# A Strontium-rich Ultra Metal-poor Star in the Atari Disk Component††thanks:
This paper includes data gathered with the 6.5 m Magellan Telescopes located
at Las Campanas Observatory, Chile.

Mohammad K. Mardini,1,2,3 Anna Frebel,2 Anirudh Chiti,4,5
1Department of Physics, Zarqa University, Zarqa 13110, Jordan
2Department of Physics and Kavli Institute for Astrophysics and Space
Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
3Joint Institute for Nuclear Astrophysics–Center for Evolution of the Elements
(JINA-CEE), East Lansing, MI 48824, USA
4Department of Astronomy $\&$ Astrophysics, University of Chicago, 5640 S
Ellis Avenue, Chicago, IL 60637, USA
5Kavli Institute for Cosmological Physics, University of Chicago, Chicago, IL
60637, USA
E-mail<EMAIL_ADDRESS>
###### Abstract

We report on the discovery of the first ultra metal-poor (UMP) star 2MASS
J20500194$-$6613298 (J2050$-$6613; [Fe/H] = $-4.05$) selected from the Gaia
BP/RP spectral catalog that belongs to the ancient Atari disk component. We
obtained a high-resolution spectrum for the star with the MIKE spectrograph on
the Magellan-Clay telescope. J2050$-$6613 displays a typical chemical
abundance pattern for UMP stars, including carbon and zinc enhancements. In
contrast, J2050$-$6613 shows extremely high [Sr/Fe] and [Sr/Ba] ratios
compared to other stars in the [Fe/H] $<-4.0$ regime. J2050$-$6613 is most
likely an early Population II star that formed from a gas cloud that was
chemically enriched by a massive Population III hypernova (E $>10^{52}$ erg).
Such a Population III core-collapse hypernova could simultaneously explain the
origin of the abundance pattern of light and heavy elements of 2MASS
J2050$-$6613 if a large amount of Sr of $\sim 10^{-5}$ M⊙ was produced,
possibly by neutrino-driven (wind) ejecta. Therefore, the abundance pattern of
2MASS J2050$-$6613 places important constraints on Sr-producing
nucleosynthesis sources operating in the Atari progenitor at the earliest
times.

###### keywords:

Early universe — Galaxy: disc — stars: abundances — stars: Population II —
stars: individual (2MASS J20500194$-$6613298)

††pagerange: A Strontium-rich Ultra Metal-poor Star in the Atari Disk
Component††thanks: This paper includes data gathered with the 6.5 m Magellan
Telescopes located at Las Campanas Observatory, Chile.–A Strontium-rich Ultra
Metal-poor Star in the Atari Disk Component††thanks: This paper includes data
gathered with the 6.5 m Magellan Telescopes located at Las Campanas
Observatory, Chile.

##  1 Introduction

The very first (so-called Population III; hereafter Pop III) stars were
massive and lived short (e.g., Abel et al., 2002). No direct observation of
Population III stars may be possible, even with the JWST telescope (Gardner et
al., 2006) due to their high redshift $\geq 20$ and extremely limited apparent
brightness. However, an alternative path to investigating Pop III stars is
studying chemically pristine, ultra-metal poor ([Fe/H]111[Fe/H]=
$\log_{10}(N_{\text{Fe}}/N_{\text{H}})_{\star}-\log_{10}(N_{\text{Fe}}/N_{\text{H}})_{\sun}$
$<-4.0$; UMP) Galactic stars. These stars preserve clean signatures of one or
few enrichment events that polluted their birth clouds as discussed in a
number of reviews (e.g., Beers & Christlieb, 2005; Frebel & Norris, 2015;
Frebel, 2018). As such, they provide information on early star formation
processes and help reconstruct the characteristics of the first stars and
their nucleosynthetic yields.

Extensive efforts have been made to discover these early, most metal-poor (Pop
II) stars over the last several decades (e.g., Christlieb et al., 2008; Frebel
et al., 2006; Da Costa et al., 2019; Starkenburg et al., 2017). As a result,
41 UMP stars (e.g., Christlieb et al., 2002; Frebel et al., 2005; Caffau et
al., 2011a; Keller et al., 2014; Placco et al., 2021b) have been confirmed
with high-resolution spectroscopic observations to date (Abohalima & Frebel,
2018). These searches have clearly shown UMP stars to be incredible rare, at a
$\sim$1 in 1,000,000 rate (Frebel, 2018). This also explains why only a few
dozen of stars have been found. More efficient search techniques appear to
hold the key to identifying these rare objects.

Techniques have been recently refined to efficiently identify the most metal-
poor candidates from narrow-band photometry (e.g., Aguado et al., 2019; Da
Costa et al., 2019; Chiti et al., 2020; Whitten et al., 2021; Placco et al.,
2022). This process can be further optimized by deriving [Fe/H] estimates from
the new spectrophotometry data products (often shortened as GaiaXP; see
Carrasco et al., 2021; Montegriffo et al., 2023; De Angeli et al., 2023) of
the third data release (DR3) of the ESA/Gaia astrometric mission (Gaia
Collaboration et al., 2016, 2023). Extensive efforts have been undertaken to
obtain accurate [Fe/H] estimates for the $\sim 220$ million sources in the
Gaia XP catalog (e.g., Rix et al., 2022; Andrae et al., 2023; Yao et al.,
2023; Zhang et al., 2023). Yet, no Gaia XP UMP stars have thus far been
discovered.

In a parallel effort, we have computed metallicity-sensitive synthetic
photometry from the Gaia XP spectrophotometric data using GaiaXPy222Available
at https://gaia-dpci.github.io/GaiaXPy-website, and utilized the grids of
synthetic photometry presented in Chiti et al. (2021) to estimate [Fe/H] for
sources in the Gaia XP data set (M. Mardini et al. 2024, in prep.). From high-
resolution spectroscopic follow-up of our lowest [Fe/H] candidates, we report
the discovery and detailed chemical abundance analysis of the relatively
bright (V = 13.1 mag) red giant star 2MASS J20500194$-$6613298 (hereafter
J2050$-$6613) with [Fe/H] = $-4.05$ and [C/Fe] = 1.24, the first confirmed UMP
star from the Gaia XP catalog. We note that the low metallicity nature of
J2050$-$6613 ([Fe/H] = $-4.25$) had independently been reported by Da Costa et
al. (2019), but no high-resolution follow-up analysis was reported for the
star.

