# Unsupervised Domain Adaptation from Synthetic to Real Images for Anchorless
Object Detection

Tobias Scheck  , Ana Perez Grassi  , Gangolf Hirtz
Faculty of Electrical Engineering and Information Technology, Chemnitz
University of Technology, Germany
<EMAIL_ADDRESS>
https://orcid.org/0000-0002-1829-0996 https://orcid.org/0000-0003-1171-903X
https://orcid.org/0000-0002-4393-5354

###### Abstract

Synthetic images are one of the most promising solutions to avoid high costs
associated with generating annotated datasets to train supervised
convolutional neural networks (CNN). However, to allow networks to generalize
knowledge from synthetic to real images, domain adaptation methods are
necessary. This paper implements unsupervised domain adaptation (UDA) methods
on an anchorless object detector. Given their good performance, anchorless
detectors are increasingly attracting attention in the field of object
detection. While their results are comparable to the well-established anchor-
based methods, anchorless detectors are considerably faster. In our work, we
use CenterNet, one of the most recent anchorless architectures, for a domain
adaptation problem involving synthetic images. Taking advantage of the
architecture of anchorless detectors, we propose to adjust two UDA methods,
viz., entropy minimization and maximum squares loss, originally developed for
segmentation, to object detection. Our results show that the proposed UDA
methods can increase the mAP from $61\,\%$ to $69\,\%$ with respect to direct
transfer on the considered anchorless detector. The code is available:
https://github.com/scheckmedia/centernet-uda.

##  1 INTRODUCTION

Object detection, which involves both locating and classifying objects in an
image, is one of the most challenging tasks in computer vision. Its difficulty
depends on the application, which can go from highly controlled environments
with few well-known objects, such as in industrial tasks, to extremely complex
and dynamic environments with a large number of varying objects, such as
outdoor traffic scenes.

With the advent of CNNs (Convolutional Neural Networks), object detection has
undergone a breakthrough. In this context, two important lines of work have
recently appeared. The first one aims to find the most efficient way to
represent objects in order to train CNNs. This has led to the development of
keypoint-based (i.e., anchorless) detectors. The second line of work addresses
the increasing need of using training datasets that differ in nature from
those of the real applications. This is due to the great cost in generating
and labeling large datasets from the real setting. However, using such
datasets that are different in nature to the intended ones poses the problem
of domain adaption, i.e., translating from one dataset to the other.

Currently, object detection with CNNs is strongly dominated by anchor-based
methods. These methods involve very popular architectures like SSD [Fu et al.,
2017], YOLO [Redmon and Farhadi, 2016], R-FCN [Dai et al., 2016], RetinaNet
[Lin et al., 2017] and Faster R-CNN [Ren et al., 2017]. To recognize an
object, these networks generate thousands of RoIs (Regions of Interest) with
different positions, sizes and shapes classifying them individually. The
number of RoIs is a design parameter and equal for all images, independent of
the real number of objects that actually appear in them. During the training,
those RoIs that have a IoU (Interception over Union) value higher than 50%
with respect to the given ground truth are considered positives, while the
rest are considered background.

On the one hand, the number of RoIs must be kept high in order to find all
possible objects in the images. On the other hand, this results in an
imbalance, since normally the number of RoIs containing background will be
much higher than those with objects. Moreover, anchor-based methods require a
careful design, where the adequate number, size, and shape of the RoIs depend
on the application.

These disadvantages have motivated researchers to investigate on new
architectures for object detectors. With Cornernet [Law and Deng, 2020], a new
generation of anchorless detectors has started to attract attention. In this
case, objects are represented by keypoints. The number of keypoints in one
image is directly proportional to the number of objects on it. By eliminating
the need of a fix and large amount of RoIs, design and imbalance issues are
not only overcome, but also the resulting efficiency increases. Our work is
based on CenterNet [Zhou et al., 2019a], an anchorless architecture that
represents objects using their center point and a dimension vector.

In practice, a big problem that all object detectors, anchor-based and
anchorless, face, is the lack of sufficient labeled data to train the
networks. Not only capturing enough images, but specially labeling them, is
very expensive. For this reason synthetic images has been gaining in
importance in this field. Game engines allow not only generating synthetic
images with realistic shapes, textures and movements, but they also label them
automatically. This way, CNNs can be trained on synthetic images to later work
as detector on real ones. In this case, synthetic images are the source domain
and real images are the target domain.

However, the difference between real and synthetic images is still a problem
for CNNs, which cannot generalize well from one domain to the other. Different
techniques has been developed to reduce the gap between target and source
domain. In this work, we are interested in UDA (Unsupervised Domain
Adaptation) methods, which use unlabeled target images to bring source and
target domain closer. We focus on two UDA techniques: entropy minimization [Vu
et al., 2019] and maximum squares loss [Chen et al., 2019]. These techniques
were developed for segmentation applications, however, given the architecture
of anchorless detectors, they can be easily adapted to object detection as
shown in this paper.

Contributions. This paper aims to translate UDA techniques from segmentation
to object detection by taking advantage of the architecture of anchorless
detectors. We first test how an anchorless architecture, viz., CenterNet,
generalizes from synthetic images to real ones and we compare its performance
with anchor-based methods. Then, we extend its architecture to consider UDA by
entropy minimization and maximum squares loss. We show that UDA methods can
improve the performance of anchorless detectors trained on synthetic images.
To our best knowledge, this is the first work to test the anchorless detector
CenterNet with synthetic images and extending it to consider UDA methods.

##  2 STATE OF THE ART

With the advent of deep learning, object recognition have reached new state-
of-the-art performances. In the last years, architectures based on anchor
boxes, like SSD [Fu et al., 2017], YOLO [Redmon and Farhadi, 2016], R-FCN [Dai
et al., 2016], RetinaNet [Lin et al., 2017] and Faster R-CNN [Ren et al.,
2017], have dominated this field.

Despite of their success, the use of anchor boxes has considerable drawbacks.
Firstly, a large number of boxes is necessary to ensure enough overlap with
the ground truth. This latter results in much more negative than positive
sample, i.e., in an unbalanced dataset impairing the training process.
Secondly, the characteristics and number of the anchor boxes should be
designed carefully and are normally customized for a given dataset or
application.

To overcome these disadvantages, a new generation of anchorless detectors have
been developed in the last year. These detectors abandon the concept of anchor
boxes and instead localize objects based on keypoints. The pioneering
anchorless detector is CornerNet [Law and Deng, 2020] – note that this paper
was available online since 2019. CornerNet describes an object as a pair of
keypoints given by the top-left and bottom-right corners of a bounding box. In
[Duan et al., 2019], an extension of CornerNet is proposed by adding the
center of the bounding box as keypoint. In [Zhou et al., 2019b], ExtremeNet is
presented where keypoints are given by objects’ extreme point. Extreme points
have the advantage over corners of being always part of the object, without
being affecting by background information.

All the aforementioned detectors use more than one keypoint, therefore, it is
necessary to group them in order to perform a detection on their basis. For
this reason, they are called point-grouping detectors [Duan et al., 2020a]. On
the other hand, detectors called point-vector detectors [Duan et al., 2020a]
are based on only one keypoint and a vector containing geometrical
characteristics of the object, like width, height, etc. In this category, we
can enumerate CenterNet [Zhou et al., 2019a], FoveaBox [Kong et al., 2020] and
FCO [Tian et al., 2019].

Our work uses CenterNet as defined in [Zhou et al., 2019a]. CenterNet models
objects using their center point, which are detected from a CNN-generated
heatmap. From this keypoint, CenterNet is able to regress other object
properties, such as size, 3D location, orientation and pose. As CenterNet only
uses one keypoint, it does not need any grouping stage, which makes it faster
than the point-grouping detectors. Zhou et al. present four architectures for
CenterNet using ResNet18, ResNet101 [He et al., 2015], DLA34 [Yu et al., 2019]
and Hourglass-104 [Newell et al., 2016] as feature extractors.

All the mentioned works have been tested on real image datasets, specifically,
most of them use COCO dataset [Lin et al., 2014]. One of the most important
disadvantages of supervised neural networks is their strong dependence on the
quantity and variety of the training images as well as the quality of their
labels. This is one of the highest hurdle when implementing CNNs in real
applications, since generating such a training dataset is associated with a
huge effort and high costs.

To overcome this predicament, the use of synthetic images have been attracting
attention in the last years. Modern game platforms allow generating
photorealistic images, introducing variations and modifications with less
effort. In addition, images are labeled automatically and without error, which
reduces efforts even more.

In the area of autonomous driving, for example, synthetic dataset like GTA 5
[Richter et al., 2016] and SYNTHIA [Ros et al., 2016] are used to train neural
networks for detection and segmentation tasks. For indoor applications, the
dataset SceneNet [McCormac et al., 2017] and SceneNN [Hua et al., 2016]
present segmentation masks, depth maps and point clouds of unoccupied rooms
with different furniture. SURREAL [Varol et al., 2017] combine real
backgrounds and synthetic persons for human segmentation and depth estimation.
THEODORE [Scheck et al., 2020] presents different indoor scenes from a top-
view using an omnidirectional camera.

Despite the good quality of some synthetic images, their difference with real
images, called reality gap, constitutes a problem for neural networks. Tested
on real images, neural networks trained with synthetic images have a worse
result than those trained with real ones. This problem has motivated an
increasing investigation in the area of unsupervised domain adaptation (UDA)
for synthetic images. In this case, synthetic and real images constitute the
source and target domain respectively.

UDA methods use unlabeled real images during the training to approximate the
source domain to the target domain and therefore to minimize the reality gap.
In [Li et al., 2020] a survey of deep domain adaptation for object recognition
is presented. All works mentioned in [Li et al., 2020] involve, however,
anchor-based architectures. In this paper, in contrast to this, we are
concerned with anchorless approaches.

