On the impact of the galaxy window function on cosmological parameter estimation

One important source of systematics in galaxy redshift surveys comes from the estimation of the galaxy window function. Up until now, the impact of the uncertainty in estimating the galaxy window function on parameter inference has not been properly studied. In this paper, we show that the uncertainty and the bias in estimating the galaxy window function will be salient for ongoing and next-generation galaxy surveys using a simulation-based approach. With a specific case study of cross-correlating Emission-line galaxies from the DESI Legacy Imaging Surveys and the Planck CMB lensing map, we show that neural network-based regression approaches to modelling the window function are superior in comparison to linear regression-based models. We additionally show that the definition of the galaxy overdensity estimator can impact the overall signal-to-noise of observed power spectra. Finally, we show that the additive biases coming from the window functions can significantly bias the modes of the inferred parameters and also degrade their precision. Thus, a careful understanding of the window functions will be essential to conduct cosmological experiments.Comment: 13 pages, 12 figures, complementary paper to an upcoming paper on Cross-Correlation of ELGs and Planck CMB lensing, accepted for publication in MNRA

Pulmonary arteriovascular malformation: a rare cause of unexplained hypoxia and acute dyspnoea in young patients

2014 BMJ Publishing Group Ltd.Pulmonary arteriovenous malformations (PAVMs) are anomalous vascular connections between arteries and veins in the lung and comprise of two types, simple and complex. PAVMs are associated with congenital conditions such as hereditary haemorrhagic telengiectasia along with acquired causes. We present a case of a 26-year-old man who presented with dyspnoea, palpitations and decreased oxygen saturation as an initial presentation of PAVM, which was treated successively with embolisation

Diverse metallicities of Fermi bubble clouds indicate dual origins in the disk and halo

The Galactic Center is surrounded by two giant plasma lobes known as the Fermi Bubbles, extending ~10 kpc both above and below the Galactic plane. Spectroscopic observations of Fermi Bubble directions at radio, ultraviolet, and optical wavelengths have detected multi-phase gas clouds thought to be embedded within the bubbles referred to as Fermi Bubble high-velocity clouds (FB HVCs). While these clouds have kinematics that can be modeled by a biconical nuclear wind launched from the Galactic center, their exact origin is unknown because, until now, there has been little information on their heavy-metal abundance (metallicity). Here we show that FB HVCs have a wide range of metallicities from <20% solar to ~320% solar. This result is based on the first metallicity survey of FB HVCs. These metallicities challenge the previously accepted tenet that all FB HVCs are launched from the Galactic center into the Fermi Bubbles with solar or super-solar metallicities. Instead, we suggest that FB HVCs originate in both the Milky Way's disk and halo. As such, some of these clouds may characterize circumgalactic medium that the Fermi Bubbles expand into, rather than material carried outward by the nuclear wind, changing the canonical picture of FB HVCs. More broadly, these results reveal that nuclear outflows from spiral galaxies can operate by sweeping up gas in their halos while simultaneously removing gas from their disks.Comment: This version of the article has been accepted for publication on Nature Astronomy after peer review. This version is not the Version of Record (https://doi.org/10.1038/s41550-022-01720-0) and does not reflect post-acceptance improvements, or any correction

Time series analysis of electric energy consumption using autoregressive integrated moving average model and Holt Winters model

With the increasing demand of energy, the energy production is not that much sufficient and that’s why it has become an important issue to make accurate prediction of energy consumption for efficient management of energy. Hence appropriate demand side forecasting has a great economical worth. Objective of our paper is to render representations of a suitable time series forecasting model using autoregressive integrated moving average (ARIMA) and Holt Winters model for the energy consumption of Ohio/Kentucky and also predict the accuracy considering different periods (daily, weekly, monthly). We apply these two models and observe that Holt Winters model outperforms ARIMA model in each (daily, weekly and monthly observations) of the cases. We also make a comparison among few other existing analyses of time series forecasting and find out that the mean absolute percentage error (MASE) of Holt Winters model is least considering the monthly data