# Measurement of alpha induced reaction cross-sections on natMo with detailed
covariance analysis

Mahesh Choudhary<EMAIL_ADDRESS>A. Gandhi  Aman Sharma  Namrata
Singh  A. Kumar<EMAIL_ADDRESS>Department of Physics, Banaras Hindu
University, Varanasi-221005, India  S. Dasgupta  J. Datta  Analytical
Chemistry Division, BARC, Variable Energy Cyclotron Center, Kolkata-700064,
India

###### Abstract

In the present study we have measured the excitation functions for the nuclear
reactions 100Mo($\alpha$,n)103Ru, natMo($\alpha$,x)97Ru,
natMo($\alpha$,x)95Ru, natMo($\alpha$,x)96gTc, natMo($\alpha$,x)95gTc and
natMo($\alpha$,x)94gTc in the energy range 11-32 MeV. We have used the stacked
foil activation technique followed by off-line gamma ray spectroscopy
technique to measure the excitation functions. In this study we have also
documented detailed uncertainty analysis for these nuclear reactions and their
corresponding covariance matrix are also presented. The excitation functions
are compared with the available experimental data from EXFOR data library and
the theoretical prediction from TALYS nuclear reaction code. The present
measurements are found to be consistent with the available experimental data.

##  I Introduction

The cross-sections measurement for the charged particle-induced reactions with
various target materials are of great interest in many fields such as
astrophysics, production of medical radioisotopes, nuclear reaction studies,
investigations and improvement of physical properties 01 ; 02 . Nowadays,
there are various radioactive materials with unique properties, playing an
important role in nuclear medicine diagnosis 03 ; 04 . Out of such
radioisotopes, 97Ru $(T_{1/2}$=2.79 days) is the one that emits low energy (Eγ
= 215.70 keV) $\gamma$-ray with a branching ratio (Iγ) $85.2\%$ that makes it
suitable for use in single photon emission computed tomography (SPECT) imaging
05 . The SPECT is a nuclear medicine tomographic imaging technique using
$\gamma$-rays that is capable of providing true 3-D image. An another
important radioisotope is 103mRh with short half-life of 56.11 min, which is
useful for augur electron therapy due to it’s very low photon/electron ratio
06 . The radioisotope 103Ru has a half-life of 39.26 days which decays via
$\beta^{-}$ decay into 103mRh with a branching ratio (Iγ) of $91\%$. Since
103mRh has a short half-life of 56.11 min, 103Ru as a parent to 103mRh
provides a good option due to its relatively longer half-life of 39.26 days.
Alpha-induced reactions with molybdenum target are important for the
production of the above-mentioned radioisotopes and these nuclear reactions
also play an important role in astrophysics for the study of p-process and
r-process nucleosynthesis. 07 . The nuclear reaction 100Mo($\alpha$,n)103Ru is
used to study the weak r-process nucleosynthesis 08 . In the present study, we
have used alpha particles as the projectile and molybdenum as the target
material. The main goal of this investigation was to extend the activation
cross-section data for nuclear reactions natMo($\alpha$,x)97Ru,
100Mo($\alpha$,n)103Ru with respect to the production of medicines related to
97Ru, 103Ru.
Since, with the increasing demand for alpha-induced reactions in nuclear
research, astrophysics, nuclear medicine diagnostics, industry, it is an
important component to accurately estimate the amount of uncertainty
propogation from the evaluated nuclear reaction cross section data of these
reactions. Although experimental data for these reactions exist in the EXFOR
data library, yet a detailed covariance analysis is not available with any of
the data. Therefore, it is important to discuss covariance analysis in detail.
Covariance analysis is a method used to estimate the uncertainty in the
measured quantity with considering cross-correlations, the quantities measured
in this study are nuclear reaction cross-sections 09 ; 10 ; 11 ; 12 . In the
present work, we provide covariance analysis of nuclear reactions
100Mo($\alpha$,n)103Ru, natMo($\alpha$,x)97Ru, natMo($\alpha$,x)95Ru,
natMo($\alpha$,x)96gTc, natMo($\alpha$,x)95gTc and natMo($\alpha$,x)94gTc,
considering micro-correlations between measurements such as $\gamma$-ray
abundance, $\gamma$-ray count, incident flux, detector efficiency etc. For the
first time covariance analysis has been performed for the present nuclear
reaction reactions. We measured the reaction cross-sections for the above
mentioned reactions in the incident energy range 11–32 MeV, for this we have
used the stacked foil activation technique. In the stacked foil activation
technique, a stack of several thin foils is irradiated by an integrated-
particle beam along with the monitor foil. After irradiation, the irradiated
foils are placed in front of the detector for measurement of the decay gamma
from the radionuclide. The excitation functions are compared with existing
experimental data available in the EXFOR data library and with theoretical
calculations performed using the TALYS nuclear reaction code 13 ; 14 ; 15 ; 16
. The TALYS nuclear reaction code is a computer code system for nuclear
reaction calculations based on the Hauser–Fesbach statistical model. This code
has separate options for level density and optical model parameters 17 ; 18 .
The present study is organized into the following six sections. The details
about experimental technique and experimental setup in section II, the details
about efficiency calibration of the detector in section III, the details about
covariance analysis, estimation of nuclear reaction cross-section and
theoretical calculation in section IV, results and discussion in section V,
the conclusion in section VI are presented.

##  II Experimental technique and Details

The experiment was performed at the K-130 cyclotron, Variable Energy Cyclotron
Center (VECC), Kolkata, India. The alpha particles was generated from ionized
helium. In this experiment, alpha particles were accelerated up to 32 $\pm$
0.20 MeV and the average beam current was 170 nA.

Figure 1: Detailed structure of the stacked foil arrangement.

The stacked foil activation 19 ; 20 ; 21 ; 22  technique has been used to
measure the reaction cross-section of alpha-induced reactions on molybdenum in
the energy range from the threshold energy of contributing reaction up to 32
MeV. In this method, a series of thin target foils are placed to form the
target as shown in Fig. 1 and each target foil (natMo in the present work) is
followed by another material (natAl in the present work) to catch the recoils
nuclides from the target foil (natMo). This catcher foil must be such that is
does not produce any radioactive product in the incident particle energy range
and it has also as low Z-material as possible to decrease the gamma
attenuation during the measurements. Therefore a pair of Mo an Al foils will
contain the total produced radioactive isotopes from the target Mo foil after
irradiation. A detailed structure of the stacked foil arrangement is shown in
Fig. 1. The main advantage of the stacked foil activation method is that we
can find the complete excitation function curve and each target foils are
irradiated with the from a single beam. The Mo and Al foils were measured
simultaneously to capture the recoil products and to calculate the nuclear
reaction cross sections of the products formed in the Mo foils. The energy
drop in the stacked foils were calculated using the Stopping and Range of Ions
in Matter (SRIM) code 23 ; 24 . The SRIM is a code that provides us details
about the energy drop of incident ions and the range in the matter. Three thin
metallic foils of natMo, natAl and natTi of thickness 12.85 $mg/cm^{2}$ , 13.5
$mg/cm^{2}$ and 1.80 $mg/cm^{2}$ respectively were used. We made a set of
stacks by placing molybdenum foils followed by aluminum foils as the Mo-Al (10
x 10 $mm^{2}$), and have been used 6 such sets to make a stacked target. In
this experiment, the Ti foil (10 x 10 $mm^{2}$ ) was used as the monitor foil
to confirm the incident beam intensity and energy. The stacked target was
irradiated for 2 hours by a 32 MeV alpha beam. Two collimators of 7 mm
diameter were used in this experiment, one placed in front of the beam and the
other placed in front of the stacked target. At the end of the experiment
setup, a Faraday cup was attached for current measurement. A schematic diagram
of the irradiation setup is shown in Fig. 2.

