Mithi Alexa Caballes de los Reyes

This is for the Planet Haus crew: Aida, Shreyas, Austin, Ben, and (of course) Izzy. This is for Tatay, whose example is one of the reasons I convinced myself I could get a PhD – maraming salamat po for all the conversations during our long walks, and as a reminder of it.

INTRODUCTION

Galactic archaeology

Type Ia supernovae
Chemical evolution models

Dwarf galaxies in cosmic voids
Star formation laws
Thesis outline

Indeed, Type Ia SNe were used in the Nobel Prize-winning discovery of the accelerated expansion of the Universe (Perlmutter et al. 1999; Riess et al. 1998). This method has been used to measure SFHs of many of the galaxies in the Local Group (e.g. Weisz et al. 2014 ).

Figure 1.1: The cycle of star formation and chemical enrichment in a galaxy, through which baryons shift between stars and the ISM

USING MANGANESE TO PROBE THE MASS OF TYPE IA SUPERNOVA PROGENITORS

Introduction

Measuring nucleosynthesis with dwarf galaxies
Manganese

The yields of various elements can then be used to infer properties of Type Ia SNe alone (McWilliam et al. 2018). Furthermore, the abundance contributions specifically from Type Ia SNe (fIa) can be calculated using the well-constrained theoretical yields of various elements of core-collapse SNe.

Observations

We identified arc lines using the NIST atomic spectra database (Kramida et al. 2014). We have modified specifications in several ways to improve the reliability of the wavelength resolution for the 1200B grid.

Abundance measurements .1 Description of pipeline.1Description of pipeline

Uncertainty analysis
Manganese abundance catalog

The right panel of Figure 2.2 shows distributions of deviation from the mean [Mn/Fe] (i.e. [Mn/Fe]− h[Mn/Fe]i) in units of the total error. To check this, we plot a histogram of the differences between MRS and HRS measurements in the right panel of Figure 2.3.

Manganese yields in Sculptor

Inferring [Mn/Fe] yields from a simple chemical evolution model With our measured manganese abundances, we can now estimate how much of this
Comparison with prior work

As described in Kirby et al. 2.6) Type Ia efficiency can then be determined from This is consistent with North et al. 2012), who published in Sculptor the largest literature catalog of manganese abundance to date.

Figure 2.4: [Mn / Fe] as a function of [Fe / H] for Sculptor dSph. Left: Observed [Mn/Fe] as a function of [Fe/H] for Sculptor dSph (black points)

Implications for Type Ia supernova physics

Comparison with theoretical models
Other dSph galaxies
Non-LTE effects

However, this central density may be unphysically low for singly degenerate Type Ia SNe (e.g. Fig. 4 in Lesaffre et al. 2006 ). We can roughly estimate the fractions of sub-MCh and near-MCh Type Ia SNe required to produce our inferred [Mn/Fe]Ia.

Figure 2.5: Type Ia supernova [Mn/Fe] yield (at [Fe / H] = − 1 . 5) measured in Sculp- Sculp-tor dSph from this work (gray shaded region, marking ± 68% confidence interval about the median), compared to theoretical yields from various models (vertical line

Summary and conclusions

Effective temperature can also affect the extent of NLTE corrections, but Teff at a given [Fe/H] in Sculptor, Leu I, and Fornax are shifted by at most 200–300 K; the resulting difference in NLTE corrections is. The resulting [Mn/Fe]Ia is roughly solar at [Fe/H] ~ −1.5, which is more consistent with yields from near-MCh models.

Appendix: Theoretical yield tables

Deflagration-to-detonation (DDT)
Pure deflagration
Sub-M Ch

Simmering” can increase the excess of neutrons (e.g. Chamulak et al. 2008; Piro and Bildsten 2008), making the initial metallicity of an MCh Type Ia SN below a threshold of effectively irrelevant. These may represent Type Iax SNe (e.g. Kromer et al. 2015), so their nucleosynthetic yields may not apply to “normal” Type Ia SNe.

Table 2.9: Theoretical yields for M Ch models.

