Multi-photon corrections to W boson mass determination at hadron colliders

The impact of higher-order final-state photonic corrections on the precise determination of the W-boson mass at the Tevatron and LHC colliders is evaluated. The W-mass shift from a fit to the transverse mass distribution is found to be about 10 MeV in the W --> mu nu channel and a few MeV in the W --> e nu channel. The calculation, which is implemented in the Monte Carlo event generator HORACE for data analysis, can contribute to reduce the uncertainty associated to the W mass measurement at present and future hadron collider experiments.Comment: 3 pages, 2 figures, to appear in the proceedings of International Europhysics Conference on High-Energy Physics (EPS 2003), Aachen, Germany, 17-23 Jul 200

Towards pocket-sized genomic analyses: cross-platform benchmark of multi-organism genomic data indexing and alignment

The current socio-economic situation as well as international objectives set by the United Nation (2030 Sustainable Agenda) underline the urgency of low-cost and environmental-friendly computational alternatives. Moreover, in recent years the bioinformatic community has shown renewed interest for Raspberry Pi (RPi) application in teaching and research projects. In the context of the BioVRPi project - which aims to develop and offer a low-cost, stable and tested bioinformatic environment - we propose an exploratory cross-platform benchmarking of multi-organism genomic analyses. The benchmark of indexing and alignment processes was carried out on the following devices: RPi 4 (Raspberry Pi OS 04-04-2022) RAM 8GB HDD storage, laptop (MacOS Big Sur v11.2.3) Intel Core i5 2GHz quad-core processor RAM 16GB SSD, and desktop (Ubuntu 20.04.4 LTS) Intel Core i7 3GHz octa-core processor RAM 32GB HDD storage. Performance assessment was evaluated on SARS-CoV-2 virus, Escherichia coli and Caenorhabditis elegans genome sequences (respective RefSeq accessions: GCF_009858895.2, GCF_000005845.2, GCF_000002985.6) since they present different degrees of genomic complexity: virus, bacterium, and nematode. To minimize variability and possible biases due to sequencing technologies used, sample reads were generated in silico from their respective reference genomes using ART Illumina v2.5.8 with the following parameters: read length 150, paired end, coverage 30X, mean fragment length 200, standard deviation 10, HiSeqX v2.5 TruSeq built-in profile. Indexing and alignment were performed with 3 alignment tools: BWA v0.7.17-r1188, Bowtie2 v2.4.5, and Minimap2 v2.17, using default parameters and scaling from 1 up to 4 threads. Benchmarking was evaluated using Hyperfine v1.13.0 with a warmup step of 3 simulations and 10 runs for each process. We performed a cross-platform benchmark of multi-organism genomic indexing and short reads alignment to evaluate RPi as a viable alternative to common bioinformatic devices. To assess its performance, we tested some of the most widely used alignment tools on SARS-CoV-2, E. coli and C. elegans genomic data (respective genome sizes: 29.9Kbp, 4.6Mbp, 100.3Mbp). The computational times for indexing and alignment are reported in Table 1. As regards indexing, we observed comparable runtimes among RPi and other platforms using BWA and Bowtie2 for SARS-CoV-2 and E. coli, whereas Minimap2 indexing showed an increase of one order of magnitude in runtimes for RPi. Nonetheless, Minimap2 showed the fastest runtimes for indexing overall. In addition, we found an increase of one order of magnitude in RPi runtimes for C. elegans for all considered tools, even though differences in runtimes across platforms showed to be stable across organisms. As regards the alignment process, we observed consistency in runtimes differences across all organisms and tools. Overall, Minimap2 performances proved to be the fastest whereas Bowtie2 displayed poor performances across all platforms, exacerbating its inefficiency on RPi. Even though BWA seems to work more efficiently on RPi than on desktop for SARS-COV-2 data, desktop and laptop showed better performances on more complex organisms as expected. Benchmarking analyses considered multi-threading up to 4 threads, the maximum available on RPi. As regards indexing on Bowtie2, multi-threading proved to be effective for C. elegans data, showing no improvement in runtimes for SARS-CoV-2 and E. coli. Conversely, alignment showed the best performances using multi-threading as expected. In conclusion, RPi showed promising results, proved to be a viable low-cost and environmental-friendly alternative to perform genomic data analysis on different organisms and turned out to be particularly efficient for microorganisms. Further advances and tools optimization for RPi ARM architecture will lead to a greater scalability for complex organisms and will be carried out by the BioVRPi project in future exploratory analyses

Coupling news sentiment with web browsing data improves prediction of intra-day price dynamics

The new digital revolution of big data is deeply changing our capability of understanding society and forecasting the outcome of many social and economic systems. Unfortunately, information can be very heterogeneous in the importance, relevance, and surprise it conveys, affecting severely the predictive power of semantic and statistical methods. Here we show that the aggregation of web users' behavior can be elicited to overcome this problem in a hard to predict complex system, namely the financial market. Specifically, our in-sample analysis shows that the combined use of sentiment analysis of news and browsing activity of users of Yahoo! Finance greatly helps forecasting intra-day and daily price changes of a set of 100 highly capitalized US stocks traded in the period 2012-2013. Sentiment analysis or browsing activity when taken alone have very small or no predictive power. Conversely, when considering a news signal where in a given time interval we compute the average sentiment of the clicked news, weighted by the number of clicks, we show that for nearly 50% of the companies such signal Granger-causes hourly price returns. Our result indicates a "wisdom-of-the-crowd" effect that allows to exploit users' activity to identify and weigh properly the relevant and surprising news, enhancing considerably the forecasting power of the news sentiment

Drell-Yan processes at the LHC

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