# Playing a 2D Game Indefinitely using NEAT and Reinforcement Learning

Jerin Paul Selvan  Dept. of Computer Engineering
Pune Institute of Computer Technology
Pune, India
<EMAIL_ADDRESS>Dr. P. S. Game  Dept. of Computer Engineering
Pune Institute of Computer Technology
Pune, India
<EMAIL_ADDRESS>
###### Abstract

For over a decade now, robotics and the use of artificial agents have become a
common thing. Testing the performance of new path finding or search space
optimisation algorithms has also become a challenge as they require simulation
or an environment to test them. The creation of artificial environments with
artificial agents is one of the methods employed to test such algorithms.
Games have also become an environment to test them. The performance of the
algorithms can be compared by using artificial agents that will behave
according to the algorithm in the environment they are put in. The performance
parameters can be, how quickly the agent is able to differentiate between
rewarding actions and hostile actions. This can be tested by placing the agent
in an environment with different types of hurdles and the goal of the agent is
to reach the farthest by taking decisions on actions that will lead to
avoiding all the obstacles. The environment chosen is a game called ”Flappy
Bird”. The goal of the game is to make the bird fly through a set of pipes of
random heights. The bird must go in between these pipes and must not hit the
top, the bottom, or the pipes themselves. The actions that the bird can take
are either to flap its wings or drop down with gravity. The algorithms that
are enforced on the artificial agents are NeuroEvolution of Augmenting
Topologies (NEAT) and Reinforcement Learning. The NEAT algorithm takes an ’N’
initial population of artificial agents. They follow genetic algorithms by
considering an objective function, crossover, mutation, and augmenting
topologies. Reinforcement learning, on the other hand, remembers the state,
the action taken at that state, and the reward received for the action taken
using a single agent and a Deep Q-learning Network. The performance of the
NEAT algorithm improves as the initial population of the artificial agents is
increased.

###### Keywords:

NeuroEvolution of Augmenting Topologies (NEAT), Artificial agent, Artificial
environment, Game, Reinforcement Learning (RL)

##  I Introduction

An intelligent agent is anything that can detect its surroundings, act
independently to accomplish goals, and learn from experience or use knowledge
to execute tasks better. The agent’s surroundings are considered an
environment in artificial intelligence. The agent uses actuators to send its
output to the environment after receiving information from it through sensors.
[11] There are several types of environments, Fully Observable vs Partially
Observable, Deterministic vs Stochastic, Competitive vs Collaborative, Single-
agent vs Multi-agent, Static vs Dynamic, Discrete vs Continuous, Episodic vs
Sequential and Known vs Unknown. An approach to machine learning known as
NEAT, or Neuroevolution of Augmenting Topologies, functions similarly to
evolution. In its most basic form, [1] NEAT is a technique for creating
networks that are capable of performing a certain activity, like balancing a
pole or operating a robot. It’s significant that NEAT networks can learn using
a reward function as opposed to back-propagation. By executing actions and
observing the outcomes of those actions, an agent learns how to behave in a
given environment via reinforcement learning, a feedback-based machine
learning technique. The agent receives compliments for each positive activity
and is penalised or given negative feedback for each negative action. In
contrast to supervised learning, reinforcement learning uses feedback to
autonomously train the agent without the use of labelled data. The agent can
only learn from its experience because there is no labelled data. In
situations like gaming, robotics, and the like, where decisions must be made
sequentially and with a long-term objective, RL provides a solution. The agent
engages with the environment and independently explores it. In reinforcement
learning, an agent’s main objective is to maximise positive rewards while
doing better.

##  II Literature Survey

Games have been used a lot to act as an environment to test algorithms. There
is a lot of research [3] done to create an AI bot that can challenge a player
in a multi-player or two-player game. Neuroevolution and Reinforcement
learning algorithms are some of the algorithms that are used to create AI bots
or artificial agents. [1], [7] and [8] have implemented a configuration of an
ANN called Neuroevolution. The algorithm does not depend on the actions taken
by the agents as a whole. [3], [4], [5], [6] and [7] use Reinforcement
Learning algorithm with Deep Q-Learning to train the agents.
The performance of the Neuroevolution algorithm depends on the objective
function, initial population, mutation rate, weights and bias added to the
network, the activation function used and overall topology of the network.
Authors in [2] talk about how superior the Neuroevolution algorithm is over
the traditional Reinforcement Learning algorithm with the Deep Q-Learning
algorithm. Neuroevolution has an upper hand when it comes to the time taken by
the artificial agent to train itself. There are other parameters that need to
be taken into consideration while using a Neural Network. The topology of the
network plays a vital role in the performance. Two strategies were proposed by
Evgenia Papavasileiou (2021) [2], using fixed topologies in the neural
networks and using augmented topologies. The network topology is a single
hidden layer of neurons, with each hidden neuron connected to every network
input and every network output. Evolution searches the space of connection
weights of this fully-connected topology by allowing high performing networks
to reproduce. The weight space is explored through the crossover of network
weight vectors and through the mutation of single networks’ weights. Thus, the
goal of fixed-topology NE is to optimise the connection weights that determine
the functionality of a network. The topology, or structure, of neural networks
also affects their functionality, and modifying the network structure has been
effective as part of supervised training.
There are two ways of making use of the environment. Authors in [3], [4], [6]
and [7] use DNN to extract the features from the frame of the game and they
form the input to the agent. However, [1], [5] and [8] make use of the game
itself and place the agent to perceive its surroundings. There are several
combinations of Reinforcement Learning algorithms possible, like Deep Neural
Networks (DNN), Long short-term memory (LSTM), Deep Q-Network (DQN) and the
like. However, depending on the type of obstacle and the type of game, its
performance varies.
Reinforcement Learning algorithm with DNN and LSTM have been used in [3]. This
algorithm addresses issues like vast search space, dependencies between the
actions taken by the agent, the state and the environment, inputs and
imperfect information. To reduce the complexity of the data generated by the
perception of the agent, data skipping techniques are implemented. There is,
however, a drawback with this algorithm. It takes a lot of time for the agent
to train. Or, for every discrete step taken by the agent, it receives a state
that belongs to a set S and it sends an action from the set A actions to the
environment. The environment makes a transition from state St to St+1 and a
gamma value [0, 1] determines the preference for immediate reward over long-
term reward. A self-playing method is used by storing the parameters of the
network to create a pool of past agents. This pool of past agents is used to
sample opponents. This method offers RL to learn the Nash equilibrium
strategy. Data skipping techniques were proposed in this paper. It refers to
the process of dropping certain data during the training and evaluation
process. Data skipping techniques proposed are: ”no-op” and ”maintain move
decision”. The network is composed of an LSTM-based architecture, which has
four heads with a shared state representation layer. An actor-critic off-
policy learning algorithm was proposed.
Botong Liu (2020) [4] has used Reinforcement Learning with DQN. The game was
split into frames, and each game image was sequentially scaled, grayed, and
adjusted for brightness. Deep Q Network algorithm was used to convert the game
decision problem into a classification and recognition problem of multi-
dimensional images and solve it using CNN. Reinforcement learning works best
for continuous decision-making problems. However, Deep Reinforcement Learning
has a limitation of not converging for which Neural fitted Q-learning and DQN
algorithms were used to overcome the issue. Since FNQ can work with numerical
information only the author suggests use of DQN. Combining Q learning with
CNN, the DQN can achieve self-learning. ReLu and maximum pooling layers are
added to the CNN. Gradient descent (Adam Optimizer) was used to train the DQN
parameters.
Q-Value function based algorithms are the focus of Aidar Shakerimov (2021)
[5]. For the DQN algorithms, improvements could be achieved in their
performance by using a cumulative reward for training actions. To speed up
training, RNN-ReLU was used instead of LSTM or GRU. LSTM or GRU performs
better than RNN-ReLU but takes 7 times more time to train. Label smoothing was
used to prevent the vanishing gradients in RNN-ReLU. However, DQN is sensitive
to seed randomization.
SARSA is a slight variation of the traditional Q-Learning algorithm. Authors
in [6] use SARSA and Q-Learning algorithms with modifications such as
$\epsilon$-greedy policy, discretization and backward updates. Some variants
of Q-Learning were also implemented such as a tabular approach, Q-value
approximation using linear regression, and NN. In the implementation, [6]
finds the SARSA algorithm to have outperformed Q-learning. The specifications
of the rewards are a positive 5 for passing a pipe, a negative 1000 for
hitting a pipe, and a positive 0.5 for surviving a frame. Feed-forward NN was
used with a 3 neuron input layer, 50 neuron hidden layer, 20 neuron hyphen
layer, and a 2 neuron output layer (ReLU activation function). The CNN is used
with preprocessed input image by removing the background, grayscale, and
resizing to 80 x 80, 2 CNN layers were used, one using sixteen 5 × 5 kernels
with stride 2, and another with thirty-two 5 × 5 kernels with stride 2.
[7] proposes the use of specific feature selection and presents the state by
the bird velocity and the difference between the bird’s position and the next
lower pipe. This reduces the feature space and eliminates the need for deeper
modules. The agent is provided with rational human-level inputs along with
generic RL and a standard 3-layer NN with a genetic optimization algorithm.
The reward for the agent is a positive 1 for every cross of the pipe and a
negative 100 if the agent dies. The Neuro evolution has the following
characteristics: the NN weights and the number of hidden layer units undergo
changes, the mutation rate is kept at 0.3, and the initial population size is
200. [8] proposes the use of two levels for the Flappy Bird game. The fitness
function is calculated by the distance traveled by the agent and the current
distance to the closest gap. The mutation rate is kept at 0.2, and there are 5
neurons in the hidden layer.