MEASURING THE TYPE IA DELAY-TIME DISTRIBUTION WITH GALACTIC ARCHAEOLOGY

Introduction
A method for measuring the Type Ia DTD in an individual galaxy

The observed rate of Type Ia SNe
Comparing with the expected rate of Type Ia SNe

The Type Ia DTD in dSphs

Data
Sculptor dSph: A proof-of-concept
The Type Ia DTD as a function of SFH
Implications for Type Ia supernova physics

Conclusions

The shape of the DTD itself depends only on the internal physics of Type Ia SNe (for a review, see Maoz et al. 2014). However, the normalization of the Type Ia velocities calculated from the model DTDs varies considerably. However, interpreting the shape of the Type Ia rate in Leo II is less straightforward.

Figure 3.1: A schematic outlining the method used to compute the Type Ia DTD in a single galaxy

A SIMPLE CHEMICAL EVOLUTION MODEL FOR SCULPTOR DSPH

Introduction

Many Local Group dSphs have simple SFHs – typically one or a few bursts of star formation, followed by relatively low levels of star formation (e.g. Weisz et al. 2014 ). Many of these simple models have been extremely successful; indeed Vincenzo et al. 2016) showed that the output of such a one-zone model can reproduce many of the observed photometric features on a dSphs CMD. In this paper, we instead use a one-zone GCE model to independently derive the SFHs of dSphs in the local group, extending previous work by e.g. Kirby et al.

Figure 4.2: The “impulse response” of elemental yields to an instantaneous 100 M

Methods

Abundance measurements
A fast, simple galactic chemical evolution model
Input nucleosynthetic yields

Conceptually, this model is similar to that used by Kirby et al. 2011), and we refer the reader to that work for more details on the individual equations. To determine the number of SNe/AGB stars at each time step, Kirby et al. This approach, similar to that of the One-zone Model for the Evolution of GAlaxies code (OMEGA; Côté et al. 2017 ), eliminates the most numerical integration from the model.

Figure 4.3: Observed [Ba/Eu] as a function of [Fe/H] from the DART dataset (orange empty points) and the line of best fit, used as a statistical correction to remove r -process contributions from [Ba/Fe].

Results: Dwarf galaxy star formation histories .1 Fitting the GCE model.1Fitting the GCE model

The star formation history of Sculptor dSph

We find that the abundances of C, Mg and Ca predicted by the GCE model are sensitive to the input CCSN yield—that is, the predicted [X/Fe] abundances change by > 0.2 dex at the peak of the the metallicity distribution function ( MDF) (-1.5 < [Fe/H] < -1.0). Here the additional conditions require that the model's final stellar and gas masses (M?,modelandMgas,model) match the observed masses (M?,obsandMgas,obs) within the observational uncertainties. We also plot the output of the best-fit GCE model, showing how the components of the galaxy change over time, in Figure 4.6.

Table 4.3 describes the inputs and outputs of this MCMC sampling: the priors, initial values from linear optimization, and the best-fit values for each parameter.

Discussion

Comparison to previous literature
Model assumptions

Using a new technique to match the CMD and the MDF simultaneously, de Boer et al. 2012) found that Sculptor had a continuous period of star formation with a duration of ~6−7 Gyr (pink dashed line in Figure 4.7). This parameterization is similar to that used in the analytical model of Kirby et al. 2013), who found that ram pressure stripping successfully reproduced the metal dispersion function of Sculptor dSph. Increasing tmin to 0.2 Gyr flattens the rate of Type Ia SNe; similar to the shallower t−0.5 power law, it increases the duration of the SFH by 50%. purple dotted line in the bottom panel of figure 4.9).

Figure 4.7: Cumulative star formation history from the best-fit GCE model for Sculptor dSph (black solid lines)

Implications for nucleosynthetic yields

Comparing CCSN and AGB yield sets
Probing Type Ia SNe using Mn and Ni
Probing r-process nucleosynthesis using Ba and Eu

The AGB yields predicted by the FRUITY models (Cristallo et al. 2015) do not predict such a large improvement. Left: Best fitting GCE models without any r-process production (cyan), with only fast CCSN-like r-process events (blue), with only delayed r-process events based on the Simonetti et al. 2019) DTD for neutron star mergers (purple), and with both fast and delayed r-process events (black). However, it also produces a steeper [Eu/Mg] trend as a function of metallicity, which is less consistent with the observed flat [Eu/Mg] trend (Skúladóttir et al. 2019).