Figure 2: The Schematic diagram of experimental setup.

TABLE I. The value of the fitting parameter of efficiency ($\varepsilon$)
curve and their uncertainty

Parameters |  Value |  Uncertainty
---|---|---
$\varepsilon_{c}$ |  0.00137 |  6.2907$\times 10^{-5}$
$\varepsilon_{0}$ |  0.00912 |  4.2340$\times 10^{-4}$
E0 (keV) |  330.9628 |  20.4224

TABLE II. Coincidence summing effect, correction factor, and efficiency of
HPGe detectors at different gamma-ray energies with their intensities for both
sample ($\varepsilon$) and point source ($\varepsilon_{p}$) geometries.

$E_{\gamma}$(keV) |  $I_{\gamma}$ |  Counts(C) |  Kc |  $\varepsilon_{p}$ |  $\varepsilon$
---|---|---|---|---|---
121.78 |  0.2853 $\pm$ 0.0016 |  509811 $\pm$ 764.71 |  1.032 |  0.027099 |  0.007785 $\pm$ 0.0000605
244.69 |  0.0755 $\pm$ 0.0004 |  93725 $\pm$ 337.41 |  1.041 |  0.018990 |  0.005726 $\pm$ 0.0000471
344.27 |  0.2659 $\pm$ 0.0020 |  265271 $\pm$ 530.54 |  1.022 |  0.014982 |  0.004589 $\pm$ 0.0000429
411.11 |  0.02238 $\pm$ 0.00013 |  17985 $\pm$ 160.06 |  1.050 |  0.012399 |  0.003820 $\pm$ 0.0000451
688.70 |  0.0085 $\pm$ 0.0008 |  4415 $\pm$ 94.48 |  1.007 |  0.007686 |  0.002401 $\pm$ 0.0002321
867.38 |  0.0423 $\pm$ 0.0003 |  18156 $\pm$ 152.51 |  1.045 |  0.006591 |  0.002070 $\pm$ 0.0000252
964.05 |  0.1451 $\pm$ 0.0007 |  62252 $\pm$ 261.45 |  1.017 |  0.006411 |  0.002019 $\pm$ 0.0000166
1085.83 |  0.1011 $\pm$ 0.0005 |  36755 $\pm$ 205.82 |  0.986 |  0.005267 |  0.001663 $\pm$ 0.0000151
1112.94 |  0.1367 $\pm$ 0.0008 |  49937 $\pm$ 236.20 |  1.007 |  0.005405 |  0.001708 $\pm$ 0.0000156
1299.1 |  0.0163 $\pm$ 0.00011 |  5351 $\pm$ 80.26 |  1.030 |  0.004968 |  0.001575 $\pm$ 0.0000272
1408.01 |  0.2087 $\pm$ 0.0009 |  64230 $\pm$ 256.92 |  1.012 |  0.004576 |  0.001453 $\pm$ 0.0000114

##  III Gamma-ray spectrometry

The activity of the samples were measured using high purity germanium detector
(HPGe) having 40 $\%$ relative efficiency. In the present work, the standard
${}^{152}Eu$ point source has been used to calculate the efficiency of the
HPGe detector for various gamma-ray energies. The half-life of the standard
${}^{152}Eu$ point source is T1/2 = 13.517 $\pm$ 0.009 years and the known
activity was reported on 17 May 1982 which is A${}_{0}=3.908\times 10^{4}$ Bq.

Figure 3: Efficiency curve of HPGE detector for a distance of 50 mm between
source and detector absorber.

The absolute efficiency calibration of the point source for the source-
detector distance 50 mm was estimated using the following equation 25 :

$\varepsilon_{p}=\frac{CK_{c}}{A_{0}I_{\gamma}\Delta{t}e^{-{\lambda}t}}$ (1)

in above equation, A0 is the known activity of standard ${}^{152}Eu$ point
source, the total number of count C was taken in 10000 second for
${\gamma}$-ray energy with absolute intensity ($I_{\gamma}$) and t is the time
interval from the date of a manufacturing point source to the start of
measurement. Here, the summing correction factor is $K_{C}$ . When the
distance between the source and absorber of the detector was 50 mm, shows a
coincidence summing effect, so it should be corrected for efficiency
calibration of the detector. The standard sample ${}^{152}Eu$ was a point
source and our samples have a finite area, so the efficiency of the point
source geometry ($\epsilon_{p}$) has to be changed for the efficiency of the
sample geometry ($\epsilon$). We have used the Monte Carlo simulation code
EFFTRAN 26 ; 27  to calculate the correction factor ($K_{C}$) of coincidence
summing effect and convert the efficiency of the point source ($\epsilon_{p}$)
geometry to the efficiency of the sample ($\epsilon$) geometry.
The calculated efficiency value for sample source geometry ($\epsilon$) and
point source geometry ($\epsilon_{p}$) placed at 50 mm from the detector
absorber are given in Table II with the correction factor (KC) of coincidence
summing effect. To calculate the efficiency of a particular $\gamma$-ray of
the product radionuclide, we have used equation 2 which is a fitting function
of interpolating the point-wise efficiencies ($E_{\gamma}$) of the
$\gamma$-ray energy of the standard source ${}^{152}Eu$ 25 ; 28 .

$\varepsilon(E_{\gamma})=\varepsilon_{o}exp({-E_{\gamma}/E_{0}})+\varepsilon_{c}$
(2)

In the above equation $\varepsilon_{o}$, $\varepsilon_{c}$, and $E_{0}$ are
the fitting parameters of the measured detection efficiencies ($\varepsilon$)
of standard point ${}^{152}Eu$. The value of the curve fitting parameters
mentioned in equation 2 are given in Table I.

##  IV DATA ANALYSIS

A. Estimation of the reaction cross section

In this work, the nuclear reaction cross-sections were calculated using the
following standard activation formula;

$\sigma=\frac{C_{\gamma}\lambda}{\varepsilon(E_{\gamma}){I_{\gamma}}D_{t}{\phi}N_{t}e^{-\lambda
t_{c}}(1-e^{-\lambda t_{irr}})(1-e^{-\lambda t_{m}})}$ (3)

In the above formula 29 , the peak area counts is $C_{\gamma}$ of a particular
$\gamma$-ray with it’s abundance ($I_{\gamma}$), $\varepsilon(E_{\gamma})$ is
the detector efficiency with dead time $D_{t}$, $\lambda$ is the decay
constant ($s^{-1}$), $N_{t}$ (($cm^{-2}$)) is the particle density in the
target material, $\phi$ is the incident particle flux per unit time($s^{-1}$).
In equation 3, the cooling time of the irradiated target is $t_{c}$(s) with
the irradiation time $t_{irr}$(s) and the measurement time $t_{m}$(s) of the
sample source. In the present study, the uncertainty in the factors
contributing to the cross-section such as the efficiency of the detector
$\varepsilon(E_{\gamma})$, gamma-ray intensity ($I_{\gamma}$), number density
of atoms ($N_{t}$), gamma-ray peak counts ($C_{\gamma}$) etc. is also taken
into account to calculate the uncertainty of the obtained cross-section.