##  2 Target Selection, Observations, and Radial Velocity

The Gaia Data Processing and Analysis Consortium developed the GaiaXPy
package, which allows one to calculate the expected flux of an object with XP
spectra through pre-loaded transmission curves. Several photometric filters
were provided that are known to be sensitive to metallicities of stars, namely
the SkyMapper $u,v,g,i$ filterset and the narrow-band Pristine Ca II K filter.
The 360-410 nm SkyMapper $v$-filter and the even narrower 390-400 nm Pristine
filter cover CaII K line at 393 nm. Photometry through these filters can
demonstrably be used to identify low-metallicity stars (e.g., Starkenburg et
al., 2017; Da Costa et al., 2019; Chiti et al., 2021).

We leverage the utility of the aforementioned metallicity-sensitive filters to
derive photometric metallicities for sources with Gaia XP spectra. We selected
a sample of $\sim$ 61 million Gaia XP sources that satisfy the following
quality cuts: i) Galactic latitude $|b|>10.0$°, ii) RUWE $<$ 1.1, iii)
c$\\_$star $<1.0$, and iv) E(B $-$ V) $<0.35$. For this sample, we then
derived photometric metallicities using grids of synthetic photometry that
were matched to observed photometry, exactly as reported in Chiti et al.
(2021). However, we also regenerated the same grid to include the Pristine
CaII K filter and repeated the same procedure to derive photometric
metallicities with broad-band SkyMapper $g,i$ photometry. This allowed two
estimates for the metallicity of each stars– one from the SkyMapper filterset
([Fe/H]SMSS), and the other from the Pristine + SkyMapper $g,i$ photometry
([Fe/H]Pris). Our [Fe/H]Pris measurements agree to within 0.39 dex with
results from high-resolution spectroscopic [Fe/H] values, down to [Fe/H]
$\sim-4.0$. For comparison, this is as nearly as good as typical medium-
resolution spectroscopic results with $\sim$ 0.30 dex uncertainties. A more
detailed description of this procedure together with a catalog of
metallicities will be presented in a forthcoming paper (M. Mardini et al.
2024, in prep.).

Table 1: Magellan/MIKE Chemical Abundances of J2050$-$6613 Species | Method | $N$ | $\log\epsilon(\mbox{X})$ | st.dev | st.err | [X/H] | [X/Fe] | $\Delta T_{\text{eff}}$ | $\Delta\log(g)$ | $\Delta v_{\text{micr}}$ | $\sigma_{tot}$
---|---|---|---|---|---|---|---|---|---|---|---
|  |  | (dex) |  |  | (dex) | (dex) |  $+100$ K |  $+0.3$ dex |  $+0.3$ km s-1 |
C | Syn | 1 |  $+$5.62 | 0.10 | 0.10 |  $-$2.81 |  $+$1.24 |  $-$0.16 |  $-$0.00 |  $-$0.00 | 0.17
O | Syn | 1 |  $<+$6.19 | … | … |  $<-$2.50 |  $<+$1.55 | … | … | … | …
Na I | EW | 2 |  $+$1.93 | 0.08 | 0.07 |  $-$4.31 |  $-$0.26 |  $-$0.05 |  $-$0.02 |  $-$0.09 | 0.12
Mg I | EW | 6 |  $+$4.12 | 0.09 | 0.04 |  $-$3.48 |  $+$0.57 |  $-$0.05 |  $-$0.06 |  $-$0.12 | 0.13
Al I | Syn | 1 |  $+$1.73 | 0.10 | 0.10 |  $-$4.72 |  $-$0.67 |  $-$0.09 |  $-$0.00 |  $-$0.13 | 0.17
Si I | Syn | 1 |  $+$3.60 | 0.10 | 0.10 |  $-$3.91 |  $+$0.14 |  $-$0.04 |  $-$0.01 |  $-$0.03 | 0.10
Ca I | EW | 3 |  $+$2.56 | 0.09 | 0.06 |  $-$3.78 |  $+$0.27 |  $-$0.10 |  $-$0.03 |  $-$0.02 | 0.11
Sc II | EW | 3 |  $-$1.34 | 0.09 | 0.05 |  $-$4.49 |  $-$0.44 |  $-$0.07 |  $-$0.08 |  $-$0.03 | 0.12
Ti I | EW | 3 |  $+$1.35 | 0.13 | 0.05 |  $-$3.60 |  $+$0.45 |  $-$0.05 |  $-$0.02 |  $-$0.05 | 0.09
Ti II | EW | 10 |  $+$1.11 | 0.16 | 0.05 |  $-$3.84 |  $+$0.21 |  $-$0.12 |  $-$0.07 |  $-$0.11 | 0.14
Cr I | EW | 3 |  $+$1.27 | 0.07 | 0.04 |  $-$4.37 |  $-$0.32 |  $-$0.04 |  $-$0.04 |  $-$0.06 | 0.09
Mn I | EW | 2 |  $+$0.20 | 0.05 | 0.05 |  $-$5.23 |  $-$1.18 |  $-$0.03 |  $-$0.02 |  $-$0.07 | 0.09
Fe I | EW | 56 |  $+$3.45 | 0.16 | 0.02 |  $-$4.05 |  $+$0.00 |  $-$0.11 |  $-$0.02 |  $-$0.09 | 0.13
Fe II | EW | 3 |  $+$3.46 | 0.05 | 0.04 |  $-$4.04 |  $+$0.01 |  $-$0.02 |  $+$0.08 |  $-$0.05 | 0.10
Co I | EW | 8 |  $+$1.33 | 0.15 | 0.05 |  $-$3.66 |  $+$0.39 |  $-$0.02 |  $-$0.03 |  $-$0.04 | 0.07
Ni I | EW | 12 |  $+$2.32 | 0.17 | 0.06 |  $-$3.90 |  $+$0.15 |  $-$0.01 |  $-$0.06 |  $-$0.03 | 0.08
Zn I | Syn | 1 |  $+$1.35 | 0.10 | 0.10 |  $-$3.21 |  $+$0.95 |  $-$0.09 |  $-$0.09 |  $-$0.08 | 0.12
Sr II | Syn | 2 |  $-$0.18 | 0.05 | 0.03 |  $-$3.05 |  $+$1.00 |  $-$0.10 |  $-$0.04 |  $-$0.07 | 0.14
Ba II | Syn | 1 |  $-$2.95 | 0.10 | 0.10 |  $-$5.13 |  $-$1.08 |  $-$0.11 |  $-$0.08 |  $-$0.11 | 0.22

Figure 1: Portions of the observed spectrum of J2050$-$6613 used to derived
chemical abundances for carbon (panel a), strontium (panel b), and barium
(panel c). Black line-connected points denote the observed data. Gold lines
represent the best-fit spectrum syntheses. Green lines represent syntheses
with diminishing contribution by the relevant element. The shaded areas
represent illustrative $\pm 0.2$ dex variations to show corresponding
abundance uncertainties. Final line abundances are listed in the legends.