For a segmentation task, Vu et al. showed that models trained on only the
source domain tend to produce low-entropy predictions on source-like (i.e.,
synthetic) images and high-entropy predictions on target-like (i.e., real)
images [Vu et al., 2019]. Based on this, the authors propose two methods using
entropy minimization (EM) to adapt from the synthetic to the real domain. One
method minimizes the entropy indirectly by adversarial loss and the second one
does it directly by entropy loss. These methods are mainly applied on image
segmentation, but also tested on object detection. For object detection, the
authors use a modified version of a SSD-300 [Liu et al., 2016]. Further, in
[Chen et al., 2019], Chen et al. observe that the gradient’s magnitude of the
entropy loss disproportionately favors those classes detected with high
probability. This latter results in a training process dominated by those
object classes that actually are easier to transfer from one domain to other.
To counteract this effect, Chen et al. propose to replace the entropy
minimization by the maximum squares loss (MSL) [Chen et al., 2019]. In this
work, we adapt and compare these two UDA methods on anchorless detectors, more
specifically on CenterNet. In contrast to using SSD-300 as proposed in [Vu et
al., 2019], incorporating EM and also MSL in CenterNet can be done without
altering its architecture.

##  3 BACKGROUND

In this section, we firstly introduce the detector CenterNet and both
previously mentioned UDA methods, i.e., EM and MSL. We then extend CenterNet’s
architecture to consider these UDA methods, in order to obtain an anchorless
detector with domain adaptation between synthetic and real images.

###  3.1 CenterNet

CenterNet is an anchorless object detector describing objects as points, which
was introduced in [Zhou et al., 2019a]. More specifically, CenterNet
identifies each object using only the center point of its RoI. Then, to
regress the object size, CenterNet uses a vector with the RoIs’ widths and
heights.

Let $\mathcal{C}=\\{1,\dots,C\\}$ be the set of all $C$ object classes to be
detected. The training dataset $\mathcal{T}$ is given by labeled images
$\mathbf{x}_{i}$ of size $H\times W\times 3$, with $1\leq i\leq|\mathcal{T}|$.
Each object in an image $\mathbf{x}_{i}$ is annotated with a surrounding RoI
and a class $c\in\mathcal{C}$, which together constitute the ground truth.

To train CenterNet, the ground truth should be converted from RoIs to
heatmaps. To this aim, for each image $\mathbf{x}_{i}\in\mathcal{T}$, a
keypoint map $K(x,y,c)$ of size $H\times W\times C$, is generated by
extracting the center points of each annotated RoIs. $K(x,y,c)$ is equal one,
only if the coordinates $(x,y)$ in $\mathbf{x}_{i}$ belong to an object’s
center of class $c$ and equal zero elsewhere. Through convolution with a Gauss
kernel, whose standard deviation is a function of the object’s size, the
keypoint map $K(x,y,c)$ is expanded to form a heatmap. The size of this
heatmap is then modified to agree with the size of the network’s output by
using a factor $R$. The final heatmap of an image $\mathbf{x}_{i}$ is denoted
by $Y(x,y,c)\in[1,0]$ and have a size of $\frac{W}{R}\times\frac{H}{R}\times
C$. The training dataset $\mathcal{T}$ and the corresponding set of heatmaps
$Y(x,y,c)$ (new ground truth) are used together with a focal loss function
$L_{h}$ [Lin et al., 2017] to train the network in order to predict new
heatmaps $\hat{Y}(x,y,c)$.

The down-sampling of the heatmap using $R$ produces a discretization error at
the location of the objects’ center. To correct this error, CenterNet also
provides an output $\hat{O}\in\mathbb{R}^{\frac{W}{R}\times\frac{H}{R}\times
2}$ with the predicted offset. Additionally, for each detected object’s
center, CenterNet also regresses the object’s size, in order to reconstruct
its RoI. The predicted size is given by the output
$\hat{S}\in\mathbb{R}^{\frac{W}{R}\times\frac{H}{R}\times 2}$. Offset and size
outputs are trained using the $L_{1}$-loss functions $L_{off}$ and $L_{size}$
respectively. Finally, the linear combination of all loss functions, i.e.,
from heatmap, offset and size gives the complete detection loss function of
CenterNet,where $\lambda_{h}$, $\lambda_{size}$ and $\lambda_{off}$ are scale
factors [Zhou et al., 2019a]:

$\displaystyle
L_{det}(\mathbf{x}_{i})=\lambda_{h}L_{h}+\lambda_{size}L_{size}+\lambda_{off}L_{off}.$
(1)

At inference time, the 100 highest peaks inside $8$ neighborhoods are
extracted from the predicted heatmap $\hat{Y}(x,y,c)$. The coordinates of each
of these peaks may indicate the center of a detected object. The probability
of each detection, given by the corresponding value of $\hat{Y}(x,y,c)$, is
used as a threshold to validate the detection. To reconstruct the RoIs of the
detected objects, the coordinates of the center points in $\hat{Y}(x,y,c)$ are
corrected using $\hat{O}(x,y)$, while the width and height dimensions are
extracted from $\hat{S}(x,y)$.

The architecture of CenterNet consists of one feature extractor followed by
three heads, one for each of the described outputs: heatmap head, offset head
and size head. In [Zhou et al., 2019a], four architectures are presented as
feature extractor: ResNet18, ResNet101 [He et al., 2015], DLA34 [Yu et al.,
2019] and Hourglass-104 [Newell et al., 2016]. We choose ResNet101 and DLA34
for our experiments because they present the best trade-off between accuracy
and runtime [Zhou et al., 2019a].

Figure 1: Heatmap and entropy map for $c=0$ (person) from source (i.e.,
synthetic) and target (i.e., real) image. Note: For ease of exposition, the
entropy map is shown only for one class.

###  3.2 UDA by Entropy Minimization

In [Vu et al., 2019] it was shown that segmentation models trained only with
synthetic images (source domain) tends to produce low-entropy predictions on
other synthetic images, but high-entropy predictions on real images (target
images). Based on this observation, it is possible to reduce the gap between
synthetic and real images by enforcing low-entropy predictions on real images.

In a segmentation architecture, the prediction output for one image
$\mathbf{x}_{i}$ consists of a segmentation map
$\hat{P}(x,y,c)\in[0,1]^{H\times W\times C}$, where the most probable class
for each pixel $(x,y)$ is given by the maximum value on the third dimension
$c$. An entropy map can be calculated from $\hat{P}(x,y,c)$ as follows:

$\displaystyle\hat{E}_{\mathbf{x}_{i}}(x,y)=\frac{-1}{\log(C)}\sum_{c=1}^{C}{\hat{P}(x,y,c)\log\hat{P}(x,y,c)}.$
(2)

An entropy loss function for an image $\mathbf{x}_{i}$ can then be defined by
adding all values of its entropy map [Vu et al., 2019]:

$\displaystyle L_{ent}(\mathbf{x}_{i})=\frac{1}{WH}\sum_{x,y}\hat{E}(x,y),$
(3)

where $W$ and $H$ are width and height dimensions of $\hat{E}(x,y)$.

The network is then training with labeled synthetic images to minimize some
segmentation loss and with unlabeled real images to minimize this entropy loss
(see Eq. (3)). In this way, the network is trained to learn the object
segmentation from the synthetic images and, at the same time, it is forced to
keep a low entropy on real images.

###  3.3 UDA by Maximum Squares Loss

Chen et al. [Chen et al., 2019] have observed that the gradient’s magnitude of
$L_{ent}$ (Eq. (3)) increases almost linearly with $\hat{P}(x,y,c)$ until a
probability value of $\approx 0.85$ and then it grows up faster tending to
infinity for a probability value of one. As a consequence, those classes $c$
that are predicted with high probability values, dominate the training
process. However, these classes – detected with high probability – are
normally the classes that are easy to transfer. Chen et al. give this problem
the name of probability imbalance. As a solution, they propose to change the
entropy loss function by the maximum squares loss (MSL) defined as:

$\displaystyle
L_{ms}(\mathbf{x}_{i})=-\frac{1}{WH}\sum_{c}\sum_{x,y}(\hat{P}(x,y,c))^{2},$
(4)

where $W$ and $H$ are width and height dimensions of $\hat{P}(x,y,c)$.

$L_{ms}$ has a linearly increasing gradient over the entire range of
$\hat{P}(x,y,c)$ values. By segmentation tasks, this latter prevents high
confidence areas from producing excessive gradients. Of course, these areas
still have larger gradients than those with lower confidence, but their
dominance is reduced in favor of the areas with lower probability and,
therefore, in favor of those classes that are more difficult to transfer.

##  4 UDA METHODS FOR CENTERNET

The UDA methods described in previous sections 3.2 and 3.3 were mainly
developed for segmentation architectures. In this work, we propose to extend
these UDA methods to the anchorless detector CenterNet. Our approach is based
on the similarity between a segmentation map $\hat{P}(x,y,c)$ and the heatmap
$\hat{Y}(x,y,c)$, as given by CenterNet.

###  4.1 EM-Extended CenterNet

Figure 2: Extended CenterNet architecture including unsupervised domain
adaptation.

Equation 2 can be used to calculate the entropy map from the CenterNet’s
heatmap instead from a segmentation map. The information contained in both
maps is, however, different. A segmentation map $\hat{P}(x,y,c)$ shows the
probability that a pixel $(x,y)$ belongs to a particular object class $c$. The
heatmap $\hat{Y}(x,y,c)$ shows the probability of pixel $(x,y)$ to be the
center of an object of class $c$. Nevertheless, a tendency to produce low-
entropy and high-entropy predictions on respectively the source and the target
domain, can also be observed with CenterNet. Figure 1 shows the heatmaps
generated by CenterNet and their calculated entropy maps for a synthetic and a
real image with class $c$ corresponding to persons. The center points detected
on the synthetic image are more defined than those on real one, and therefore
present a lower entropy.