##  III Methodology

The NEAT algorithm implementation is dependent on the objective function,
crossover, mutation, and a population of agents. For a given position of the
bird, say (x, y), there are two actions that the agent can make. Either the
bird flaps its wings or it does not flap its wings. The vertical and
horizontal distances traveled by the agent are determined by the following
equations.

$d_{vertical}=v_{jump}.t+\frac{1}{2}.a.t^{2}$ (1) $d_{horizontal}=v_{floor}.t$
(2) $d_{floor}=v_{floor}.t$ (3) $d_{pipe}=v_{pipe}.t$ (4)

Eq. (1) determines the vertical displacement of the agent, where _a_ is the
acceleration that is a constant [12].

Figure 1: Details of the game environment [10]

As shown in the Fig. 2, the y coordinate of the agent, the distance between
the top pipe and the agent _(y - T’)_ and the distance between the bottom pipe
and the agent _(T’)_ are the inputs to the neural network. The gap between the
top and the bottom pipe is fixed to 320 pixels, and the heights are randomly
generated. The distance between subsequent pipes is also kept constant. With
respect to the NEAT algorithm, the fitness of the agent is determined by the
number of pipes that the agent is able to cross without collision. As soon as
the agent collides with the pipe, hits the roof, or falls down to the ground,
the agent is removed from the environment. The performance of the algorithm
depends on the initial population that is taken into consideration. The
activation function used is the hyperbolic tangent function. The mutation rate
is kept at 0.03.

Figure 2: Parameters required as input to the NN

The encoding of the chromosome is shown in Table. I. The weight of the
connection from a node in a layer to another node in the other layer and the
dropped value is also considered as part of the encoding. If the connection is
to be dropped, it is encoded with the value 0 otherwise, it has the value 1.

Figure 3: Diagramatic view of the encoded chromosome in Table I TABLE I: Encoding of a chromosome before crossover and mutation Weight | 0.25 | 2.31 | 1.55 | 0.98 | 5.11 | 1.17 | 0.07
---|---|---|---|---|---|---|---
From | 1 | 2 | 3 | 1 | 3 | 4 | 2
To | 2 | 3 | 2 | 3 | 4 | 3 | 4
Enabled | 1 | 0 | 1 | 1 | 1 | 1 | 1

With reference to Fig. 3 and Table. I, the edges between the nodes are
represented by the rows ’From’ and ’To’. The Table. I shows the encoding of
the network before mutation. After the mutation, or rather after topology
augmentation, the encoding of the edges is shown in Table. II. The resultant
connections are shown in Fig. 4. The edges that are in red are the edges that
were dropped, and the edges that are in green are the ones that have been
added as a result of the mutation. The cross-over process happens between any
two randomly selected parents. The next population is determined by the
fitness of the individual agents.

Figure 4: Diagramatic view of the encoded chromosome in Table. II TABLE II: Encoding of a chromosome after crossover and mutation Weight | 0.25 | 5.11 | 1.17 | 0.98 | 2.31 | 1.55 | 0.07
---|---|---|---|---|---|---|---
From | 1 | 2 | 4 | 1 | 3 | 3 | 4
To | 3 | 4 | 2 | 4 | 2 | 4 | 3
Enabled | 1 | 1 | 1 | 0 | 1 | 1 | 0

##  IV Results

The implementation of the algorithm requires no historic data or any dataset.
The algorithm makes use of the sensory data perceived from the environment by
the artificial agent as the program runs. The inputs to the algorithm are the
_y_ position of the agent, the vertical distance of the agent from the top
pipe, and the vertical distance of the agent from the lower pipe. The output
of the algorithm is the action that the agent is to take i.e. jump or drop
down owing to gravity. NEAT algorithm was implemented by taking different
initial populations. Fig. 5, Fig. 6 and Fig. 7 shows the average score and the
scores reached in every generation, when the game is played by the agents over
50 generations.

Figure 5: Gameplay when initial population is 80

Figure 6: Gameplay when initial population is 100

Figure 7: Gameplay when initial population is 120

The change in the average scores over the change in the initial population is
separately shown in Fig. 8 for generations 30 to 50. The average score of the
agent is steadily increasing from when the initial population is 20 to 100.
The maximum score is observed when the population is 160. The average fitness
value of the population is higher when the initial population size is 100.
This is shown in Fig. 9. The initial training phase is less than 5
generations. When the initial population has fewer agents, it takes more
generations to spike the average score of the game. This can be observed from
Fig. 10.

Figure 8: Average scores over initial population change (Gen 30 - Gen 50)

Figure 9: Average Fitness of the population over initial population change

Figure 10: Speed of agents getting trained over initial population change

Table. III shows the average score and the maximum score gained by the agent
over 50 generations. A maximum score of 1025 is obtained when the initial
population is 160 and the gameplay run till 50 generations.

TABLE III: Scores over change in initial population Initial Population | Average Score | Max Score
---|---|---
20 | 158.2 | 583
40 | 187.04 | 771
60 | 200.06 | 756
80 | 250.28 | 765
100 | 244.56 | 911
120 | 220.66 | 544
140 | 255.82 | 565
160 | 293.72 | 1025

## Conclusion and Future Scope

By using a 2D game, the performance of the algorithms can be determined very
efficiently. Unlike simulation, the creation of an environment gives better
control over the environment. Through various iterations by changing the
initial population size, the average score gained by the agent has increased.
The initial population of agents also affects the training speed. The more the
agents, the quicker the training is done. The highest average score is
obtained when the initial population is set to 100 individuals. It can be
concluded that the performance of the algorithm increases as the initial
population is increased. The implemented algorithm can be extended to making
use of Reinforcement Learning with multiple agents and using Augmented
Topologies along with the Deep Q-Learning model.

