11institutetext: University of Maryland, College Park MD 20740, USA

# Right Place, Right Time!
Towards ObjectNav for Non-Stationary Goals

Vishnu Sashank Dorbala* Bhrij Patel* Amrit Singh Bedi 11 0000-0001-5697-5219
11 0000-0002-8296-4537  22 0000-0002-8807-2695 Dinesh Manocha 11
0000-0001-7047-9801

###### Abstract

We present a novel approach to tackle the ObjectNav task for non-stationary
and potentially occluded targets in an indoor environment. We refer to this
task Portable ObjectNav (or P-ObjectNav), and in this work, present its
formulation, feasibility, and a navigation benchmark using a novel memory-
enhanced LLM-based policy. In contrast to ObjNav where target object locations
are fixed for each episode, P-ObjectNav tackles the challenging case where the
target objects move during the episode. This adds a layer of time-sensitivity
to the navigation scheme, and is particularly relevant in scenarios where the
agent needs to find portable targets (e.g. misplaced wallets) in human-centric
environments. A P-ObjectNav agent needs to estimate not just the correct
location of an object, but also the time at which the object is at that
location for visual grounding _—_ raising a question about the feasibility of
this task. We address this concern by studying two cases for object placement:
one where the objects placed follow a routine or a path, and the other where
they are placed at random. We dynamize Matterport3D for these experiments, and
modify PPO and LLM-based navigation policies for evaluation. Using PPO, we
observe that agent performance in the random case stagnates, while the agent
in the routine-following environment continues to improve, allowing us to
infer that P-ObjectNav is solvable in environments with routine-following
object placement. Further, using memory-enhancement on an LLM-based policy, we
set a benchmark for P-ObjectNav. The memory-enhanced agent significantly
outperforms non-memory based counterparts across object placement scenarios by
$71.76\%$ and $74.68\%$ on average when measured by Success Rate (SR) and
Success Rate weighted by Path Length (SRPL), showing the influence of memory
on the improving the performance of a P-ObjectNav agent. Our code and dataset
for object placement will be made publicly available for reproducibility.

###### Keywords:

Embodied AI Large-Language Models Object Navigation

##  1 Introduction

We present an approach to tackle Object Navigation (ObjectNav) in dynamic
scenarios with non-stationary targets. While embodied tasks including
ObjectNav [3, 40, 49], ImageNav [26, 12, 48], PointNav [15, 52, 41] and even
Audio-Guided navigation [8, 9] have been studied in static environments [21,
18, 39, 41], their adoption to dynamic scenarios with moving goals has been
limited. In this work, we present an approach to tackle such dynamic scenarios
by formulating the Portable Object Navigation (P-ObjectNav) task, which
requires an agent to consider environmental changes for path planning and
visually identifying non-stationary targets.

Figure 1: We present a new approach to tackle ObjectNav with non-stationary
and potentially occluded target objects by defining the P-ObjectNav task. We
first dynamize scans from the Matterport3D [7] dataset, by placing portable
objects to images corresponding to their natural rooms at various times (eg.
wallet in office between timesteps 1-25 in the image above). We then perform
navigation on this modified dataset and set a benchmark using a memory-
enhanced LLM agent. Beyond temporal changes in placement, our dataset also
introduces occlusions and spatial transformations for added realism, which we
study using SoTA object detectors.

Recent developments in Embodied AI have led to the emergence of several unique
navigation challenges that rely on of various forms of perception. In
ObjectNav for instance, an agent is tasked with navigating to an instance of a
specified target object class, such as “couch” or “fridge”, relying on visual
object grounding [28, 32] to know if it has reached its goal. However, these
tasks often assume static environments, neglecting the dynamic nature of the
real world where objects can move and need not be entirely visible.

Navigation in dynamic environments has been typically limited to low-level
planning tasks such as crowd avoidance [43, 34, 4] and socially aware
navigation [6, 33], where an agent actively performs real-time maneuvers to
find a path that is smooth and collision-free while trying to minimize time.
On the other hand, high-level planning is less time-sensitive and usually
involves sequential decision-making using percepts obtained from waypoints or
nodes on a topological graph [10, 45] to reach a target destination.
Literature on the combined case of high-level planning in dynamic environments
with non-stationary targets has been scarce to non-existent.

Performing this kind of high-level navigation in dynamic environments is
particularly relevant in household scenarios involving finding small objects
that a user tends to misplace. For instance, if a user routinely leaves their
glasses in the garage, an agent tasked with finding them needs an estimate of
not just where the glasses are, but also when the glasses are usually there.
Analogous to Chatbots that provide personalized assistance based on historical
data [27], a P-ObjectNav agent must be able to identify user behaviors over
time in a dynamically changing environment to make personalized decisions on
locating a target object.

In defining such non-stationary target objects, a natural question arises
regarding the feasibility of P-ObjectNav. Much akin to an endless game of hide
and seek, if objects are constantly being moved, would an agent ever be able
to find any target? In low-level planning, this falls under the pursuit-evader
class [1, 46] of problems. In a high-level planning task however, such
situations are for the most part left unaddressed. As such, we first seek to
prove feasibility, by comparing agent performance in random and routine-
following object placement scenarios. An agent placed in a routine following
scenario must intuitively show continuous improvement in finding portable
targets over an agent attempting to find targets that are randomly placed.

