Rubin-LSST

Abstract:

Many of the species in decline around the world are subject to different environmental stressors across their range, so replicated large-scale monitoring programmes, are necessary to disentangle the relative impacts of these threats. At the same time as funding for long-term monitoring is being cut, studies are increasingly being criticised for lacking statistical power. For those taxa or environments where a single vantage point can observe individuals or ecological processes, time-lapse cameras can provide a cost-effective way of collecting time series data replicated at large spatial scales that would otherwise be impossible. However, networks of time-lapse cameras needed to cover the range of species or processes create a problem in that the scale of data collection easily exceeds our ability to process the raw imagery manually. Citizen science and machine learning provide solutions to scaling up data extraction (such as locating all animals in an image). Crucially, citizen science, machine learning-derived classifiers, and the intersection between them, are key to understanding how to establish monitoring systems that are sensitive to – and sufficiently powerful to detect –changes in the study system. Citizen science works relatively ‘out of the box’, and we regard it as a first step for many systems until machine learning algorithms are sufficiently trained to automate the process. Using Penguin Watch (www.penguinwatch.org) data as a case study, we discuss a complete workflow from images to parameter estimation and interpretation: the use of citizen science and computer vision for image processing, and parameter estimation and individual recognition for investigating biological questions. We discuss which techniques are easily generalizable to a range of questions, and where more work is needed to supplement ‘out of the box’ tools. We conclude with a horizon scan of the advances in camera technology, such as on-board computer vision and decision making.

Abstract:

We report the time-resolved spectral analysis of a bright near-infrared and moderate X-ray flare of Sgr A ⋆ . We obtained light curves in the M , K , and H bands in the mid- and near-infrared and in the 2 − 8 keV and 2 − 70 keV bands in the X-ray. The observed spectral slope in the near-infrared band is νL ν ∝ ν 0.5 ± 0.2 ; the spectral slope observed in the X-ray band is νL ν ∝ ν −0.7 ± 0.5 . Using a fast numerical implementation of a synchrotron sphere with a constant radius, magnetic field, and electron density (i.e., a one-zone model), we tested various synchrotron and synchrotron self-Compton scenarios. The observed near-infrared brightness and X-ray faintness, together with the observed spectral slopes, pose challenges for all models explored. We rule out a scenario in which the near-infrared emission is synchrotron emission and the X-ray emission is synchrotron self-Compton. Two realizations of the one-zone model can explain the observed flare and its temporal correlation: one-zone model in which the near-infrared and X-ray luminosity are produced by synchrotron self-Compton and a model in which the luminosity stems from a cooled synchrotron spectrum. Both models can describe the mean spectral energy distribution (SED) and temporal evolution similarly well. In order to describe the mean SED, both models require specific values of the maximum Lorentz factor γ max , which differ by roughly two orders of magnitude. The synchrotron self-Compton model suggests that electrons are accelerated to γ max ∼ 500, while cooled synchrotron model requires acceleration up to γ max ∼ 5 × 10 4 . The synchrotron self-Compton scenario requires electron densities of 10 10 cm −3 that are much larger than typical ambient densities in the accretion flow. Furthermore, it requires a variation of the particle density that is inconsistent with the average mass-flow rate inferred from polarization measurements and can therefore only be realized in an extraordinary accretion event. In contrast, assuming a source size of 1 R S , the cooled synchrotron scenario can be realized with densities and magnetic fields comparable with the ambient accretion flow. For both models, the temporal evolution is regulated through the maximum acceleration factor γ max , implying that sustained particle acceleration is required to explain at least a part of the temporal evolution of the flare.

Abstract:

The VEGAS imaging survey of the Hydra I cluster has revealed an extended network of stellar filaments to the south-west of the spiral galaxy NGC 3314A. Within these filaments, at a projected distance of ∼40 kpc from the galaxy, we discover an ultra-diffuse galaxy (UDG) with a central surface brightness of μ0, g ∼ 26 mag arcsec−2 and effective radius Re ∼ 3.8 kpc. This UDG, named UDG 32, is one of the faintest and most diffuse low-surface-brightness galaxies in the Hydra I cluster. Based on the available data, we cannot exclude that this object is just seen in projection on top of the stellar filaments and is thus instead a foreground or background UDG in the cluster. However, the clear spatial coincidence of UDG 32 with the stellar filaments of NGC 3314A suggests that it might have formed from the material in the filaments, becoming a detached, gravitationally bound system. In this scenario, the origin of UDG 32 depends on the nature of the stellar filaments in NGC 3314A, which is still unknown. The stellar filaments could result from ram-pressure stripping or have a tidal origin. In this letter we focus on the comparison of the observed properties of the stellar filaments and of UDG 32 and speculate on their possible origin. The relatively red colour (g − r = 0.54 ± 0.14 mag) of the UDG, similar to that of the disk in NGC 3314A, combined with an age older than 1 Gyr and the possible presence of a few compact stellar systems, points towards a tidal formation scenario.

Abstract:

Utilizing optical and near-infrared broad-band photometry covering >5 deg2 in two of the most well-studied extragalactic legacy fields (COSMOS and XMM-LSS), we measure the galaxy stellar mass function (GSMF) between 0.1 < z < 2.0. We explore in detail the effect of two source extraction methods (SExtractor and ProFound) in addition to the inclusion/exclusion of Spitzer IRAC 3.6 and 4.5 μm photometry when measuring the GSMF. We find that including IRAC data reduces the number of massive (log10(M/M⊙) > 11.25) galaxies found due to improved photometric redshift accuracy, but has little effect on the more numerous lower-mass galaxies. We fit the resultant GSMFs with double Schechter functions down to log10(M/M⊙) = 7.75 (9.75) at z = 0.1 (2.0) and find that the choice of source extraction software has no significant effect on the derived best-fitting parameters. However, the choice of methodology used to correct for the Eddington bias has a larger impact on the high-mass end of the GSMF, which can partly explain the spread in derived M* values from previous studies. Using an empirical correction to model the intrinsic GSMF, we find evidence for an evolving characteristic stellar mass with δlog10(M*/M⊙)/δz = −0.16±0.05(−0.11±0.05)⁠, when using SExtractor (ProFound). We argue that with widely quenched star formation rates in massive galaxies at low redshift (z < 0.5), additional growth via mergers is required in order to sustain such an evolution to a higher characteristic mass.