## References

  * [1] M. G. Cordeiro, Paulo B. S. Serafim, Yuri B. Nogueira, “A Minimal Training Strategy to Play Flappy Bird Indefinitely with NEAT”, 2019 18th Brazilian Symposium on Computer Games and Digital Entertainment (SBGames), pp. 384-390, DOI: 10.1109/SBGames.2019.00014.
  * [2] Evgenia Papavasileiou, Jan Cornelis and Bart Jansen, “A Systematic Literature Review of the Successors of “NeuroEvolution of Augmenting Topolo- gies”, Evolutionary Computation, vol: 29, Issue: 1, March 2021, pp. 1-73, DOI: 10.1162/evcoa00282.
  * [3] Inseok Oh, Seungeun Rho, Sangbin Moon and Seongho Son, “Creat- ing Pro-Level AI for a Real-Time Fighting Game Using Deep Reinforcement Learning”, IEEE Transactions on Games, 2021, pp. 1-10, doi: 10.1109/TG.2021.3049539.
  * [4] Botong Liu, “Implementing Game Strategies Based on Reinforce- ment Learning”, ICRAI 2020: 2020 6th International Conference on Robotics and Artificial Intelligence, November 2020, pp 53–56, DOI: https://doi.org/10.1145/3449301.3449311.
  * [5] Evalds Urtans, Agris Nikitenko, “Survey of Deep Q-Network variants in PyGame Learning Environment”, ICDLT ’18: Proceedings of the 2018 2nd International Conference on Deep Learning Technologies, June 2018, pp 27–36, DOI: https://doi.org/10.1145/3234804.3234816.
  * [6] Tai Vu, Leon Tran, ”FlapAI Bird: Training an Agent to Play Flappy Bird Using Reinforcement Learning Techniques”, experimental projects with community collaborators, 2020, DOI: https://doi.org/10.48550/arXiv.2003.09579.
  * [7] Andre Brandao, Pedro Pires, Petia Georgieva, ”Reinforcement Learning and Neuroevolution in Flappy Bird Game”, Pattern Recognition and Image Analysis, 2019, pp.225-236, DOI: DOI:10.1007/978-3-030-31332-620.
  * [8] Yash Mishra, Vijay Kumawat, Selvakumar Kamalanathan, ”Performance Analysis of Flappy Bird Playing Agent Using Neural Network and Genetic Algorithm”, Information, Communication and Computing Technology, 2019, pp.253-265, DOI:10.1007/978-981-15-1384-821.
  * [9] A. McIntyre, M. Kallada, C. G. Miguel, and C. F. da Silva, “neat-python,” https://github.com/CodeReclaimers/neat-python.
  * [10] T. Ruscica and J. Keromnes, ”Flappy Bird game images”, https://github.com/techwithtim/NEAT-Flappy-Bird/tree/master/imgs.
  * [11] S. Kumar, ”Understand Types of Environments in Artificial Intelligence”, https://www.aitude.com/understand-types-of-environments-in-artificial-intelligence/, 2020.
  * [12] D. Zhu, ”How I Built an Intelligent Agent to Play Flappy Bird”, Analytics Vidhya, 2020.


# Nucleosynthesis Contribution of Neutrino-dominated Accretion Flows to the
Chemical Evolution of Active Galactic Nuclei

Yan-Qing Qi Department of Astronomy, Xiamen University, Xiamen, Fujian 361005,
China<EMAIL_ADDRESS><EMAIL_ADDRESS>Tong Liu Department of
Astronomy, Xiamen University, Xiamen, Fujian 361005, China;
<EMAIL_ADDRESS><EMAIL_ADDRESS>Zhen-Yi Cai CAS Key Laboratory for
Research in Galaxies and Cosmology, Department of Astronomy, University of
Science and Technology of China, Hefei, Anhui 230026, China<EMAIL_ADDRESS>School of Astronomy and Space Science, University of Science and Technology of
China, Hefei, Anhui 230026, China  Mouyuan Sun Department of Astronomy, Xiamen
University, Xiamen, Fujian 361005, China<EMAIL_ADDRESS><EMAIL_ADDRESS>
###### Abstract

Recent observations of quasars show high line-flux ratios in their broad
emission lines and the ratios appear to be independent of redshift up to
$z\gtrsim 6$, which indicate that the broad-line regions of these early
quasars are surprisingly metal-rich. Here, we revisit the chemical evolution
of high-redshift quasars by adding a new ingredient, i.e., the neutrino-
dominated accretion flows (NDAFs) with outflows, on top of the conventional
core-collapse supernovae (CCSNe). In the presence of the chemical contribution
from NDAFs with outflows, the total metal mass (i.e., the summation of the
conventional CCSN and NDAFs with outflows) per CCSN depends weakly upon the
mass of the progenitor star if the mass is in the range of $\sim
25-55~{}M_{\odot}$. We model the chemical evolution by adopting a improved
open-box model with three typical initial mass functions (IMFs). We find that,
with the additional chemical contribution from NDAFs with outflows, the quasar
metallicity can be enriched more rapidly in the very early Universe ($z\sim
10$) and reaches higher saturation than the no-NDAF case at $z\sim 8$, after
which they evolve slowly with redshift. The quasar metallicity can reach $\sim
20~{}Z_{\odot}$ ($Z_{\odot}$ denotes the metallicity of the Sun; and $\sim
20\%$ of which is produced by NDAF outflows) at $z\sim 8$ for the “top-heavy”
IMF model in Toyouchi et al. (2022), which readily explains the quasar
observations on the super-solar metal abundance and redshift-independent
evolution.

accretion, accretion disks - black hole physics - galaxies: abundances -
nuclear reactions, nucleosynthesis, abundances - supernovae: general

##  1 Introduction

High-redshift luminous active galactic nuclei (AGNs), i.e., quasars, provide
valuable probes to the metallicity of the early universe, thanks to their high
luminosities and various metallic emission lines in the rest-frame ultraviolet
(UV) spectra. According to photoionization models (e.g., Hamann et al., 2002;
Nagao et al., 2006), UV emission-line ratios like $(\rm
Si\,{\textsc{iv}}+O\,{\textsc{iv\scriptsize]}})/C\,{\textsc{iv}}$,
$\rm(C\,{\textsc{iii\scriptsize]}}+Si\,{\textsc{iii\scriptsize]}})/C\,{\textsc{iv}}$,
$\rm Al\,{\textsc{iii}}/C\,{\textsc{iv}}$, $\rm
N\,{\textsc{v}}/C\,{\textsc{iv}}$, $\rm He\,{\textsc{ii}}/C\,{\textsc{iv}}$,
$\rm Fe\,{\textsc{ii}}/Mg\,{\textsc{ii}}$, and $\rm
N\,{\textsc{v}}/He\,{\textsc{ii}}$ can be used to characterize the broad-line
regions (BLRs) metal abundances of high-redshift ($z\gtrsim 6$) quasars; UV
spectroscopic observations identify two noteworthy properties in the BLRs of
high-redshift quasars, i.e., super solar metal abundances and no-redshift
evolution (e.g., Jiang et al., 2007; De Rosa et al., 2014; Wang et al., 2022).
Hence, there is a rapid star formation and elemental enrichment within the
first gigayear. The supermassive black holes (SMBHs) in these early quasars
are already massive ($\sim 10^{8}-10^{10}~{}M_{\odot}$) according to near-
infrared (NIR) spectroscopic observations (e.g., Jiang et al., 2007; Kurk et
al., 2007; Wu et al., 2015; Yang et al., 2021), indicating a rapid mass growth
history.