B. Covariance Analysis

A covariance analysis is a mathematical tool that can help to describe the
detailed uncertainty with the cross-correlation between different measured
quantities. The principle of covariance emphasizes the formulation of physical
laws using only measurements of physical quantities that can be correlated.
The covariance matrix of cross-sections Iσ can be written as 30 ; 31 ;

$I_{\sigma}=H_{x}C_{x}H_{x}^{T}$ (4)

Where Iσ is the covariance matrix of measured reaction cross-sections with
order m $\times$ m, $C_{x}$ is the covariance matrix with order n $\times$ n
of different attributes in activation formula (equation 2) like as
$\gamma$-ray intensity ($I_{\gamma}$), detector efficiency
$\varepsilon(E_{\gamma})$, incident flux ($\phi$), counts of the peak area
($C_{\gamma}$), decay constant ($\lambda$) etc. In equation (4), $H_{x}$ is
the sensitivity matrix with element;

$H_{xij}=\frac{\partial\sigma_{i}}{\partial
x_{j}};(i=1,2,3,...m;~{}j=1,2,3,...n)$ (5)

Here m has value equal to total number of measured cross-sections for a
particular nuclear reaction and has value equal to total number of attributes
in the activation formula. Let us consider $x_{j}$ (j = 1,2,3….n), various
variable parameters (which are mentioned above) required in the calculation of
the cross-sections, then the covariance matrix (cx) of these variables can be
obtained from the following equation 32 ;

$C_{x}(x_{j},x_{k})=Cor(x_{j},x_{k})(\Delta x_{j}\Delta x_{k})$ (6)

Where $Cor(x_{j},x_{k})$ is the correlation coefficient between two variable
$x_{j}$, $x_{k}$ and it has value $0\leq Cor(x_{j},x_{k})\leq 1$.
$Cor(x_{j},x_{k})$ = 1, when j = k i.e., these two variable $x_{j}$, $x_{k}$
are fully correlated.

C. Theoretical Calculations

The theoretical results for the reactions 100Mo($\alpha$,n)103Ru,
natMo($\alpha$,x)97Ru, natMo($\alpha$,x)95Ru, natMo($\alpha$,x)96gTc,
natMo($\alpha$,x)95gTc and natMo($\alpha$,x)94gTc has been done using the
statistical nuclear model code TALYS. The TALYS code is a Fortran based
theoretical nuclear reaction model code and used for calculating various
physical observable related to nuclear reactions. In the TALYS nuclear code,
we can simulate the nuclear reactions that involve neutrons, photons, protons,
deuterons, tritons, 3He- and alpha-particles as a projectile, in the 1 keV -
200 MeV energy range and for target nuclides of mass 12 and heavier. The main
purpose of TALYS nuclear code is to provide a complete set of answers for the
nuclear reactions, all open channels and associated cross sections, spectra
and angular distributions. In the TALYS nuclear code, there are 6 level
densities models in which three options for the phenomenological level density
model and three options for the microscopic level density 33 ; 34 . Of these
six level density models, ldmodel-1 is the constant temperature and Fermi gas
model, ldmodel-2 is the back-shifted Fermi gas model, ldmodel-3 is the
generalized superfluid model, ldmodel-4 is the microscopic level density
(Skyrme Force) from the Gorley table, ldmodel-5 is the microscopic level
density (Skyrem force) from Hilaire’s combinatorial tables and ldmodel-6 is
the microscale density (temperature-dependent HFB, Gogni force) from Hilaire’s
combinatorial tables 35 ; 36 ; 37 ; 38 ; 39 ; 40 . The theoretical
calculations for present nuclear reaction cross sections, we have used all
these six level density models and also presented a comparison of the result
of theoretical calculations using these different level density models with
experimental data.

##  V RESULTS AND DISCUSSION

In this section, we have presented measured reaction cross-sections with their
uncertainties and covariance matrix of natMo($\alpha$,x) reactions in the
energy range from the respective threshold for each contributing reaction to
about 32 MeV. The obtained reaction cross-section in the present work are
plotted in Fig. 4-9 along with the available experimental data from the EXFOR
and theoretical results from the TALYS-1.9 code.The details of the reactions
and decay data of the measured radioisotopes are shown in Table III and the
value of the measured cross-sections for the present reactions along with
their covariance matrix are presented in Tables IV-IX. Whichever level density
model gives the best theoretical result along with the experimental data, the
theoretical result is shown in a solid line, and other theoretical results are
shown in a dash-dash line with different colors in the graph. The experimental
data are shown in dark black circles and available experimental data in EXFOR
are shown in different shapes with their error bar along the y-axis.

TABLE III. Reactios and the decay data of the measured radioisotopes.

Radionuclide |  Half-life |  $E_{\gamma}$ |  $I_{\gamma}$ | Reaction |  Q-value
---|---|---|---|---|---
|  ($t_{1/2}$) |  (keV) | (%) |  |  (MeV)
95Ru |  1.643 h |  336.40 | 69.90 |  92Mo($\alpha$,n) |  -9.002
|  |  1096.00 | 20.90 |  94Mo($\alpha$,3n) |  -26.748
|  |  626.83 | 17.80 |  |
|  |  1178.70 | 05.10 |  |
|  |  806.28 | 4.04 |  |
97Ru |  2.791 d |  215.70 | 85.62 |  94Mo($\alpha$,n) |  -7.940
|  |  324.49 | 10.79 |  95Mo($\alpha$,2n) |  -15.309
|  |  |  |  96Mo($\alpha$,3n) |  -24.463
|  |  |  |  97Mo($\alpha$,4n) |  -31.284
103Ru |  39.26 d |  497.20 | 91.00 |  100Mo($\alpha$,n) |  -4.572
|  |  610.33 | 5.76 |  103Tc decay (100 %) |
94gTc |  293 min |  702.7 | 99.6 |  92Mo($\alpha$,p+n) |  -15.586
|  |  849.7 | 95.7 |  94Mo($\alpha$,3n) |  -33.334
|  |  871.1 | 99.9 |  94mTc decay ($<0.1\%$) |
|  |  916.1 | 7.6 |  |
95gTc |  20.0 h |  765.78 | 93.80 |  92Mo($\alpha$,p) |  -5.652
|  |  1073.71 | 3.74 |  94Mo($\alpha$,t) |  -14.918
|  |  947.67 | 1.95 |  94Mo($\alpha$,2n+p) |  -23.399
|  |  |  |  95Mo($\alpha$,3n+p) |  -30.769
|  |  |  |  95mTc decay (3.88 %) |
|  |  |  |  95Ru decay (97 %) |
96gTc |  4.28 d |  778.2 | 99.76 |  94Mo($\alpha$,p+n) |  -15.528
|  |  812.5 | 82 |  95Mo($\alpha$,p+2n) |  -22.897
|  |  849.9 | 98 |  96Mo($\alpha$,p+3n) |  -32.051
|  |  1126.85 | 15.2 |  96mTc decay (98%) |

###  V.1 103Ru (T1/2 = 39.26 d)

The measured excitation function as a function of the incident alpha energy
for the nuclear reaction 100Mo($\alpha$,n)103Ru with available experimental
data from the EXFOR and the TALYS nuclear code theoretical calculation is
shown in Fig. 4. The radionuclide 103Ru is produced through the nuclear
reaction 100Mo($\alpha$,n)103Ru and by the decay of 103Tc which has short half
life of 54.2 sec. The reaction cross-sections for the nuclear reaction
100Mo($\alpha$,n)103Ru were obtained from a $\gamma$-ray of 497.08 keV energy
and intensity Iγ = 91 $\%$, which decay from 103Ru radionuclide. The counting
of this $\gamma$-ray was done after cooling time of about 2 days. It is clear
from Fig. 5 that present experimental data for this reaction are found to be
in good agreement with the reaction data reported by R. A. Esterlund+ et
al.(1965), 41 , but our results slightly lower than available experimental
data reported by H. P. Graf+ et al.(1974), 42 . The theoretical results (shown
in the magenta colour by solid line) made using ldmodel-3 are best in line
with the present experimental data and follow the trend of experimental
results of this reaction. The measured reaction cross sections along with
their uncertainties and covariance metrics for the reaction
100Mo($\alpha$,n)103Ru are presented in Table IV.

