16 Search Results

This paper presents the results of a systematic study of projection biases in the Weak Lensing analysis of cosmic shear and the combination of galaxy clustering and galaxy-galaxy lensing using data collected during the first-year of running the Dark Energy Survey experiment. The study uses $$\Lambda$$CDM as the cosmological model and two-point correlation functions for the WL analysis. The results in this paper show that, independent of the WL analysis, projection biases of more than $$1\sigma$$ exist, and are a function of the position of the true values of the parameters $$h$$, $$n_{s}$$, $$\Omega_{b}$$, and $$\Omega_{\nu}h^{2}$$ with respect to theirmore » prior probabilities. For cosmic shear, and the combination of galaxy clustering and galaxy-galaxy lensing, this study shows that the coverage probability of the $$68.27\%$$ credible intervals ranges from as high as $$93\%$$ to as low as $$16\%$$, and that these credible intervals are inflated, on average, by $$29\%$$ for cosmic shear and $$20\%$$ for the combination of galaxy clustering and galaxy-galaxy lensing. The results of the study also show that, in six out of nine tested cases, the reduction in error bars obtained by transforming credible intervals into confidence intervals is equivalent to an increase in the amount of data by a factor of three.« less

We present a systematic study of projection biases in the weak lensing analysis of the first year of data from the Dark Energy Survey (DES) experiment. In the analysis we used a $$\Lambda$$CDM model and three two-point correlation functions. We show that these biases are a consequence of projecting, or marginalizing, over parameters like $$h$$, $$\Omega_b$$, $$n_s$$ and $$\Omega_\nu h^2$$ that are both poorly constrained and correlated with the parameters of interest like $$\Omega_m$$, $$\sigma_8$$ and $$S_8$$. Covering the relevant parameter space we show that the projection biases are a function of where the true values of the poorly constrainedmore » parameters lie with respect to the parameter priors. For example, biases in the position of the posteriors can exceed the 1.5$$\sigma$$ level if the true values of $$h$$ and $$n_s$$ are close to the top of the prior's range and the true values of $$\Omega_b$$ and $$\Omega_\nu h^2$$ are close to the bottom of the range of their priors. We also show that in some cases the 1D credible intervals can be over-specified by as much as 30% and coverage can be as low as 27%. Finally we estimate these projection biases for the analysis of three and six years worth of DES data.« less

Production cross sections of Y(1S), Y(2S), and Y(3S) states decaying into μ⁺μ^- in proton-lead (pPb) collisions are reported using data collected by the CMS experiment at $$\sqrt{S_{NN}}$$ = 5.02 TeV. A comparison is made with corresponding cross sections obtained with data measured at the same collision energy and scaled by the Pb nucleus mass number. The nuclear modification factor for Y(1S) is found to be R_pPb(Y(1S)) = 0.806 ± 0.024 (stat) ± 0.059 (syst). Similar results for the excited states indicate a sequential suppression pattern, such that R_pPb(Y(1S)) > R_pPb(Y(2S)) . R_pPb(Y(3S)). The suppression of all states is much lessmore » pronounced in pPb than in PbPb collisions, and independent of transverse momentum $$p^Y_T$$ and center-of-mass rapidity y$$^Y_{CM}$$ of the individual Y state in the studied range $$p^Y_T$$ < 30 GeV/c and |y$$^Y_{cm}$$| <1.93. Models that incorporate final-state effects of bottomonia in pPb collisions are in better agreement with the data than those which only assume initial-state modifications.« less

Neutrinos are particles that interact rarely, so identifying them requires large detectors which produce lots of data. Processing this data with the computing power available is becoming even more difficult as the detectors increase in size to reach their physics goals. Liquid argon time projection chamber (LArTPC) neutrino experiments are expected to grow in the next decade to have 100 times more wires than in currently operating experiments, and modernization of LArTPC reconstruction code, including parallelization both at data- and instruction-level, will help to mitigate this challenge. The LArTPC hit finding algorithm is used across multiple experiments through a commonmore » software framework. In this paper we discuss a parallel implementation of this algorithm. Using a standalone setup we find speedup factors of two times from vectorization and 30–100 times from multi-threading on Intel architectures. The new version has been incorporated back into the framework so that it can be used by experiments. On a serial execution, the integrated version is about 10 times faster than the previous one and, once parallelization is enabled, more speedups comparable to the standalone program are achieved.« less

