8. EVALUACIÓN Y FORMULACIÓN FINANCIERA DEL PROYECTO
8.4 ANALISIS FINANCIERO
The power of the MaNGA survey lies in the combination of its large sample with the ability to spatially resolve internal structures in galaxies. Such dataset is now allowing us to move beyond a long-standing dichotomy in the observational study of galaxy evolution. On one side, observations of small samples 17Integral field spectroscopy refers to the experimental technique whereby several spectra of an extended source are
obtained across a two-dimensional field one the sky. Di↵erent hardware solutions are possible to implement IFS, including an image slicer, lenslet array and fibres. A detailed discussion of the hardware used in the MaNGA IFS survey is presented in Sec. 2.1.1.
of bright nearby galaxies (e.g. SINGS, Kennicutt et al. 2003a) have provided us with outstanding detail regarding the distribution of star formation and chemical abundances in the local Universe. These studies, however, are fundamentally limited to probing only limited (and perhaps biased) sub- samples of the overall galaxy population. At the other extreme, large surveys like SDSS have provided enormous statistics, albeit at the cost of treating galaxies as point sources. The aim of this thesis is to contribute to bridging this gap, and allow a characterisation of the distribution of line emission, star
formation and chemical abundances in a large sample of galaxies (⇠ 103) on ⇠ kpc scales.
In chapters 3 and 4 I focus on the study for star formation and quiescence, and I aim to tackle the following questions.
1. Where do spatially resolved regions appear on classical BPT diagnostic diagrams, and how applicable are these diagnostics in distinguishing star-forming from quiescent regions?
2. How may we define ‘quiescence’ using spatially resolved spectroscopic data? How are quies- cent regions distributed in galaxies?
3. Does the presence of a large number of quiescent regions correlate with other fundamental galaxy properties, such as stellar mass, SFR, colour, bulge-to-disc ratio?
4. How widespread is line emission on the red sequence and what powers the observed emission? The ultimate goal of this study is to be able to identify trends with environment (Sec. 4.4 for a first look at the problem) and be able to quantify where and when the quenching process proceeds inside galaxies. The kpc-scale resolution of MaNGA, combined with its large sample, covering both the blue cloud, the red sequence and the sparsely-populated green valley, is ideal to conduct this type of study. Chapters 5 and 6 of this thesis are dedicated to the subject of chemical abundances and chemical evolution modelling. In those chapters I aim to answer the following questions.
1. How does the metallicity radial gradient change as a function of mass and other fundamental galaxy properties (such as bulge-to-disc ratio, SFR)?
2. Can chemical abundances at large galacotcentric radii (R> 2.0 Re) provide evidence for direct
accretion of pristine gas or metal transport via galactic fountains?
3. When appropriate knowledge of the gas content of galaxies is available, can the observed gas phase abundance gradient be used to constrain the importance of gas flows (inflows and out- flows) over the history of a galaxy?
4. Can abundance ratios of chemical elements with di↵erent nucleosynthetic origin be used to shed light on the star formation and gas flow history of galaxies?
It is worth noting that a fundamental limitation to the study of chemical evolution in the MaNGA sample is the lack of spatially-resolved cold gas observations. In order to circumvent this problem, in chapter 6 I abandon the MaNGA sample and present a case study for a single nearby galaxy, for which a unique combination of IFS and cold gas data are available.
Chapter
2
The MaNGA survey
Partially adapted from ‘P-MaNGA Galaxies: emission-lines properties - gas ionization and chemical abundances from prototype observations’, F. Belfiore, et. al., 2015, MNRAS, 449, 867.
2.1 An overview of the MaNGA instrument and data
In this section I describe the overall properties of the MaNGA survey, in terms of its hardware, ob- serving strategy, sample selection and data reduction, with a specific focus on the aspects which are most relevant to the study of the ionised ISM. MaNGA compares well with other ongoing IFS surveys (SAMI, CALIFA) in terms of information-gathering power and data quality. In particular, it o↵ers a wider wavelength range than other surveys, by extending into the near-IR (Fig. 2.1). If one quantifies the survey information-gathering power by a product of the étendue (defined as the product of the system throughput, telescope collecting area and solid angle covered by the fibres) and the spectral resolution (R) of the survey, MaNGA compares favourably to other surveys thanks to its high system throughput, although SAMI is superior in its red arm thanks to its higher spectral resolution.
Details on the MaNGA instrument, including the design, testing, and assembly of the MaNGA fibre bundles are given in Drory et al. (2015). Wake et al. (in preparation) present the sample design, optimisation, and final selection of the survey. Details on the observing strategy and imaging quality requirements can be found in Law et al. (2015), while a discussion of the MaNGA science require- ments, survey execution and initial data quality is presented in Yan et al. (2016b). The software and data framework as well as the reduction pipeline is described in Law et al. (2016). The spectrophoto- metric calibration procedure is described in Yan et al. (2016a). Details on the MaNGA data publicly released in SDSS Data Release (DR) 13 are presented in Albareti et al. (2016).
