| 1. |
- Ahdida, C., et al.
(author)
-
Sensitivity of the SHiP experiment to Heavy Neutral Leptons
- 2019
-
In: Journal of High Energy Physics (JHEP). - 1126-6708 .- 1029-8479. ; :4
-
Journal article (peer-reviewed)abstract
- Heavy Neutral Leptons (HNLs) are hypothetical particles predicted by many extensions of the Standard Model. These particles can, among other things, explain the origin of neutrino masses, generate the observed matter-antimatter asymmetry in the Universe and provide a dark matter candidate. The SHiP experiment will be able to search for HNLs produced in decays of heavy mesons and travelling distances ranging between O(50 m) and tens of kilometers before decaying. We present the sensitivity of the SHiP experiment to a number of HNL's benchmark models and provide a way to calculate the SHiP's sensitivity to HNLs for arbitrary patterns of flavour mixings. The corresponding tools and data files are also made publicly available.
|
|
| 2. |
- Ahdida, C., et al.
(author)
-
The experimental facility for the Search for Hidden Particles at the CERN SPS
- 2019
-
In: Journal of Instrumentation. - : Institute of Physics Publishing (IOPP). - 1748-0221. ; 14
-
Journal article (peer-reviewed)abstract
- The Search for Hidden Particles (SHiP) Collaboration has shown that the CERN SPS accelerator with its 400 GeV/c proton beam offers a unique opportunity to explore the Hidden Sector [1-3]. The proposed experiment is an intensity frontier experiment which is capable of searching for hidden particles through both visible decays and through scattering signatures from recoil of electrons or nuclei. The high-intensity experimental facility developed by the SHiP Collaboration is based on a number of key features and developments which provide the possibility of probing a large part of the parameter space for a wide range of models with light long-lived super-weakly interacting particles with masses up to O(10) GeV/c(2) in an environment of extremely clean background conditions. This paper describes the proposal for the experimental facility together with the most important feasibility studies. The paper focuses on the challenging new ideas behind the beam extraction and beam delivery, the proton beam dump, and the suppression of beam-induced background.
|
|
| 3. |
- Ahdida, C., et al.
(author)
-
Fast simulation of muons produced at the SHiP experiment using Generative Adversarial Networks
- 2019
-
In: Journal of Instrumentation. - : IOP PUBLISHING LTD. - 1748-0221. ; 14
-
Journal article (peer-reviewed)abstract
- This paper presents a fast approach to simulating muons produced in interactions of the SPS proton beams with the target of the SHiP experiment. The SHIP experiment will be able to search for new long-lived particles produced in a 400 GeV/c SPS proton beam dump and which travel distances between fifty metres and tens of kilometers. The SHiP detector needs to operate under ultra-low background conditions and requires large simulated samples of muon induced background processes. Through the use of Generative Adversarial Networks it is possible to emulate the simulation of the interaction of 400 GeV/c proton beams with the SHiP target, an otherwise computationally intensive process. For the simulation requirements of the SHiP experiment, generative networks are capable of approximating the full simulation of the dense fixed target, offering a speed increase by a factor of O(10(6)). To evaluate the performance of such an approach, comparisons of the distributions of reconstructed muon momenta in SHiP's spectrometer between samples using the full simulation and samples produced through generative models are presented. The methods discussed in this paper can be generalised and applied to modelling any non-discrete multi-dimensional distribution.
|
|
| 4. |
- Ferizi, U., et al.