The gas powering bright AGNs is subject to instability and fragmentation due
to self-gravity and a fraction of it might collapse to form local stars, which
undergo gas accretion to be massive (e.g., Goodman & Tan, 2004; Cantiello et
al., 2021; Jermyn et al., 2021). These stars will explode as supernovae (SNe),
polluting the environment with heavy elements and providing a possible
explanation for the observed high metal environment in AGNs, and leave behind
some stellar remnants. SNe Ia are thermonuclear explosions that originate from
a white dwarf (WD) accreting companion or double-WD mergers. They are thought
to be one of the most significant nucleosynthetic factories for heavy metals,
especially iron-peak elements (see, e.g., Arnett, 1996; Hillebrandt &
Niemeyer, 2000; McKernan et al., 2020). Massive stars ($\gtrsim
8~{}M_{\odot}$) end up as core-collapse SNe (CCSNe) or hypernovae. They are
considered to produce the majority of $\alpha-$elements (e.g., oxygen, sulfur,
neon, and argon). The nucleosynthesis processes in such energetic SNe has
received a great deal of attention (e.g., Nomoto et al., 2006; Heger &
Woosley, 2010; Sukhbold et al., 2016). Because SNe Ia has a longer
evolutionary timescale than CCSNe, iron enrichment from $\alpha-$element is
thought to be delayed by $\sim 1~{}\rm Gyr$. The $\rm
Fe\,{\textsc{ii}}/Mg\,{\textsc{ii}}$ flux ratio has been used as a proxy for
the $\rm Fe/\alpha-$element abundance. Surprisingly, no significant redshift
evolution was discovered up to $z\sim 7$, despite the fact that the
measurement uncertainty is non-negligible in many cases (e.g., Kurk et al.,
2007; De Rosa et al., 2014; Wang et al., 2022).

The observed BLR gas is likely to originate from the thermal diffused hot gas
continuously supplied by the AGN disk (e.g., Wang et al., 2011). Shocks from
SN explosions interacting with interstellar clouds can lead to the evaporation
of clumps and thus the production of hot gas, which is photoionized by the
ionizing continua from the central engine and undergoes dynamical and thermal
instabilities to drive the formation of the BLR (e.g., Wang et al., 2012).
Therefore, the metal abundance in the AGN disk may determine that in the BLR.

A stellar-mass black hole (BH) with an accretion disk often forms in the
center of a collapsar ($\gtrsim 25~{}M_{\odot}$). The fall-back matter of the
collapsar activates BH hyperaccretion processes, which power relativistic jets
through the neutrino-antineutrino annihilation mechanism in the initial
accretion phase. The disk that releases the gravitational energy of the BH is
named the neutrino-dominated accretion flow (NDAF; for a recent review, see
e.g., Liu et al., 2017). Following the decreasing accretion rates, the
Blandford-Znajek mechanism (Blandford & Znajek, 1977) that extracts the
rotational energy of the BH will dominate over Poynting flux. For NDAFs in the
centers of collapsars, the abundance of free protons and neutrons in the disk
outflows will participate nucleosynthesis, or even induce the CCSN explosions
(e.g., Pruet et al., 2004; Surman et al., 2006; Song & Liu, 2019), and inject
the heavy elements to the circumstances (Liu et al., 2021). Such a chemical
enrichment process is often disregarded in previous works.

In this paper, we investigate the contribution of CCSNe with or without NDAF
outflows to the metallicity evolution of quasars using three typical initial
mass functions (IMFs). The paper is organized as follows. In Section 2, we
give the metallicity evolution model by considering the gas accretion to the
central SMBH, star formation, and SNe (with or without NDAFs) of a quasar. In
Section 3, we show the evolution of metallicity within a quasar as a function
of IMFs. Conclusions and discussion are made in Section 4. We adopt the
cosmology of $\Omega_{0}=1-\Omega_{\Lambda}=0.3$ and $H_{0}=70~{}\rm
km~{}s^{-1}~{}Mpc^{-1}$ here.

##  2 Model

###  2.1 Stellar Yields

The amount of newly formed and pre-existing elements ejected at the death of
all masses of stars, known as stellar yields, is a key parameter in
calculating galactic chemical evolution. Numerous studies find that the
production and ejection of stellar elements are a function of the initial mass
and chemical composition of the star (e.g., Heger & Woosley, 2010; Nomoto et
al., 2013). Furthermore, if massive stars ($\gtrsim 25~{}M_{\odot}$) explode
as CCSNe, the central core falls back to the remnant BHs with accretion disks.
The collapsar initial mass-supply rates can keep the accretion processes in
the NDAF phase. The metallicity of the progenitor stars determines the
electron fraction $Y_{\rm e}$ at the outer boundary of NDAFs (e.g., Surman &
McLaughlin, 2005; Surman et al., 2006; Liu et al., 2021). Liu et al. (2012)
and Liu et al. (2014) discovered that the Bernoulli parameter is positive
especially in the middle and outer regions of the NDAF, implying that outflows
may occur, which is similar to the classical advection-dominated accretion
flows (e.g., Gu et al., 2009). Furthermore, many numerical simulations find
strong NDAF outflows (e.g., Fernández et al., 2015; Just et al., 2015; Siegel
& Metzger, 2017). The initial density, temperature, and materials available
for synthesis, all of which are dependent on the state of the disk, are the
main parameters that influence the nucleosynthesis in disk outflows. CCSNe and
NDAF outflows have the very similar electron fractions (i.e., relative proton-
rich condition). Hence, the nucleosynthesis in NDAF outflows is expected to be
similar to that in CCSNe (e.g., Surman et al., 2011; Liu et al., 2021). Liu et
al. (2021) found that the density profiles and temperatures of the NDAF with
outflows are high enough to trigger and maintain the nucleosynthesis process.
And they calculated the ${}^{56}\rm Ni$ yields supplied by CCSNe and NDAF
outflows and found that the total ${}^{56}\rm Ni$ yields are independent of
the mass of the progenitor ($\sim 25-55~{}M_{\odot}$). Similarly, we assume
that the total metal yields of CCSNe with NDAFs as a constant value for a
progenitor star in that mass range. The stellar yields for the case of no NDAF
outflows are taken from the Online Yields Table (2013)111$\rm
http://star.herts.ac.uk/~{}chiaki/works/YIELD\\_CK13.DAT$.

Figure 1 shows the total metal yields $q(m)$ ejected by SNe with (the red
solid line) or without (the black dashed line) the NDAF outflows as a function
of the progenitor mass for $Z=0$ (Nomoto et al., 2013). The metal loading
processes caused by stellar winds from giant stars or compact binary mergers
are not taken into account.

###  2.2 IMFs

Figure 1: Total metal yields from SNe with (red solid line) or without (black
dashed line) contribution of NDAF outflows as a function of progenitor star
mass.

The total yields from a generation of stars depend on the IMF. Although the
formation of the first stars has been studied theoretically, their IMFs have
yet to be clarified. Moreover, little is known about how the IMF changes as a
function of galactic conditions. Here, we make an approximation, that is the
IMF is time-independent, i.e., $\phi(m,t)=\phi(m)$, where $m$ denotes the
stellar mass in units of the solar mass. The most common parameterizations of
the IMF are either a single power law or a power law with multiple breaks,
which is defined by $\phi(m)\equiv dN/dm\propto m^{-\alpha_{i}}$ in a mass
range of $0.1-100~{}M_{\odot}$, where $\alpha_{i}$ is the index of the IMF and
$0.1~{}M_{\odot}$ and $100~{}M_{\odot}$ are the lower and upper limits of the
stellar masses, respectively, and it is normalized as

$\int^{100}_{0.1}m\phi(m)dm=1.$ (1)

We quote three typical IMF types here, i.e., Toyouchi et al. (2022)
characterized a top-heavy IMF with a single slope $\alpha=-0.5$ (hereafter the
Toyouchi IMF), which describe the stellar population in AGN disk; Zhang et al.
(2018) assumed a bottom-heavy IMF with a single slope $\alpha=2.7$ (hereafter
the Zhang IMF); and Ballero et al. (2007) approximated the IMF with
$\alpha_{1}=1.3$ for $0.1\leq m\leq 0.5$, and $\alpha_{2}=1.95$ for $0.5\leq
m\leq 100$ (hereafter the Ballero IMF). In the above IMFs, $\sim
25-55~{}M_{\odot}$ stars have the highest percentage in Toyouchi IMF, and, the
lowest percentage in Zhang IMF.