The MaNGA survey is an ongoing e↵ort at the time of writing, having started on 1st July 2014 and being scheduled to run until 2020, utilising half of the dark time available in SDSS-IV. MaNGA observations are carried out by a team of professional observers at Apache Point Observatory (APO) and routinely supervised by members of the MaNGA team. The reduction of the MaNGA data is coordinated by the MaNGA data team. My contribution to this e↵ort has been limited the study of the e↵ect of error covariance introduced by the cube reconstruction algorithm (Sec. 2.1.4) and aspects of data validation, especially during the MaNGA prototype instrument phase (Sec. 2.3). The MaNGA
34 Chapter 2. The MaNGA survey 0 1 2 3 4 5
etendue (10
4m
2deg
2)
MaNGA SAMI CALIFA 0 .5 1etendue x R (m
2deg
2)
MaNGA SAMI CALIFA 300 400 500 600 700 800 900 1000 1100 1 2flux density (f
)
wavelength (nm)
log M* > 11.5 9 < log M* < 10 Break H H H H H Break P P P P[OII] [NeIII] [OIII] HeI [OI] [NII] [SII] [ArIII] [OII] [SIII] [SIII]
D4000 CaI G Ca Fe Fe MgI Ca+Fe Fe Fe NaD NaI Ca
T
FeH CaI
TiO TiO TiO TiO TiO TiO
Figure 10. MaNGA’s simultaneous information gathering power, reckoned by two metrics, ´etendue (top panel) and R×´etendue (middle panel), as defined in the text.
Two examples of stacked z ∼ 0.15 spectra from the BOSS survey (bottom; Dawson2013) illustrate the available spectral features for ISM and stellar composition
and kinematic analysis in galaxies bracketing the sample range in M∗.
total exposure times in median conditions. The expected S/N
distributions for the two main samples are presented in Figure9.
Although the observational seeing is expected to be ∼1.
′′5,
the reconstructed PSF in combined datacubes after dithering
and fiber sampling is ∼2.
′′5 (FWHM). Because of the regular
hexagonal packing of MaNGA IFUs, a defined three-point dither
pattern can be adopted that achieves uniform spatial sampling
for all targets. To ensure that the resulting exposure map
provides even coverage at all wavelengths in the face of DAR,
observations are restricted to observability windows set by the
field declination that ensure acceptable levels of variance over
the course of an hour-long dither set. Once a three-exposure set is
initiated, it should be completed (possibly on subsequent nights)
at the same corresponding hour angle. Different sets initiated at
different times within the observability window are combined
in the reduction pipeline with acceptance criteria based on the
atmospheric seeing (FWHM< 2
′′–2.
′′5) and transparency.
6. IFU SURVEY COMPARISON
MaNGA follows in the footsteps of previous IFU surveys,
not only in increasing sample size, but in terms of building
on important lessons learned regarding design, instrumentation,
and analysis. These surveys were introduced in Section
1.
In this section we describe a more detailed comparison of
instrumentation and survey designs, focusing on campaigns
targeting more than ∼100 galaxies: ATLAS
3D, DiskMass,
CALIFA, SAMI, and MaNGA.
In Figure
10
we compare two metrics of the simultane-
ous information-gathering power of the CALIFA, SAMI, and
MaNGA survey instrumentation, displayed as a function of
wavelength. Relevant to both metrics is the total system effi-
ciency (ϵ), which is adopted from the literature for CALIFA
(Roth et al.
2004; Kelz et al.
2006; S´anchez et al.
2012) and
SAMI (Bryant et al.
2014). For MaNGA, ϵ is a factor of 1.1
greater than the BOSS throughput curves from Smee et al.
(2013), who did not correct for the PSF aperture losses from
a 2
′′diameter fiber in FWHM 1.
′′1 seeing. In this way, our def-
inition of ϵ can be described as the ratio of the flux detected
from a source with uniform surface brightness divided by the
incident flux from this source (before atmospheric losses) that
could have been collected by the IFU.
51In the top panel of Figure
10, ´etendue is the product of
the telescope collecting area (A; accounting for the central
obstruction), solid angle covered by all fibers (Ω), and ϵ. In
the photon-limited regime, ´etendue is a measure of how quickly
one can map the sky to a given S/N at a given spectral resolution.
51 In the case of SAMI, Bryant et al. (2014) report throughput curves for the
first-generation SAMI instrument that are not consistent with those measured
for the same instrumentation in Croom et al. (2012). This reflects updates to
the throughput calculation performed in Bryant et al. (2014), including the
adoption of the same definition of ϵ used here (J. Bryant 2014, private communication).
14
Figure 2.1:Top: MaNGA’s information-gathering power, quantified by the products of the étendue and the spectral reso- lution (R) as a function of wavelength, compared to that of other modern IFS galaxy surveys. Bottom: Two examples of stacked z ⇠ 0.15 spectra from the BOSS survey (Dawson et al., 2013), illustrating the available spectral features for ISM and stellar composition and kinematics available in the MaNGA wavelength range. From Bundy et al. (2015).
data analysis pipeline, which performs spectral fitting and derives physical parameters (e.g. line fluxes, kinematics etc) from the reduced datacubes, is an ongoing e↵ort which I have been deeply involved in. The output of the MaNGA data analysis pipeline is undergoing quality control testing at the time of writing. In Sec. 2.2 I discuss the development of my own independent software and algorithmic framework to analyse the reduced MaNGA data. The results presented in this thesis are obtained by means of this custom-made analysis software.