To identify additional metal-poor stars with [Fe/H] $<-3.0$, we selected $\sim
25$ candidates with [Fe/H]SMSS $\leq-3.0$ and [Fe/H]Pris $\leq-3.0$, and no
high-resolution spectroscopic abundances in the literature. We then obtained
high-resolution spectra of 20 stars with MIKE/Magellan (Bernstein et al.,
2003). Given the reliability of our derived photometric metallicities, we were
able to skip the time-consuming process of vetting candidates first, typically
done with medium-resolution spectra and Ca II K line measurements. We observed
J2050$-$6613 on 2023 April 22 for a total of 2000 s using the
0$\aas@@fstack{\prime\prime}$7 slit. Our observation setup yielded a high
spectral resolution of R $\sim$ 35,000 and 30,000 in the blue ($\lambda<5000$)
and red ($\lambda>5000$) wavelength regime of our spectrum that covers a wide
(3300 Å to 9400 Å) wavelength range. We carried out a standard data reduction
procedure using the reduction pipeline developed for Magellan/MIKE
observations (Kelson, 2003)333Available at
http://obs.carnegiescience.edu/Code/python. The reduced data has high S/N of
110 at $\lambda\sim 4000$ Å.

We cross-correlated our MIKE spectrum against a restframe spectrum of HD122563
(a red giant with [Fe/H] $\sim-2.8$) around the Ca triplet region to calculate
a heliocentric radial velocity. We find RV = 195.6 $\pm 0.5$ km s-1. We also
retrieved RV measurements from Gaia DR3 of (RV = 197.32 $\pm 2.04$ km s-1 Gaia
Collaboration et al., 2023), from RAVE DR5 of (RV = 196.50 $\pm 4.18$ km s-1
Kunder et al., 2017), and from RAVE DR6 of (RV = 196.10 $\pm 4.28$ km s-1
Steinmetz et al., 2020). These available RV measurements for J2050$-$6613
suggests no binarity.

##  3 Equivalent widths and Stellar Parameters

We adopted an atomic and molecular data linelist generated by the
linemake444Available at https://github.com/vmplacco/linemake code (Placco et
al., 2021a). We measured equivalent widths (EW) for detectable lines (see
Table 1) in our high-resolution spectrum using the Spectroscopy Made Hard
(SMH) software (Casey, 2014)555Available at https://github.com/andycasey/smhr
by fitting Gaussian profiles to the absorption features. We note that the
selected lines for the EW measurements were checked for no carbon
contamination. Also, we visually examined each of the lines for potential
blending and corrected the fits for any unreliable continuum placements.

We determined the atmospheric stellar parameters for J2050$-$6613 using a
combination of photometric and spectroscopic approaches, similarly to what was
presented in Mardini et al. (2023). We estimate the photometric effective
temperature ($T_{\rm{eff}}$) using accurate G, BP and RP magnitudes for our
star, as reported in the source catalog published by Gaia Collaboration et al.
(2023). We estimated the bolometric correction following Casagrande &
VandenBerg (2018a, b). We used the best polynomial fit of the
color–$T_{\rm{eff}}$ from Mucciarelli et al. (2021) to derive $T_{\rm{eff}}$.
To obtain the median $T_{\rm{eff}}$ and its uncertainty, we generated 10,000
random realizations drawn from each input parameter and their corresponding
uncertainties. This way, we calculated our final $T_{\rm{eff}}$ = 5000 $\pm$
100 K which is the median value of the distribution and its standard
deviation.

We used our EW measurements, the latest version of MOOG (Sneden, 1973)
666Available at https://github.com/alexji/moog17scat, and one-dimensional
plane-parallel model atmospheres with $\alpha$-enhancement (Castelli & Kurucz,
2004) wrapped within SMH to derive LTE chemical abundances of Fe I and Fe II
lines. We then used the Fe I and Fe II abundances to iteratively constrain the
surface gravity ($\log g$) and microturbulence (v${}_{\text{micro}}$) by
adopting $\log g$ that show agreement between the Fe I and Fe II mean
abundances and enforcing no trends between the reduced EW and Fe I abundances.
Our final stellar parameters are $T_{\rm{eff}}$ = 5000 $\pm$ 100 K, $\log g$=
1.80 $\pm$ 0.30 dex, v${}_{\text{micro}}$= 1.40 $\pm$ 0.30 km s-1, and [Fe/H]
= $-$4.05 $\pm$ 0.16 dex. We note that we adopted typical uncertainties for
$\log g$ and v${}_{\text{micro}}$. Also, we adopted the standard deviation of
the Fe I abundances as the uncertainty for [Fe/H]. For completeness, we also
obtained stellar parameters from a traditional spectroscopic approach and
found $T_{\rm{eff}}$ = 4350 K, $\log g$ = 0.21 dex, v${}_{\text{micro}}$ =
1.86 km s-1, and [Fe/H] = $-4.75$ dex. It is well established that
spectroscopic temperatures are cooler by several hundred degrees than
photometrically derived ones. Correcting the spectroscopic temperature
following the approach presented in Frebel et al. (2013) would yield
$T_{\rm{eff}}$ = 4585 K, $\log g$ = 0.79 dex, v${}_{\text{micro}}$ = 1.52 km
s-1, and [Fe/H] = $-4.48$ dex. However, no straightforward explanation for the
large ($\sim 415$ K) difference between our derived temperatures can be drawn,
but note that these temperatures yield [Fe/H] $\lesssim-$4.0. Thus,
J2050$-$6613 is truly a UMP star.