Figure 4: The excitation function of reaction 100Mo($\alpha$,n)103Ru along
with available experimental data from EXFOR and theortical results from TALYS
nuclear code. Figure 5: The excitation function of reaction
natMo($\alpha$,x)97Ru along with available experimental data from EXFOR and
theortical results from TALYS nuclear code.

TABLE IV. The measured reaction cross sections of the reaction
100Mo($\alpha$,n)103Ru with their uncertainties and covariance matrix.

Eα (MeV) |  Cross section (mb) |  covariance matrix |  |
---|---|---|---|---
|  ($\sigma$ $\pm$ $\bigtriangleup\sigma$) |  |  |  |  |  |
11.56 |  43.704 $\pm$ 6.941 |  48.188 |  |  |  |  |
15.93 |  51.831 $\pm$ 8.207 |  56.765 |  67.364 |  |  |  |
19.65 |  15.297 $\pm$ 2.448 |  16.751 |  19.869 |  5.992 |  |  |
23.01 |  5.353 $\pm$ 0.871 |  5.861 |  6.952 |  2.051 |  0.759 |  |
27.22 |  3.171 $\pm$ 0.509 |  3.473 |  4.119 |  1.215 |  0.425 |  0.259 |
31.01 |  3.043 $\pm$ 0.492 |  3.333 |  3.953 |  1.166 |  0.408 |  0.241 |  0.242

TABLE V. The measured reaction cross sections of the reaction
natMo($\alpha$,x)97Ru with their uncertainties and covariance matrix.

Eα (MeV) |  Cross section (mb) |  covariance matrix |  |
---|---|---|---|---
|  ($\sigma$ $\pm$ $\bigtriangleup\sigma$) |  |  |  |  |  |
11.56 |  25.422 $\pm$ 3.640 |  13.256 |  |  |  |  |
15.93 |  86.794 $\pm$ 12.430 |  45.253 |  154.511 |  |  |  |
19.65 |  134.539 $\pm$ 19.269 |  70.147 |  239.490 |  371.317 |  |  |
23.01 |  176.741 $\pm$ 25.314 |  92.151 |  314.615 |  487.683 |  640.808 |  |
27.22 |  203.241 $\pm$ 29.108 |  105.968 |  361.787 |  560.804 |  736.719 |  847.294 |
31.01 |  209.541 $\pm$ 30.010 |  109.252 |  373.000 |  578.186 |  759.553 |  873.438 |  900.654

###  V.2 97Ru(T1/2 = 2.83 d)

The measured excitation function as a function of the incident alpha energy
for the nuclear reaction natMo($\alpha$,x)97Ru with available experimental
data from the EXFOR and the TALYS nuclear code theoretical calculation is
shown in Fig. 6. The reaction cross-sections for the nuclear reaction
natMo($\alpha$,x)97Ru were obtained from a $\gamma$-ray of 215.70 keV energy
and intensity Iγ = 85.62 $\%$, which decay from 97Ru radionuclide. The
counting of this $\gamma$-ray was done after cooling time of about 2 days.
From Fig. 6 it is clear that present experimental data for this reaction are
found to be in good agreement with the reaction data reported by F.Tarkanyi+
et al (2017), and F.Ditroi+ et al (2012) 43 ; 44 . The theoretical results
(shown in the red solid line) made using ldmodel-2 are best in line with the
present experimental data and follow the trend of experimental results of this
reaction but theortical results are lower than experimental data in the energy
range 21-24 MeV. The measured reaction cross sections along with their
uncertainties and covariance metrics for the reaction natMo($\alpha$,x)97Ru
are presented in Table V.

TABLE VI. The measured reaction cross sections of the reaction
natMo($\alpha$,x)95Ru with their uncertainties and covariance matrix.

Eα (MeV) |  Cross section (mb) |  covariance matrix |  |
---|---|---|---|---
|  ($\sigma$ $\pm$ $\bigtriangleup\sigma$) |  |  |  |  |  |
11.56 |  11.609 $\pm$ 1.264 |  1.599 |  |  |  |  |
15.93 |  52.467 $\pm$ 5.158 |  5.869 |  26.609 |  |  |  |
19.65 |  87.196 $\pm$ 8.855 |  9.753 |  44.083 |  78.411 |  |  |
23.01 |  39.708 $\pm$ 4.056 |  4.441 |  20.074 |  33.362 |  16.451 |  |
27.22 |  12.160 $\pm$ 1.276 |  1.360 |  6.147 |  10.216 |  4.652 |  1.628 |
31.01 |  4.669 $\pm$ 0.521 |  0.522 |  2.360 |  3.923 |  1.786 |  0.547 |  0.272

TABLE VII. The measured reaction cross sections of the reaction
natMo($\alpha$,x)96gTc with their uncertainties and covariance matrix.

Eα (MeV) |  Cross section (mb) |  covariance matrix
---|---|---
|  ($\sigma$ $\pm$ $\bigtriangleup\sigma$) |  |  |  |
19.65 |  0.256 $\pm$ 0.071 |  0.005 |  |  |
23.01 |  1.345 $\pm$ 0.132 |  0.008 |  0.017 |  |
27.22 |  21.119 $\pm$ 1.975 |  0.131 |  0.261 |  3.903 |
31.01 |  36.412 $\pm$ 3.391 |  0.226 |  0.450 |  6.660 |  11.504

###  V.3 95Ru(T1/2 = 1.643 h)

In this work, Fig. 7 shows the measured excitation function as a function of
the incident alpha energy in the energy range 11– 32 MeV of the nuclear
reaction natMo($\alpha$,x)95Ru with available experimental data from the EXFOR
and the TALYS nuclear code theoretical calculation. The reaction cross-
sections for the nuclear reaction natMo($\alpha$,x)95Ru were obtained from a
$\gamma$-ray of 336.40 keV energy and intensity Iγ = 69.9 $\%$, which decay
from 95Ru radionuclide. The counting of this $\gamma$-ray was done after a
short cooling time. It is clear from Fig. 7 that for natMo($\alpha$,x)95Ru
reaction the maximum value of cross-sections at incident alpha energy is found
at about 18 MeV. The present experimental data for this reaction follow the
trend of the experimental data reported by F.Tarkanyi+ et al.(2017), and
F.Ditroi+ et al(2012) 43 ; 44 , but our results are slightly less than the
available experimental data in the incident energy range 17–25 MeV. The
theoretical results (shown in the red solid line) made using ldmodel-2 are
best in line with the present experimental data and follow the trend of
experimental results of this reaction. The measured reaction cross sections
along with their uncertainties and covariance metrics for the reaction
natMo($\alpha$,x)95Ru are presented in Table VI.