###  2.3 The Metallicity Equation

Some galaxy chemical evolution models (e.g., Tinsley, 1980), which usually
consider gas flows and dilute nucleosynthesis products with unenriched
material from outside the galaxy, aim to explain the composition of galaxies
based on the stellar yields and the mixing of stellar ejecta with interstellar
gas. We improve this chemical evolution model and apply it to the the chemical
evolution of the gas of a high-redshift quasar, named the “open-box” chemical
evolution model, which uses SNe and NDAF outflows as chemical enrichment
sources and has only gas accretion to the central SMBH without gas inflow from
the environment. Therefore, this can emphasize the role of nucleosynthesis in
the NDAF outflows as an additional metal source and reduce the dependence on
the composition of outside.

The quasars are powered by the process of accreting materials onto the central
SMBH and emit near the Eddington limit. The mass accretion rate is
parameterized in terms of the Eddington accretion rate $\dot{M}_{\rm
Edd}=L_{\rm Edd}/\eta c^{2}$, where the Eddington luminosity is provided by
$L_{\rm Edd}=4\pi GM_{\rm smbh}m_{\rm p}c/\sigma_{\rm T}$, $M_{\rm smbh}$ is
the SMBH mass, $\sigma_{\rm T}$ is the Thompson cross section for electron
scattering, and $m_{\rm p}$ is the proton mass. Here, we take the assumed
radiative efficiency $\eta=0.1$, and thus get SMBH accretion rate at $\lambda$
times Eddington rate to grow the central SMBH,

$\dot{M}_{\rm smbh}\approx\frac{dM_{\rm smbh}}{dt}=\lambda\dot{M}_{\rm Edd}.$
(2)

Then, the equations describe the evolution of star formation, gas, and metal
abundance as follows

$\dot{\Psi}=M_{\rm g}~{}/~{}t_{\star},$ (3) $\frac{dM_{\rm
g}}{dt}=-\dot{\Psi}+R-\dot{M}_{\rm smbh},$ (4) $\frac{d(M_{\rm
g}X)}{dt}=-\dot{\Psi}X+E-\dot{M}_{\rm smbh}X,$ (5)

where $\dot{\Psi}$ is the star formation rate (SFR; e.g., see Equation (A4) of
Cai et al., 2013), $M_{\rm g}$ is the total mass of gas, $t_{\star}$ is a
typical star formation timescale, $R$ is the rate of mass ejection at the end
of stellar evolution, $X$ is the mass fraction of metals, and $E$ is the rate
of metal ejection from SNe with or without NDAF outflows, respectively.

We use the instantaneous recycling approximation, i.e., a star was formed at
time $t-\tau(m)$ if it dies at time $t$, and $\tau(m)$ is the lifetime of
stars with mass $m$. The mass ejection rate at time $t$ is

$R(t)=\int_{m_{\star}(t)}^{m_{\rm max}}(m-m_{\rm
rem})~{}\phi(m)~{}\dot{\Psi}[t-\tau(m)]dm,$ (6)

where $m_{\rm rem}(m)$ is the remnant mass, and $m_{\star}$ is the mass of the
star for which $\tau(m_{\star})=t$. The remnant mass $m_{\rm rem}(m)$ is
provided by the Online Yields Table (2013). The lifetime of a star with mass
$m$ is estimated according to the work of the Geneva group (Schaller et al.,
1992)

$\tau(m)\approx 11.3m^{-3}+0.06m^{-0.75}+0.0012~{}~{}{\rm Gyr}.$ (7)

The overall amount of metals produced by SNe with or without NDAFs is

$\displaystyle E(t)\simeq$ $\displaystyle\int_{m_{\star}(t)}^{m^{\rm max}}\
q(m)~{}\phi(m)~{}\dot{\Psi}[t-\tau(m)]~{}dm,$ (8)

where $q(m)$ is the metal yield as shown in Figure 1. We further assume that
the initial metallicity of the star has no effect on $q(m)$, i.e., the metal
under consideration is a primary element. Metal production of the progenitor
mass with $Z=0$ and $Z_{\odot}$ is compared (see Figure 4 in Nomoto et al.,
2013), which slightly decreases at high metallicity.

###  2.4 Observational consequences for the BLR

We collect 24 quasars at $z\gtrsim 5.7$ from Jiang et al. (2007) and Wang et
al. (2022) and utilize the theoretical relation
$\textrm{log}~{}Z/Z_{\odot}=1.0+1.33~{}\log(\rm
N\,{\textsc{v}}/C\,{\textsc{iv}})$ to convert the line-flux ratios $\rm
N\,{\textsc{v}}/C\,{\textsc{iv}}$ to metallicities $Z$ (Hamann et al., 2002;
Wang et al., 2010). These results are shown in Figure 2, which imply that
quasar BLRs were enriched in metals at $z\sim 6$, and that the enrichment of
BLR metal abundances must have happened much earlier and/or much faster. The
conversion of line ratios to metallicities has substantial systematic
uncertainties. The ionizing parameter, the hardness of the ionizing continuum,
temperature, density, and other parameters can all have effects on the
conversion (see Maiolino & Mannucci, 2019, and reference therein). Considering
only show a comparison of the present model with the observed samples in high-
redshift quasars, we will not attempt to understand the differences between
each metal abundance indicator in this work, nor will we reach any conclusions
about which should be used as the true metal abundance.

##  3 Metallicity within the quasar

Figure 2: Metallicity evolutions in scenarios of SNe with (solid lines) or
without (dashed lines) NDAF outflows by using yields and three typical IMFs
described in Section 2.2. Different observed samples are from Wang+22 (black
rectangles; Wang et al., 2022) and Jiang+07 (red circles; Jiang et al., 2007).

The primary mechanisms for the seed formation of SMBHs are the collapse of a
massive star, runaway collisions in stellar clusters, and the direct collapse
of a gas cloud into a massive BH (for a review, see Latif & Ferrara, 2016).
Based on the measured bolometric luminosities and BH masses, Yang et al.
(2021) derived the Eddington ratios of high-redshift quasars with a mean of
1.08. We begin by assuming that the seed BH with $100~{}M_{\odot}$ at $z\sim
11$ grows in the Eddington limit $\lambda=1$ until the accretion rate changes
to zero when the gas surrounding the SMBH is about to be consumed. For a
quasar with initial gas mass $M_{\rm g,0}=10^{9}~{}M_{\odot}$ (e.g., Storchi-
Bergmann & Schnorr-Müller, 2019), and star formation timescale
$t_{\star}=0.1~{}\rm Gyr$, we show the chemical evolution altered with
different IMF types in Figure 2 by considering the SNe with or without
nucleosynthesis of NDAF outflows.

