Rubin-LSST

Abstract:

© 2020 IOP Publishing Ltd and Sissa Medialab. The statistical power of Lyman-α forest Baryon Acoustic Oscillation (BAO) measurements is set to increase significantly in the coming years as new instruments such as the Dark Energy Spectroscopic Instrument deliver progressively more constraining data. Generating mock datasets for such measurements will be important for validating analysis pipelines and evaluating the effects of systematics. With such studies in mind, we present LyaCoLoRe: A package for producing synthetic Lyman-α forest survey datasets for BAO analyses. LyaCoLoRe transforms initial Gaussian random field skewers into skewers of transmitted flux fraction via a number of fast approximations. In this work we explain the methods of producing mock datasets used in LyaCoLoRe, and then measure correlation functions on a suite of realisations of such data. We demonstrate that we are able to recover the correct BAO signal, as well as large-scale bias parameters similar to literature values. Finally, we briefly describe methods to add further astrophysical effects to our skewers-high column density systems and metal absorbers-which act as potential complications for BAO analyses.

Abstract:

Time-lapse cameras facilitate remote and high-resolution monitoring of wild animal and plant communities, but the image data produced require further processing to be useful. Here we publish pipelines to process raw time-lapse imagery, resulting in count data (number of penguins per image) and ‘nearest neighbour distance’ measurements. The latter provide useful summaries of colony spatial structure (which can indicate phenological stage) and can be used to detect movement – metrics which could be valuable for a number of different monitoring scenarios, including image capture during aerial surveys. We present two alternative pathways for producing counts: (1) via the Zooniverse citizen science project Penguin Watch and (2) via a computer vision algorithm (Pengbot), and share a comparison of citizen science-, machine learning-, and expert- derived counts. We provide example files for 14 Penguin Watch cameras, generated from 63,070 raw images annotated by 50,445 volunteers. We encourage the use of this large open-source dataset, and the associated processing methodologies, for both ecological studies and continued machine learning and computer vision development.

Abstract:

© 2020, The Author(s). A search for new resonances decaying into a pair of jets is reported using the dataset of proton-proton collisions recorded at s = 13 TeV with the ATLAS detector at the Large Hadron Collider between 2015 and 2018, corresponding to an integrated luminosity of 139 fb−1. The distribution of the invariant mass of the two leading jets is examined for local excesses above a data-derived estimate of the Standard Model background. In addition to an inclusive dijet search, events with jets identified as containing b-hadrons are examined specifically. No significant excess of events above the smoothly falling background spectra is observed. The results are used to set cross-section upper limits at 95% confidence level on a range of new physics scenarios. Model-independent limits on Gaussian-shaped signals are also reported. The analysis looking at jets containing b-hadrons benefits from improvements in the jet flavour identification at high transverse momentum, which increases its sensitivity relative to the previous analysis beyond that expected from the higher integrated luminosity. [Figure not available: see fulltext.]