We then present a novel memory-enhanced approach to tackle ObjectNav in non-
stationary environments i.e., the P-ObjectNav task. Figure 1 presents an
overview of our approach.

Main Results. Our work makes the following key contributions:

  * •

We develop a novel approach to tackle ObjectNav in scenarios with non-
stationary and potentially occluded target objects. As part of our approach,
we formulate the Portable ObjectNav task, establish its feasibility, and set a
navigational benchmark using a novel memory-enhanced LLM-based policy. To the
best of our knowledge, we are the first to tackle ObjectNav in this setup.

  * •

We customize the Matterport3D dataset to accommodate the dynamic nature of our
task by placing portable objects following both temporal and positional
placement strategies. We will be releasing this dataset to promote research in
this area.

  * •

We establish the feasibility of P-ObjectNav by comparing performance of a PPO-
agent in random and routine-based temporal object placement scenarios.
Continuous improvement in the routine case over sporadic performance in the
random case demonstrates the viability of our task in scenarios involving an
orderly placement of target objects.

  * •

Finally, we set a baseline for the P-ObjectNav task employing memory-
enhancement an LLM-based agent, highlighting a drastic performance improvement
of over $70\%$ when measured across Success Rate (SR) and Success Rate
Weighted by Path Length (SRPL).

##  2 Related Works

###  2.1 Embodied Navigation

Recent developments in Embodied Simulators [16, 44, 36, 7, 37] and subsequent
works [3, 41, 30, 5, 38, 50] have established ObjectNav as a popular task in
the community. These also include zero-shot approaches that employ various
foundation models [20, 21, 18, 57]. However, ObjectNav has predominantly been
done in static environments, with fixed targets. While Habitat 3.0 [36]
introduces the Social Rearrangement task, this is a human-robot teaming task
where the agent is expected to rearrange objects in the house in tandem with
the human. Our work deals strictly with a passive human case, where the agent
is expected to find a non-stationary target without human assistance.

Planning in non-stationary environments has been studied in the past [47, 58,
35], with recent approaches even utilizing LLMs in conjunction with multi-arm
bandits [14]. These schemes usually propose memory augmentations with
hierarchical planning procedures and frame the problem from an obstacle
avoidance standpoint [47, 29, 55]. In contrast, our work considers the case
where the target itself is non-stationary.

Rudra et. al in [42] define small portable objects around the house, and
propose a contextual bandit scheme that aims to learn the likelihood of
finding an object at various waypoints. In their case,, however, object
locations are shuffled only after each episode, meaning it finally boils down
to an ObjectNav task in a static environment. In contrast, we tackle a truly
dynamic case, where objects are moving even during the episode. This
definition adds a layer of complexity as the embodied agent must now navigate
towards a constantly shifting target object, for which it needs to identify
specific routines and object movement patterns in the environment.

###  2.2 Visual-Language Grounding

An important component of ObjectNav is the object detection module that allows
the embodied agent to perceive and understand the household scene. Object
detection and semantic segmentation datasets like MS-COCO [31], ImageNet-2K
[17], and LVIS [23] have brought great strides in pre-training visual models
for everyday items. Furthermore, recent developments in vision foundation
models [25, 53, 28] have allowed for zero-shot object detection and
segmentation in household environments. With the advent of LLMs, a lot of new
approaches for Object Navigation, especially in zero-shot conditions [21, 18,
56, 22, 19, 51] that rely solely on commonsense knowledge or LLM planning
capabilities have garnered much interest. In our work, we adopt one of these
approaches, LGX, and enhance it with memory to set a benchmark for
P-ObjectNav.

##  3 P-ObjectNav: Task Overview & Approach

In this section, we first formalize the P-ObjectNav task and establish
conditions for determining feasibility. We then discuss our environment setup
as a modification of Matterport3D with various temporal and positional object
placement strategies. Finally, we describe our PPO and LLM-based navigation
policies and set a memory-enhanced benchmark.

###  3.1 Formulation

P-ObjectNav tackles ObjectNav in a constantly changing dynamic environment,
with non-stationary target objects. This setting introduces a unique challenge
for the agent that needs it to be at the right place, at the right time to
successfully ground a target object. The agent in each episode is tasked with
finding as many portable target objects as it can.

While the object positions are non-stationary, we hypothesize that the object
routes need to be constant for an agent to learn patterns for successful task
completion. We consider this constancy in object routes to be representative
of human habits as routines [13]. As such, a P-ObjectNav agent aims to
optimize,

$\pi^{*}=\arg\max_{\pi}P(o_{p}|\pi(t),t)$

where $\pi$ is the agent’s policy, which maximizes the probability of
intersecting with a portable object $o_{p}$ at time $t$ and location
$l=\pi(t)$.