To introduce entropy minimization (EM) for domain adaptation into CenterNet,
we first need to calculate the entropy map $\hat{E}(x,y)$ from the heatmap
$\hat{Y}(x,y,c)$ and then to include the entropy loss, as defined in Eq. (3).
To calculate the entropy from the heatmap $\hat{Y}(x,y,c)$, predicted by
CenterNet, this one must first go through a Softmax function. The resulting
heatmap $Y^{\prime}(x,y,c)=\text{Softmax}\\{\hat{Y}(x,y,c)\\}$ ensures that
the sum of all center predictions along the dimension $c$ is equal to one. The
calculation of the entropy map for an image $\mathbf{x}_{i}$ is then performed
by replacing the segmentation map $\hat{P}(x,y,c)$ with $Y^{\prime}(x,y,c)$ in
Eq. (2) leading to:

$\displaystyle\hat{E}_{\mathbf{x}_{i}}(x,y)=\frac{-1}{\log(C)}\sum_{c=1}^{C}{Y^{\prime}(x,y,c)\log
Y^{\prime}(x,y,c)}.$ (5)

Figure 2 shows a schematic of the extended CenterNet architecture including
EM. The training set contains now a set of labeled synthetic images and a set
of unlabeled real images. The labeled synthetic images follow the blue
continuous lines to contribute to the detection loss function $L_{det}$ (Eq.
(1)). The real images, on the other hand, follow the red discontinuous lines
going only through the heatmap head and contributing to the entropy loss
function $L_{ent}$ as defined in Eq. (3) times a scale factor $\lambda_{ent}$.
The final loss function for a given training image $\mathbf{x}_{i}$ is given
by:

$\displaystyle
L(\mathbf{x}_{i})=L_{det}(\mathbf{x}_{i})+\lambda_{ent}L_{ent}(\mathbf{x}_{i}).$
(6)

###  4.2 MSL-Exteded CenterNet

To implement the maximum squares loss (MSL) in CenterNet, it is only necessary
to replace $\hat{P}(x,y,c)$ in Eq. (4) by
$Y^{\prime}(x,y,c)=\text{Softmax}\\{\hat{Y}(x,y,c)\\}$ obtaining:

$\displaystyle
L_{ms}(\mathbf{x}_{i})=-\frac{R}{WH}\sum_{c}\sum_{x,y}(Y^{\prime}(x,y,c))^{2}.$
(7)

Figure 2 also shows the extended CenterNet architecture including MSL. Similar
to EM, the labeled synthetic images contribute to the detection loss function
$L_{det}$ (Eq. (1)) and the unlabeled real images to the maximum squares loss
as defined in Eq. (7) time a scale factor $\lambda_{ms}$.

The final loss function for a given training image $\mathbf{x}_{i}$ is then
given by:

$\displaystyle
L(\mathbf{x}_{i})=L_{det}(\mathbf{x}_{i})+\lambda_{ms}L_{ms}(\mathbf{x}_{i}).$
(8)

##  5 RESULTS

Table 1: Results for anchor-based methods, CenterNet and CenterNet with unsupervised domain adaptation. Class $AP_{50}$ | Armchair | Chair | Person | Table | TV | Walker | mAP
---|---|---|---|---|---|---|---
SSD [Scheck et al., 2020] | 0.021 | 0.231 | 0.904 | 0.824 | 0.545 | 0.623 | 0.525
R-FCN [Scheck et al., 2020] | 0.262 | 0.039 | 0.849 | 0.859 | 0.000 | 0.640 | 0.442
Faster R-CNN [Scheck et al., 2020] | 0.148 | 0.141 | 0.873 | 0.980 | 0.943 | 0.613 | 0.616
CenterNet/DLA34 | 0.162 | 0.451 | 0.886 | 0.949 | 0.940 | 0.656 | 0.674
CenterNet/ResNet101 | 0.261 | 0.148 | 0.818 | 0.839 | 0.933 | 0.682 | 0.613
CenterNet/DLA34 EM | 0.202 | 0.525 | 0.912 | 0.854 | 0.967 | 0.679 | 0.690
CenterNet/ResNet101 EM | 0.285 | 0.382 | 0.849 | 0.915 | 0.953 | 0.648 | 0.672
CenterNet/DLA34 MSL | 0.106 | 0.476 | 0.857 | 0.924 | 0.937 | 0.712 | 0.668
CenterNet/ResNet101 MSL | 0.244 | 0.490 | 0.867 | 0.873 | 0.990 | 0.676 | 0.690

###  5.1 Implementation

For our experiments we use three datasets of indoor scenes captured with a
top-view omnidirectional camera. This kind of datasets are tipical for AAL
(Ambient Assisted Living) applications and are a good example of the need and
advantages of synthetic images, since there is no dataset with a sufficiently
large number of real omnidirectional images and their corresponding labels for
AAL.

In this work, we use THEODORE [Scheck et al., 2020] as synthetic (source)
dataset, CEPDOF [Duan et al., 2020b] as unlabeled real (target) dataset and
finally FES (Fisheye Evaluation Suite)[Scheck et al., 2020] as test dataset.
THEODORE and FES are annotated datasets with $C=6$ object classes: armchair,
TV, table, chair, person and walker. THEODORE has 25,000 synthetic images,
whereas FES has 301 real images. On the other hand, CEPDOF was developed for
the network RAIPD [Duan et al., 2020b], which is designed for omnidirectional
images. CEPDOF has 25,000 annotated frames with rotated bounding boxes only
for the class person. In our work, we used CEPDOF for the unsupervised domain
adaptation such that these notations are neither required nor relevant. We
test with two feature extractors for CenterNet: ResNet101 and DLA34. For
DLA34, we include, as suggested in [Zhou et al., 2019a], deformable
convolutions before upsampling with transposed convolutions. In the case of
ResNet101, for convenience in the implementation, no deformable convolutions
are considered.

The input size $H\times W$ of CenterNet is $512\times 512$ pixels for training
and $800\times 800$ for testing. In addition, we use a scale factor $R=4$ that
gives a heatmap of $128\times 128\times 6$ pixels for training. The focal loss
function [Lin et al., 2017] used in CenterNet for the heatmap requires two
parameter $\alpha=2$ and $\beta=4$. The scale factors are selected as follows:
$\lambda_{h}=1$, $\lambda_{off}=1$, $\lambda_{size}=0.1$,
$\lambda_{ent}=0.0001$ and $\lambda_{ms}=0.3$. The threshold to validate a
peak as a detected center of an object is given by $\hat{Y}(x,y,c)\geq 0.1$.

For training we use Adam optimizer, learning rate 0.0001, weight decay 0.0001,
manual step decay at epoch 30 with a gamma factor of 0.1 and batch sizes of 16
for each domain. As augmentation techniques we use: flipping, rotation,
translation, scaling, cropping, motion blur, adding Gaussian noise and
changing hue and brightness.

Each experiment is repeated with and without a UDA method. The experiments
without UDA allow us to evaluate the performance of the anchorless network
CenterNet by direct transfer, i.e., when it is trained only with synthetic
images (THEODORE) and tested on real ones (FES). The results are compared with
those presented in (Scheck et al., 2020), which were obtained based on anchor-
based detectors on the same datasets that we use. Finally, all experiments are
repeated, but now incorporating the described UDA methods. This allows
evaluating whether the detection performance on real images improves by the
proposed approaches in this paper.

Figure 3:  The bars show, for both feature extractors, the mean values of the
heatmaps obtained from the ground truths (GT) and by applying direct transfer
(baseline) and the proposed UDA approaches. The GTs represent the optimal
heatmaps generated from the centers and size of the given bounded boxes.

###  5.2 CenterNet vs. Anchor-based Detectors without UDA

As mentioned above, our first experiment consists in comparing the performance
of (the anchorless) CenterNet and of anchor-based methods when applying direct
transfer.

The work in [Scheck et al., 2020] presents the results of three anchor-based
detectors, SSD [Fu et al., 2017], R-FCN [Dai et al., 2016] and Faster R-CNN
[Ren et al., 2017] by training on THEODORE and testing on FES. We repeat the
same experiments using CenterNet/DLA34 and CenterNet/ResNet101 without domain
adaption.

Table 1 shows the resulting Average Precision (AP) per class and the final
mean Average Precision (mAP) for each architecture. CenterNet/DLA34 presents a
improvement on the mAP value with respect to the anchor-based methods.
However, this improvement is specially dominated by the class chair. This
class together with the class armchair are underrepresented in THEODORE, where
they are both characterized by a single 3D-mesh, i.e., there is only one
armchair and one chair model in the entire dataset. As a consequence, these
two classes give in all experiments very low AP results. Although the results
of CenterNet and anchor-based methods are similar, the performance of the
first one is superior given its better time performance. Table 2 shows the
frames per second (FPS) achieved by testing with each architecture on a Nvidia
GeForce RTX 2080 TI and using an input resolution of $640\times 640$.
CenterNet is significantly faster than the anchor-based networks, achieving
with DLA34 almost twice as many FPS as SSD.

Table 2: Testing speed for the different architectures on a Nvidia GeForce RTX 2080 TI and using an input resolution of $640\times 640$. Architecture | FPS
---|---
SSD [Scheck et al., 2020] | 25
R-FCN [Scheck et al., 2020] | 22
Faster R-CNN [Scheck et al., 2020] | 24
CenterNet/DLA34 | 49
CenterNet/ResNet101 | 39

###  5.3 UDA-Extended CenterNet

As shown in Fig. 1, the heatmap $\hat{Y}(x,y,c)$ predicted by CenterNet via
direct transfer presents object’s centers, that are more defined or compact on
synthetic images (source images) than on real ones (target images). By
incorporating unlabeled real images during the training and extendeding
CenterNet to include the loss functions in Eq. (6) and Eq. (8), the network is
forced to increase the compactness of the detected centers also on real
images. This latter can be visualized by comparing the mean value of the
resulting heatmaps with and without domain adaptation. Figure 3 presents the
heatmaps’ mean values for CenterNet/DLA34 and CenterNet/ResNet101 for EM and
MSL as well as for direct transfer (Baseline). Additionally, also the mean
values of the ground truth (GT) heatmaps are shown for comparison. In the case
of CenterNet/ResNet101, both proposed methods, EM ad MSL, achieve a reduction
on the heatmaps’ mean values. For CenterNet/DLA34 only the EM method obtained
a lower mean value as the baseline.