As shown in Figure 2, if the NDAF outflow nucleosynthesis is added, the metal
abundance can be rapidly raised to higher values at $z\sim 10$ than the no-
NDAF case. This is because in the very early Universe, only massive and short-
lived stars end their lives and the accompanying NDAF outflow nucleosynthetic
is prominent. The metallicity increases with time until it reaches a
saturation value at $z\sim 8$. If the SNe and NDAF outflow contributions are
considered simultaneously, the metal abundances can grow to $20.2$, $10.6$,
and $1.0~{}Z_{\odot}$ for the IMFs adopted from Toyouchi IMF (blue solid
line), Ballero IMF (green solid line) and Zhang IMF (purple solid line),
respectively. However, if only SNe explosions are considered, the gas can be
enriched to $\sim 16.5~{}Z_{\odot}$ in Toyouchi IMF (blue dashed line), $\sim
6.5~{}Z_{\odot}$ in Ballero IMF (green dashed line) but only to $\sim
0.7~{}Z_{\odot}$ in Zhang IMF (purple dashed line). NDAF outflows act as the
important sources of nucleosynthesis, resulting in a higher metal abundance
ratio in a short period of time and a higher (by a factor of $\sim 120\%$)
saturation value, which naturally explains all the observational data for the
Toyouchi IMF case, including those the super solar metal abundance and non-
redshift evolution of high-redshift quasars.

In addition, the metallicity is affected by the choice of popular IMFs, which
typically offer various ratios of the massive stars. It is worth noting that
the metallicity is greatest when the IMF is Toyouchi IMF (blue lines; Toyouchi
et al., 2022) and lowest when the IMF is the Zhang IMF (purple lines; Zhang et
al., 2018). This is because stars with the mass of $25-100~{}M_{\odot}$ have
the highest ratio in the IMF for Toyouchi IMF and the lowest ratio in the IMF
for Zhang IMF. For the Toyouchi IMF, the metallicity is already high enough to
explain all the observations even without taking into account the contribution
of NDAF outflows. The Toyouchi IMF supports the frequent occurrence of massive
stars, so that the stellar mass distribution is expected to be “top-heavy”
(e.g., Goodman & Tan, 2004; Mapelli et al., 2012). Such a special IMF probably
only holds for stars in quasar disks (e.g., Toyouchi et al., 2022).
Observations of the central nuclear region ($\sim 0.5\rm~{}pc$) of the Milky
Way reveal the presence of hundreds of massive young OB stars and Wolf-Rayet
stars (e.g., Levin & Beloborodov, 2003), with no low-mass stars, which is
contrary to the number predicted by the standard Salpeter IMF. Compare the
predictions of the model with observations of chemical abundance ratios in
quasars, constraints can be put on the IMFs.

AGN activity and star formation are heavily reliant on the availability of
cold gas, which is the fuel. Because massive stars make up a large proportion
in the top-heavier IMF, the death of massive stars can return a large amount
of gas in time to keep the mass of quasar system at a high value and maintain
the SMBH accretion for a longer time. As Gao & Solomon (2004) points out, SFR
scales linearly with the dense molecular gas mass. Hence, the top-heavier the
IMF, the higher the SFR. With Eddington accretion and radiation efficiency of
0.1 in Toyouchi IMF, it would take $\sim\rm 0.7~{}Gyr$ for a seed BH of
$100~{}M_{\odot}$ to grow to a $\sim 10^{9}~{}M_{\odot}$ SMBH, which is more
massive than those in the cases of Ballero IMF and Zhang IMF, and within the
observed SMBH mass range ($\sim 10^{8}-10^{10}~{}M_{\odot}$).

##  4 Conclusions and Discussion

In this paper, we calculate the metallicity evolution of high-redshift quasars
with different IMFs by considering the contributions of SNe with or without
NDAF outflows. We find that if the contributions of NDAF outflows are added,
the metal abundance can be raised to a higher value at an earlier time than
the no-NDAF case, thereby offering in a natural explanation for the unexpectly
high abundance of metals in high-redshift quasars. In addition, the
metallicity reaches its maximum value at $z\sim 8$, which can be increased by
at least $\sim 20\%$ compared to the no-NDAF case, and then evolves slowly
with the redshift. During an episode of the SMBH activity, the quasar
metallicity can be used as a “clock” to estimate its age.

Metal-rich environments are fully established when the IMF is “top-heavy”,
which supports the frequent occurrence of massive stars. For the Toyouchi IMF,
the metal abundance can reach more than ten times the solar metal abundance
even without accounting for the contribution of NDAF outflows. The
nucleosynthesis contributions of NDAF outflows also significantly increase the
metal abundance by $\sim 20\%$ and should be reasonably included in the
chemical evolution of galaxies and AGNs.

The measurements of the metallicity in quasar BLR may lead to other intriguing
findings since the BLR metallicity depends upon either SMBH mass or quasar
luminosity (e.g., Warner et al., 2003; Nagao et al., 2006; Matsuoka et al.,
2011; Xu et al., 2018), and thus can shed light on the co-evolution of
galaxies and their central SMBHs.

This work was supported by the National Natural Science Foundation of China
under grants 12173031, 11873045, and 11973002, and the science research grants
from the China Manned Space Project with Nos. CMS-CSST-2021-A06 and CMS-
CSST-2021-B11.

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[table]capposition=top Further author information: (Send correspondence to
J.M.)
J.M.: E-mail<EMAIL_ADDRESS>
# Panoramic SETI: Program Update and High-Energy Astrophysics Applications

Jérôme Maire  Center for Astrophysics & Space Sciences, University of
California San Diego, CA, USA  Shelley A. Wright  Center for Astrophysics &
Space Sciences, University of California San Diego, CA, USA  Department of
Physics, University of California San Diego, CA, USA  Jamie Holder  Department
of Physics and Astronomy, University of Delaware, DE, USA.  David Anderson
Space Sciences Laboratory, University of California Berkeley, CA, USA  Wystan
Benbow  Center for Astrophysics $|$ Harvard & Smithsonian, Cambridge, MA, USA
Aaron Brown  Center for Astrophysics & Space Sciences, University of
California San Diego, CA, USA  Maren Cosens  Center for Astrophysics & Space
Sciences, University of California San Diego, CA, USA  Department of Physics,
University of California San Diego, CA, USA  Gregory Foote  Department of
Physics and Astronomy, University of Delaware, DE, USA.  William F. Hanlon
Center for Astrophysics $|$ Harvard & Smithsonian, Cambridge, MA, USA  Olivier
Hervet  Santa Cruz Institute for Particle Physics and Department of Physics,
University of California, Santa Cruz, CA, USA  Paul Horowitz  Department of
Physics, Harvard University, Cambridge, MA, USA  Andrew W. Howard  Astronomy
Department, California Institute of Technology, Pasadena, CA, USA  Ryan Lee
Space Sciences Laboratory, University of California Berkeley, CA, USA  Wei Liu
Space Sciences Laboratory, University of California Berkeley, CA, USA
Department of Astronomy, University of California Berkeley, CA, USA  Rick
Raffanti  Techne Instruments, Oakland, CA, USA  Nicolas Rault-Wang  Space
Sciences Laboratory, University of California Berkeley, CA, USA  Department of
Astronomy, University of California Berkeley, CA, USA  Remington P. S. Stone
University of California Observatories, Lick Observatory, CA, USA  Dan
Werthimer  Space Sciences Laboratory, University of California Berkeley, CA,
USA  Department of Astronomy, University of California Berkeley, CA, USA
James Wiley  Center for Astrophysics & Space Sciences, University of
California San Diego, CA, USA  Department of Physics, University of California
San Diego, CA, USA  David A. Williams  Santa Cruz Institute for Particle
Physics and Department of Physics, University of California, Santa Cruz, CA,
USA

###### Abstract

Optical SETI (Search for Extraterrestrial Intelligence) instruments that can
explore the very fast time domain, especially with large sky coverage, offer
an opportunity for new discoveries that can complement multimessenger and time
domain astrophysics. The Panoramic SETI experiment (PANOSETI) aims to observe
optical transients with nanosecond to second duration over a wide field-of-
view ($\thicksim$2,500 sq.deg.) by using two assemblies of tens of telescopes
to reject spurious signals by coincidence detection. Three PANOSETI
telescopes, connected to a White Rabbit timing network used to synchronize
clocks at the nanosecond level, have been deployed at Lick Observatory on two
sites separated by a distance of 677 meters to distinguish nearby light
sources (such as Cherenkov light from particle showers in the Earth’s
atmosphere) from astrophysical sources at large distances. In parallel to this
deployment, we present results obtained during four nights of simultaneous
observations with the four 12-meter VERITAS gamma-ray telescopes and two
PANOSETI telescopes at the Fred Lawrence Whipple Observatory. We report
PANOSETI’s first detection of astrophysical gamma rays, comprising three
events with energies in the range between $\thicksim$15 TeV and $\thicksim$50
TeV. These were emitted by the Crab Nebula, and identified as gamma rays using
joint VERITAS observations.