In our case, we consider a high-level planning problem, requiring the agent to
make decisions at nodes on a topological graph. To maximize the number of
objects found within an episode or $n$ timesteps, the agent’s subgraph of
sequential decision-making nodes i.e., $G_{i}(t_{n})$ at trial $i$ over $n$
timesteps must maximize the intersection with the ground truth portable object
subgraph over that time period $G^{\prime}(t_{n})$. The optimal subgraph
$G_{o}(t_{n})$ for $n$ timesteps could be expressed as,

$G_{o}(t)=\underset{G_{i}(t),\;i\in\\{1,\ldots,I\\}}{\arg\max}\left|G_{i}(t)\cap
G^{\prime}(t)\right|\\\ $ $I=f(N_{a},n)\\\ $

Here, $I$ represents the total number of possibilities or trials for a
subgraph to evolve over $n$ timesteps from a particular starting point
$N_{a}$. Given the time constraint, $I$ is finite on an undirected graph,
meaning a solution for $G_{o}$ must exist. However, if $G^{\prime}(t)$ is not
constant for each trial, $G_{o}$ would keep varying.

Thus, when objects are placed according to a fixed routine, as the ground
truth portable object subgraph $G^{\prime}(t)$ remains constant during each
trial in $G_{i}(t)$, an optimal policy is learnable over a set of trials.
Conversely, if objects are moved at random without any underlying pattern, the
environment becomes completely non-stationary from the perspective of the
agent, and $G^{\prime}(t)$ would change for each trial in $G_{i}(t)$, i.e,
maximizing the variance of $G_{o}(t)$. This scenario nullifies the agent’s
ability to learn beyond mere chance. As such, to prove feasibility, we need to
experimentally prove our theoretical assumption that -

$\lim_{t\to\infty}(R_{routine}(t)-R_{random}(t))\gg 0$

where $R_{routine}$ represents the agent’s performance in a routine following
environment, and $R_{random}$ represents performance when P-Object motions are
random. We hypothesize that over time, an agent performing P-ObjectNav in a
routine-following environment should perform much better than an agent placed
in a random environment.

Toward this objective, we conduct P-ObjectNav experiments in a dynamized
Matterport3D environments using both PPO-based and LLM-based decision-making
agents.

###  3.2 Object Placement

ObjectNav can be considered as a fusion of two components, i.e., Sequential
Decision Making pertaining to the agent’s high-level navigation scheme, and
Visual-Language Grounding which deals with the identifying the location of the
target object from an image. To setup ObjectNav in a dynamic scenario, we
associate object placement complexities in terms of Temporal and Positional
placement with both these components.

Room | Portable Objects
---|---
Bedroom |  Charger, Water Bottle, Smartwatch, Laptop, Notebook, Toothbrush, Mug, USB Flash Drive, Phone, Headphones, Hat
Garage |  Screwdriver, Flashlight, Mug, Phone, Headphones, Hat
Dining |  Salt and Pepper Shakers, Portable Speaker, Charger, Water Bottle, Mug, Bowl, Phone, Headphones, Hat
Office |  Charger, Laptop, Hat, Notebook, USB Flash Drive, Mug, Phone, Headphones
Bathroom |  Toothbrush, Phone, First-Aid Kit
Kitchen |  Salt and Pepper Shakers, Hat, Mug, Bowl, Phone, Headphones, First-Aid Kit
Lounge |  Playing Cards, Mug, Portable Speaker, Charger, Water Bottle, Laptop, Phone, USB Flash Drive, Dice, Headphones, Hat
Gym |  Dumbbells, Jumprope, Smartwatch, Phone, Headphones, Hat
Outdoor |  Jumprope, Smartwatch, Portable Speaker, Phone, Water Bottle, Headphones, Hat
Recreation |  Playing Cards, Dice, Water Bottle, Headphones, Hat
Table 1: Rooms and Portable Objects: We map $21$ portable objects to a set of
rooms available in our Matterport3D scans. This mapping is used to set object
locations during our Routine-based movement schemes. During each episode, the
objects are placed in different rooms during for a range of timesteps.
Commonly moved objects such as phone, headphones, hat are associated with $9$
different rooms, while less commonly shifted ones such as dumbbells appear
only in the Gym. The frequency of portable objects ranges from $1-9$.

Given the computational overhead of existing simulators, we chose to modify
Matterport3D [7] and make it dynamic for our task. This setup allows us to
treat P-ObjectNav as a high-level sequential decision-making problem in which
the agent needs to determine the right sequence of discrete node “hops" to
reach the portable target.

Figure 2: Object Placement: Portable objects are placed at various timesteps
on different nodes in the Matterport scan. As the agent explores the
environment, the portable object keeps shifting positions. Here, the portable
object mug is placed on a kitchen node $N_{k}$ at time $T_{n}$. If the agent
reaches the kitchen before $T_{n}$, it would fail to see the portable object.

We consider a set of $10$ randomly chosen scans from Matterport3D [7] for
experimentation. Each house scan contains topological graphs consisting of
nodes representing panoramic images of the scene, and edges representing the
distance between them. Additionally, each node contains information about the
room type. We use these room types to obtain a mapping of portable objects,
listed in Table 1. This mapping is used to determine routine movements
described below.