##  4 Chemical abundances

We determined elemental abundance ratios ([X/Fe]) from both spectrum synthesis
and EW analysis of absorption lines detected in our Magellan/MIKE spectrum. We
adopted solar abundances ($\log\epsilon(\mbox{X})_{\odot}$) from Asplund et
al. (2009). A summary of the EW fitting procedure and analysis is provided in
Section 3. For the spectrum synthesis approach, we generated synthetic spectra
to match the observational data to derive chemical abundances for seven
elements (see the second column of Table 1). Figure 1 shows an illustrative
example of our spectral synthesis fitting. We took into consideration the
effects of hyperfine splitting when determining the abundances from the Sr II
and Ba II lines, as discussed in Bergemann et al. (2012) and Gallagher et al.
(2010), respectively. Our final abundances are listed in Table 1. We also used
the strongest line of the O I triplet $\sim$ 7770 Å to place an upper limit on
the oxygen abundance. We determined reasonable line uncertainties for the
elements derived from spectral synthesis (C, Al, Si, Zn, Sr, and Ba) by
comparing a range of synthetic spectra that were consistent with the continuum
(see e.g., the shaded area in Figure 1).

The fifth column presents the standard deviation of the mean abundances as
derived from individual line measurements. Note that for elements with few
available lines ($N$<4; e.g., Na), the standard deviations were adjusted to
account for the small number samples, following Keeping (1962). This prevents
uncertainties from being underestimated due to small number statistics. To
arrive at these adjusted values, we use $\sigma$ = Rk, where k is a factor
computed for each N derived to approximate Gaussian statistics for small N
samples. Values of k can be found in table 2.5 of Keeping (1962). R is the
maximum range covered by individual measurements. In the sixth column, we
report the standard error, based on st.err = Rk⋆, and again, corrected for
small number samples. The k⋆ values are taken from table 2.5 of Keeping
(1962).

We also estimated the effects of the uncertainty in our stellar parameters on
the derived atmospheric abundances. The ninth ($\Delta T_{\text{eff}}$), tenth
($\Delta\log(g)$), and eleventh ($\Delta v_{\text{micr}}$) columns list the
systematic uncertainties due to changes in one stellar parameter within their
uncertainties ($\Delta T_{\rm{eff}}$ = 100 K, $\Delta\log g$ = 0.3 dex, and
$\Delta$ v${}_{\text{micro}}$ = 0.3 km s-1). We finally calculated the total
uncertainties as quadratic sums of each uncertainty estimate, using our
calculated standard errors.

Overall, J2050$-$6613 is a carbon enhanced ([C/Fe] = 1.24) UMP ([Fe/H] =
$-4.05$) star, adding to the overwhelming number of carbon-rich halo stars at
the lowest metallicities. An evolutionary correction of [C/Fe] = $+0.08$,
based on Placco et al. (2014b), is applied here. The other abundances of
J2050$-$6613 also agree well with those of other known UMP stars (i.e.,
enhancements in $\alpha$-elements and Zn relative to iron). The neutron-
capture element abundances are somewhat unusual in J2050$-$6613\. The Ba
abundance is low ([Ba/Fe] $=-1.04$), as it is typical for other halo stars at
[Fe/H] $\sim-4$. However, the Sr abundance, of [Sr/Fe] $=+0.95$, is among the
highest of UMP stars. This leads to one of the largest known [Sr/Ba] ratios
([Sr/Ba] = 1.99). Note that using the corrected spectroscopic stellar
parameters would yield [Sr/Fe] $=+1.10$ and [Ba/Fe] $=-1.30$. We illustrate
this behavior in panels a and b of Figure 2 where we show [Sr/H] and [Ba/H] as
a function of [Fe/H]. Panel c of Figure 2 shows [Sr/Ba] versus [Ba/Fe]. The
blue asterisk denotes the location of our star, and orange points are chemical
abundances data for metal-poor stars compiled in Abohalima & Frebel (2018).
Some relevant comparison metal-poor stars (as discussed in Section 5) are
plotted with symbols as listed in the legend.

##  5 Possible Origin Scenarios for J2050$-$6613

We now utilize the observed chemical abundance signature of J2050$-$6613 to
gain insights into its birth environment and how its progenitor gas cloud was
chemically enriched by the first stars. The empirical transition discriminant
($D_{\text{trans}}$) criterion from Frebel et al. (2007) employs carbon and
oxygen abundances to set a limit for sufficient fragmentation for early low-
mass Pop II star formation through C and O fine-structure line cooling.

To calculate $D_{\text{trans}}$ for J2050$-$6613, we used our measurement of
[C/H] and the upper limit on [O/H]. Since we only have an upper limit for O we
cannot derive a $D_{\text{trans}}$ value that would meaningfully suggest
whether it is above the threshold of $D_{\text{trans}}$ $=-3.50$. However,
given that our [C/O] $>-$0.31 agrees with the suggested range of $-0.7<$ [C/O]
$<0.2$ by Frebel & Norris (2013) for metal-poor stars, we adopt [C/O] = 0.2 to
estimate $D_{\text{trans}}$. This way we obtain $D_{\text{trans}}\gtrsim-2.8$.
This reasonable lower limit is already well above the threshold. We note that
if we were to apply any NLTE corrections to the O triplet, the O abundance
would decrease even further and effectively not change the $D_{\text{trans}}$
value. This suggests that the composition of the birth gas cloud fragmented
sufficiently for low-mass stars to form, and in the case of J2050$-$6613 to
form with a high natal C abundance.

For CEMP-no stars ([C/Fe] $>0.7$ and [Ba/Fe] $<0.0$), such as J2050$-$6613,
Rossi et al. (2023) suggested a criterion for identifying their origin based
on their carbon, magnesium and iron abundances. A similar approach was
suggested by Hartwig et al. (2018) although they did not specifically consider
CEMP-no stars. J2050$-$6613 has A(C) = 5.62 and [C/Mg] = 0.67, which indicates
predominant prior enrichment by massive metal-free stars to the local birth
environment (e.g., Placco et al., 2016; Almusleh et al., 2021; Mardini et al.,
2019b), likely by those that produced a lot of carbon.