Figure 4.10: Comparisons among different Type Ia yields in the best-fit GCE model (solid lines) for manganese (top) and nickel (bottom)

Conclusions

In a separate analysis of Sculptor dSph, Skúladóttir et al. 2019) use the flat [Eu/Mg] trend to argue the opposite: that the primary source of Eu should not be significantly delayed relative to the primary source of Mg. This is consistent with studies of the r-process in the Milky Way and in the universe (e.g. Cescutti et al. 2015; Côté et al. 2019; Matteucci et al. 2014; Siegel et al. 2019; Wehmeyer et al. 2015) , showing that a combination of delayed NSMs and fast CCSN-like events can successfully reproduce the observed trend and distribution of [Eu/Fe] in the Milky Way. The best-fitting free parameters produce yield patterns similar to those of Nomoto et al.

Appendix: Nucleosynthetic yield parameterizations

As Ni is not included in the fit to our GCE model (Section 4.2.2), it does not affect any of our results; however, we note that Nomoto et al. 2013) yield significantly underpredicts the observed [Ni/Fe] at low [Fe/H]. The functions listed here that describe the CCSN throughput are for ther-process; as described in the text, we use the Ba yields from Li et al. To aid interpretation, we report an average yield of Z = 0.002 per CCSN—i.e. the best-fit analytic function Z = 0.002, integrated over the CCSN parent mass range of the 13–40 M yield sets and multiplied by a factor of 500 (from about 1 CCSN produced for every 500 M formed stars) – in the upper right corner of each plot.

Table 4.4: Analytic functions describing IMF-weighted CCSN and AGB yields.

THE STELLAR KINEMATICS OF VOID DWARF GALAXIES USING KCWI

Introduction

In this chapter, I will describe an observational program designed to measure the spatially resolved properties of a sample of local void dwarf galaxies. Environmental effects are also thought to influence the stellar kinematics of Local Group dwarf galaxies. I will demonstrate in this chapter that void dwarf galaxies provide a useful test of these scenarios.

Data

Sample selection
Observations and data reduction

Optical spectroscopy has previously been used to search for central supermassive black holes in dwarf galaxies (e.g. Moran et al. 2014; Reines et al. 2013; for most void galaxies these are the galaxy index numbers from the KIAS Value Added Galaxy Catalog ( Choi et al. 2010 ) The photometrically derived stellar masses are obtained from the MPA/JHU value-added catalog ( Kauffmann et al. 2003 ).

Table 5.1: General properties of void and field dwarf galaxy sample.

Analysis

Binning and covariance correction
Continuum fitting and kinematics measurements

This ratio, noted, is assumed to be a function of bin size, with the form suggested by Husemann et al. I estimate the value of the free parameters {α, β, N threshold} following the procedure of Law et al. The same, but for the integrated spectrum, which has higher S/N than any associated spectrum.

Figure 5.1: The ratio η = true / no covar as a function of bin size for an example with 4 stacked exposures

Discussion

Measurements for Local Group galaxies—ultrafaint dwarf galaxies (green triangles), MW and M31 satellites (orange filled squares), and isolated dwarf galaxies (open orange squares)—are taken from Wheeler et al. Since the dwarf galaxies in my sample are typically extremely isolated, they can be used to extend this trend to greater distances. Therefore, any comparison with measurements of galaxies in the Local Group is not a direct "apples to apples" comparison.

Table 5.3: Stellar kinematics of void and field dwarf galaxies.

Conclusions

Appendix: Velocity and velocity dispersion maps

GLOBAL STAR FORMATION LAWS IN LOCAL SPIRAL AND DWARF GALAXIES

Introduction

Furthermore, many spatially resolved studies report a shallower star formation rate slope n than that found by K98. Moreover, recent investigations of the global star formation law have found some inconsistencies with the K98 study. To investigate the issues raised above, we revise the global star formation law with improved multi-wavelength data.

Data

Sample selection
Diameters
SFR surface densities
Gas surface densities
Other properties

The final SFR and SFR densities are listed in Table 6.4 at the end of the section. -add” is the ID of the co-add (image produced by combining single exposure frames). The uncertainties in the mean surface densities for HI and H2, reported in Table 6.4, are dominated by the signal-to-noise of the maps (HI and CO), and corrections for spatial undersampling of the disks (CO).