(author)
-
Diffusion MRI microstructure models with in vivo human brain Connectome data: results from a multi-group comparison
- 2017
-
In: NMR in Biomedicine. - : Wiley. - 0952-3480 .- 1099-1492. ; 30:9, s. Article no e3734 -
-
Journal article (peer-reviewed)abstract
- A large number of mathematical models have been proposed to describe the measured signal in diffusion-weighted (DW) magnetic resonance imaging (MRI). However, model comparison to date focuses only on specific subclasses, e.g. compartment models or signal models, and little or no information is available in the literature on how performance varies among the different types of models. To address this deficiency, we organized the White Matter Modeling Challenge' during the International Symposium on Biomedical Imaging (ISBI) 2015 conference. This competition aimed to compare a range of different kinds of models in their ability to explain a large range of measurable in vivo DW human brain data. Specifically, we assessed the ability of models to predict the DW signal accurately for new diffusion gradients and b values. We did not evaluate the accuracy of estimated model parameters, as a ground truth is hard to obtain. We used the Connectome scanner at the Massachusetts General Hospital, using gradient strengths of up to 300mT/m and a broad set of diffusion times. We focused on assessing the DW signal prediction in two regions: the genu in the corpus callosum, where the fibres are relatively straight and parallel, and the fornix, where the configuration of fibres is more complex. The challenge participants had access to three-quarters of the dataset and their models were ranked on their ability to predict the remaining unseen quarter of the data. The challenge provided a unique opportunity for a quantitative comparison of diverse methods from multiple groups worldwide. The comparison of the challenge entries reveals interesting trends that could potentially influence the next generation of diffusion-based quantitative MRI techniques. The first is that signal models do not necessarily outperform tissue models; in fact, of those tested, tissue models rank highest on average. The second is that assuming a non-Gaussian (rather than purely Gaussian) noise model provides little improvement in prediction of unseen data, although it is possible that this may still have a beneficial effect on estimated parameter values. The third is that preprocessing the training data, here by omitting signal outliers, and using signal-predicting strategies, such as bootstrapping or cross-validation, could benefit the model fitting. The analysis in this study provides a benchmark for other models and the data remain available to build up a more complete comparison in the future.
|
|
| 5. |
- Law, L. L., et al.
(author)
-
Cardiorespiratory Fitness Modifies Influence of Sleep Problems on Cerebrospinal Fluid Biomarkers in an At-Risk Cohort
- 2019
-
In: Journal of Alzheimers Disease. - : IOS Press. - 1387-2877 .- 1875-8908. ; 69:1, s. 111-121
-
Journal article (peer-reviewed)abstract
- Background: Previous studies indicate that cardiorespiratory fitness (CRF) and sleep are each favorably associated with Alzheimer's disease (AD) pathophysiology, including reduced amyloid-beta (A beta) and tau pathology. However, few studies have examined CRF and sleep in the same analysis. Objective: To examine the relationship between sleep and core AD cerebrospinal fluid (CSF) biomarkers among at-risk healthy late-middle-aged adults and determine whether CRF modifies this association. Methods: Seventy-four adults (age = 64.38 +/- 5.48, 68.9% female) from the Wisconsin Registry for Alzheimer's Prevention participated. Sleep was evaluated using the Medical Outcomes Study Sleep Scale, specifically the Sleep Problems Index I (SPI), which incorporates domains of sleep disturbance, somnolence, sleep adequacy, and shortness of breath. Higher scores indicate greater sleep problems. To assess CRF, participants underwent a graded exercise test. CSF was collected via lumbar puncture, from which A beta(42), total-tau (t-tau), and phosphorylated-tau (p-tau) were immunoassayed. Regression analyses examined the association between SPI and CSF biomarkers, and the interaction between SPI and CRF on these same biomarkers, adjusting for relevant covariates. Results: Higher SPI scores were associated with greater p-tau (p = 0.027) and higher t-tau/A beta(42) (p = 0.021) and p-tau/A beta(42) (p = 0.009) ratios. Analyses revealed significant SPI*CRF interactions for t-tau (p = 0.016), p-tau (p = 0.008), and p-tau/A beta(42)(p = 0.041); with a trend for t-tau/A beta(42) (p = 0.061). Specifically, the relationship between poorer sleep and these biomarkers was significant among less fit individuals, but not among those who were more fit. Conclusion: In a late-middle-aged at-risk cohort, CRF attenuated the association between poor sleep and levels of select CSF biomarkers. This suggests fitness may play an important role in preventing AD by protecting against pathology, even in impaired sleep.
|
|