Table 1 shows the average precision (AP) values for CenterNet/ResNet101 and
CenterNet/DLA34 after applying EM and MSL for all classes. We can observe
that, except for the class table, all other AP values have increased by
applying one of the proposed UDA methods. The best mAP result is given by
CenterNet/DLA34 EM and CenterNet/ResNet101 MSL, where the mAP values increase
with reference to the baseline from $0.674$ to $0.69$ and from $0.613$ to
$0.69$ respectively.

This means also an increment of the mAP value with respect to anchor-based
methods from $0.616$ (achieved by Faster R-CNN) to $0.69$ (achieved by
CenterNet/DLA34 EM and CenterNet/ResNet101 MSL). Moreover, as mentioned
before, CenterNet/DLA34 is almost twice as fast as all tested anchor-based
architectures (Table 2).

##  6 CONCLUSIONS

In this work, we extended two unsupervised domain adaptation (UDA) methods to
an anchorless object detector, viz., CenterNet. We consider omnidirectional
synthetic images as source domain and omnidirectional real images as target
domain. Taking advantage of the CenterNet’s architecture, we adapted two
segmentation UDA methods, namely, minimization entropy (EM) and maximum
squares loss (MSL), to the case of object detection. Our results show that the
performance of CenterNet obtained via direct transfer can be improved by
applying the proposed UDA methods. This latter validates the use of ME and MSL
in order to reduce the gap between source and target domain for object
detection in an anchorless architecture as illustrated for the case CenterNet.
The proposed method also enjoys the speed advantage of the anchorless
detectors, being up to twice as fast as the anchor-based methods. As future
work, we plan to test our UDA methods with other anchorless detectors,
adversarial approaches and including other image datasets.

## ACKNOWLEDGEMENTS

This work is funded by the European Regional Development Fund (ERDF) under the
grant number 100-241-945.

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# A sub-solar metallicity on the ultra-short period planet HIP 65Ab

Luc Bazinet Institut Trottier de Recherche sur les Exoplanètes, Département de
Physique, Université de Montréal, 1375 Avenue Thérèse-Lavoie-Roux, Montreal,
QC H2V 0B3, Canada  Stefan Pelletier Institut Trottier de Recherche sur les
Exoplanètes, Département de Physique, Université de Montréal, 1375 Avenue
Thérèse-Lavoie-Roux, Montreal, QC H2V 0B3, Canada  Observatoire astronomique
de l’Université de Genève, 51 chemin Pegasi 1290 Versoix, Switzerland  Björn
Benneke Institut Trottier de Recherche sur les Exoplanètes, Département de
Physique, Université de Montréal, 1375 Avenue Thérèse-Lavoie-Roux, Montreal,
QC H2V 0B3, Canada  Ricardo Salinas Departamento de Física y Astronomía,
Universidad de La Serena, Av. Juan Cisternas 1200 Norte, La Serena, Chile
Gregory N. Mace Department of Astronomy and McDonald Observatory, The
University of Texas at Austin, 2515 Speedway, Stop C1400, Austin, TX 78712,
USA

###### Abstract

Studying and understanding the physical and chemical processes that govern hot
Jupiters gives us insights on the formation of these giant planets. Having a
constraint on the molecular composition of their atmosphere can help us
pinpoint their evolution timeline. Namely, the metal enrichment and carbon-to-
oxygen ratio can give us information about where in the protoplanetary disk a
giant planet may have accreted its envelope, and subsequently, indicate if it
went through migration. Here we present the first analysis of the atmosphere
of the hot Jupiter HIP 65Ab. Using near-infrared high-resolution observations
from the IGRINS spectrograph, we detect H2O and CO absorption in the dayside
atmosphere of HIP 65Ab. Using a high-resolution retrieval framework, we find a
CO abundance of log(CO) = $-3.85^{+0.33}_{-0.36}$, which is slightly under
abundant with expectation from solar composition models. We also recover a low
water abundance of log(H2O) = $-4.42\pm{0.18}$, depleted by 1 order of
magnitude relative to a solar-like composition. Upper limits on the abundance
of all other relevant major carbon- and oxygen-bearing molecules are also
obtained. Overall, our results are consistent with a sub-stellar metallicity
but slightly elevated C/O. Such a composition may indicate that HIP 65Ab
accreted its envelope from beyond the water snowline and underwent a disk-free
migration to its current location. Alternatively, some of the oxygen on HIP
65Ab could be condensed out of the atmosphere, in which case the observed gas-
phase abundances would not reflect the true bulk envelope composition.

††software: Astropy (Robitaille et al., 2013; Collaboration et al., 2018,
2022), NumPy (Harris et al., 2020), SciPy (Virtanen et al., 2020), Matplotlib
(Hunter, 2007), emcee (Foreman-Mackey et al., 2013), corner (Foreman-Mackey,
2016)

##  1 Introduction

Hot Jupiters offer the best opportunity to study atmospheric properties of
exoplanets. With their large radius and elevated temperatures, they are ideal
for emission spectroscopy. With no analogue in our Solar System, these extreme
worlds are interesting to study from a planet formation perspective.
Specifically, measuring elemental abundance ratios in hot Jupiter atmospheres
can shed insight into their evolutionary and migratory pasts (Öberg et al.,
2011; Madhusudhan et al., 2014; Lothringer et al., 2021). These key elements
include oxygen and carbon, the two most important metal building blocks of
planets (Line et al., 2021). Water and other oxygen-bearing molecules in
Jupiter and Saturn’s atmosphere are condensed due to their cold temperature.
Finding the elemental abundances in hot gaseous planets is considerably
easier, as their higher temperatures enables water to remain in gas-form.

High-resolution cross-correlation spectroscopy (HRCCS, Snellen et al., 2010;
Brogi et al., 2012; Birkby, 2018) coupled with a Bayesian retrieval framework
has proven to be a powerful tool for the characterization of hot Jupiters
(e.g., Brogi & Line, 2019; Gandhi et al., 2019; Gibson et al., 2020, 2022).
HRCCS uses data from high resolution ($R\gtrsim 25,000$) spectrographs to
distinguish between planetary lines and the undesirable telluric and stellar
lines. These different spectral lines can be untangled because the planetary
lines’ radial velocity undergo several km s-1 shifts during continuous hours-
long observations, while the telluric and stellar lines remain stationary or
quasi-stationary. The dayside temperature structure and volume mixing ratios
of atmospheric constituents dictate the observed line shapes and contrasts.
From observing the emission spectrum of a planet, we can therefore infer the
underlying physical properties of its atmosphere using a Bayesian retrieval
framework coupled with an atmospheric forward model (e.g., Line et al., 2021).

Just recently discovered in 2020, HIP 65Ab is a massive
($M_{p}=3.213\pm{0.078}$ $M_{J}$) hot Jupiter ($T_{\mathrm{eq}}=1411\pm{15}$
K) on an ultra short 0.98 day orbit around its K4V ($K_{mag}=8.29$) host star
(Nielsen et al., 2020; Wong et al., 2020). Being a grazing transiting planet
with an impact parameter $b=1.17_{-0.08}^{+0.10}$, its radius is relatively
poorly constrained ($R_{p}=2.03_{-0.49}^{+0.61}$ $R_{J}$). With its short
orbital period, relatively large radius and cold host star, it is a prime
target for atmospheric characterization using high-resolution spectroscopy.

The atmosphere of HIP 65Ab has yet to be studied in great details. Here, we
use HRCCS with a Markov Chain Monte Carlo based retrieval framework to find
the abundances of major carbon- and oxygen-bearing molecules. This work offers
a first look into the dayside atmosphere of HIP 65Ab.

In Section 2, we discuss the observations of HIP 65Ab with IGRINS. Section 3
presents the data reduction steps and the telluric lines removal process. The
modelling of HIP 65Ab’s atmosphere and the retrieval setup are discussed in
Section 4. In Section 5, we present the results from the cross-correlations
and retrievals. These results are discussed in Section 6. We conclude this
work in Section 7.

##  2 Observations

We observed HIP 65Ab with the Immersion GRating INfrared Spectrometer (IGRINS)
(Park et al., 2014; Mace et al., 2018) for 3.5 hours during the night of 7
July 2022 (Program ID GS-2022A-Q-137, PI: Pelletier). Our data set composed of
56 exposures of the western dayside hemisphere of the planet with orbital
phases from $\phi=0.57$ to $0.717$ (Figure 1). Exposures were taken in an ABBA
nodding pattern to reduce background contamination. The exposure time of each
AB pair was 140 seconds, with an average SNR per exposure of about 63.

IGRINS is a high resolution spectrograph ($R=45,000$) installed on the Gemini
South 8.1 m telescope in Chile. It observes in the near infrared in two
continuous bands between 1.43 and 2.52 µm, corresponding to the photometric
$H$ and $K$ bands, respectively. This wavelength range contains strong
molecular bands from major carbon- and oxygen-bearing molecules, such as H2O,
CO, CH4 and CO2 (Gandhi et al., 2020).

Figure 1: To scale diagram of the phase cover of our HIP 65Ab observations.
The phase covered is from $\phi=0.57$ to $0.717$ where $\phi=0$ is the center
of the transit and $\phi=0.5$ is the middle of the secondary eclipse. The 3.5
hours observation used in this analysis is of the post-eclipse (western)
dayside of HIP 65Ab.

##  3 Data reduction and telluric removal

The raw data was processed with the IGRINS Pipeline Package (Mace et al.,
2018; Lee & Gullikson, 2016) which extracts the 1D spectra and does an initial
wavelength calibration. To proceed with our analysis, we bundled the IGRINS
pipeline products in a data cube of 56 (exposures) $\times$ 54 (spectral
orders) $\times$ 2048 (pixels).

We noticed a $\sim$1 km s-1 drift of the wavelength solution throughout the
continuous observing sequence, which has been reported in previous works using
IGRINS (e.g., Brogi et al., 2023; Line et al., 2021). To fix this issue, we
used a similar technique as in Line et al. (2021), however, instead of
correcting each exposure individually, we corrected the drift using a linear
radial-velocity shift in time. More precisely, we used the same method as in
Line et al. (2021) to find the shift of each spectral order for all exposures.
We then found the best fitting line of the shift as a function of exposure.
Finally, we shifted the whole time series according to this linear fit. Using
a linear fit as opposed to shifting each exposure individually serves to
prevent any sporadic shifts to be added to the data in certain outlier cases.
This correction was done separately for spectral orders in the $H$ and $K$
bands, where we found a shift of 0.3 and 1.3 km s-1 throughout the night,
respectively.

