Software Challenges For HL-LHC Data Analysis

The high energy physics community is discussing where investment is needed to prepare software for the HL-LHC and its unprecedented challenges. The ROOT project is one of the central software players in high energy physics since decades. From its experience and expectations, the ROOT team has distilled a comprehensive set of areas that should see research and development in the context of data analysis software, for making best use of HL-LHC's physics potential. This work shows what these areas could be, why the ROOT team believes investing in them is needed, which gains are expected, and where related work is ongoing. It can serve as an indication for future research proposals and cooperations

A systematic study of Lyman-Alpha transfer through outflowing shells: Model parameter estimation

Outflows promote the escape of Lyman-$\alpha$ (Ly$\alpha$) photons from dusty interstellar media. The process of radiative transfer through interstellar outflows is often modelled by a spherically symmetric, geometrically thin shell of gas that scatters photons emitted by a central Ly$\alpha$ source. Despite its simplified geometry, this `shell model' has been surprisingly successful at reproducing observed Ly$\alpha$ line shapes. In this paper we perform automated line fitting on a set of noisy simulated shell model spectra, in order to determine whether degeneracies exist between the different shell model parameters. While there are some significant degeneracies, we find that most parameters are accurately recovered, especially the HI column density ($N_{\rm HI}$) and outflow velocity ($v_{\rm exp}$). This work represents an important first step in determining how the shell model parameters relate to the actual physical properties of Ly$\alpha$ sources. To aid further exploration of the parameter space, we have made our simulated model spectra available through an interactive online tool.Comment: 10 pages, 6 figures. Matches version published in ApJ. Our grid of Lyman alpha spectra can be accessed at http://bit.ly/man-alpha through an interactive online too

Information Extraction, Data Integration, and Uncertain Data Management: The State of The Art

Information Extraction, data Integration, and uncertain data management are different areas of research that got vast focus in the last two decades. Many researches tackled those areas of research individually. However, information extraction systems should have integrated with data integration methods to make use of the extracted information. Handling uncertainty in extraction and integration process is an important issue to enhance the quality of the data in such integrated systems. This article presents the state of the art of the mentioned areas of research and shows the common grounds and how to integrate information extraction and data integration under uncertainty management cover

Challenges in the automated classification of variable stars in large databases

With ever-increasing numbers of astrophysical transient surveys, new facilities and archives of astronomical time series, time domain astronomy is emerging as a mainstream discipline. However, the sheer volume of data alone - hundreds of observations for hundreds of millions of sources – necessitates advanced statistical and machine learning methodologies for scientific discovery: characterization, categorization, and classification. Whilst these techniques are slowly entering the astronomer’s toolkit, their application to astronomical problems is not without its issues. In this paper, we will review some of the challenges posed by trying to identify variable stars in large data collections, including appropriate feature representations, dealing with uncertainties, establishing ground truths, and simple discrete classes