####  3.2.1 Temporal Placement

To introduce dynamism in the Sequential Decision Making described earlier, we
modify the Matterport3D dataset to place portable objects on various nodes at
different timesteps. This is illustrated in figure 2.

We compute trajectories of the portable objects in an offline manner. For each
portable object, we compute a random partition of timesteps $T$. To emulate
realism, each object has a waiting period, $w$, where once it moves to a new
location, it will wait there for a certain number of timesteps before being
able to move again. Therefore, each element in $T$ has at least $w$ elements.
For each element in $T$, we assign a room for the portable object to be in
during those timesteps. The room is determined by a given object movement
scheme that we detail below. Note that the new location chosen could also be
the same location the portable object is in currently, i.e., the object can
stay where it was.

We look at three forms of object movement schemes for our feasibility study:

  1. 1.

Random Movement: As a baseline, we consider the case where portable object
placement is completely random, in random rooms at random times. Their
locations do not follow the mapping listed in Table 1.

  2. 2.

Semi-Routine Movement: Portable object locations in this case are in
accordance to the mapping, but the timesteps of placement remain variable
between episodes. For instance, a toothbrush will move only between the
bedroom and bathroom, but the time during which it moves would change between
episodes.

  3. 3.

Fully-Routine Movement: In this case, objects follow a specific placement
pattern or a path that remains constant from episode to episode. For instance,
a toothbrush in the bathroom during timesteps $t_{1}$-$t_{2}$ would remain
there at that time during all episodes.

####  3.2.2 Spatial Placement

To introduce dynamism in the form of Visual-Language Grounding for
P-ObjectNav, we consider spatial transformations and occlusions on the target
objects. This is illustrated in figure 3.

Figure 3: Spatial Placement: To introduce dynamism in terms of visual-language
grounding of the target, we consider various positions, orientations and
occlusions of the target object in the scene. Given images of the scene and a
stock image of the portable target object, we obtain segmentation masks from
both. The target object is then placed onto the scene such that it stays above
of segmentations below the center of the image. To mimic occlusions, we cut-
off varying percentages of the of the target segmentation during the overlay.

We use FastSAM [54] to obtain segmentation masks for both the scene and the
portable target object. For the scene segmentations, we filter out as set of
$k$ largest segments below the center of the image, to avoid placing target
objects on ceilings. We then place the target objects on a randomly chosen
segment from this set. This allows for spatial variance, allowing us to
evaluate grounding approaches against various backgrounds. We also add
occlusion, by cutting off a percentage of the target segment and pasting it on
the scene segmentations. More details about this are in the supplementary.

###  3.3 Navigation Policies

We develop two embodied navigation agents for P-ObjectNav as follows:

PPO: We train the PPO agent for 2000 episodes with 50 timesteps per episode.
In each episode, the task is to find the most amount of portable objects. The
episode ends when either the agent has found all portable objects or it has
exceeded the time limit.

For the observation space, the agent is given an ($n+1$)-dimensional vector
where $n$ is the number of nodes in the Matterport3D scan. Each element except
for the last one, contains a binary value where the value is only 1 if the
node corresponding to that index is adjacent to the current and is 0
otherwise. The last element in the observation space is the timestep. The
action space is an integer representing the index of the node in the
observation space input vector the agent will travel to next.

For each transition, the agent receives the following rewards:

  * •

For each transition in the environment, the agent receives a reward of +1 for
every new portable object from Table 1 it finds.

  * •

If the action $a$ is an index corresponding to a node that is not adjacent to
the current node, the agent receives a penalty of -1.

Formally, let $A$ be the set of adjacent nodes at timestep $t$, and let $P(t)$
be the set of new portable objects seen at $t$.

$R(t)=|P(t)|-\mathbbm{1}_{a\centernot\in A}$ (1)

We analyze the PPO training in Section 4 to highlight the feasibility of
learning portable object trajectories when using a routine-based object
movement-scheme as opposed to a fully random scheme.

LLM-Based: We implement LGX [18], an LLM-based SOTA model for zero-shot
ObjectNav. Given a target object, at each timestep, LGX takes a list of
objects around the agent and asks an LLM (GPT) to predict a target to navigate
towards. The agent continues to do this till it has found the target.

In our implementation, we slightly modify LGX to our dynamic Matterport3D
environment. At each timestep, we pass YOLO v8 [24] to obtain a list of
objects in the scene, and then prompt GPT-4 with the object list asking for an
object prediction from the list that might lead to a portable object. We then
use the scene graph from Matterport3D to hop to the next node.

Memory Enhancement: Given the need to finetune agent behaviour to a specific
object placement routine, we enhance LGX with memory by passing historical
actions and timesteps to the prompt across trials. We do this in two ways -

  1. 1.

Complete: Here, we pass in all the objects in view as well as the current GPT
prediction to the system prompt of the agent. As the token size of GPT is
limited to 4096 tokens, we maintain a horizon, where we remove the earliest
appended observation when an overflow of tokens occurs.

  2. 2.