Assuming J2050$-$6613 is a second-generation star, then its atmospheric
abundance pattern can be expected to reflect the chemical yields of the Pop
III progenitor. To constrain the progenitor characteristics, we matched 10,000
abundance patterns, generated from normal Gaussian distributions of our
derived abundances and uncertainties, to theoretical Pop III models taken from
Heger & Woosley (2010) and using the StarFit777Available at http://starfit.org
code. We find a reasonable fit for most elements, although the observed carbon
abundance often appears to be underestimated by the models. The best-fit
parameters of $\sim 97\%$ of our generated patterns suggest a massive
progenitor (29.5 and 24.0 M$\odot$, respectively, as seen in panel d of Figure
2). These models have high explosion energies of E = 1052 erg. Such energetic
hypernovae most likely enriched the birth gas cloud of J2050$-$6613\. In
contrast, few models (2.5%) have lower masses (M = $10$ \- $11$ M⊙). They
match the observed [C/Fe] for our star but consistently underestimate [Zn/Fe].
Zinc is produced in the complete Si-burning shell deep in the star.
Consequently, the production of a substantial amount of Zn relies on the
energy released during an explosion and is commonly associated with hypernovae
(Umeda & Nomoto, 2005). Along with Zn enhancements, Pop III hypernova models
also predict an enhancement in Co alongside lower values for Mn and Cr (Umeda
& Nomoto, 2005).

Qualitatively, J2050$-$6613 follows this principal pattern. However, the
hypernova models still somewhat underproduce Zn by about 0.3 dex as well as
carbon. In contrast, the lower mass progenitor models cannot produce nearly
enough Zn which we take as a benchmark for constraining the progenitor. We
thus adopt the properties of a high-mass hypernovae for the Pop III
progenitor. We note that it is common for some of the individual abundances to
be fit poorly; as discussed by Heger & Woosley (2010) and also Magg et al.
(2020) this is at least partially due to uncertainties in our observations
(i.e., stellar parameters and atmospheric abundances), theoretical models
(e.g., yield predictions), as well as the fitting algorithm. Keeping these
caveats in mind, and notwithstanding, this comparison provides a helpful
indication of the potential stellar mass and explosion energy of the
progenitor.

Figure 2: Observed abundance trends of [Sr/H] (panel a) and [Ba/H] (panel b)
as a function of [Fe/H]. Panel c represents the [Sr/Ba] versus [Ba/Fe]. The
blue asterisk denotes the abundances for J2050$-$6613\. Gray points represent
the data for metal-poor stars collected by JINAbase. Other noteworthy metal-
poor stars are indicated with various symbols. Solid lines in panel d show the
best-fit Pop III theoretical yields (with progenitor properties) from Heger &
Woosley (2010) and color-coded by their fractional occurrence. Panel e shows
LTE elemental abundance patterns for J2050$-$6613 and the other noteworthy
metal-poor stars (see the legend). Metal-poor star data taken from: Roederer
et al. (2014); Barklem et al. (2005); Ryan et al. (1991); Cohen et al. (2004);
Hollek et al. (2011); Bonifacio et al. (2012); Mardini et al. (2019a, b, 2020,
2022a); Aguado et al. (2017a); Yong et al. (2013); Frebel et al. (2010);
Hansen et al. (2015); Jacobson et al. (2015); Ezzeddine et al. (2019); Spite
et al. (2014); Cayrel et al. (2004); Norris et al. (2007); Francois et al.
(2007); Aguado et al. (2021); Lai et al. (2004); (Mashonkina et al., 2017);
Almusleh et al. (2021); Placco et al. (2020); Ryan et al. (1996); Aguado et
al. (2018); Aoki et al. (2007); Aoki et al. (2013); Cohen et al. (2013); Honda
et al. (2011); Lai et al. (2008); Casey & Schlaufman (2015); Masseron et al.
(2006); Rich & Boesgaard (2009); Ryan et al. (1999); Spite et al. (1999);
Depagne et al. (2000); Spite et al. (2000); Sivarani et al. (2006); Norris et
al. (2001); Mardini et al. (2019c); Hansen et al. (2018); Placco et al.
(2014a); Caffau et al. (2011b); Frebel et al. (2005); Frebel et al. (2008);
Behara et al. (2010); Caffau et al. (2011a); Aguado et al. (2017b); Carretta
et al. (2002); For & Sneden (2010); Keller et al. (2014); Frebel et al.
(2015); Caffau et al. (2013); Plez & Cohen (2005); Christlieb et al. (2002);
Frebel et al. (2019); Placco et al. (2023).

We now discuss possible origins of the observed neutron-capture elements.
J2050$-$6613 exhibits a strong enhancement in Sr ([Sr/Fe] = 0.95), in stark
contrast to its much more “typical” Ba ([Ba/Fe] = $-$1.04) abundance. Among
the other known UMP stars, only HE 1327$-$2326 ([Fe/H] $=-5.71$; [Sr/Fe] =
1.08; [Ba/Fe] $<$ 1.39; Frebel et al. 2008) and J0224$-$5737 ([Fe/H] $=-3.97$;
[Sr/Fe] = 1.08; [Ba/Fe] $<$ -0.91; Jacobson et al. 2015) have similar Sr
enhancement levels; however, these stars only have Ba upper limits available.
For comparison, the Sr abundances of other UMP halo stars (e.g., Frebel et al.
2014, Andales et al. 2023) typically have much lower values as low as [Sr/Fe]
$\sim-1.8$ at [Fe/H] $\sim-4.0$ which is nearly three dex lower than what we
observed in J2050$-$6613.

However, there are several other stars with large [Sr/Ba] ratios which we
highlight in figure 2: J0929+0838 ([Fe/H] = $-2.34$; Ezzeddine et al. 2020),
HE0109$-$4510 ([Fe/H] = $-2.96$; Hansen et al. 2015), J1422+0031 ([Fe/H] =
$-3.03$; Aoki et al. 2013), and, again, J0224$-$5737 ([Fe/H] = $-3.97$;
Jacobson et al. 2015). Note that the latter two stars only have upper limits
on their Ba abundances, implying lower limits on their [Sr/Ba] values.
Nevertheless, all these objects cluster above all other stars that form the
main observed trend, thus opening up the parameter space. For completeness, we
also add HE 1327$-$2326; note that its high upper limit of [Ba/Fe] $<$ 1.39
renders its low [Sr/Ba] $>-0.31$ lower limit largely meaningless.