TABLE VIII. The measured reaction cross sections of the reaction
natMo($\alpha$,x)95gTc with their uncertainties and covariance matrix.

Eα (MeV) |  Cross section (mb) |  covariance matrix |  |
---|---|---|---|---
|  ($\sigma$ $\pm$ $\bigtriangleup\sigma$) |  |  |  |  |  |
11.56 |  6.476 $\pm$ 0.790 |  0.624 |  |  |  |  |
15.93 |  55.844 $\pm$ 6.675 |  5.145 |  44.559 |  |  |  |
19.65 |  100.185 $\pm$ 12.010 |  9.241 |  79.821 |  144.246 |  |  |
23.01 |  72.122 $\pm$ 8.660 |  6.662 |  57.547 |  103.398 |  74.995 |  |
27.22 |  25.325 $\pm$ 3.043 |  2.342 |  20.237 |  36.365 |  26.225 |  9.260 |
31.01 |  11.026 $\pm$ 1.380 |  1.021 |  8.823 |  15.856 |  11.436 |  4.023 |  1.905

###  V.4 96gTc, (T1/2 = 4.28 d )

The measured excitation function as a function of the incident alpha energy
for the nuclear reaction natMo($\alpha$,x)96gTc with available experimental
data from the EXFOR and the TALYS nuclear code theoretical calculation is
shown in Fig. 7. The radionuclide 96gTc is produced through the nuclear
reactions natMo($\alpha$,x) and by the internal decay of 96mTc which has half
life of 52 min. The reaction cross-sections for the nuclear reaction
natMo($\alpha$,x)96gTc were obtained from a $\gamma$-ray of 313.23 keV energy
and intensity Iγ = 2.43 $\%$.

Figure 6: The excitation function of reaction natMo($\alpha$,x)95Ru along with
available experimental data from EXFOR and theortical results from TALYS
nuclear code.

The counting of this $\gamma$-ray was done after cooling time of about 2 days,
leading to complete decay of the parent. There is no experimental data
available in the EXFOR data library for the nuclear reaction
natMo($\alpha$,x)96gTc. It is clear from Fig. 7 that the present experimental
data of this nuclear reaction are found in good agreement with the theoretical
results made using ldmodel-2. The measured reaction cross sections along with
their uncertainties and covariance metrics for the nuclear reaction
natMo($\alpha$,x)96gTc are presented in Table VII.

Figure 7: The excitation function of reaction natMo($\alpha$,x)96gTc along
with available experimental data from EXFOR and theortical results from TALYS
nuclear code. Figure 8: The excitation function of reaction
natMo($\alpha$,x)95gTc along with available experimental data from EXFOR and
theortical results from TALYS nuclear code.

TABLE IX. The measured reaction cross sections of the reaction
natMo($\alpha$,x)94gTc with their uncertainties and covariance matrix.

Eα (MeV) |  Cross section (mb) |  covariance matrix
---|---|---
|  ($\sigma$ $\pm$ $\bigtriangleup\sigma$) |  |  |  |
19.65 |  1.096 $\pm$ 0.254 |  0.064 |  |  |
23.01 |  43.347 $\pm$ 9.550 |  2.298 |  91.210 |  |
27.22 |  76.425 $\pm$ 16.810 |  4.051 |  160.168 |  282.606 |
31.01 |  97.846 $\pm$ 21.518 |  5.186 |  205.043 |  361.490 |  463.061

###  V.5 95gTc(cum), (T1/2 = 20 h)

The measured excitation function as a function of the incident alpha energy in
the energy range 11– 32 MeV of the reaction natMo($\alpha$,x)95gTc with
available experimental data from the EXFOR and the TALYS nuclear code
theoretical calculation is shown in Fig. 8. The reaction cross-sections for
the nuclear reaction natMo($\alpha$,x)95gTc were obtained from a $\gamma$-ray
of 1073.71 keV energy and intensity Iγ = 3.74 $\%$. There is small
contribution of 3.83 % isomeric transition (95mTc, T1/2 = 61 d). To minimize
the contribution of this isomeric transition we have used short cooling time.
During the measurement, special attention was also paid to ensure that the
contribution of the short-lived parent 95Ru (T1/2 = 1.65 h) should be minimum.
From Fig. 8, it is clear that present experimental data for this reaction are
found to be in good agreement with the reaction data reported by F. Tarkanyi
et al 43 . The present experimental data follows the trend of the theoretical
results made using ldmodel-6, but the theoretical results are slightly lower
than the experimental data in the energy range of about 11–23 MeV and slightly
higher in the energy range of about 24–32 MeV. The measured reaction cross
sections along with their uncertainties and covariance metrics for the nuclear
reaction natMo($\alpha$,x)95gTc are presented in Table VIII.

Figure 9: The excitation function of reaction natMo($\alpha$,x)94gTc along
with available experimental data from EXFOR and theortical results from TALYS
nuclear code.

###  V.6 94gTc, (T1/2 = 293 min)

The measured excitation function as a function of the incident alpha energy
for the nuclear reaction natMo($\alpha$,x)94gTc with available experimental
data from the EXFOR and the TALYS nuclear code theoretical calculation is
shown in Fig. 9. The radionuclide 94gTc is produced through the nuclear
reactions natMo($\alpha$,x) and by the decay of 94Ru which has half life of
51.8 min.The short-lived isomeric state (94mTc) has a very small contribution
(less than 0.1%) that can be neglected. The reaction cross-sections for the
nuclear reaction natMo($\alpha$,x)94gTc were obtained from a $\gamma$-ray of
916.1 keV energy (Iγ = 7.6 $\%$) and The activity was measured of this
$\gamma$-ray after the complete decay of the parent. From Fig. 9, it is clear
that present experimental data for this reaction are found to be in good
agreement with the reaction data reported by F. Tarkanyi et al 43 . The
present experimental data are in good agreement with the theoretical results
made using ldmodel-2 and ldmodel-4. The measured reaction cross sections along
with their uncertainties and covariance metrics for the reaction
natMo($\alpha$,x)94gTc are presented in Table IX.

##  VI Conclusion

In the present work, the reaction cross-sections of natMo($\alpha$,n)103Ru,
natMo($\alpha$,x)97Ru, natMo($\alpha$,x)95Ru, natMo($\alpha$,x)96gTc,
natMo($\alpha$,x)95gTc and natMo($\alpha$,x)94gTc nuclear reactions in the
energy range about 11-32 MeV are determined using stack foil activation
technique along with covariance analysis. The documentation of detailed
uncertainty analysis for these nuclear reactions and their corresponding
covariance matrix are presented for the first time. The reaction cross-
sections obtained for the above nuclear reactions are found to be consistent
with experimental data available in the EXFOR data library and theoretical
prediction from the TALYS nuclear code. The ldmodel-2 gives the best
theoretical results for the nuclear reactions natMo($\alpha$,x)97Ru,
natMo($\alpha$,x)95Ru, natMo($\alpha$,x)96gTc, natMo($\alpha$,x)94gTc, the
ldmodel-3 gives the best theortical results for the nuclear reaction
100Mo($\alpha$,n)103Ru and the ldmodel-6 gives the best theoretical results
for the nuclear reactions natMo($\alpha$,x)95gTc. The present data analysis of
the measured reaction cross sections took into account the necessary
improvements as a result of the coincidence summing factor and the geometric
factor in efficiency. The present experimental results with detailed
covariance analysis are important in astrophysics, nuclear medicine, and
improving the nuclear reaction codes.