Selective: Rather than passing the entire action history of the agent, we
perform memory selection by utilizing a memory tree. During each timestep, we
update the memory tree with the observations of the agent as well as the
prediction that GPT makes. We then check if a path exists on the tree from the
current node to a set of seen observations containing a portable object. If a
path does exist, we prompt the LLM with the cost of travelling to that node,
asking if it wants to backtrack, and if so, execute the traceback.

##  4 Experimentation & Results

We decouple the navigation and target grounding performance in our
experiments. This separation allows us to better analyze the performance of
each module.

Figure 4: Training curves for PPO agents trained on semi-routine (orange) and
random movement (blue). (Left) The number of portable objects found at each
episode. At max, $11$ objects can be found. (Right) The total rewards up until
the end of each episode. The semi-routine converges to a higher number of
objects and rewards per episode than random movement which corresponds to
humans leaving things with no habits.

Figure 5: Statistics on the PPO agents trained. (Left) Node coverage by each
agent as a percentage of the total number of nodes (i.e., $53$ in the test
scan). (Right) Object-wise finding frequency. Note the uniform distribution in
the semi-routine case, the camping behavior in the fully-routine case, and the
sporadic behavior in the random case.

###  4.1 PPO Training

Figure 4 shows the training curves between two PPO agents, one that trained on
semi-routine portable object movement and another trained on random movement.
The plots show that the agent trained on semi-routine movement was able to
learn and improve performance, while the agent trained on random movement had
no signal to learn from. For ease of training and proof of feasibility, we
consider only $10$ portable objects across $53$ nodes of a single scan.

We monitor the number of unique objects that the agent has found across
episodes on the graph on the left, while we monitor the cumulative reward per
episode on the graph on the right of Figure 4. Note the improved performance
of semi-routine agent over the random agent, while the fully routine agent
seems to perform much worse. In order to analyze this better, we draw
statistics about the nodes and objects that the agent views.

In figure 5, the graph on the left shows the area covered by each node on
average during each episode, as well as the total unique area covered across
all episodes. While all the agents explore lesser as time progresses, notice
the extremely low coverage of the agent in the fully-routine environment, due
to it’s camping nature. The graph on the right shows a object-wise frequency
distribution of the total number of objects found during the entire period of
training. We observe that the fully routine agent tends to camp at one
location where the portable speaker is at (which also has a larger waiting
time $w$). This negatively affects its overall performance and explains the
reward curve shown in as shown in Figure 4.

While the camping behaviour for the fully-routine case is not ideal, the agent
placed in the semi-routine environment shows continuous improvement as
suggested by its cumulative reward. This result is in line with our
formulation presented earlier, and suggests the need for an orderly placement
of objects where the locations are fixed, but not the timesteps for ideal
learnability. The performance of the semi-routine following agent also
satisfies our initial hypothesis introduced in equations 3.1 and 3.1 from the
formulation section 3.1. We can thus experimentally infer that P-ObjectNav is
feasible in environments with routine-based object placement that has a soft
constraint on time, but a hard constraint on position.

###  4.2 LLM Inference

We evaluate our LLM based policies using two metrics, adapted from the
original ObjectNav task.

  * •

Success Rate (SR) represents the average success across all trials, and is a
raw measure of portable object finding success. This is the number of episodes
where a portable object was found in each trial, divided by the total number
of timesteps, averaged across all trials. It is defined as -

$SR=\frac{100}{\text{Num. Trials}}*\Sigma\frac{\text{Suc. Ep.}}{\text{Total
Ep.}}$

  * •

Success Rate Weighted By Path Length (SRPL) is defined similar to SPL for
ObjectNav [2], but is a comparative metric. It is defined as the success rate
divided by the path length (or number of steps). As the number of steps taken
is usually much larger than the success rate, we take the log of the step
count, and the percentage of this value. It is represented as -

$SRPL=100*\frac{SR}{\log(\text{Path Length})}$

As we expect a high SR, and from a low number of steps, a higher value is
preferred from this metric.

Policy | Object Placement |  SR ($\%$) $\uparrow$ |  SRPL ($\%$) $\uparrow$
---|---|---|---
LGX | Random | $4.12$ | $0.45$
Semi-Routine | $2.96$ | $0.34$
Fully-Routine | $7.14$ | $0.82$
LGX + Complete Memory | Random | $5.45$ | $0.74$
Semi-Routine | $13.08$ | $2.40$
Fully-Routine | $11.53$ | $1.38$
LGX + Selective Memory | Random | $7.00$ | $0.93$
Semi-Routine | 25.24 | $2.97$
Fully-Routine | $20.95$ | $2.46$
Table 2: Evaluation of P-ObjectNav Policies Across Different Scenarios: We
conduct experiments with agents with and without memory on the $3$ different
object placement scenarios described in section 4. We experiment with two
types of memory, complete where we pass the entire list of observations, and
selective, where we only pass relavant nodes when a path to a portable object
exists. Note the overall improved performance of the selective memory enhanced
agent across all object placement scenarios. Further, observe that the semi-
routine scenario gives us best results overall. We attribute this to fully-
routine scenarios being overtly strict in terms of orderly placement for time
and location.

Table 2 presents the results of LGX on the P-ObjectNav task. We run the agent
for $10$ trials of a modified Matterport3D scan, with each trial having $20$
episodes and $30$ timesteps.