Following the initial processing of the raw data, we want to remove the
contribution from HIP 65Ab’s host star and the Earth’s atmosphere to only keep
the planet spectrum buried in some intrinsic noise. To do this, we closely
followed the data detrending procedure described in Pelletier et al. (2021,
2023).

As a first step, we corrected the pixels that deviate significantly from their
spectral channel. Pixels that are more than 5 $\sigma$ away from the temporal
mean are flagged as bad pixels. If these bad pixels form a group of 3 or less
pixels, they are interpolated using neighbouring pixels. Bigger groups of
adjacent bad pixels are completely masked.

Afterwards, we masked the wavelengths regions with high telluric
contamination. More specifically, the masking was applied to the following
regions: 1.43 to 1.4465 µm, 1.7995 to 2.03 µm and 2.48 to 2.52 µm,
corresponding to the edges of the $H$ and $K$ bands. Another mask was
introduced to remove the deep features that dip below 70% of the continuum
flux. We also masked 200 pixels on both edges of each order, where the blaze
transmittance is lower, to remove edge residual effects observed in some
cases. Finally, we discard any remaining spectral orders where more than 80%
of the data points are masked by the aforementioned steps. This resulted in
the removal of 10 of the 54 spectral orders, all of which are at the edges of
the $H$ and $K$ bands where telluric absorption is strongest.

Because of the varying observing conditions between each exposure (e.g., due
to airmass, seeing, throughout variations), the photon count for each pixel
varies greatly throughout the time series (Figure 2, panel a). To bring each
observed spectrum to the same continuum level, we used the same procedure as
in Pelletier et al. (2021). However, we used a 51 pixel width box filter
modulated with a Gaussian filter of 100 pixel standard deviation (Gibson et
al., 2019, 2020). This process corrects for any continuum variations between
exposures and enables a differential analysis to be performed on the time
series of spectra (Figure 2, panel b). A bigger filter has been also tested as
this has been found to sometimes influence retrieval results (Gibson et al.,
2022). However, in our case, we retrieve similar results in both cases.

At this stage, remaining telluric lines are still the dominating variance
contained in the spectra. However, these are stationary in the earth’s frame
of reference. Another major source of variance in the data is the spectral
lines produced by the host star HIP 65A. These stellar lines are shifted by
less than a pixel throughout the 3.5 hour observation caused mainly by the
barycentric movement of the earth and reflex motion of HIP 65Ab. For
comparison, HIP 65Ab’s shift is about 55 pixels ($\sim$ 110 km s-1) throughout
the 3.5 h observation due to its short orbital period.

The stability of telluric and stellar lines relative to the rapidly moving
spectral lines emitted from HIP 65Ab’s atmosphere can be used to remove these
unwanted contributions while leaving the planetary signal mostly unaffected.
For this, we first divide the spectra by a second order fit of the median
spectrum of each order. This is done individually for each exposure. Without
change in airmass, stellar activity and other time dependant changes, this
reduction would theoretically be enough to remove the telluric and stellar
line while keeping the planetary signal intact. Unfortunately, such variations
and noises are present, leaving residuals that still overwhelm the planetary
signal (Figure 2, panel c).

Figure 2: Overview of the data detrending steps applied to the observed 3.5
hour IGRINS spectroscopic time series targeting HIP 65Ab’s dayside atmosphere.
a) The extracted data from the IGRINS pipeline of an example spectral order in
the $K$ band. b) The same data, but continuum alignment. c) After the fitted
median spectra is removed from each exposure. d) With three principal
components removed and noisy spectral channels masked. This last panel is fed
to the cross-correlation and retrieval frameworks. The applied steps help
remove undesirable telluric and stellar contributions, leaving behind the
underlying planetary signal buried in noise.

Next, principal component analysis (PCA) is used to remove the remaining
contamination from telluric and stellar residuals. PCA finds the orthogonal
components of a data set with the most variance. The first few components of
the PCA will be orientated toward the telluric and stellar lines as they are
the dominating source of time dependant variance. The planetary lines shift in
wavelength by a considerable amount compared to telluric and stellar lines,
therefore, the PCA will not prioritise these. However, removing an excessive
amount of components can eventually chip away HIP 65Ab’s signal. Finding the
optimal number of components removed is therefore a balance between the
complete removal of the undesirable lines of telluric or stellar nature, and
the preservation of lines from HIP 65Ab’s atmosphere (Birkby et al., 2017;
Cabot et al., 2019; Cheverall et al., 2023; Smith et al., 2024). As done with
other high-resolution cross-correlation analyses (e.g., Holmberg &
Madhusudhan, 2022), we calculated the detection strength as a function of
number of principal components removed for the real signal and 200 injected
signals at random locations in $K_{p}$-$V_{\mathrm{sys}}$ space away from the
real signal (Figure 3). We found that the signal is best recovered if at least
2 principal components are removed, and changes little if up to 10 are
removed. For our analysis, we opted to remove 3 components.

Finally, a mask is applied to any remaining noisy spectral channels that
deviate more than 5 $\sigma$ of their spectral order. This final step removed
about 0.3% of the data.

The results of all these detrending steps is a time series of HIP 65Ab’s
spectra dominated by noise (Figure 2, panel d). These spectra are fed to the
cross-correlation and retrieval frameworks to, ultimately, characterise HIP
65Ab’s atmosphere.

Figure 3: Evolution of the cross-correlation detection strength as a function
of number of principal components removed for both the real signal, and 200
injected samples. The orange line represents the detection of the real signal
from HIP 65Ab observed in the data for different number of removed principal
components. Each grey line represents an injection-recovery test (black line =
the average), where we inject an atmospheric model at random orbital
parameters away from the expected signal in $K_{p}-V_{\mathrm{sys}}$ space and
measure the strength at those parameters as a function of components removed.
The strength of the injected model was calibrated to roughly match the real
signal line. Both the real signal and the injected signal exhibit similar
behavior when varying the number of principal components removed. In all
cases, at least 2 principal components need to be removed to well-reveal the
planetary signal, which then remains persistent up to at least 10 components
removed. In our final analysis, we opted to remove 3 components.

##  4 Modelling and retrieval setup

In order to detect chemical species, the data must be cross-correlated with
appropriate atmospheric models. Here, we use the SCARLET framework (Benneke &
Seager, 2012, 2013; Benneke, 2015; Knutson et al., 2014; Kreidberg et al.,
2014; Benneke et al., 2019a, b; Pelletier et al., 2021, 2023) to generate
models of HIP 65Ab’s dayside thermal emission. SCARLET generates line-by-line
high-resolution ($R=250,000$) emission spectra given the temperature-pressure
profile, the composition and the cloud structure of a planet. Molecular
opacities used for this analysis are H2O (Polyansky et al., 2018; Barber et
al., 2006), CO (Rothman et al., 2010; Li et al., 2015), CH4 (Rothman et al.,
2010; Hargreaves et al., 2020), CO2 (Yurchenko et al., 2020), SiO (Yurchenko
et al., 2022), HCN (Harris et al., 2006; Barber et al., 2014) and C2H2 (Chubb
et al., 2020). Collision-induced absorption by H2-H2 and H2-He (Borysow, 2002)
are also considered.

For each generated model, three sources of broadening are then taken into
account: rotational, exposure, and instrumental. Rotational broadening is due
to the line-of-sight spin of the planet, $V_{\mathrm{rot}}=v\mathrm{sin}(i)$
(see Eq. 4 from (Reiners & Schmitt, 2002), assuming $\epsilon=1$), causing
different parts of the HIP 65Ab’s dayside rotating towards (blueshifted) and
away (redshifted) from the observer. Exposure broadening considers the
broadening effect caused by the radial velocity change of the planet during a
given exposure. HIP 65Ab has an ultra-short period of 0.98 days, therefore it
travels a non-negligible distance during an exposure. This creates a
detectable blurring of the molecular lines in the data that needs to be
considered in the modelling. The average blurring per AB pair by this source
is about 1.2 km s-1. Finally, to account for the instrumental broadening, the
spectrum is convolved with a Gaussian profile matching IGRINS’ spectral
resolution ($R=45,000$).

As a first approximation of the composition of HIP 65Ab, we used SCARLET to
produce chemical equilibrium models of HIP 65Ab assuming a self-consistent
temperature structure with a zero-albedo equilibrium temperature of 1411 K. At
each atmospheric layer, SCARLET uses the thermochemical equilibrium code
FastChem 2 (Stock et al., 2022, 2018) to compute the volume mixing ratios
(VMR) of chemical species given the temperature, the pressure and the
elemental abundances of a volume of gas. We produced two models: one with a
solar metallicity and C/O, and the other at a solar metallicity but with a C/O
of 1 (Figure 4). In both cases, the models predicts that the molecular
composition is dominated by H2O, CO, CH4 and SiO in the low atmosphere. As HIP
65Ab’s temperature is significantly less than the dissociation temperature of
water (Parmentier et al., 2018), OH is never predicted to be in measurable
abundances (Figure 4). In our search for molecular species in HIP 65Ab’s
atmosphere, we therefore looked for signals from H2O, CO, CH4, SiO as well as
CO2, HCN and C2H2.

Figure 4: Mixing ratios of notable carbon- and oxygen-bearing molecules in
chemical equilibrium, as predicted by FastChem 2 (Stock et al., 2022, 2018),
compared to retrieved abundances (Section 5.2). The chemical equilibrium
models are calculated for a solar metallicity with a uniform temperature
profile of 1411 K, the equilibrium temperature of HIP 65Ab. The solid lines
are the mixing ratios for a model with solar C/O, while the dashed lines
represent a C/O of 1. The retrieved H2O and CO abundances are illustrated with
their 1$\sigma$ uncertainties. The other abundance constraints are shown as
their 3$\sigma$ upper limits. All shown molecules are included in our
analysis, with the exception of OH, which is never expected to have measurable
abundances at the pressures probed. The retrieved CO is slightly under-
abundant compared to equilibrium models. The H2O is also depleted relative to
these solar abundance model predictions. SiO is poorly constrained due to it
having almost no opacity in the IGRINS bandpass. Overall, our results are
consistent with HIP 65Ab having a sub-solar metallicity, and show a preference
towards a super-solar C/O.