###### Acknowledgements.

The author (Mahesh Choudhary) gratefully thanks to the Council of Scientific
and Industrial Research (CSIR), Government of India, for financial support in
the form of Senior Research Fellowships. (File No 09/013(882)/2019-EMR-1).
Also, one of the authors (A. Kumar) would like to thank the SERB, DST,
Government of India [Grant No. CRG/2019/000360], and Institutions of Eminence
(IoE) BHU [Grant No. 6031].
We acknowledge the kind support provided by Prof. A. K. Tyagi, Director,
Chemistry Group, BARC, Mumbai and Prof. Chandana Bhattacharya, Head,
Experimental Nuclear Physics Division, VECC, Kolkata towards the successful
execution of the experiment. We would also like to thank the Cyclotron (K-130)
staff, VECC, Kolkata for providing us high quality of the beam during the
experiment.

## References

  * (1) Qaim, Syed M., Ingo Spahn, Bernhard Scholten, and Bernd Neumaier. Radiochimica acta, 104(9), 601-624 (2016).
  * (2) M. S. Uddin, A. Hermanne, S. Sudar, M. N. Aslam, B. Scholten, H. H. Coenen, S. M. Qaim, Applied radiation and isotopes, 69, 699 (2011).
  * (3) B. Mukhopadhyay , and K Mukhopadhyay . J. Nucl. Med. Radiat. Ther. 2(2):1000115, (2011).
  * (4) A. A. Alharbi, et al., In Radioisotopes-Applications in Bio-Medical Science. IntechOpen, (2011).
  * (5) M. Sitarz, E. Nigron, A, Guertin, F. Haddad, and T. Matulewicz, Instruments, 3(1), 7 (2019).
  * (6) B. Bartos, E. Kowalska, A. Bilewicz, and G. Skarnemark, Journal of radioanalytical and nuclear chemistry, 279(2), 655-657 (2009).
  * (7) W. Rapp, et al., Physical Review C, 78(2), 025804 (2008).
  * (8) T. N. Szegedi, et al., A. Physical Review C, 104(3), 035804 (2021).
  * (9) A. Gandhi, et al., Physical Review C. 102, 014603 (2020).
  * (10) A. Gandhi, et al., European Physical Journal A. 57, 1 (2021).
  * (11) A. Gandhi, et al., Chinese Physics C 46 1 014002-014002-8 (2022)
  * (12) A. Gandhi, A. Sharma, R. Pachuau, N. Singh, P. N. Patil, M. Mehta, and A. Kumar, The European Physical Journal Plus, 136(8), 1-17 (2021).
  * (13) N. Otuka et al., Nucl. Data Sheets 120, 272 (2014).
  * (14) IAEA-EXFOR Experimental nuclear reaction database, https://www-nds.iaea.org/exfor (Data retrieved on December 2021).
  * (15) A. J. Koning and D. Rochman, Nucl. Data Sheets 113, 2841 (2012).
  * (16) A.J. Koning, S. Hilaire and M.C. Duijvestijn, TALYS-1.0”, Proceedings of the International Conference on Nuclear Data for Science and Technology, April 22-27, 2007, Nice, France, editors O. Bersillon, F. Gunsing, E. Bauge, R. Jacqmin, and S. Leray, EDP Sciences, 211 (2008).
  * (17) Mert Sekerci, Radiochimica Acta, 108(6), 459-467 (2020).
  * (18) Mahesh Choudhary, Aman Sharma, A. Gandhi, Namrata Singh, and A. Kumar, IJPAP 58(05) (2020).
  * (19) M. S. Uddin, K. S. Kim, M. Nadeem, S. Sudar, and G. N. Kim, The European Physical Journal A, 53(5), 1-10 (2017).
  * (20) S. Takacs, M. P. Takacs, A. Hermanne, F. Tarkanyi, and R. A. Rebeles, Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms, 297, 44-57 (2013)
  * (21) S. Takacs, M. P. Takacs, A. Hermanne, F. Tarkanyi, and R. A. Rebeles, Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms,278, 93-105 (2012).
  * (22) T. Siiskonen, J. Huikari, T. Haavisto, J. Bergman, S. J. Heselius, J. O. Lill, T. Lonnroth and K. Perajarvi, Applied Radiation and Isotopes,67(11), 2037-2039 (2009).
  * (23) J. F. Ziegler, M. D. Ziegler, and J. P. Biersack, Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms, 268(11-12), 1818-1823 (2010).
  * (24) Peter Sigmund, and Schinner Andreas, Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms 410 78-87 (2017).
  * (25) L. R. M. Punte et al., Phys. Rev. C 95, 024619 (2017).
  * (26) T. Vidmar, Nucl. Instrum. Methods Phys. Res. A 550, 603 (2005).
  * (27) T. Vidmar, G. Kanisch, and G. Vidmar, App. Radiat. Isot. 908, 69 (2011).
  * (28) R. Pachuau et al., Nucl. Phys. A 992, 121613 (2019).
  * (29) M. Aikawa, M. Saito, S. Ebata, Y. Komori, and H. Haba, Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms,427, 91-94 (2018).
  * (30) D. L. Smith, and N. Otuka, Nuclear Data Sheets, 113(12), 3006-3053 (2012).
  * (31) B. Lawriniang Journal of Radioanalytical and Nuclear Chemistry, 319(3), 695-701 (2019).
  * (32) N. Otuka, Radiation Physics and Chemistry, 140, 502-510 (2017).
  * (33) A. Gandhi, et al., Indian Journal of Physics 93.10, 1345-1351 (2019).
  * (34) A. Gandhi, et al., Journal of Radioanalytical and Nuclear Chemistry 322.1, 89-97 (2019).
  * (35) A. Gilbert and A.G.W. Cameron, Can. J. Phys. 43, 1446 (1965).
  * (36) W. Dilg, W. Schantl, H. Vonach, and M. Uhl, Nucl. Phys. A 217, 269 (1973).
  * (37) A.V. Ignatyuk, J.L. Weil, S. Raman, and S. Kahane, Phys. Rev. C 47, 1504 (1993).
  * (38) S. Goriely, F. Tondeur, and J. M. Pearson, A Hartree-Fock nuclear mass table, At. Data Nucl. Data Tables 77, 311 (2001).
  * (39) S. Goriely, S. Hilaire and A.J. Koning, Phys. Rev. C 78, 064307 (2008).
  * (40) S. Hilaire, M. Girod, S. Goriely, and A. J. Koning, Phys. Rev. C 86, 064317 (2012).
  * (41) R. A. Esterlund, and B. D. Pate, Nuclear Physics, 69(2), 401-422 (1965).
  * (42) H. P. Graf, and H. Muenzel, Journal of Inorganic and Nuclear Chemistry, 36(12), 3647-3657 (1974).
  * (43) F. Tarkanyi, A. Hermanne, F. Ditroi, S. Takacs, and A. Ignatyuk, Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms, 399, 83-100 (2017).
  * (44) F. Ditroi, A. Hermanne, F. Tarkanyi, S. Takacs, and A. V. Ignatyuk, Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms, 285, 125-141 (2012).