We make some salient observations. First, the memory enhanced agents perform
significantly better on average compared to agents without memory. This is
evident, as a LGX’s zero-shot performance is reliant on commonsense knowledge
from GPT for decision making, which might not capture personalized object
routines.

Second, LGX with Complete Memory performs much worse than LGX with Selective
memory. As such, passing raw memory without any selectivity does not
necessarily give good results. This can be attributed to an “input overload”
where the LLM’s output is not desirable since it does not have a direct path
to a portable object from the data it has been given.

Third, in using LGX with Selective Memory, the Semi-Routine object placement
gives us the best results overall. The Semi-Routine case considers a soft
constraint on the target’s time of placement allowing it to be moved at any
time during the episode, while the location of the target is fixed to a set of
rooms. This is an interesting result, which we can attribute to this case
being a middle ground between aggressive (random) and conservative (fully-
routine) placement behaviors. We infer that the fully-routine case is too
strict, since the agent might be in the vicinity of a portable object, i.e.,
the right room, but might not find it due to it being at the wrong node in the
room.

Finally, we notice that performance of the memory-enhanced agent in routine
based placement scenarios is far superior than the agent in a random placement
scenario. We can infer from this that memory is a strong component of what
makes a successful P-ObjectNav agent. Unlike ObjectNav, where the fixed nature
of object locations over time allows for agents to rely on commonsense
reasoning for path planning, P-ObjectNav performance is not commonsense
driven, but instead sensitive to memory. In the selective case, our agent uses
a Memory Tree to store observations, and returns a path to the agent only when
it is exists. As such, we perform a compression of memory via out memory tree,
which is interfaced to the LLM prompt. In the complete case, all previous
observations are passed to the agent, giving it temporal data to reason about
where to go next. The inclusion of memory allows for agents to store specific
object placement patterns in the environment, allowing for improved inference
over time.

##  5 Conclusion

We present an approach to tackle the ObjectNav task with non-stationary target
objects. In extending ObjectNav, our approach formulates a new task called
Portable ObjectNav or P-ObjectNav, defining conditions for its feasibility. In
our experiment setup, we first customize a Matterport3D environment with non-
stationary portable objects, simulating “dynamic” behavior by introducing
temporal and spatial complexities. Our setup considers three cases, two of
which involve objects being placed in a routine-following manner, and the
other where objects are randomly moved around. Our results with a PPO-based
agent on our Matterport3D environment show that over time, the performance of
an agent placed in a random environment stagnates, while a routine following
agent continues to improve. We can infer from this that object-shifting
behaviors can be learned in a dynamic environment with routine-following
placement behavior, proving our condition for feasibility. Finally, we perform
P-ObjectNav with an LLM-based agent, and infer that only commonsense
knowledge-based navigation is insufficient for good performance. We enhance
the LLM with a memory interface, and the drastic improvement in performance
suggests the need for memory in agents to capture object-specific placement
patterns in the environment for improved inference. Code, dataset and
additional ablation experiments will be released and provided in the
supplementary.

##  6 Limitations and Broader Impacts

As we introduce a new task involving portable objects, we encounter a few
limitations. We currently perform analysis on individual modules for
navigation and visual grounding. As such, errors in the navigation stage would
not affect the grounding and vice versa. Coupling these experiments would
provide a better sense of realism to the high-level policy learning task.

While we provide a theoretical guarantee that an optimal path for maximizing
the number of portable objects found exists if the object placement follows a
routine, we only consider show this for a simplified case of a fully connected
graph. One interesting route is to consider habits of various frequencies to
better model short-term, long-term, and seasonal habits.

Yet another interesting direction, is to combine generative modeling of images
with 2D simulators such as Minigrid [11]. As high-level navigation involves
sequential decision-making at nodes rather than low-level planning, we believe
a 3D simulation environment is too computationally expensive while also being
unnecessarily challenging to setup. As such, one direction to explore is that
of a generative 2D simulator that could generate images to mimic human and
dynamic obstacles at various viewpoints.

In terms of broader impact, we believe our work provides a strong foundation
in establishing the ObjectNav task for non-stationary environments. As such,
we believe it to have an impact on the Embodied AI community, in fostering a
new direction of research for navigation in dynamic, human-centric
environments.