Sr abundances in combination with significantly lower Ba values are often
attributed to the operation of the limited-$r$ process (Frebel, 2018).
However, in comparison to the representative limited-$r$ star HD122563 (Honda
et al., 2011), J2050$-$6613 shows a much higher relative Sr abundance as well
as a more extreme [Sr/Ba] ratio (HD122563 has [Sr/Ba] $\sim$0.8 dex). We added
HD122563 to panel e of Figure 2 for comparison. Altogether, it appears that
there is a new class of objects with such high [Sr/Ba] ratios of $\gtrsim$ 2\.
We plot the stars common element abundances in panel e of Figure 2. As can be
seen, all stars have qualitatively very similar patterns. Possible exception
may be some variations in the Ba abundances given that several stars only have
upper limits at present.

To learn more about these stars and their nucleosynthetic origins, we now
discuss core-collapse supernovae (CCSN) as a major source of neutron-capture
elements (e.g., Pruet et al., 2006; Arcones et al., 2007; Wanajo et al.,
2011). Specifically, we attempt to derive yield predictions for Sr and Ba.
Assuming that J2050$-$6613 formed from a gas cloud with $10{{}^{5}}$ M⊙, the
observed abundances imply a large Sr yield of $\sim 10^{-5}$ M⊙ alongside a Ba
yield of only $\sim 10^{-8}$ M⊙. Whether these very different yields are
physically plausible to be produced within one massive first star remains to
be seen. In any case, it appears that CCSN must produce extremely variable Sr
yields as reflected in the different [Fe/H] levels observed. Adding rotation
to the models can, however, enhance the Sr production (e.g., Frischknecht et
al., 2016). Also, neutrino-driven winds that follows the successful CCSN might
synthesize additional Sr (e.g., Bliss et al., 2020). Interestingly, our
suggested Sr yield appears to (coincidentally) be similar to what the kilonova
AT2017gfo produced (Watson et al., 2019). However, the coalescence time of
binary neutron star mergers argues against a compact object merger as the
origin of Sr observed in our star. However, if CCSN yields are found to
produce only a narrow range of Sr yields, the new class of high [Sr/Ba] stars
suggests a likely contribution from an additional source (with the possible
caveat that this source should produce negligible amounts of iron as to not
overproduce the stellar [Fe/H] of the initial/main source). A discussion of Sr
yields and associated astrophysical sites, can be found in Hansen et al.
(2013).

In summary, the abundance signature of J2050$-$6613 suggests that its Pop III
progenitor was massive and very energetic, and that it must have produced
copious amounts of Sr but little Ba, possibly through a limited r-process
while also having a low Fe yield to account for the UMP nature of the star.
However, other scenarios, possibly involving two sources or sites, should be
further explored to place new constraints on the nucleosynthetic history of
this class of very low metallicity, high [Sr/Ba] stars, including
J2050$-$6613\. Interestingly, since J2050$-$6613 is also another confirmed
ultra-metal-poor member of the so-called Atari disk (Mardini et al., 2022b),
this suggests that significant C and Sr production and/or the limited
$r$-process may have been a major pathways for element nucleosynthesis in the
progenitor system of the Atari component. Atari itself then likely formed from
a very early ($\geq 9$ Gyr) radial merger event of that progenitor that
entered the proto-galactic disk to form a component separate from the
canonical thick disk. For more details, interested readers are referred to
Mardini et al. (2022b). In any case, the Atari disk appears to contain a
significant amount of the most metal-poor stars, including five stars with
[Fe/H] $<-$4.0. J2050$-$6613 adds to this growing number of truly ancient UMP
stars of accreted second generation stars present in the Galactic disk.

## Acknowledgements

We thank Vinicius Placco, Ian Roederer, and Shinya Wanajo for helpful
discussions. This work was initiated at an event supported by the National
Science Foundation under Grant No. PHY-1430152 (JINA Center for the Evolution
of the Elements). This work is supported by Basic Research Grant (Super AI) of
Institute for AI and Beyond of the University of Tokyo. M.K.M. acknowledges
partial support from NSF grant OISE 1927130 (International Research Network
for Nuclear Astrophysics/IReNA). A.F. acknowledges support from NSF grants
AST-1716251 and AST-2307436. A.C. is supported by a Brinson Prize Fellowship
at the University of Chicago/KICP.

This work has made use of data from the European Space Agency (ESA) mission
Gaia (https://www.cosmos.esa.int/gaia), processed by the Gaia Data Processing
and Analysis Consortium (DPAC,
https://www.cosmos.esa.int/web/gaia/dpac/consortium). Funding for the DPAC has
been provided by national institutions, in particular the institutions
participating in the Gaia Multilateral Agreement.

## Data Availability

The individual line measurements are provided as supplementary material. The
reduced spectra can be obtained by reasonable request to the corresponding
author.

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00footnotetext: Licensed under a Creative Commons Attribution 4.0
International License (CC BY 4.0). Copyright remains with the author(s).

# Seizing the Means of Production: Exploring the Landscape of Crafting,
Adapting and Navigating Generative AI Models in the Visual Arts

Ahmed M. Abuzuraiq<EMAIL_ADDRESS>0000-0002-3604-7623 Simon Fraser
UniversityCanada and  Philippe Pasquier<EMAIL_ADDRESS>0000-0001-8675-3561
Simon Fraser UniversityCanada

###### Abstract.

In this paper, we map out the landscape of options available to visual artists
for creating personal artworks, including crafting, adapting and navigating
deep generative models. Following that, we argue for revisiting model
crafting, defined as the design and manipulation of generative models for
creative goals, and motivate studying and designing for model crafting as a
creative activity in its own right.

generative AI, generative models, model crafting, visual arts, personalization

††conference: ; ; ††copyright: acmlicensed

##  1\. Introduction

Large-scale Text-to-image Generation Models (LTGMs), including Dalle-E
(OpenAI, 2024) and Stable Diffusion (Podell et al., 2023), are trained on
large datasets with many high-end GPUs. Users of these models can produce
diverse and high-quality visuals through meticulously written text prompts.
These models mark a significant shift from the era when artists used
personally-trainable generative models like Generative Adversarial Networks
(GANs) and Variational Autoencoders (VAEs). LTGMs have made the generation of
visuals accessible to everyone, but this shift in attention has overshadowed
the practice of ”model crafting”, whereas artists personalize their work by
experimenting with training sets, model architectures, and hyperparameters in
addition to combining, adapting and manipulating pre-trained models (Broad et
al., 2021a). Model crafting offered artists a sense of craftsmanship and
ownership over the creative process and its outcomes. However, the high
resource requirements and costs associated with training LTGMs have made them
practically impossible for individual artists to train111For example, a recent
work by Chen et al. (Chen et al., 2024) propose a model that can be trained
with a fraction of the cost it takes to train a large model such as Stable
Diffusion V1.5. However this fraction amounts to $26,000., and replaced
process ownership with a contested product ownership (Epstein et al., 2023).
In this paper, we map out the landscape of options available to artists for
creating personal artworks, on LTGMs or personally-trainable models, and argue
for revisiting model crafting as a significant venue for artistic
personalization.