###  4.1 Retrieval setup

To be able to retrieve the atmospheric parameters on HIP 65Ab, the spectrum
needs to be Doppler shifted according to the relative speed of HIP 65Ab at
each exposure. The equation that dictates the radial velocity shift $V_{p}(t)$
at a given time for a planet of zero eccentricity, as is the case for HIP
65Ab, is given as:

$V_{p}(t)=K_{p}\sin(2\pi\phi(t))+V_{\mathrm{sys}}+V_{\mathrm{bary}}(t),$ (1)

where $\phi(t)$ is the orbital phase of the planet at time $t$,
$V_{\mathrm{bary}}(t)$ is the barycentric radial velocity of the Earth,
$K_{p}$ is the Keplerian velocity and $V_{\mathrm{sys}}$ is the systemic
velocity.

We use a Bayesian analysis to characterize HIP 65Ab’s atmosphere and constrain
its orbital and atmospheric parameters. The Markov Chain Monte Carlo code
emcee (Foreman-Mackey et al., 2013) was used for the retrieval of atmospheric
and orbital parameters. Volume mixing ratios (VMRs) of H2O, CO, CH4, C2H2,
CO2, HCN and SiO were fitted. The orbital parameters $K_{p}$ and
$V_{\mathrm{sys}}$ were also fitted. To find the temperature profile of HIP
65Ab, we fitted 12 points evenly spaced in the log pressure space between 0.01
mbar and 100 bar. The prior used on the temperature points is described in
Pelletier et al. (2021) (See their Eq. 11) where we used a smoothing parameter
$\sigma_{s}$ of 350 K dex-2. An optically-thick grey cloud deck is also
considered in the retrieval with its cloud-top pressure $P_{c}$ being fitted.
The priors on all molecular abundances were log-uniform from $10^{-12}$ to 1,
with the added condition that mixing ratios queried by the retrieval summing
to more than unity are rejected from the prior space (Benneke & Seager, 2012).
H2 and He is then used as a filler gas such that the sum of all mixing ratios
is equal to one. We adopted a log-uniform prior for the cloud-top pressure of
the grey cloud between 0.01 mbar and 100 bar. A uniform prior is used on the
parameter $K_{p}$ between $\pm{50}$ km s-1 of its literature value (192.7 km
s-1, calculated with planetary parameters given in Nielsen et al., 2020).
$V_{\mathrm{sys}}$’s uniform prior is also between $\pm{50}$ km s-1 of its
known value (21.04 km s-1, Gaia Collaboration, 2018).

The rotational broadening was also fitted with its uniform prior being between
5.18 and 20 km s-1. The lower bound of 5.18 was chosen to be the equatorial
rotational velocity assuming a planetary radius of 1 RJ. This choice is
motivated by the fact that a radius of 1 RJ or lower for HIP 65Ab is
nonphysically low based on its mass of 3.213 MJ. We expect the retrieved
broadening to be close to the tidally-locked equatorial rotation of HIP 65Ab
which is about 10.5 km s-1, assuming a radius of 2.03 RJ.

The likelihood treatment used in this analysis is based on the framework of
Gibson et al. (2020). The cross-correlation function is given by

$\mathrm{CCF}=\sum^{N}_{i=1}\frac{d_{i}m_{i}}{\sigma^{2}_{i}},$ (2)

where $m$ is the planetary atmospheric model, $d$ is the cleaned data (Figure
2, panel d), $\sigma$ is the uncertainty on the data as calculated in Gibson
et al. (2020), and $N$ is the number of data points. The CCF-to-likelihood
mapping is then of the form

$\ln\mathcal{L}=-\frac{N}{2}\ln\left[\frac{1}{N}\left(\sum^{N}_{i=1}\frac{d_{i}^{2}+\alpha^{2}m_{i}^{2}}{\sigma^{2}_{i}}-2\alpha\mathrm{CCF}\right)\right],$
(3)

where $\alpha$ is a factor for the uncertainty in the scaling of the model
(Brogi & Line, 2019; Gibson et al., 2020). As done by previous works and
supported by the poorly constrained radius, we opted to fit this parameter.
The prior used on $\alpha$ is a log-uniform between 10-2 and 102.

##  5 Results

###  5.1 Cross-correlation results

To search for the chemical inventory of HIP 65Ab, we start by cross-
correlating molecular models with the detrended data following Eq. 2. The
cross-correlation results are phase-folded at each point in a
$K_{p}-V_{\mathrm{sys}}$ grid according to Eq. 1.

We detect H2O and CO absorption features at signal-to-noise ratios of 4.2 and
3.2, respectively, with maxima in $K_{p}-V_{\mathrm{sys}}$ space near the
expected orbital parameters (Figure 5). In comparison to similar IGRINS
observations of the dayside of hot Jupiter WASP-77Ab (Line et al., 2021), the
signal strengths are relatively weak considering that HIP 65Ab has the highest
Eclipse Spectroscopy Metric (ESM, Kempton et al., 2018) at 2.2 µm to date, if
assuming a radius of 2.03 $R_{J}$. This suggests that HIP 65Ab likely has a
radius on the lower estimate of its current reported value with large
uncertainties ($R_{p}=2.03_{-0.49}^{+0.61}R_{J}$), and thus is not as
favourable for emission spectroscopy as its face-value ESM would imply.

Figure 5:  $K_{p}-V_{\mathrm{sys}}$ cross-correlation signal-to-noise maps for
the observed water (top) and carbon monoxide (bottom) signals in HIP 65Ab’s
dayside atmosphere. The white dotted lines indicate the known orbital
parameters ($K_{p}=192.7$ km s-1 and $V_{\mathrm{sys}}=21.04$ km s-1, Nielsen
et al., 2020; Gaia Collaboration, 2018). The maximum cross-correlation peaks
(4.2 and 3.2 S/N for H2O and CO, respectively) occur close to the expected
$K_{p}$ and $V_{\mathrm{sys}}$.

We also cross-correlated models of SiO, CH4, HCN, C2H2 and CO2, but no
significant detections were recorded (Appendix Fig. 8).

###  5.2 Retrieval results

We obtain bounded abundance constraints for H2O and CO, respectively finding
values of log(H2O) = $-4.42\pm{0.18}$ and log(CO) = $-3.85^{+0.33}_{-0.36}$.
Both retrieved abundances are under the solar model prediction values. We also
recover upper limits on the VMR of SiO, CO2, CH4, HCN, C2H2 (see Figure 4 and
Table 1). We note that a peak is present in the CH4 posterior at the abundance
expected for a solar-like composition in chemical equilibrium and also a
positive correlation is found in its $K_{p}-V_{\mathrm{sys}}$ (Appendix Fig.
8). However, given that the cross-correlation signal is not significant, and
its abundance posterior in the retrieval is not bounded, we do not consider
this a detection. Still, this may warrant further observations of HIP 65Ab.
CH4 was previously claimed to be detected in the dayside of the hot Jupiter HD
102195b using high-resolution spectroscopy (Guilluy et al., 2019), but a re-
analysis of the same data with an updated CH4 line list no longer showed any
signal (Gandhi et al., 2020).

Our results do not favour grey clouds above the bar pressure level, likely
indicating that HIP 65Ab is not dominated by high-altitude clouds on its
dayside.

We retrieved a non-inverted temperature profile (Figure 6, middle panel). This
is expected for planets in this temperature range (e.g., Mansfield et al.,
2021; Baxter et al., 2020). The data probe a wide range of pressures in the
mid atmosphere of the planet, where the absolute temperature gradient is high
and the atmosphere is not too thick or thin (Figure 6, middle panel red and
blue curves). Due to this, the temperature profile is better constrained at
those pressure levels. This causes the retrieved temperature at the top and
bottom of the atmosphere to be unconstrained and the median gravitates towards
the middle of the prior. This is what causes the seemingly inverted profile at
the top of the atmosphere. Furthermore, our temperature prior prefers a
profile with a lower curvature, explaining the straight profile at the bottom
of the atmosphere where the data has little-to-no sensitivity.

We infer the model scaling factor to be log($\alpha$) = $-0.44\pm{0.21}$.
Although log($\alpha$) = $0$ (or $\alpha$ = $1$) is still within the retrieved
3 $\sigma$ bounds, this value is low, suggesting that our model is
overestimating the line contrast of the true signal. In order to verify this,
we ran a retrieval where we fixed the scaling factor to 1, to see what impact
this had on the other retrieved parameters. We found that this mainly only
affected the retrieved thermal structure, where fixing $\alpha=1$ resulted in
a smaller lapse rate at the pressures probed, with the retrieved temperature
being similar in the upper atmosphere but slightly colder in the lower
atmosphere. The interplay between temperature gradient and scaling factor can
be understood as two competing effect to affect the line depth of spectral
features in the model. In this case, increasing $\alpha$ from its previously
retrieved value (by setting it to 1) is now compensated by a lower temperature
gradient, acting to similarly decrease the depth of spectral lines in the
model.

