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Abstract:

Context

The William Herschel Telescope Enhanced Area Velocity Explorer (WEAVE) is a new, massively multiplexing spectrograph that allows us to collect about one thousand spectra over a 3 square degree field in one observation. The WEAVE Stellar Population Survey (WEAVE-StePS) in the next 5 years will exploit this new instrument to obtain high-S/N spectra for a magnitude-limited (IAB = 20.5) sample of ∼25 000 galaxies at moderate redshifts (z ≥ 0.3), providing insights into galaxy evolution in this as yet unexplored redshift range.

Aims

We aim to test novel techniques for retrieving the key physical parameters of galaxies from WEAVE-StePS spectra using both photometric and spectroscopic (spectral indices) information for a range of noise levels and redshift values.

Methods

We simulated ∼105 000 galaxy spectra assuming star formation histories with an exponentially declining star formation rate, covering a wide range of ages, stellar metallicities, specific star formation rates (sSFRs), and dust extinction values. We considered three redshifts (i.e. z = 0.3, 0.55, and 0.7), covering the redshift range that WEAVE-StePS will observe. We then evaluated the ability of the random forest and K-nearest neighbour algorithms to correctly predict the average age, metallicity, sSFR, dust attenuation, and time since the bulk of formation, assuming no measurement errors. We also checked how much the predictive ability deteriorates for different noise levels, with S/NI,obs = 10, 20, and 30, and at different redshifts. Finally, the retrieved sSFR was used to classify galaxies as part of the blue cloud, green valley, or red sequence.

Results

We find that both the random forest and K-nearest neighbour algorithms accurately estimate the mass-weighted ages, u-band-weighted ages, and metallicities with low bias. The dispersion varies from 0.08–0.16 dex for age and 0.11–0.25 dex for metallicity, depending on the redshift and noise level. For dust attenuation, we find a similarly low bias and dispersion. For the sSFR, we find a very good constraining power for star-forming galaxies, log sSFR ≳ −11, where the bias is ∼0.01 dex and the dispersion is ∼0.10 dex. However, for more quiescent galaxies, with log sSFR ≲ −11, we find a higher bias, ranging from 0.61 to 0.86 dex, and a higher dispersion, ∼0.4 dex, depending on the noise level and redshift. In general, we find that the random forest algorithm outperforms the K-nearest neighbours. Finally, we find that the classification of galaxies as members of the green valley is successful across the different redshifts and S/Ns.

Conclusions

We demonstrate that machine learning algorithms can accurately estimate the physical parameters of simulated galaxies for a WEAVE-StePS-like dataset, even at relatively low S/NI, obs = 10 per Å spectra with available ancillary photometric information. A more traditional approach, Bayesian inference, yields comparable results. The main advantage of using a machine learning algorithm is that, once trained, it requires considerably less time than other methods.

Abstract:

MOSAIC is a wide-field spectrograph, combining multiple-object spectroscopy and integral field units, to cover the ELT focal plane with a field-of-view of 7.8 arcmin from the blue to the near-infrared, 390 to 1800nm. In the current Phase B design, AO is GLAO 91̽��ed by four LGS in a fixed asterism and with multiple NGS. Although the GLAO correction is modest compared to other ELT instrumentation, the use of the integrated M4/M5 correction elements and the existing LGS allows for an efficient design which is outlined. MOSAIC GLAO will use the ELT PFS guide-probes to compensate for high- frequency tip/tilt errors, greatly relaxing the requirements on the instrumental NGS sensors. The Phase A architecture used the same pick-off mirrors as the IFU instruments to feed the NGS-WFS from anywhere in the focal plane, which was mandatory for the proposed MOAO design. The reduced performance requirements at Phase B allows us to take advantage, instead, of the four 2 arcmin diameter field-of-view through the LGS cutouts, arranged in a square pattern at an off-axis distance of 3.75 arcmin. In each LGS cutout, a wide-field-imager is implemented–alongside one LGS WFS–to acquire multiple NGS that 91̽��s both slow tip/tilt measurements, isolating instrument-Nasmyth flexure, solving for the astrometric distortion expected from errors in the ELT optical path, and 91̽��ing the alignment of MOS apertures with the field. The latter is a key requirement for MOSAIC, leading to 40mas accuracy in MOS aperture positioning and 40mas rotation displacement at the edge of the scientific field.

Abstract:

MOSAIC is an instrument for the Extremely Large Telescope (ELT). The instrument has started phase B, and apart from technical and financial requirements, MOSAIC has the additional requirement to investigate and minimise its environmental impact. The first step is to estimate the carbon footprint (and other effects) in a ‘Life Cycle Analysis’, for the instrument development up to Provisional Acceptance in Chile. This paper presents a preliminary analysis, aimed at identifying potential contributors to environmental impact. Investigated contributors are: materials, Full-Time-Equivalents, travel, and transport of the instrument. Not yet investigated (due to lack of information or certainty) are: electronics, test facilities and prototyping. Uncertainty in input data and conversion factors leads to error bars of a factor 2 or larger. Therefore, the outcome of the analysis can be used for internal comparison of contributors only, and it should not be used for comparison to other instruments or disciplines.

Abstract:

MOSAIC is the Muti-Object Spectrograph for the 39m ESO Extremely Large Telescope. The instrument development has recently been reorganized in different channels to be implemented progressively. The Laboratoire d’Astrophysique de Marseille (LAM) is in charge of the instrument “Assembly, Integration, Test and Verification (AIT/V)” phases. AITV for AO instruments, in laboratory as at the telescope, always represent numerous technical challenges. We already started the preparation and planning for the instrument level AIT activities, from identification of needs, challenges, risks, to defining the optimal AIT strategy.

In this paper, we present the state of this study, discuss a new approach with distributed AIT activities and controlled remotely over different sites. We describe AIT/V scenarios with phased implementation, starting with the Front-End and Visible channels AIT phases. We also show our capacity, experience (several MOS instruments, ELT HARMONI) and expertise to lead the instrument MOSAIC AIT/V activities both in Europe and at the telescope in Chile.