Abstract The transverse momentum ($$p_{\textrm{T}}$$ ) differential production cross section of the promptly produced charm-strange baryon$$\mathrm {\Xi _{c}^{0}}$$ (and its charge conjugate$$\overline{\mathrm {\Xi _{c}^{0}}}$$ ) is measured at midrapidity via its hadronic decay into$$\mathrm{\pi ^{+}}\Xi ^{-}$$ in p–Pb collisions at a centre-of-mass energy per nucleon–nucleon collision$$\sqrt{s_{\textrm{NN}}}~=~5.02$$ TeV with the ALICE detector at the LHC. The$$\mathrm {\Xi _{c}^{0}}$$ nuclear modification factor ($$R_{\textrm{pPb}}$$ ), calculated from the cross sections in pp and p–Pb collisions, is presented and compared with the$$R_{\textrm{pPb}}$$ of$$\mathrm {\Lambda _{c}^{+}}$$ baryons. The ratios between the$$p_{\textrm{T}}$$ -differential production cross section of$$\mathrm {\Xi _{c}^{0}}$$ baryons and those of$$\mathrm {D^0}$$ mesons and$$\mathrm {\Lambda _{c}^{+}}$$ baryons are also reported and compared with results at forward and backward rapidity from the LHCb Collaboration. The measurements of the production cross section of prompt$$\Xi ^0_\textrm{c}$$ baryons are compared with a model based on perturbative QCD calculations of charm-quark production cross sections, which includes only cold nuclear matter effects in p–Pb collisions, and underestimates the measurement by a factor of about 50. This discrepancy is reduced when the data is compared with a model that includes string formation beyond leading-colour approximation or in which hadronisation is implemented via quark coalescence. The$$p_{\textrm{T}}$$ -integrated cross section of prompt$$\Xi ^0_\textrm{c}$$ -baryon production at midrapidity extrapolated down to$$p_{\textrm{T}}$$ = 0 is also reported. These measurements offer insights and constraints for theoretical calculations of the hadronisation process. Additionally, they provide inputs for the calculation of the charm production cross section in p–Pb collisions at midrapidity.
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Rates of convergence for regression with the graph poly-Laplacian
Abstract In the (special) smoothing spline problem one considers a variational problem with a quadratic data fidelity penalty and Laplacian regularization. Higher order regularity can be obtained via replacing the Laplacian regulariser with a poly-Laplacian regulariser. The methodology is readily adapted to graphs and here we consider graph poly-Laplacian regularization in a fully supervised, non-parametric, noise corrupted, regression problem. In particular, given a dataset$$\{x_i\}_{i=1}^n$$ and a set of noisy labels$$\{y_i\}_{i=1}^n\subset \mathbb {R}$$ we let$$u_n{:}\{x_i\}_{i=1}^n\rightarrow \mathbb {R}$$ be the minimizer of an energy which consists of a data fidelity term and an appropriately scaled graph poly-Laplacian term. When$$y_i = g(x_i)+\xi _i$$ , for iid noise$$\xi _i$$ , and using the geometric random graph, we identify (with high probability) the rate of convergence of$$u_n$$ togin the large data limit$$n\rightarrow \infty $$ . Furthermore, our rate is close to the known rate of convergence in the usual smoothing spline model.
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- PAR ID:
- 10475993
- Publisher / Repository:
- Springer Science + Business Media
- Date Published:
- Journal Name:
- Sampling Theory, Signal Processing, and Data Analysis
- Volume:
- 21
- Issue:
- 2
- ISSN:
- 2730-5716
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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