Figure 6: Summary of the results from the free chemistry retrieval of our HIP
65Ab IGRINS dayside data set. The left corner plot includes the posteriors of
planetary and orbital parameters. log($\Sigma$(VMR)) is the sum of the Volume
Mixing Ratios (VMR) of all the retrieved species (shown separately for
clarity). log(PCloud) is the (base 10) logarithm of the cloud-top pressure in
units of bars. $V_{\mathrm{rot}}$ is the retrieved rotational broadening of
HIP 65Ab. log($\alpha$) is the model scaling factor used in the likelihood
(Eq. 3). $K_{p}$ and $V_{\mathrm{sys}}$ are the orbital parameters as
described in Eq. 1. The top middle panel is the retrieved vertical non-
inverted temperature profile of HIP 65Ab with a smoothing prior
$\sigma_{s}=350$ K dex-2. The orange line is the median temperature profile
while the darker and lighter orange shaded areas are the 1$\,\sigma$ (68%) and
2$\,\sigma$ (95%) regions, respectively. The 12 black points the pressure
levels where temperature points are retrieved. The dark green dash-dotted line
is the condensation curve of MgSiO3. The blue and red curves represent the
contribution functions for H2O and CO, respectively. The H2O probes around the
1 bar level, while the CO probes higher, around the 0.1 bar level. The right
panels are the retrieved posteriors of the VMR of the fitted chemical species.
The vertical lines represents the predicted thermochemical equilibrium
abundance at 0.3 bar for a solar metallicity atmosphere with a uniform
temperature profile of 1411 K (see Figure 4). The solid lines are from a model
with solar C/O and the dashed lines are from a model with a C/O of 1. H2O and
CO are well constrained while only upper limits are inferred on other
molecules. The retrieved CO VMR is slightly below but consistent with the
chemical equilibrium model. The H2O abundance lies between the solar C/O and
C/O=1 model predictions.

The retrieved rotational broadening of HIP 65Ab
($V_{\mathrm{rot}}=8.9^{+1.5}_{-1.7}$ km s-1) is slightly lower than the
tidally-locked equatorial rotational broadening ($\sim$10.5 km s-1, assuming a
radius of 2.03 RJ). While the rotation rate of the planet should be a lower-
limit of the observed line broadening, with dynamical effect acting on top of
this, finding a rotational broadening below tidally-locked equatorial rate is
unexpected. For example, recent dayside observations of the hot Jupiter
WASP-43b using CRIRES+ found a rotational broadening larger than expected
(Lesjak et al., 2023). They attributed this high broadening to a strong
equatorial jet of several km s-1. As HIP 65Ab has a similar equilibrium
temperature than WASP-43b, it might have similar atmospherical dynamics and
would make this scenario probable.

Table 1: HIP 65Ab Retrieval Results Free chemistry retrieval
---
log(H2O) | $-4.42\pm{0.18}$
log(CO) | $-3.85^{+0.33}_{-0.36}$
log(SiO) |  $<-1.11$ ($3\,\sigma$ upper limit)
log(CO2) |  $<-3.64$ ($3\,\sigma$ upper limit)
log(CH4) |  $<-5.15$ ($3\,\sigma$ upper limit)
log(HCN) |  $<-5.77$ ($3\,\sigma$ upper limit)
log(C2H2) |  $<-5.68$ ($3\,\sigma$ upper limit)
PCloud |  $>2.18$ bar ($3\,\sigma$ lower limit)
log($\alpha$) | $-0.44\pm{0.21}$
$V_{\mathrm{rot}}$ |  $8.9^{+1.5}_{-1.7}$ km s-1
$K_{p}$ |  $188.6^{+5.6}_{-5.4}$ km s-1
$V_{\mathrm{sys}}$ |  $14.6^{+4.3}_{-4.1}$ km s-1
C/O (with SiO free) | $0.71^{+0.16}_{-0.47}$
C/O (SiO $<$ 3$\times$ solar) | $0.77^{+0.12}_{-0.20}$
$[$C/H$]$ | $-0.65^{+0.33}_{-0.35}$
$[$O/H$]$ | $-0.66^{+0.54}_{-0.31}$
Chemical equilibrium retrieval
C/O | $0.72^{+0.13}_{-0.30}$
Metallicity, [M/H] | $-0.76^{+0.42}_{-0.48}$

One hypothesis that could explain the relatively low retrieved
$V_{\mathrm{rot}}$ value would be if HIP 65Ab had a localized hot spot near
the sub-stellar point. The retrieved temperatures should be a weighted average
of the observed projected face of the planet. Therefore, if the planet has a
hot spot in its atmosphere (e.g., Coulombe et al., 2023), the observed flux,
being proportional to temperature to the fourth power, would come
disproportionately from this hotter region. In this scenario, the retrieved
temperature profile, and all the retrieved molecular abundances, would be
driven by the atmospheric conditions in this higher temperature but smaller-
in-size region. This would act to reduces the effective (flux-weighted) radius
of HIP 65Ab and could explain the smaller rotational broadening retrieved.

Another possibility is that HIP 65Ab might simply have a radius smaller than
the 2.03 RJ used in this analysis. Without dynamical processes in HIP 65Ab’s
atmosphere, the retrieved rotational broadening of $8.9$ km s-1 would indicate
that HIP 65Ab’s radius would be 1.72 RJ, which is still within 1$\sigma$ of
the radius recovered from transit (Nielsen et al., 2020). With the relatively
poor detection significance of molecular species given the ESM, and the fact
that HIP 65Ab is an outlier in the radius-mass space (Nielsen et al., 2020,
see their Figure 10), it is likely that the radius is even smaller than 1.72
RJ. In this scenario, the retrieved rotational broadening would be higher than
the tidally-locked rotational broadening, likely due to additional atmospheric
dynamical effects.

In the retrieval, we observe correlations between the sum of the volume mixing
ratios of the retrieved species (log($\Sigma$(VMR))), the cloud-top pressure
PCloud, the model scaling factor (log($\alpha$)), the temperature structure,
and the rotational broadening ($V_{\mathrm{rot}}$) (Figure 6, left corner
plot). These degeneracies arise from the fact that these parameters all affect
the modelled line depths and shapes, stressing the importance of fitting these
simultaneously.

We retrieved the Keplerian and systemic velocities of $188.6^{+5.6}_{-5.4}$ km
s-1 and $14.6^{+4.3}_{-4.1}$ km s-1, respectively. $K_{p}$ is slightly lower
than the expected value (192.7 km s-1, calculated with planetary parameters
given in Nielsen et al., 2020), but still consistent within 1 $\sigma$. The
retrieved $V_{\mathrm{sys}}$ is blueshifted relative to the literature value
(21.04 km s-1, Gaia Collaboration, 2018), but remains consistent at the 2
$\sigma$ sigma level.

From retrieval results, we derive the ratio of carbon and oxygen atoms in the
gas-phase of HIP 65Ab’s atmosphere. The C/O is calculated using the following
formula:

$\mathrm{C/O}=\frac{n_{\mathrm{CO}}+n_{\mathrm{CO_{2}}}+n_{\mathrm{CH_{4}}}+n_{\mathrm{HCN}}+2n_{\mathrm{C_{2}H_{2}}}}{n_{\mathrm{H_{2}O}}+n_{\mathrm{CO}}+2n_{\mathrm{CO_{2}}}+n_{\mathrm{SiO}}}.$
(4)

We note that while we include SiO here because it can be an important oxygen-
bearing molecule in hot Jupiter atmospheres (Figure 4), its abundance is
poorly constrained by the retrieval due to SiO having no significant spectral
features over the IGRINS wavelength range (Yurchenko et al., 2022). Including
SiO, the calculated gas-phase C/O is $0.71^{+0.16}_{-0.47}$, with the
relatively large lower uncertainty due to SiO-rich compositions that cannot be
ruled out from the IGRINS data alone. If we instead omit scenarios where the
SiO abundance is unrealistically high (greater than three times solar, or
approximately 0.015% of the planetary atmosphere, Figure 4), we measure an
atmospheric C/O of $0.77^{+0.12}_{-0.20}$, now mostly driven by CO and H2O as
the main carbon- and oxygen-bearing molecules in the gas phase on HIP 65Ab.
This cutoff ensures that SiO is still included in the C/O determination, while
excluding unlikely scenarios where both CO and H2O are sub-solar but SiO would
be super-solar. However, we note that Si-rich atmospheric scenarios are
possible under certain formation conditions, which would naturally give rise
to atmospheres that are H2O-poor due to a significant amount of oxygen atoms
being bound in SiO (Chachan et al., 2023).

A possible explanation for the elevated C/O is that the oxygen is undetectable
as it may be condensed out of the gas phase. Oxygen-bearing compounds such as
MgSiO3 may form in the atmosphere of HIP 65Ab acting as a sink for oxygen
atoms. We find that the temperature in the mid-atmosphere is lower than the
condensation temperature of MgSiO3 (Figure 6, middle panel), indicating that
condensation of that species is possible. This mechanism may even be stronger
on the colder nightside, essentially trapping the oxygen in liquid or solid
form.

We further calculate elemental abundance normalized to solar ([C/H] and [O/H])
defined as

$\mathrm{[C/H]}=\log_{10}\left(\frac{n_{\mathrm{CO}}+n_{\mathrm{CO_{2}}}+n_{\mathrm{CH_{4}}}+n_{\mathrm{HCN}}+2n_{\mathrm{C_{2}H_{2}}}}{2n_{\mathrm{H_{2}}}(n_{\mathrm{C_{\odot}}}/n_{\mathrm{H_{\odot}}})}\right)$
(5)

and

$\mathrm{[O/H]}=\log_{10}\left(\frac{n_{\mathrm{H_{2}O}}+n_{\mathrm{CO}}+2n_{\mathrm{CO_{2}}}+n_{\mathrm{SiO}}}{2n_{\mathrm{H_{2}}}(n_{\mathrm{O_{\odot}}}/n_{\mathrm{H_{\odot}}})}\right).$
(6)

The derived gas-phase [C/H] and [O/H] are $-0.65^{+0.33}_{-0.35}$ and
$-0.66^{+0.54}_{-0.31}$, respectively. This indicates a sub-solar metallicity,
consistent within 2 $\sigma$ of the solar values. The [Fe/H] of the host star
HIP 65A is slightly super-solar (Nielsen et al., 2020), therefore HIP 65Ab’s
metal enrichment can also be considered to be sub-stellar.

To verify our results, we also ran chemically consistent retrievals, fitting
the C/O and overall metallicity. Under chemical equilibrium assumptions, the
retrieved C/O is $0.72^{+0.13}_{-0.30}$ and the metallicity is
$-0.76^{+0.42}_{-0.48}$, consistent with the values obtained from the free
retrieval which makes no assumption on the chemistry but uses uniform-with-
altitude abundance profiles.