###### keywords:

Nanosecond timing, transients, Cherenkov shower, Cosmic Rays, Gamma Rays, SETI

##  1 INTRODUCTION

Astronomical instruments with precision time resolution can be employed to
search for technosignatures by means of detecting nano- to milli-second light
pulses that could be emitted, for instance, for the purpose of interstellar
communications or energy transfer. Numerous programs have been conducted to
search for technosignatures using optical wavelengths [1, 2, 3, 4, 5, 6, 7]
including near-infrared[8]. With 0.32 sq.deg. of instantaneous field-of-view,
the first optical SETI all-sky surveys[9, 10] used a transit observing
approach to cover the sky in 150 clear nights. To efficiently and constantly
survey the entire sky, however, groups of single-aperture telescopes capable
of collecting light instantaneously from different parts of the sky are still
required.

The Panoramic SETI experiment (PANOSETI[11, 12, 13, 14]) is an all-sky
observatory project aiming to detect transients that will cover a wide range
of timescales in a search for nanosecond to second pulsed light signals across
all optical wavelengths. Each part of the sky is observed simultaneously from
two locations for direct detection and confirmation of transients. Based upon
two assemblies of twenty-four 0.46-m Fresnel-lens telescopes equipped with
fast, low-noise silicon photo-multipliers operating in the 0.32–0.85 $\mu$m
spectral range, the PANOSETI instrument can detect flashes of light that could
have been sent from kiloparsec distances and beamed toward our direction. For
instance, a pulse of light emitted by a laser delivering 20 ns shaped pulses
of megajoule energies and collimated with a 10-m telescope located at 1 kpc
away from us would be orders of magnitude brighter than the entire broadband
visible stellar background from our perspective[4].

The small aperture, wide field-of-view, and low cost of the PANOSETI
telescopes also make them potentially well-suited for gamma-ray astronomy at
the highest energies. When a high-energy gamma-ray photon or cosmic-ray
particle enters the Earth’s atmosphere, it initiates a particle cascade which
can be detected via the pulse of blue Cherenkov radiation it emits. Ground-
based gamma-ray telescopes exploit this effect, using large aperture ($>$ 10
m) mirrors and fast photo-detector cameras to record Cherenkov images of the
showers. Subsequent analysis of these images allows determination of the
nature of the primary (photon or particle), its arrival direction and its
energy. The energy range covered by this technique is typically from around
100 GeV to tens of TeV, reaching 100 TeV only for the brightest sources.
Hundreds of astrophysical gamma-ray sources, both Galactic and extragalactic,
have been identified using this technique111http://tevcat.uchicago.edu/. At
higher energies, direct detection of the shower particles is used; recent
results from the LHAASO collaboration have revealed the existence of gamma-ray
emitters extending up to PeV energies[15]. The main benefit of this particle
detection approach is that it provides the large effective area ($>$1 km2)
which is required to measure the extremely low flux of gamma rays at the
highest energy. Cherenkov telescopes can operate in the same energy range, but
are typically non-imaging, to reduce unit cost (e.g., TAIGA-HiScore [16]). The
unit cost of PANOSETI telescopes is approximately two orders of magnitude less
than a large aperture imaging Cherenkov telescope, such as those that make up
the VERITAS array. A large array of low-cost PANOSETI telescopes, each
equipped with a 1,000 pixel camera, would permit applying the benefits of the
imaging technique (excellent angular resolution, energy resolution and
background discrimination) to the PeV regime.

We describe in Sect.2 the recent deployment of a third PANOSETI telescope at
Lick Observatory providing a baseline separation of 677-m. We report in Sect.3
results obtained from joint PANOSETI and VERITAS observations at the Whipple
Observatory in November 2021.

##  2 PANOSETI: Program Update

The PANOSETI experiment aims to observe 2,350 square degrees instantaneously
by making use of multiple large field-of-view telescopes. PANOSETI is
currently in its final design phase, and at final production two dedicated
observatories will house 24 telescopes per site. Each part of the sky is
observed simultaneously from two locations for direct detection and
confirmation of optical transients.

Each telescope is equipped with a 0.46-m f$/$1.32 Fresnel-lens which focuses
the light onto a 32x32-pixel photon-counting detector subdivided into 16
adjacent 8x8-pixel Multi-Pixel Photon Counter (Hamamatsu S13361-3050AE-08)
detector arrays operating in the 0.32 - 0.85 $\mu$m spectral range. These
silicon photomultipliers (SiPMs) are comprised of Geiger-mode-operated
avalanche photodiodes highly linear in pulse intensity, with a high internal
gain to enable single-photon detection while featuring low dark count ($<$1
Mcps per SiPM), high photon detection efficiency in the visible (45%), and
nanosecond timing resolution. Each PANOSETI detector contains four custom
readout boards that make use of a 64-bit Application-Specific Integrated
Circuit (ASIC) capable of pulse shaping and trigger detection of individual
pulses. Each detector board reads four 8x8 pixel SiPM arrays that feed four
ASICs, amplifying the signal and delivering a per-pixel trigger signal to a
Field Programmable Gate Array (FPGA). A 1 Gb per second fiber connection
provides data communications to a central multi-telescope 10 Gb network switch
connected to a central computer.

For timing synchronicity between telescopes, PANOSETI makes use of a White
Rabbit[17, 18] precision time distribution system that provides 1 ns time-
stamping for each triggered event. The PANOSETI White Rabbit system is
connected to a GPS receiver for absolute timing synchronicity.

To detect optical pulse widths ranging from nanoseconds to seconds, each
telescope’s custom readout electronics system is configured to support two
different operational modes that can be run simultaneously[18]. The Pulse
Height mode starts data acquisition when any pixel signal exceeds a
programmable threshold, triggering the readout of the entire quadrant board.
These events are then cross-correlated in time with the other telescopes to
check for any other triggered events. This Pulse Height mode has been standard
for previous optical SETI experiments [19, 1, 2, 4, 20] and is optimized for
detecting pulse widths shorter than $\thicksim$100 ns. Concurrently, the
Continuous Imaging Mode employs counters on every pixel that count over-
threshold events to produce images at a programmable frame rate. This mode is
used for detecting transients above 20 microsecond duration. Over-threshold
events for each pixel are counted by the FPGA and all pixel counters are sent
over ethernet to the central computer system, along with an accurate time
stamp for each frame.

Our team is conducting laboratory and on-sky verification of our prototype
telescopes and focal plane electronics. To verify our White Rabbit timing
system and false alarm rates, we first installed two prototype units at Lick
Observatory at a single site to measure on-sky performance.

During prototyping efforts, the deployment of pairs of telescopes has proven
the benefit of using long baselines to minimize the number of false alarms by
providing enough parallax to easily distinguish nearby transient light sources
(such as Cherenkov showers) from astrophysical sources at large distances
[21]. Therefore, a third telescope was deployed at Lick Observatory in June
2022 at a distance of 677 meters from the first site (Fig.1). It uses a 2.2-m
diameter remote-controlled dome and is fiber-connected to the first site for
data transfer, remote operations, and time synchronization.