## References

  * [1] AlDahak, A., Elnagar, A.: A practical pursuit-evasion algorithm: Detection and tracking. In: Proceedings 2007 IEEE International Conference on Robotics and Automation. pp. 343–348 (2007). https://doi.org/10.1109/ROBOT.2007.363810
  * [2] Anderson, P., Chang, A., Chaplot, D.S., Dosovitskiy, A., Gupta, S., Koltun, V., Kosecka, J., Malik, J., Mottaghi, R., Savva, M., et al.: On evaluation of embodied navigation agents. arXiv preprint arXiv:1807.06757 (2018)
  * [3] Batra, D., Gokaslan, A., Kembhavi, A., Maksymets, O., Mottaghi, R., Savva, M., Toshev, A., Wijmans, E.: Objectnav revisited: On evaluation of embodied agents navigating to objects. arXiv preprint arXiv:2006.13171 (2020)
  * [4] Cai, K., Wang, C., Cheng, J., De Silva, C.W., Meng, M.Q.H.: Mobile robot path planning in dynamic environments: A survey. arXiv preprint arXiv:2006.14195 (2020)
  * [5] Campari, T., Lamanna, L., Traverso, P., Serafini, L., Ballan, L.: Online learning of reusable abstract models for object goal navigation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 14870–14879 (2022)
  * [6] Cancelli, E., Campari, T., Serafini, L., Chang, A.X., Ballan, L.: Exploiting proximity-aware tasks for embodied social navigation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 10957–10967 (2023)
  * [7] Chang, A., Dai, A., Funkhouser, T., Halber, M., Niessner, M., Savva, M., Song, S., Zeng, A., Zhang, Y.: Matterport3d: Learning from rgb-d data in indoor environments. arXiv preprint arXiv:1709.06158 (2017)
  * [8] Chen, C., Al-Halah, Z., Grauman, K.: Semantic audio-visual navigation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 15516–15525 (2021)
  * [9] Chen, C., Jain, U., Schissler, C., Gari, S.V.A., Al-Halah, Z., Ithapu, V.K., Robinson, P., Grauman, K.: Soundspaces: Audio-visual navigation in 3d environments. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part VI 16. pp. 17–36. Springer (2020)
  * [10] Chen, K., Chen, J.K., Chuang, J., Vázquez, M., Savarese, S.: Topological planning with transformers for vision-and-language navigation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 11276–11286 (2021)
  * [11] Chevalier-Boisvert, M., Dai, B., Towers, M., de Lazcano, R., Willems, L., Lahlou, S., Pal, S., Castro, P.S., Terry, J.: Minigrid & miniworld: Modular & customizable reinforcement learning environments for goal-oriented tasks. CoRR abs/2306.13831 (2023)
  * [12] Choi, Y., Oh, S.: Image-goal navigation via keypoint-based reinforcement learning. In: 2021 18th International Conference on Ubiquitous Robots (UR). pp. 18–21. IEEE (2021)
  * [13] Clark, F., Sanders, K., Carlson, M., Blanche, E., Jackson, J.: Synthesis of habit theory. OTJR: occupation, participation and health 27(1_suppl), 7S–23S (2007)
  * [14] de Curtò, J., de Zarzà, I., Roig, G., Cano, J.C., Manzoni, P., Calafate, C.T.: Llm-informed multi-armed bandit strategies for non-stationary environments. Electronics 12(13), 2814 (2023)
  * [15] Deitke, M., Batra, D., Bisk, Y., Campari, T., Chang, A.X., Chaplot, D.S., Chen, C., D’Arpino, C.P., Ehsani, K., Farhadi, A., et al.: Retrospectives on the embodied ai workshop. arXiv preprint arXiv:2210.06849 (2022)
  * [16] Deitke, M., VanderBilt, E., Herrasti, A., Weihs, L., Salvador, J., Ehsani, K., Han, W., Kolve, E., Farhadi, A., Kembhavi, A., Mottaghi, R.: ProcTHOR: Large-Scale Embodied AI Using Procedural Generation (Jun 2022). https://doi.org/10.48550/arXiv.2206.06994
  * [17] Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: A large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition. pp. 248–255 (Jun 2009). https://doi.org/10.1109/CVPR.2009.5206848
  * [18] Dorbala, V.S., Mullen Jr, J.F., Manocha, D.: Can an embodied agent find your “cat-shaped mug”? llm-based zero-shot object navigation. IEEE Robotics and Automation Letters (2023)
  * [19] Dorbala, V.S., Sigurdsson, G.A., Thomason, J., Piramuthu, R., Sukhatme, G.S.: Clip-nav: Using clip for zero-shot vision-and-language navigation. In: Workshop on Language and Robotics at CoRL 2022 (2022)
  * [20] Gadre, S.Y., Wortsman, M., Ilharco, G., Schmidt, L., Song, S.: Clip on wheels: Zero-shot object navigation as object localization and exploration. arXiv preprint arXiv:2203.10421 (2022)
  * [21] Gadre, S.Y., Wortsman, M., Ilharco, G., Schmidt, L., Song, S.: Cows on pasture: Baselines and benchmarks for language-driven zero-shot object navigation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 23171–23181 (2023)
  * [22] Guan, T., Yang, Y., Cheng, H., Lin, M., Kim, R., Madhivanan, R., Sen, A., Manocha, D.: Loc-zson: Language-driven object-centric zero-shot object retrieval and navigation (2023)
  * [23] Gupta, A., Dollar, P., Girshick, R.: Lvis: A dataset for large vocabulary instance segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (June 2019)
