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A range of Bayesian tools has become widely used in cosmological data treatment and parameter inference (see Kunz, Bassett & Hlozek (2007), Trotta (2008), Amendola, Marra & Quartin (2013)). With increasingly big datasets and higher precision, tools that enable us to further enhance the accuracy of our measurements gain importance. Here we present an approach based on internal robustness, introduced in Amendola, Marra & Quartin (2013) and adopted in Heneka, Marra & Amendola (2014), to identify biased subsets of data and hidden correlation in a model independent way.
Gamma-ray bursts are usually classified through their high-energy emission into short-duration and long-duration bursts, which presumably reflect two different types of progenitors. However, it has been shown on statistical grounds that a third, inte
Since their serendipitous discovery, Fast Radio Bursts (FRBs) have garnered a great deal of attention from both observers and theorists. A new class of radio telescopes with wide fields of view have enabled a rapid accumulation of FRB observations, c
From the set of nearly 500 spectroscopically confirmed type~Ia supernovae and around 10,000 unconfirmed candidates from SDSS-II, we select a subset of 108 confirmed SNe Ia with well-observed early-time light curves to search for signatures from shock
In this paper we report a systematic search for an emission line around 3.5 keV in the spectrum of the Cosmic X-ray Background using a total of $sim$10 Ms Chandra observations towards the COSMOS Legacy and CDFS survey fields. We find a marginal evide
As massive black holes (MBHs) grow from lower-mass seeds, it is natural to expect that a leftover population of progenitor MBHs should also exist in the present universe. Dwarf galaxies undergo a quiet merger history, and as a result, we expect that