##  6 Discussion

###  6.1 Implication for HIP 65Ab’s formation

The present-day envelope composition of giant planets if the results of their
evolution history. Retrieving atmospheric elemental abundance ratios such as
the carbon-to-oxygen ratio and the metallicity are thus essential in the
analysis of the giant planet formation. As envelope accretion occurs in a
protoplanetary disk with a varying solid and gas composition as a function of
the distance from the host star, the formation location of a planet may then
determine its present-day atmospheric composition (e.g., Öberg et al., 2011;
Madhusudhan et al., 2014; Öberg & Bergin, 2016; Booth et al., 2017). We can
thus use the elemental ratios calculated from our retrieval results to
speculate about what formation and migration scenarios may have given rise to
HIP 65Ab’s atmospheric composition.

If we assume that the measured abundances in HIP 65Ab’s atmosphere are
representative of the bulk envelope, one scenario that would naturally result
in a sub-solar metallicity and somewhat super-solar C/O is if HIP 65Ab was
formed beyond the H2O or CO2 snowlines (Öberg et al., 2011). The
protoplanetary gas-phase metal enrichment drops with orbital separation as
molecules condense into ices in the cold outer regions. Therefore, planets
undergoing runaway gas accretion further out in the disc should have sub-solar
metallicity with super-solar C/O, as inferred here. HIP 65Ab would then have
migrated to its current orbital location. The disk-free migration is the most
probable migration scenario (Madhusudhan et al., 2014) as HIP 65Ab would
accrete few metals, and hence maintain its primordial sub-solar metallicity.

Alternatively, if a significant portion of metals are held in condensates
(e.g., MgSiO3) or in the interior (Thorngren & Fortney, 2019), it is possible
that HIP 65Ab has a bulk content that is closer to solar/stellar. In this
sense, our measurement of the C/H and O/H in the atmosphere serves as a lower-
limit of the bulk envelope composition. If this is the case, formation
scenarios inwards of the water snowline and/or via gravitational instability
can also not be ruled-out.

###  6.2 Benchmarking our HRCCS pipeline

Figure 7:  Benchmarking of our analysis framework and comparison to WASP-77Ab.
Here we show the H2O and CO abundances, and C/O retrieved for HIP 65Ab
compared to IGRINS emission observations of WASP-77Ab from Line et al. (2021).
Our analysis of the WASP-77Ab data (purple) well-reproduces the molecular
abundances and C/O reported by Line et al. (2021) (green). In contrast, using
our pipeline, HIP 65Ab (orange) shows a comparatively depleted H2O abundance
and higher C/O. Here we show the retrieved C/O from the main free chemistry
retrieval with high SiO scenarios removed. The black lines represent the
abundances taken at 0.3 bar of a solar metallicity model (solid is C/O =
solar, dashed is C/O = 1) assuming an isothermal 1411 K atmosphere (Figure 4).

In order to benchmark our analysis, we tested our high-resolution cross-
correlation spectroscopy (HRCCS) pipeline by recreating established literature
results. We opted to recreate a small-scale version of the analysis presented
in Line et al. (2021), similar to what they have done with the HyDRA-H code
(Gandhi et al., 2019). We used public data of WASP-77Ab taken with IGRINS on
14 December 2020. WASP-77Ab is a hot Jupiter ($T_{\mathrm{eq}}=1740$ K) in a
1.36 day period (Maxted et al., 2012). It has a radius of $1.21R_{J}$ and a
mass of $1.76M_{J}$. We refer the reader to Line et al. (2021) for more
information on their analysis and results.

For our retrieval of WASP-77Ab, we only fitted the abundance of H2O and CO and
fixed the model scaling factor to $\log(\alpha)=0$. We obtain retrieval
results that are fully consistent with Line et al. (2021). From our analysis
of the WASP-77Ab IGRINS data we measure abundances of log(H2O) =
$-3.93^{+0.10}_{-0.11}$ and log(CO) = $-3.81\pm{0.20}$. These values are
extremely close with Line et al. (2021) who retrieved log(H2O) =
$-3.93^{+0.10}_{-0.09}$ and log(CO) = $-3.77^{+0.18}_{-0.16}$ (Figure 7). This
is encouraging as we are using a fully independent HRCCS reduction pipeline
(Pelletier et al., 2021), modelling framework (Benneke, 2015), and likelihood
prescription (Gibson et al., 2020), while still producing almost identical
results. Our results are also consistent with the HyDRA-H framework retrieval
presented in Line et al. (2021). This test serves as a useful benchmark of our
pipeline, and also serves as a further validation of the results reported in
Line et al. (2021).

With both planets analysed using our framework, we note differences between
the retrieved abundances of WASP-77Ab and HIP 65Ab (Figure 7). The retrieved
HIP 65Ab CO abundance is similar to that of WASP-77Ab. HIP 65Ab’s H2O content,
however, is lower compared to WASP-77Ab. Its VMR is about than 0.5 dex lower
than WASP-77Ab, illustrating again the depletion of H2O in HIP 65Ab’s
atmosphere. This difference causes the overall higher observed C/O on HIP
65Ab. A possible explanation to explain HIP 65Ab’s lower water abundance
relative to WASP-77Ab is if oxygen is further condensed out of the gas-phase
(in species such as MgSiO3) in HIP 65Ab than in WASP-77Ab. HIP 65Ab
($T_{\mathrm{eq}}=1411$ K) has a lower temperature than WASP-77Ab
($T_{\mathrm{eq}}=1740$ K), so an oxygen-bearing molecule with condensation
temperature between the temperatures of the two planets could explain the
difference in the measured gas-phase oxygen content between them, while still
having the same global oxygen content.

##  7 Conclusion

We analysed 3.5 hours of IGRINS dayside emission spectroscopic data from the
hot Jupiter HIP 65Ab. Using a Bayesian analysis approach, we constrained the
water and carbon monoxide abundance in its atmosphere to be log(H2O) =
$-4.42\pm{0.18}$ and log(CO) = $-3.85^{+0.33}_{-0.36}$. Other carbon- and
oxygen-bearing molecules were not detected significantly, with only upper
limits obtained on their abundances. Our results suggest that HIP 65Ab has a
sub-solar metallicity, with a slightly elevated super-solar C/O. The measured
composition is likely either the result of HIP 65Ab’s formation and evolution
history, or the result of missing metals being condensed out of the gas phase
of the atmosphere.

The retrieved temperatures reveals a non-inverted temperature structure in HIP
65Ab’s atmosphere. This profile is consistent with expectations for a hot
Jupiter of HIP 65Ab’s equilibrium temperature. Our results favour a dayside
atmosphere for HIP 65Ab that is not dominated by high-altitude clouds. $K_{p}$
and $V_{\mathrm{sys}}$ are retrieved at values near the expected literature
values. We retrieved a rotational broadening smaller than the tidally-locked
equatorial speed of HIP 65Ab, and also recover a low scaling factor. These
findings, combined with the relatively weak cross-correlation detections
compared to similar IGRINS observations of WASP-77Ab strongly suggests the HIP
65Ab has a radius on the significant lower end of its reported values of
$R_{p}=2.03_{-0.49}^{+0.61}R_{J}$.

From our retrieved molecule volume mixing ratios, we derived elemental
abundance ratios, finding HIP 65Ab to have C/H and O/H values that are sub-
solar, with a slightly elevated C/O ratio. Such an atmospheric composition is
consistent with HIP 65Ab having formed further out in the protoplanetary disc,
before migrating to its present-day orbital position. This likely would have
occuved via disc-free migration mechanisms in order to not accrete a high
amount of planetesimals in order to preserve its sub-solar metallicity.

Finally, we benchmark our analysis framework on IGRINS data of WASP-77Ab (Line
et al., 2021), finding that we are able to obtain fully consistent results
despite our fully independent detrending and atmospheric modelling pipelines.

##  8 Acknowledgments

We thank the reviewer for providing comments which improved the quality of
this manuscript. This work used The Immersion Grating Infrared Spectrometer
(IGRINS) that was developed under a collaboration between the University of
Texas at Austin and the Korea Astronomy and Space Science Institute (KASI)
with the financial support of the US National Science Foundation under grants
AST-1229522, AST-1702267 and AST-1908892, McDonald Observatory of the
University of Texas at Austin, the Korean GMT Project of KASI, the Mt. Cuba
Astronomical Foundation and Gemini Observatory. This work is based on
observations obtained at the international Gemini Observatory, a program of
NSF’s NOIRLab, which is managed by the Association of Universities for
Research in Astronomy (AURA) under a cooperative agreement with the National
Science Foundation on behalf of the Gemini Observatory partnership: the
National Science Foundation (United States), National Research Council
(Canada), Agencia Nacional de Investigación y Desarrollo (Chile), Ministerio
de Ciencia, Tecnología e Innovación (Argentina), Ministério da Ciência,
Tecnologia, Inovações e Comunicações (Brazil), and Korea Astronomy and Space
Science Institute (Republic of Korea). L.B. acknowledges funding by the
Natural Sciences and Engineering Research Council of Canada (NSERC). S.P. is
partially supported by the Technologies for Exo-Planetary Science (TEPS) NSERC
CREATE Trainee Program. B.B. acknowledges funding by the NSERC, and the Fonds
de Recherche du Québec – Nature et Technologies (FRQNT). This project has been
carried out within the framework of the National Centre of Competence in
Research PlanetS supported by the Swiss National Science Foundation under
grant 51NF40_205606. The authors acknowledge the financial support of the
SNSF. We are thankful to Michael Line for making the WASP-77Ab IGRINS data and
associated data reduction codes publicly available.

Gemini-South (IGRINS)

$K_{p}-V_{\mathrm{sys}}$ maps for other notable carbon- and oxygen-bearing
molecules.

Figure 8: $K_{p}-V_{\mathrm{sys}}$ cross-correlation maps (same as Figure 5),
but for molecules showing no significant detections near the expected
planetary position. Despite these non-detections, we still fit these species
in the retrieval to derive upper limits on their abundances.

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