Abstract: We study the impact of the cosmological parameters uncertainties on the measurements of primordial non-Gaussianity through the large-scale non-Gaussian halo bias effect. While this is not expected to be an issue for the standard Lambda CDM model, it may not be the case for more general models that modify the large-scale shape of the power spectrum. We consider the so-called local non-Gaussianity model, parametrized by the f(NL) non-Gaussianity parameter which is zero for a Gaussian case, and make forecasts on f(NL) from planned surveys, alone and combined with a Planck CMB prior. In particular, we consider EUCLID- and LSST-like surveys and forecast the correlations among f(NL) and the running of the spectral index alpha(s), the dark energy equation of state w, the effective sound speed of dark energy perturbations c(s)(2), the total mass of massive neutrinos M-nu = Sigma m(nu), and the number of extra relativistic degrees of freedom N-nu(rel). Neglecting CMB information on f(NL) and scales k > 0.03h/Mpc, we find that, if N-nu(rel) is assumed to be known, the uncertainty on cosmological parameters increases the error on f(NL) by 10 to 30% depending on the survey. Thus the f(NL) constraint is remarkable robust to cosmological model uncertainties. On the other hand, if N-nu(rel) is simultaneously constrained from the data, the f(NL) error increases by similar to 80%. Finally, future surveys which provide a large sample of galaxies or galaxy clusters over a volume comparable to the Hubble volume can measure primordial non-Gaussianity of the local form with a marginalized 1-sigma error of the order Delta f(NL) similar to 2 – 5, after combination with CMB priors for the remaining cosmological parameters. These results are competitive with CMB bispectrum constraints achievable with an ideal CMB experiment.

Abstract: In this work we study the effects on the spatial resolution when depth of interaction (001) information is included in the parameterization of the line of response (LOR) for a small animal positron emission tomography (PET) system. One of the most important degrading factors for PET is the parallax error introduced in systems that do not provide DOI information of the recorded gamma-rays. Our group has designed a simple and inexpensive method for DOI determination in continuous scintillation crystals. This method is based, on one hand, in the correlation between the scintillation light distribution width in monolithic crystals and the DOI, and, on the other hand, on a small modification of the widely applied charge dividing circuits (CDR). In this work we present a new system calibration that includes the DOI information, and also the development of the correction equations that relates the LOR without and with DOI information. We report the results obtained for different measurements along the transaxial field of view (FOV) and the image quality enhancement achieved specially at the edge of the FOV.

Abstract: The Galactic Center Excess (GCE) in GeV gamma rays has been debated for over a decade, with the possibility that it might be due to dark matter annihilation or undetected point sources such as millisecond pulsars (MSPs). This study investigates how the gamma-ray emission model (-yEM) used in Galactic center analyses affects the interpretation of the GCE's nature. To address this issue, we construct an ultra-fast and powerful inference pipeline based on convolutional Deep Ensemble Networks. We explore the two main competing hypotheses for the GCE using a set of-yEMs with increasing parametric freedom. We calculate the fractional contribution (fsrc) of a dim population of MSPs to the total luminosity of the GCE and analyze its dependence on the complexity of the ryEM. For the simplest ryEM, we obtain fsrc = 0.10 f 0.07, while the most complex model yields fsrc = 0.79 f 0.24. In conclusion, we find that the statement about the nature of the GCE (dark matter or not) strongly depends on the assumed ryEM. The quoted results for fsrc do not account for the additional uncertainty arising from the fact that the observed gamma-ray sky is out-of-distribution concerning the investigated ryEM iterations. We quantify the reality gap between our ryEMs using deep-learning-based One-Class Deep Support Vector Data Description networks, revealing that all employed ryEMs have gaps to reality. Our study casts doubt on the validity of previous conclusions regarding the GCE and dark matter, and underscores the urgent need to account for the reality gap and consider previously overlooked “out of domain” uncertainties in future interpretations.