# Telling Creative Stories Using Generative Visual Aids

Safinah Ali
MIT Media Lab
Cambridge, MA, USA
<EMAIL_ADDRESS>
Devi Parikh
Facebook AI Research; Georgia Institute of Technology
Menlo Park, CA, USA
<EMAIL_ADDRESS>
Work was conducted while author was at Facebook AI Research.

###### Abstract

Can visual artworks created using generative visual algorithms inspire human
creativity in storytelling? We asked writers to write creative stories from a
starting prompt, and provided them with visuals created by generative AI
models from the same prompt. Compared to a control group, writers who used the
visuals as story writing aid wrote significantly more creative, original,
complete and visualizable stories, and found the task more fun. Of the
generative algorithms used (BigGAN, VQGAN, DALL-E, CLIPDraw), VQGAN was the
most preferred. The control group that did not view the visuals did
significantly better in integrating the starting prompts. Findings indicate
that cross modality inputs by AI can benefit divergent aspects of creativity
in human-AI co-creation, but hinders convergent thinking.

##  1 Introduction

Generative AI algorithms have found applications in visual art, stylizing
images and videos. Combined with Contrastive Language–Image Pre-training (or
CLIP) by OpenAI [1], that learns visual concepts from natural language
supervision, it has become possible to leverage a combination of generative
approaches and CLIP to create images from language prompts. Repeated
generation until the image sufficiently represents the prompt as scored by
CLIP eventually creates a visual that is semantically relevant to the prompt.
This approach of combining CLIP with GANs such as StyleGAN, BigGAN, VQGAN,
etc. to create visuals from textual prompts has led to a recent trend in
creative technologists and artists sharing surreal AI-generated art from
language prompts [2, 3].

We study whether artwork created using generative algorithms in one medium can
inspire creativity in another. Artists find creative inspiration from external
stimulants, even across mediums [4]. Musician Debussy’s La Mer was inspired
from The Great Wave [5]. Graphics have been used to inspire reading, writing
and thinking [6, 7]. Teachers have found that using manually curated pictures
helped students’ narrative building ability and interest in writing [8, 9].
Here, we study whether art created using GANs and other visual generative
procedures help inspire creative storytelling. As opposed to manually curating
artwork, generative algorithms automate the process of generating images
contextual to a language prompt. Further, it is difficult to manually curate
(e.g. using search engines) visuals for unrealistic fictional prompts, such as
“an octopus playing a Ukulele”, but they can be generated using generative
approaches. Generative art also contains surrealistic elements, which could
introduce unexpected elements for the writer, which could influence the
stories.

##  2 Methods

We designed Visual Stories, a web interface where writers write a creative
story from a starting prompt. They are aided with visuals generated using the
same prompt for creative inspiration, using: (1) CLIP + BigGAN [10, 11]. (2)
CLIP + VQGAN (Vector Quantized Generative Adversarial) Networks [12]. (3)
CLIPDraw (stroke-based drawings) [13]. (4) CLIP+DALL-E (Decoder of the
discrete VAE used for DALL-E) [14].

To understand whether the generative visuals benefited writer’s creativity, we
ran a human experiment, where adult participants who are fluent in English
were recruited from Amazon Mechanical Turk (AMT) to write creative stories
from fixed starting prompts. Participants were randomly divided into two
groups: one that could toggle between four visuals generated from the starting
prompt they were provided (V+), and one that saw no visuals (V-). After the
task, participants reflected on their writing experience, creativity of the
stories, and usefulness of the visuals ( 5.3). The story writing web interface
consisted of instructions (5.1), a starting prompt for the stories, a text
box, a button to view and toggle through visuals (Figure 1). External judges
blind to the conditions rated the stories using metrics from the “convergent
integrative thinking” segment for verbal creativity of the Evaluation of
Potential Creativity (EPoC) battery [15] ( 5.2).

Figure 1: Visuals used for the starting prompt “A galaxy in a melting pot.”
generated using BigGAN, DALL-E decoder, CLIPDraw and VQGAN.

##  3 Findings

362 stories were analyzed. Participants in the V+ condition, scored the task
as more fun (4.126 ± 0.908) than participants in the V- condition (4.307 ±
0.808) (p = 0.0453). They also wrote longer stories (828 characters as
compared to 796 characters in V-). 65% writers found the visuals inspiring.
Images generated using VQGAN were the most frequently used images, and VQGAN,
BigGAN and CLIPDraw generated images were used significantly more than DALL-E
(p = 0.0048). The visuals inspired “characters”, “setting” and “details” of
the stories the most.

A chi-square test of goodness-of-fit test was performed to determine whether
the stories written by participants in the two study conditions were equally
rated on the specified metrics of creativity by external raters. Rejecting the
null hypothesis, preference for the two categories was not equally distributed
in the population, and rated higher for the intervention group on the metrics
of “Creativity”, “Originality”, “Level of completion”, “Visualizability”, and
“Plot” (p<0.05). Preference for “Integration of prompt” was rated
significantly higher for the control group (p<0.05).

We found that the generated images influenced people’s creative process and
outcome. They found the process more fun and the graphics helpful. External
evaluation indicated that it benefited writers’ divergent-exploratory
thinking, and the stories were more creative and original. However, it
hindered convergent-integrative thinking - the participants did not integrate
the starting prompt as well as the control group. This could be caused by
participants being distracted by the images, or follow the arc of the images
and use the visuals as the prompt, steering away from the starting textual
prompt. This could also indicate that perhaps the visual images were not
sufficiently relevant to the starting prompt. Stories from the V+ condition
were preferred by raters to be converted to an illustration or animation,
implying that the stories helped the reader visualize it, influencing not just
the writing process, but also the reading process.

Generative approaches coupled with CLIP allow the creation of high quality
visuals that are able to visualize unrealistic scenarios. Different approaches
also bring unique aesthetic styles. While these are currently shared as
digital artwork, in this work we explore how these can also be used as
effective creative writing aids. Future work could explore how they could aid
creativity in other media, such as music production. Not requiring manual
curation makes them effective automated aids for creative writing. Instead of
static images pre-generated from text, future work could use parts of the
written story to dynamically update the aesthetics of the generation.

##  4 Ethical Implications

In this work, we demonstrate how visual art generated by AI was a helpful
writing aid. However, we also found that the writers veered away from the
starting textual prompt, and towards the visuals. A cross medium AI
collaborator helped with divergent thinking, but hindered convergent
integrative thinking, and while designing creative collaborative AI, we must
reflect on the cost it may potentially have in hindering the task at hand.
There is also the risk of creativity aids becoming dependencies. For instance,
one writer reflected that before the visuals, they were not sure what to
write. Relying on these visual aids may hinder their verbal creativity when
the visuals are missing.