##  2\. The Landscape of Personalization

In a 2018 interview (Zachariou, 2018), computational artist Memo Akten
outlined a spectrum of approaches for artistic creation with deep generative
models along two dimensions: data (creating one’s own, curating, or reusing
existing sets) and models (designing original algorithms, modifying existing
ones, or using pre-trained models). Having tried every combination, Akten
argued that as artists move from using a custom to existing models/data in
their work, it becomes ”harder to give it a unique spin and make it your own”.
Inspired by this analysis, we explore the current landscape of options that
artists have for creating personal artworks, with findings detailed in Table 1
and discussed later. This landscape of options expands on the ”spectrum of
working” by Ploin et al. (Ploin et al., 2022), and emphasizes the degree of
artistic control and the systems available to artists on different parts of
the landscape.

Table 1. Finer creative control over generative models increase as we move (down) between navigating, adapting or crafting models, it also increases as we move (left) from training on existing, curated or manually created datasets or as we move (left) from large to small datasets. |  Personalization Method |  | Data
---
Small ¡—¿ Large
| Parts
---
Updated
| Model
---
Training
Examples of Systems Used
Created |  Curated |  Existing |  Weights |  Network |  From Scratch |  Pre-trained
Navigate |  | Prompting, sampling,
---
interpolating, ..etc
moving here down ($\downarrow$) or left ($\leftarrow$) increases creative control and needed effort | ✗ | ✗ | ✗ | ✓ |  | Img2img (202, 2024b) or Latent Projection (202, 2024a)
---
Adapt | Few-shot model adaptation | ✓ | ✗ | ✗ | ✓ |  | Textual Inversion (202, 2024b, c, g)
---
Model fine-tuning | ✓ | ✗ | ✗ | ✓ |  | Tuning a pre-trained model (202, 2024a, i)
---
Train an off-the-shelf model | ✓ | ✗ | ✓ | ✗ |  | Picking a GAN variation (202, 2024a, i)
---
Craft | Active Divergence (Broad et al., 2021a) | ✓ | ✓ | ✓ | ✓ | Network Blending (202, 2024a)
| Train a modified model
---
✓ | ✓ | ✓ | ✗ |  | Customization frameworks (Sankaran et al., 2018; Pal and Das, 2019)
---
| Build own model
---
✓ | ✓ | ✓ | ✗ |  | Deep Learning libraries (Paszke et al., 2019; Abadi et al., 2015)
---

###  2.1. Navigating Generative Spaces

By interacting with a generative model, artists navigate the generative space
of the model, i.e. its space of possibilities, to find aesthetics that
interest them. Navigation can take place through prompting (e.g., (OpenAI,
2024; 202, 2024f)), latent projection and semantic sliders (e.g., on Autolume
(202, 2024a)), canvas-like (Rost and Andreasson, 2023) and paining-like (Chung
and Adar, 2023) interactions, or by embedding generative models into bespoke
workflows (202, 2024c; Derivative, 2024), among others (Shi et al., 2023).
Steering interfaces that provide semantically meaningful controls improve
artists’ control, self-efficacy, and creative ownership (Louie et al., 2020).
We contend that navigation becomes more effective when it takes place in the
generative space of expressive (Louie et al., 2022) and personalized models
(Abuzuraiq and Pasquier, 2024).

###  2.2. Adapting Generative Spaces

When navigating the model’s generative space proves cumbersome or if it
consistently fails to produce personal results, artists can adapt generative
models, which helps in shaping and focusing the scope of navigation. Few-shot
model adaptation relies on a handful of example images and can introduce novel
concepts into the generative space of the model with minimal changes. Full
fine-tuning (and training from scratch) requires more example images but it
can produce visual results for domains that are difficult to model with a few
images such as Arabic Calligraphy (Sobhan Sarbandi, 2021). Numerous techniques
for adapting and personalizing generative models exist (Robb et al., 2020; Hu
et al., 2021; Gal et al., 2022; Ruiz et al., 2023). Furthermore, visual arts
creation tools (e.g., (202, 2024b, c, d, e, i, h, a)) often integrate adaption
into their interfaces, whether they operate on personally-trainable or large
generative models, such that the adapted models become available for
downstream navigation (Abuzuraiq and Pasquier, 2024)

###  2.3. Crafting Generative Spaces

During model navigation a generative model is not modified, instead artists
only sample from its generative space (Figure 1 \- 1). During model adaption,
the weights of the model are changed which results in a different generative
space to navigate in (Figure 1 \- 2). On the other hand, model crafting
involves manipulating the model’s architecture including designing new
architectures, or editing and mixing existing models (c.f. in Active
Divergence), which can produce qualitatively different and more personal
generative spaces than is possible with model adaption alone, by virtue of
affording finer control. Conceptually, model crafting can be defined as a
creative exploration in the design space of generative models (Figure 1 \- 3).
Given the high computational cost of training large models, model crafting for
artists is limited to small models in practice, noting that ”small” depends on
the artist’s access to computational resources.

Figure 1. Three modes for creating personal works using generative models: (1)
model navigation, (2) model adaption, and (3) model crafting. In the first
mode, artists navigate within a fixed generative space to find the aesthetics
they like, e.g. by prompting. In the second, navigation takes places within an
adapted generative space. In the third, artists (co-)explore the space of
generative models (straight arrows) as well as navigating within the
generative space of each model (curved arrows).