Figure 1: LEFT: Two PANOSETI telescopes were deployed inside the Astrograph
dome at Lick Observatory. RIGHT: The deployment of a third telescope at a
distance of 677 m from the Astrograph confirmed the benefit of using a long
baseline to identify and characterize transient phenomena (Left and right
photos: courtesy Laurie Hatch Photography).

##  3 PANOSETI Coordinated Observations with VERITAS

###  3.1 Experimental setup

The PANOSETI team performed four nights of simultaneous observations with the
four 12-meter VERITAS gamma-ray telescopes [22] at the Fred Lawrence Whipple
Observatory on November 4-7 2021. Coordinated observations of CALIPSO (LIDAR
satellite), the Crab Nebula, and other potential sources of gamma rays were
performed. During the first two nights of coordinated observations, the two
PANOSETI telescopes were located beside the VERITAS T4 telescope (Fig.2). For
the last two nights, one of the PANOSETI telescopes was moved beside the
VERITAS T2 telescope at a distance of 127 meters from the other PANOSETI
telescope. Two long fibers were temporarily deployed for data communications
between the telescopes, the host computer and the White Rabbit switch.

During all of the coordinated observations, the PANOSETI telescopes were
aligned to the same celestial coordinates targeted by the four VERITAS
telescopes and made use of Celestron CGX equatorial mounts for tracking. The
PANOSETI telescope backends were also aligned in position angle.

Figure 2: Pictured here are two PANOSETI 0.5-m telescopes with one of the 12-m
VERITAS telescopes (T4) in the background. Joint observations were performed
in November 2021.

###  3.2 Data & Analysis

During joint observations, the PANOSETI detectors were set in Pulse Height
mode to detect and record events above a minimum threshold of 11.5 photo-
electrons in a single pixel. All PANOSETI triggered events recorded during the
Crab Nebula observations were cross-correlated in time with the VERITAS
detected events to search for matched events. The same procedure was used to
search for events coincident in time between PANOSETI telescopes that occurred
within the same 100 ns time window, after correcting for time delays due to
the separation and pointing directions of the telescopes.

Each PANOSETI shower image is parameterized using the Hillas method [23] to
characterize the morphology of the shower (such as the direction of shower
main axis, the length and the width of the apparent shower, and its asymmetry,
as illustrated on Figure 3-left). A direct comparison of Hillas parameter
values with those obtained with VERITAS data is then possible. A subsequent
step of intersecting the shower main axes measured by the two separated
PANOSETI telescopes allows us to determine the arrival direction of the hadron
or gamma ray that induced the Cherenkov shower (Figure 3-right), as performed
routinely with data from the four VERITAS telescopes[23]. The PANOSETI
127-meter-long baseline used during the Crab Nebula observations introduces a
small but measurable parallax angle for flashes of light generated in Earth’s
atmosphere ($\thicksim$0.2∘ to $\thicksim$0.8∘, i.e., $\thicksim$0.7 to
$\thicksim$2.6 pixels for a shower maximum at 10 km above sea level and above
30∘ elevation).

Figure 3: Examples of hadron-induced showers imaged with PANOSETI along with
Hillas parameters (left panel). The intersection of shower main axes measured
on the two telescopes determines the arrival direction (right panel).

###  3.3 Results

####  3.3.1 Timing synchronicity between VERITAS and PANOSETI

Joint observations of the NASA/CNES Cloud-Aerosol Lidar and Infrared
Pathfinder Satellite (CALIPSO), which sends repeated 20 ns optical pulses at a
frequency of 20.16Hz, were performed at the Fred Lawrence Whipple Observatory
on November 6 2021. CALIPSO was observed during culmination at 80.6∘
elevation. VERITAS T4 telescope detected $\thicksim$75 pulses, with an average
pulse intensity of $\thicksim$4000 photo-electrons. PANOSETI detected 180
pulses with peak intensities between $\thicksim$20 and 50 photo-electrons,
depending on how the satellite was centered on the pixels.

Figure 4 shows the difference in time between successive timestamps obtained
during joint observations of CALIPSO. These simultaneous measurements of the
CALIPSO pulses confirmed the sub-microsecond synchronicity between PANOSETI
and VERITAS telescopes.

Figure 4: (top-left) CALIPSO optical pulse as seen by one of the PANOSETI
telescopes at the Whipple Observatory on November 6 2021. The figure shows the
difference in time between successive pulses obtained with one of the PANOSETI
telescopes and the closest VERITAS telescope (T4), showing sub-microsecond
synchronicity between the VERITAS and PANOSETI telescopes. PANOSETI was some
120km distant from CALIPSO’s 70m footrpint: glints from the latter’s aperture
were adequate for 100% detection.

####  3.3.2 PANOSETI gamma-ray detections

The night of November 7 2021 was dedicated to joint observations of the Crab
Nebula, during which one of the PANOSETI telescopes was deployed beside the
VERITAS T4 telescope. The PANOSETI telescope hardware was fiber-connected for
data transfer and timing synchronicity to the other PANOSETI telescope, itself
deployed beside the VERITAS T2 telescope.

During 6.2 hours of continuous observations, 10,540 Cherenkov showers (largely
cosmic-ray background) observed with PANOSETI were confirmed by VERITAS. Among
these, 3,122 showers were detected by the two PANOSETI telescopes set to
detect events above 11.5 photo-electrons. Three of these showers were
identified by the four VERITAS telescopes as being very likely due to gamma
rays emitted by the Crab Nebula (Fig. 5). The reconstructed energy of the
primary gamma rays obtained from the VERITAS data analysis was 15, 36 and 51
TeV.

Figure 5: One of the three Cherenkov showers that were induced by gamma rays
emitted from the Crab Nebula, imaged by one of the PANOSETI detector quadrants
(left) and VERITAS T4 telescope (right). This event was identified by the four
VERITAS telescopes to be a gamma ray with an energy of 15 TeV. Detected within
the same microsecond, the length, width and orientation of the shower measured
by the Hillas parameters are in excellent agreement between the two
instruments.

##  4 Summary

We have demonstrated that PANOSETI has the potential to explore the extreme
Universe through the detection of ultra-high-energy gamma rays. PANOSETI is
capable of detecting gamma rays (and cosmic rays) with energies greater than
$\thicksim$10 TeV. PANOSETI’s first detection of gamma rays emitted from the
Crab Nebula was confirmed by joint VERITAS observations.

With its 677-m long-baseline prototype telescopes recently deployed at Lick
Observatory, PANOSETI is entering its final design phase with on-sky
characterizations. At final production, two dedicated observatories will house
24 telescopes per site. New pairs of telescopes will be added progressively to
increase the size of its total instantaneous field-of-view, for an all-sky
search of optical technosignatures and astrophysical transients.

###### Acknowledgements.

We would like to acknowledge the Lick Observatory staff and engineers for
their help in the installation of the PANOSETI telescopes into the Astrograph
dome and Barnard sites. We also thank the VERITAS Collaboration and the
technical support staff at the Fred Lawrence Whipple Observatory for their
cooperation in obtaining joint observations and for the use of their data. We
thank the Bloomfield Family Foundation for supporting SETI research at UC San
Diego in the CASS Optical and Infrared Laboratory. We also thank Franklin
Antonio for valuable comments and support. Harvard SETI was supported by The
Planetary Society and The Bosack/Kruger Charitable Foundation. UC Berkeley’s
SETI efforts involved with PANOSETI are supported by NSF grant 1407804, the
Breakthrough Prize Foundation, and the Marilyn and Watson Alberts SETI Chair
fund.

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