation statements)

References 80 publications

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“…where

with the centre of mass energy of the collision

13 TeV. The parameters p 1 , p 2 , and p 3 are obtained from an unbinned fit to the side-band data in using the package (Eschle et al, 2020 ). This Ansatz has been used previously in analyses performed at the LHC (ATLAS Collaboration, 2016 ) and is similar to that used in more recent searches with the omission of the last free parameter (CMS Collaboration, 2018 ; ATLAS Collaboration, 2020 ).…”

Section: Methodsmentioning

confidence: 99%

CURTAINs for your sliding window: Constructing unobserved regions by transforming adjacent intervals

et al. 2023

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We propose a new model independent technique for constructing background data templates for use in searches for new physics processes at the LHC. This method, called Curtains, uses invertible neural networks to parameterise the distribution of side band data as a function of the resonant observable. The network learns a transformation to map any data point from its value of the resonant observable to another chosen value. Using Curtains, a template for the background data in the signal window is constructed by mapping the data from the side-bands into the signal region. We perform anomaly detection using the Curtains background template to enhance the sensitivity to new physics in a bump hunt. We demonstrate its performance in a sliding window search across a wide range of mass values. Using the LHC Olympics dataset, we demonstrate that Curtains matches the performance of other leading approaches which aim to improve the sensitivity of bump hunts, can be trained on a much smaller range of the invariant mass, and is fully data driven.

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“…where

with the centre of mass energy of the collision

Section: Methodsmentioning

confidence: 99%

CURTAINs for your sliding window: Constructing unobserved regions by transforming adjacent intervals

et al. 2023

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“…To measure |H − |, φ − , and φ + , an unbinned maximum-likelihood fit to the three-dimensional angular distribution of signal-weighted data is performed using zfit [30]. For the fit, the B 0 → D * − (D * + s → D + s γ) candidates from the m(D * − D * + s ) fit in section 6 are used with per-candidate signal weights assigned.…”

Section: Angular Fit To Datamentioning

confidence: 99%

Angular analysis of $$ {B}^0\to {D}^{\ast -}{D}_s^{\ast +} $$ with $$ {D}_s^{\ast +}\to {D}_s^{+}\gamma $$ decays

Aaij¹,

Beteta²,

Ackernley³

et al. 2021

J. High Energ. Phys.

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The first full angular analysis of the $$ {B}^0\to {D}^{\ast -}{D}_s^{\ast +} $$ B 0 → D ∗ − D s ∗ + decay is performed using 6 fb−1 of pp collision data collected with the LHCb experiment at a centre-of-mass energy of 13 TeV. The $$ {D}_s^{\ast +}\to {D}_s^{+}\gamma $$ D s ∗ + → D s + γ and D*− → $$ {\overline{D}}^0{\pi}^{-} $$ D ¯ 0 π − vector meson decays are used with the subsequent $$ {D}_s^{+} $$ D s + → K+K−π+ and $$ {\overline{D}}^0 $$ D ¯ 0 → K+π− decays. All helicity amplitudes and phases are measured, and the longitudinal polarisation fraction is determined to be fL = 0.578 ± 0.010 ± 0.011 with world-best precision, where the first uncertainty is statistical and the second is systematic. The pattern of helicity amplitude magnitudes is found to align with expectations from quark-helicity conservation in B decays. The ratio of branching fractions [ℬ($$ {B}^0\to {D}^{\ast -}{D}_s^{\ast +} $$ B 0 → D ∗ − D s ∗ + ) × ℬ($$ {D}_s^{\ast +}\to {D}_s^{+}\gamma $$ D s ∗ + → D s + γ )]/ℬ(B0 → D*−$$ {D}_s^{+} $$ D s + ) is measured to be 2.045 ± 0.022 ± 0.071 with world-best precision. In addition, the first observation of the Cabibbo-suppressed Bs → D*−$$ {D}_s^{+} $$ D s + decay is made with a significance of seven standard deviations. The branching fraction ratio ℬ(Bs → D*−$$ {D}_s^{+} $$ D s + )/ℬ(B0 → D*−$$ {D}_s^{+} $$ D s + ) is measured to be 0.049 ± 0.006 ± 0.003 ± 0.002, where the third uncertainty is due to limited knowledge of the ratio of fragmentation fractions.

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“…Increasingly, GPUs are also used for offline data analysis such as fitting complex theoretical distributions with many free parameters to large data samples, for example, using Nvidia's CUDA API [8], or with TensorFlow-based frameworks [9,10]. As dataset sizes in particle physics are expected to increase exponentially in the coming years, while CPU clock speeds plateau, hardware accelerators are expected become increasingly important in online and offline computing.…”

Section: Introductionmentioning

confidence: 99%

Studying the Potential of Graphcore® IPUs for Applications in Particle Physics

Maddrell-Mander

Mohan

Marshall

et al. 2021

Comput Softw Big Sci

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This paper presents the first study of Graphcore’s Intelligence Processing Unit (IPU) in the context of particle physics applications. The IPU is a new type of processor optimised for machine learning. Comparisons are made for neural-network-based event simulation, multiple-scattering correction, and flavour tagging, implemented on IPUs, GPUs and CPUs, using a variety of neural network architectures and hyperparameters. Additionally, a Kálmán filter for track reconstruction is implemented on IPUs and GPUs. The results indicate that IPUs hold considerable promise in addressing the rapidly increasing compute needs in particle physics.

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zfit: Scalable pythonic fitting

Cited by 7 publications

References 80 publications

CURTAINs for your sliding window: Constructing unobserved regions by transforming adjacent intervals

CURTAINs for your sliding window: Constructing unobserved regions by transforming adjacent intervals

Angular analysis of $$ {B}^0\to {D}^{\ast -}{D}_s^{\ast +} $$ with $$ {D}_s^{\ast +}\to {D}_s^{+}\gamma $$ decays

Studying the Potential of Graphcore® IPUs for Applications in Particle Physics

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