####  2.3.1. Active Divergence

Artists often seek novelty and surprise from generative models. However,
generative deep learning models are, by definition, trained to model a target
distribution. Active divergence methods (Broad et al., 2021a), as the name
indicates, are a collection of methods that diverge generative models from a
target distribution to produce novel and out-of-distribution samples. Active
divergence methods, such as network rewriting (Bau et al., 2020), bending
(Broad et al., 2021b), and blending (Pinkney and Adler, 2020), involve re-
purposing and manipulating pre-trained models for art creation, with or
without data.

####  2.3.2. Model Crafting vs. Model Development

Developing generative models, and machine learning models in general, is a
mature field with support tools that span the entire development lifecycle
including data preparation and pre-processing, hyperparameters optimization,
model version control, and training process tracking as well as tools for
testing and deploying models (Budras et al., 2022; Vidal Domínguez, 2023).
Artists can lean into this ecosystem in developing generative models for
creative applications, but the emphasis on efficiency, generality, and
scalability, around which those tools are built is not always relevant for art
creation. In fact, when it comes to art creation, the bias and overfitting
associated with small datasets or simple models can be desired (Vigliensoni et
al., 2022). Art creation involves experimentation, visual exploration, and
reflection and is characterized by the lack of optimal solutions, which is why
we prefer the term Model Crafting over Model Development. If we concede that
crafting for creative endeavors is different from model development for common
purposes, we can deduce that it also requires different tool support. As the
system examples in Table 1 and previous sections show, model navigation and
adaption for the visual arts are well supported with no-code tools while
crafting largely happens on coding editors and paper sketches (Wongsuphasawat
et al., 2017). A notable exception is Autolume (202, 2024a; Kraasch and
Pasquier, 2022), a no-code visual synthesis system that combines model
navigation (via semantic sliders and latent projection), adaption ( fine-
tuning), and crafting (via network blending).

##  3\. Designing for Model Crafting

3.1. Challenges Model crafters and researchers who design tools for them face
multiple challenges including: (1) the prolonged training times for training
generative models, and (2) the interdisciplinary and technical expertise
required for model crafting. However, we join Abuzuraiq and Pasquier
(Abuzuraiq and Pasquier, 2024) in arguing that the proliferation of cloud
computing and the advances on data-efficient, limited-data and few-shot
generative models (Abdollahzadeh et al., 2023; Li et al., 2022; Yang and Wang,
2023; Moon et al., 2022) can lead high-quality models that are faster to train
(technical-perspective). Furthermore, that the adoption of ”small data”
(Vigliensoni et al., 2022) and ”slow technology” (Hallnäs and Redström, 2001)
mindsets can provide effective lenses for the design of model crafting tools
(design-perspective).

3.2. Opportunities Large generative AI systems impact the artist’s ability to
perceive generated works as their own (i.e. authorship), and the systems’
black-boxed nature impacts the artist’s sense of control over the results
(i.e., agency). In this context, model crafting offers the following distinct
advantages:

A. Owning both the Creative Process and its Products: Creative AI, epitomized
by recent generative AI systems, emphasizes the products over processes
(Colton, 2019). When people are presented with a generative AI system that
creates impressive results without offering them means to understand or
contribute to its process, their agency is decreased and they lose an
opportunity to reflect on and own their creative process, in addition to
peering into human and computational creativity at large. The advent of large
text-to-image generative models raised questions over the attribution
(authorship) of what is produced and challenged artist’s sense of ownership
(agency) when using those models, possibly due to the incurred loss of control
(Steinbrück and Stankowski, 2023). To reclaim agency over the created
products, artists personalize and adapt generative models. However, product
ownership (and authorship) can also come as a by-product of process ownership,
which can be achieved through model crafting along with carefully curated or
created datasets.

B. Sense of Craftsmanship: Model crafting is challenging and requires weaving
expertise that spans coding, deep learning, and specific domain expertise. But
it is also a rewarding activity that can be a source of pride and
craftsmanship, which is a motivation they share with makers in the Do-It-
Yourself (DIY) community (Milne, 2014). Crafters of generative systems also
often report surprising (Elgammal, 2023) and serendipitous (Lehman et al.,
2020) encounters as they experiment with generative systems.

3.3. Creativity Support for Model Crafting: Given the advantages outlined
above, we encourage researchers to study and design tools for the model
crafters. The work in this direction is scarce, so we weave together some
starting points. First, by analogy to sketching in design, Lam et al (Lam et
al., 2023), propose a system for Model Sketching by which users explore high-
level concepts (e.g. profanity or racism) based on which ML classifiers are
subsequently created. In the same vein of simplifying ML model design for non-
experts, multiple visual programming frameworks for deep learning are
introduced (Tamilselvam et al., 2019; Calò and De Russis, 2023), as well as
direct manipulation tools for visualizing and experimenting with deep learning
models (Smilkov et al., 2016). To reduce the barrier for non-expert users,
Croisdale et al. (Croisdale et al., 2023) propose a data-flow system of cards
for exploring multi-modal generative workflows. Similarly, Compton et al.
(Compton and Mateas, 2017) present physical cards for creating generative
pipelines. Finally, crafting involves finer control including mixing and
matching. Ulberg et al. (Ulberg et al., 2020) suggest visualizing and crafting
model weights for better control over neural networks for the arts. Shimizu et
al. (Shimizu et al., 2023) enable creating bespoke text-to-media mappings
between generative models with a few examples.

##  4\. Conclusion & Future Work

Crafting, adapting and using generative systems has a long and diverse lineage
in many academic fields and communities of practice, even if not under the
term Generative AI or if not based on machine learning 222Generative systems
can be rule-based, probabilistic or constraint satisfaction systems, or they
can use genetic search algorithms, and machine learning models (specifically
generative deep learning). Other names included procedural content generation
in games, generative design in architectural or industrial design, generative
music, and computational or generative art to name a few. Through model
crafting, artists can ”hack” or subvert generative AI systems, allowing them
to create critical statements about AI systems and how they relate to society
(Ploin et al., 2022). Such inquiries into the inner working of AI systems have
resulted in pioneering works such as DeepDream (Mordvintsev et al., 2015), a
predecessor on visualizing neural networks’ activations. Finally, the work on
creative model crafting for personally-trainable models can inform the design
and development of large generative models as well. In the future, we will
continue to design for and study model crafting as a creative endeavor in its
own right.

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