Figure 6.1: Color-magnitude diagram of the spiral (black solid points) and dwarf (cyan triangles) galaxies in our sample

Star formation scaling laws

The revised star formation law for spiral galaxies
Separate atomic and molecular gas components in spiral galaxies As in K98, we now separately consider the atomic and molecular hydrogen gas com-
Molecular gas conversion factors
The star formation law for both spiral and dwarf galaxies
Alternative star formation laws

We therefore take the linmix result to be the fiducial star formation law for the combined sample. The inclusion of dwarf galaxies also reduces the slope of the star formation law from n − 1.41 (for spiral galaxies alone) to n − 1.26 (for both spirals and dwarfs). Note that the choice of X(CO) does not affect this qualitative result, because adding a correction for undetected molecular gas only drives these galaxies towards higher Σ gas (without changing the ΣSFR), and thus further below the star formation law for spiral galaxies.

Figure 6.3: The global star formation law for spirals (black circles), using a constant Milky Way X ( CO )

Second-order correlations

We therefore present here a rudimentary test for second-order correlations in the global law of star formation. Both Figure 6.9 and Table 6.6 illustrate that for spiral galaxies (black points) the second-order correlations in the star formation law are weak at best. That is, can any of the second-order parameters explain the largest spread in the star formation law?

Figure 6.8: The extended Schmidt relation for spirals (black circles) and dwarfs (cyan triangles), including galaxies with only HI measurements (rightward arrows) and upper limits on SFR measurements (downward arrows)

Systematic uncertainties

SFR calibrations
Molecular gas densities
Choice of diameters

This metallicity effect does not reduce the slope of the star formation law for the combined sample of dwarf and spiral galaxies. Since the fraction of atomic gas is a function of Σgas, it can affect the slope of the star formation law. Here we demonstrate this effect by considering an extreme case: doubling the diameters for a subset of our galaxies increases the slope of the star formation law by ∼0.2 dex.

Comparison with literature

However, this example highlights the importance of choosing the right diameters when studying the law of star formation. Finally we note that until recently, a turnover or threshold in the star formation law was seen only in spatially resolved data (e.g., Bigiel et al. 2008; in particular, Leroy et al. 2008 ) performed a thorough analysis of this local-scale star formation threshold by examining the radial profiles of the star formation efficiency ΣSFR/Σgas as a function of various parameters.

Physical interpretations

The star formation law for spiral galaxies
Low-density threshold
Second-order correlations

Therefore, the extended Schmidt law can better describe low-gas galaxies, eliminating the low-density circulation observed in the star formation law. On the other hand, the star formation law for dwarf galaxies tends to show stronger second-order correlations with other galactic parameters. The parameter that significantly reduces the scatter in the star formation law for dwarf galaxies is the total gas fraction fgas = Mgas/(Mgas + M∗).

Summary

Decreasing the dynamical time thus increases the mass inside the star-forming radius; for a givenΣgas, it increases Σ∗, causing the dependence on Σ∗ in the extended Schmidt law. We found that much of the scatter in the star formation law is intrinsic, motivating a search for second-order correlations in the star formation law (Section 6.4). We found that there are no significant second-order correlations for spiral galaxies, but that second-order correlations with total gas fraction (MMgas . gas+M∗), Σ∗ or τdyn can explain much of the scatter in the star formation law for dwarf galaxies.

Appendix: Photometry procedure and corrections .1 UV and IR photometry.1UV and IR photometry

Photometry systematics and corrections

Python package photutils (now an associated package of the Astropy library; see Robitaille et al. 2013) to identify all point sources. Both the axis ratio b/a and the position angle θ of the ellipse were determined from RC3. The median difference between our Hα aperture (R25 aperture) measurements and the Gil de Paz et al. 2007 ) fluxes are indicated by a dashed orange (dashed blue) line; the orange (blue) shaded area marks ±1 median absolute deviation.

Figure 6.10: Comparison between our measured FUV fluxes and the FUV fluxes reported in Gil de Paz et al

Appendix: References for gas surface densities

Tables 6.2 and 6.3 list the final aperture-corrected UV and IR fluxes used in this work.