Wide-field astronomical surveys are often affected by the presence of undesirable reflections (often known as "ghosting artifacts" or "ghosts") and scattered-light artifacts. The identification and mitigation of these artifacts is important for rigorous astronomical analyses of faint and low-surface-brightness systems. However, the identification of ghosts and scattered-light artifacts is challenging due to a) the complex morphology of these features and b) the large data volume of current and near-future surveys. In this work, we use images from the Dark Energy Survey (DES) to train, validate, and test a deep neural network (Mask R-CNN) to detect and localize ghosts and scattered-lightmore » artifacts. We find that the ability of the Mask R-CNN model to identify affected regions is superior to that of conventional algorithms and traditional convolutional neural networks methods. We propose that a multi-step pipeline combining Mask R-CNN segmentation with a classical CNN classifier provides a powerful technique for the automated detection of ghosting and scattered-light artifacts in current and near-future surveys.« less

We investigate the feasibility of using deep learning techniques, in the form of a one-dimensional convolutional neural network (1D-CNN), for the extraction of signals from the raw waveforms produced by the individual channels of liquid argon time projection chamber (LArTPC) detectors. A minimal generic LArTPC detector model is developed to generate realistic noise and signal waveforms used to train and test the 1D-CNN, and evaluate its performance on low-level signals. We demonstrate that our approach overcomes the inherent shortcomings of traditional cut-based methods by extending sensitivity to signals with ADC values below their imposed thresholds. This approach exhibits great promisemore » in enhancing the capabilities of future generation neutrino experiments like DUNE to carry out their low-energy neutrino physics programs.« less

We describe the use of a 1DCNN to recognize signals in the raw waveforms generated from individual channels of LArTPC detectors used in neutrino experiments. In addition, this method also identifies the region-of-interest (ROI) localizing the position of this signal within the full waveform.

Wide-field astronomical surveys are often affected by the presence of undesirable reflections (often known as “ghosting artifacts” or “ghosts”) and scattered-light artifacts. The identification and mitigation of these artifacts is important for rigorous astronomical analyses of faint and low-surface-brightness systems. In this work, we use images from the Dark Energy Survey (DES) to train, validate, and test a deep neural network (Mask R-CNN) to detect and localize ghosts and scattered- light artifacts. We find that the ability of the Mask R-CNN model to identify affected regions is superior to that of conventional algorithms that model the physical processes that leadmore » to such artifacts, thus providing a powerful technique for the automated detection of ghosting and scattered-light artifacts in current and near-future surveys.« less

Machine learning algorithms are becoming increasingly prevalent and performant in the reconstruction of events in accelerator-based neutrino experiments. These sophisticated algorithms can be computationally expensive. At the same time, the data volumes of such experiments are rapidly increasing. The demand to process billions of neutrino events with many machine learning algorithm inferences creates a computing challenge. We explore a computing model in which heterogeneous computing with GPU coprocessors is made available as a web service. The coprocessors can be efficiently and elastically deployed to provide the right amount of computing for a given processing task. With our approach, Services formore » Optimized Network Inference on Coprocessors (SONIC), we integrate GPU acceleration specifically for the ProtoDUNE-SP reconstruction chain without disrupting the native computing workflow. With our integrated framework, we accelerate the most time-consuming task, track and particle shower hit identification, by a factor of 17. This results in a factor of 2.7 reduction in the total processing time when compared with CPU-only production. For this particular task, only 1 GPU is required for every 68 CPU threads, providing a cost-effective solution.« less

Tmore » he second-order Fourier coefficients ( $v_2$) characterizing the azimuthal distributions of Υ(1S) and Υ(2S) mesons produced in PbPb collisions at $\sqrt{s_{{}_{\mathrm{NN}}}}=5.02\text{eV}$ are studied. he Υ mesons are reconstructed in their dimuon decay channel, as measured by the CMS detector. he collected data set corresponds to an integrated luminosity of $1.7{\text{nb}}^{-1}$. he scalar product method is used to extract the $v_2$ coefficients of the azimuthal distributions. Results are reported for the rapidity range $\left|y\right|<2.4$, in the transverse momentum interval $0<p_{\mathrm{}}<50\text{GeV}/c$, and in three centrality ranges of 10–30%, 30–50% and 50–90%. In contrast to the $\mathrm{J}/\mathit{\psi }$ mesons, the measured $v_2$ values for the Image 5 mesons are found to be consistent with zero.« less