Plasma dechirper and lens for electron beams from laser wakefield acceleration in a tailored density profile
Authors:
T. L. Steyn,
A. Panchal,
O. Vasilovici,
F. M. Herrmann,
S. Schöbel,
P. Ufer,
O. Khomyshyn,
Y. -Y. Chang,
I. Moulanier,
M. Masckala,
M. Samir,
C. Ballage,
M. LaBerge,
P. Désesquelles,
F. Massimo,
S. Dobosz Dufrénoy,
U. Schramm,
A. Irman,
B. Cros
Abstract:
Achieving high-quality electron beams from laser wakefield accelerators critically relies on density tailoring to control electron dynamics during injection, acceleration, and extraction. We report on the experimental observation of electron beam acceleration and shaping, in transverse momentum and longitudinal phase space, controlled by plasma density tailoring in a gas cell. Electron beams with…
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Achieving high-quality electron beams from laser wakefield accelerators critically relies on density tailoring to control electron dynamics during injection, acceleration, and extraction. We report on the experimental observation of electron beam acceleration and shaping, in transverse momentum and longitudinal phase space, controlled by plasma density tailoring in a gas cell. Electron beams with a FWHM charge of 40 pC at an energy of 190 MeV, 3.4\% energy spread and an rms divergence of 0.46 mrad, corresponding to a transverse momentum spread of 0.2 $m_e c$, have been measured. These beams have a peak spectral brightness of up to 8 pC/MeV/mrad. Simulations using experimental parameters as input show that acceleration in the plasma plateau leads to chirped electron beams which then undergo transverse momentum spread reduction in a plasma down-ramp followed by dechirping in a 10~mm long plasma tail, leading to the measured peaked spectra. %\textcolor{blue}{A control experiment with and without the LPT confirms these results.} The comparison of experimental results with and without long plasma tail confirms this analysis.
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Submitted 29 April, 2026;
originally announced April 2026.
Uncertainty Quantification of the 4th kind; optimal posterior accuracy-uncertainty tradeoff with the minimum enclosing ball
Authors:
Hamed Hamze Bajgiran,
Pau Batlle Franch,
Houman Owhadi,
Mostafa Samir,
Clint Scovel,
Mahdy Shirdel,
Michael Stanley,
Peyman Tavallali
Abstract:
There are essentially three kinds of approaches to Uncertainty Quantification (UQ): (A) robust optimization, (B) Bayesian, (C) decision theory. Although (A) is robust, it is unfavorable with respect to accuracy and data assimilation. (B) requires a prior, it is generally brittle and posterior estimations can be slow. Although (C) leads to the identification of an optimal prior, its approximation s…
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There are essentially three kinds of approaches to Uncertainty Quantification (UQ): (A) robust optimization, (B) Bayesian, (C) decision theory. Although (A) is robust, it is unfavorable with respect to accuracy and data assimilation. (B) requires a prior, it is generally brittle and posterior estimations can be slow. Although (C) leads to the identification of an optimal prior, its approximation suffers from the curse of dimensionality and the notion of risk is one that is averaged with respect to the distribution of the data. We introduce a 4th kind which is a hybrid between (A), (B), (C), and hypothesis testing. It can be summarized as, after observing a sample $x$, (1) defining a likelihood region through the relative likelihood and (2) playing a minmax game in that region to define optimal estimators and their risk. The resulting method has several desirable properties (a) an optimal prior is identified after measuring the data, and the notion of risk is a posterior one, (b) the determination of the optimal estimate and its risk can be reduced to computing the minimum enclosing ball of the image of the likelihood region under the quantity of interest map (which is fast and not subject to the curse of dimensionality). The method is characterized by a parameter in $ [0,1]$ acting as an assumed lower bound on the rarity of the observed data (the relative likelihood). When that parameter is near $1$, the method produces a posterior distribution concentrated around a maximum likelihood estimate with tight but low confidence UQ estimates. When that parameter is near $0$, the method produces a maximal risk posterior distribution with high confidence UQ estimates. In addition to navigating the accuracy-uncertainty tradeoff, the proposed method addresses the brittleness of Bayesian inference by navigating the robustness-accuracy tradeoff associated with data assimilation.
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Submitted 13 September, 2022; v1 submitted 24 August, 2021;
originally announced August 2021.