## References

  * Radford et al. [2021] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al.  Learning transferable visual models from natural language supervision.  _arXiv preprint arXiv:2103.00020_ , 2021.
  * Snell [2021] Charlie Snell.  Alien dreams: An emerging art scene.  2021\.  URL https://ml.berkeley.edu/blog/posts/clip-art/.
  * Murdock [2021a] Ryan Murdock.  Thoughts on deepdaze, bigsleep, and aleph2image.  2021a.  URL https://rynmurdock.github.io/2021/02/26/Aleph2Image.html.
  * Duncum [2004] Paul Duncum.  Visual culture isn’t just visual: Multiliteracy, multimodality and meaning.  _Studies in art education_ , 45(3):252–264, 2004\.
  * Zinn [2014] Joshua Zinn.  Great paintings that inspired music.  2014\.  URL https://www.houstonpublicmedia.org/articles/arts-culture/2014/08/11/52509/hpm-top-ten-list-great-paintings-that-inspired-music/.
  * Claggett and Brown [1992] Fran Claggett and Joan Brown.  _Drawing Your Own Conclusions. Graphic Strategies for Reading, Writing, and Thinking._ ERIC, 1992.
  * Childers et al. [1998] Pamela B Childers, Eric H Hobson, and Joan A Mullin.  _ARTiculating: Teaching Writing in a Visual World._ ERIC, 1998.
  * Ali and Hasanah [2014] Aschawir Ali and Uswatun Hasanah.  Using series pictures to develop the students’ideas in english narrative writing.  _Scholarly Journal_ , 2014.
  * Asrifan [2015] Andi Asrifan.  The use of pictures story in improving students’ ability to write narrative composition.  _International Journal of Language and Linguistics_ , 3(4):244–251, 2015.
  * Brock et al. [2018] Andrew Brock, Jeff Donahue, and Karen Simonyan.  Large scale gan training for high fidelity natural image synthesis.  _arXiv preprint arXiv:1809.11096_ , 2018.
  * Murdock [2021b] Ryan Murdock.  The big sleep: Bigganxclip, 2021b.  URL https://colab.research.google.com/drive/1NCceX2mbiKOSlAd_o7IU7nA9UskKN5WR?usp=sharing#scrollTo=reChJi_vrNI_.
  * Esser et al. [2021] Patrick Esser, Robin Rombach, and Bjorn Ommer.  Taming transformers for high-resolution image synthesis.  In _Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition_ , pages 12873–12883, 2021.
  * Frans et al. [2021] Kevin Frans, LB Soros, and Olaf Witkowski.  Clipdraw: Exploring text-to-drawing synthesis through language-image encoders.  _arXiv preprint arXiv:2106.14843_ , 2021.
  * Ramesh et al. [2021] Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever.  Zero-shot text-to-image generation.  _arXiv preprint arXiv:2102.12092_ , 2021.
  * Barbot et al. [2016] Baptiste Barbot, Maud Besançon, and Todd Lubart.  The generality-specificity of creativity: Exploring the structure of creative potential with epoc.  _Learning and Individual Differences_ , 52:178–187, 2016\.
  * Hennessey et al. [1999] Beth A Hennessey, Teresa M Amabile, and Jennifer S Mueller.  Consensual assessment.  _Encyclopedia of creativity_ , 1:347–359, 1999.

##  5 Appendix

###  5.1 Story Writing Instructions

  * •

In this task, you will be writing a creative story.

  * •

Use the given starting sentence and continue it to create your original
creative story.

  * •

Write a minimum of 500 characters (about 100 words) and maximum of 1000
characters to complete the task.

  * •

You can choose to view some visual inspiration to help with the story using
the yellow button on the top.

  * •

Most creative stories will be awarded a bonus.

  * •

Please do not paste text from the internet and try to create your own story!

  * •

Please do not complete this HIT multiple times. It will only be accepted once.

###  5.2 Story scoring

We adopted story rating metrics from the “convergent integrative thinking”
segment for verbal creativity of the Evaluation of Potential Creativity (EPoC)
battery [15] as well as the Consensual Assessment Technique (CAT) [16]. More
details of EPoC, CAT and the questions asked can be found in Appendix 1.

The EPoC test was chosen since it involves a verbal creativity assessment task
similar to the task we used, where subjects are asked to create a story from a
fixed beginning. While the EPoC is developed for children, it has been used
with adults. The CAT is a powerful tool used by creativity researchers in
which judges are asked to rate the creativity of creative products such as
stories, collages, poems, and other artifacts. Creativity is rated through
comparisons with other artifacts in the pool, and not as absolute scores for
each individual story. The CAT has been used in many contexts to compare the
creativity of two groups. Raters were asked to compare the stories on the
following metrics:

  * •

Which of these stories is more creative (or is more novel or original)?

  * •

Please explain your answer in 1-2 sentences

  * •

The writer of which story is more imaginative?

  * •

Please explain your answer in 1-2 sentences

  * •

Which story better integrates the starting prompt provided?

  * •

Which story has a more elaborate setting (or has the setting explained in
detail)?

  * •

Which story has more elaborate characters (or has characters explained in
detail)?

  * •

Which story has a better narrative composition or storyline?

  * •

Which story was more interesting for you to read?

  * •

Which story do you think has an overall higher quality?

  * •

Which story seems more complete?

  * •

Hypothetically, if you had to make an illustration, visual image or animation
from one of these stories, which one would you rather pick?

###  5.3 Post-test questionnaire

Participants were asked the following questions after writing their stories:

  * •

Did the visual images inspire you for creative story writing?

  * •

On a scale of 1-5, how fun was the writing process?

  * •

Would you want to share the story you wrote with your friends? [Yes, No,
Maybe]

  * •

Do you think the story you wrote was creative? [Yes, it was very creative;
Yes, but I can do better; It was not great, but not bad either; It was not
that creative; No, it was not creative at all].

  * •

In 1-2 sentences, describe how the visuals inspire you for writing.

  * •

In 1-2 sentences, describe what you wish the website could provide that could
better help you with writing.

  * •

Which parts of the story did the visuals help you with? [Characters, setting,
details, plot, conflict, resolution, tone, none]

###  5.4 Study findings

Figure 2: 65% writers found the visuals inspiring. Images generated using
VQGAN were the most used images, and VQGAN, BigGAN and CLIPDraw generated
images were used significantly more than DALL-E generated images (X2 (3, N =
751) = 12.947 , p = 0.0048). Of the features of stories that the visuals
inspired, “characters”, “setting” and “details” were the most common.

###  5.5 Sample stories

####  5.5.1 With the visual prompt (Figure 1)

"A galaxy in a melting pot was the first impression Betsy had of the abstract
painting. And yet, as she studied it more closely, the swirling blue black
cyclone and bright star-like spots began to seem almost familiar to her. It
was as if bits here and there had attached to her psyche as snippets of
memories she had all but forgotten. She continued to observe, and as she did,
the painting seemed to awaken from its inert state and began to move. It
started slowly but then picked up speed. Betsy was mesmerized. She became
frightened but was unable to pull away. More and more invisible tendrils clung
to her. She gasped. A tear trickled out of her eye. And suddenly the painting
pulled her into itself."

####  5.5.2 Without the visual prompt

"A galaxy in a melting pot was what the Captain called it. And we were tasked
with the mission of saving those on surrounding planets. It was a gargantuan
task for any society and we had only 1000 ships at our disposal, which would
convey only half the surrounding planets population before the likely
destruction caused by the supernova. Some planets instituted a lottery system,
with families with children at the top of the list. Others were less organized
as there were crowds vying to get on the ships and crowd control had to be
instituted. But there was one planet that was unique. They refused to let
anyone go unless everyone could go. Hearing this, the federation of planets
decided to put them at the head of the line and insure that everyone could go
on their planet. To accomplish this, they had do de-prioritize the planet
where everyone was competing against each other to board the ships. In the
end, that planet would be the last one served."