  * [24] Jocher, G., Chaurasia, A., Qiu, J.: Ultralytics YOLO (Jan 2023), https://github.com/ultralytics/ultralytics
  * [25] Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A.C., Lo, W.Y., Dollár, P., Girshick, R.: Segment anything (2023)
  * [26] Krantz, J., Lee, S., Malik, J., Batra, D., Chaplot, D.S.: Instance-specific image goal navigation: Training embodied agents to find object instances. arXiv preprint arXiv:2211.15876 (2022)
  * [27] Labadze, L., Grigolia, M., Machaidze, L.: Role of ai chatbots in education: systematic literature review. International Journal of Educational Technology in Higher Education 20(1), 56 (2023)
  * [28] Li, L.H., Zhang, P., Zhang, H., Yang, J., Li, C., Zhong, Y., Wang, L., Yuan, L., Zhang, L., Hwang, J.N., Chang, K.W., Gao, J.: Grounded Language-Image Pre-training (Jun 2022). https://doi.org/10.48550/arXiv.2112.03857
  * [29] Li, W., Hong, R., Shen, J., Yuan, L., Lu, Y.: Transformer memory for interactive visual navigation in cluttered environments. IEEE Robotics and Automation Letters 8(3), 1731–1738 (2023). https://doi.org/10.1109/LRA.2023.3241803
  * [30] Liang, Y., Chen, B., Song, S.: Sscnav: Confidence-aware semantic scene completion for visual semantic navigation. In: 2021 IEEE international conference on robotics and automation (ICRA). pp. 13194–13200. IEEE (2021)
  * [31] Lin, T.Y., Maire, M., Belongie, S.J., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft coco: Common objects in context. In: European Conference on Computer Vision (2014), https://api.semanticscholar.org/CorpusID:14113767
  * [32] Liu, S., Zeng, Z., Ren, T., Li, F., Zhang, H., Yang, J., Li, C., Yang, J., Su, H., Zhu, J., et al.: Grounding dino: Marrying dino with grounded pre-training for open-set object detection. arXiv preprint arXiv:2303.05499 (2023)
  * [33] Mavrogiannis, C.I., Knepper, R.A.: Decentralized multi-agent navigation planning with braids. In: Algorithmic Foundations of Robotics XII: Proceedings of the Twelfth Workshop on the Algorithmic Foundations of Robotics. pp. 880–895. Springer (2020)
  * [34] Mohanan, M., Salgoankar, A.: A survey of robotic motion planning in dynamic environments. Robotics and Autonomous Systems 100, 171–185 (2018). https://doi.org/https://doi.org/10.1016/j.robot.2017.10.011, https://www.sciencedirect.com/science/article/pii/S0921889017300313
  * [35] Morris, T., Dayoub, F., Corke, P., Wyeth, G., Upcroft, B.: Multiple map hypotheses for planning and navigating in non-stationary environments. In: 2014 IEEE international conference on robotics and automation (ICRA). pp. 2765–2770. IEEE (2014)
  * [36] Puig, X., Undersander, E., Szot, A., Cote, M.D., Yang, T.Y., Partsey, R., Desai, R., Clegg, A.W., Hlavac, M., Min, S.Y., et al.: Habitat 3.0: A co-habitat for humans, avatars and robots. arXiv preprint arXiv:2310.13724 (2023)
  * [37] Ramakrishnan, S.K., Gokaslan, A., Wijmans, E., Maksymets, O., Clegg, A., Turner, J., Undersander, E., Galuba, W., Westbury, A., Chang, A.X., et al.: Habitat-matterport 3d dataset (hm3d): 1000 large-scale 3d environments for embodied ai. arXiv preprint arXiv:2109.08238 (2021)
  * [38] Ramakrishnan, S.K., Chaplot, D.S., Al-Halah, Z., Malik, J., Grauman, K.: Poni: Potential functions for objectgoal navigation with interaction-free learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 18890–18900 (2022)
  * [39] Ramrakhya, R., Batra, D., Wijmans, E., Das, A.: Pirlnav: Pretraining with imitation and rl finetuning for objectnav. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 17896–17906 (2023)
  * [40] Ramrakhya, R., Undersander, E., Batra, D., Das, A.: Habitat-web: Learning embodied object-search strategies from human demonstrations at scale. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 5173–5183 (2022)
  * [41] Raychaudhuri, S., Campari, T., Jain, U., Savva, M., Chang, A.X.: Mopa: Modular object navigation with pointgoal agents. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. pp. 5763–5773 (2024)
  * [42] Rudra, S., Goel, S., Santara, A., Gentile, C., Perron, L., Xia, F., Sindhwani, V., Parada, C., Aggarwal, G.: A contextual bandit approach for learning to plan in environments with probabilistic goal configurations. In: 2023 IEEE International Conference on Robotics and Automation (ICRA). pp. 5645–5652. IEEE (2023)
  * [43] Sathyamoorthy, A.J., Liang, J., Patel, U., Guan, T., Chandra, R., Manocha, D.: Densecavoid: Real-time navigation in dense crowds using anticipatory behaviors. In: 2020 IEEE International Conference on Robotics and Automation (ICRA). pp. 11345–11352. IEEE (2020)
  * [44] Savva, M., Kadian, A., Maksymets, O., Zhao, Y., Wijmans, E., Jain, B., Straub, J., Liu, J., Koltun, V., Malik, J., Parikh, D., Batra, D.: Habitat: A Platform for Embodied AI Research (Nov 2019)
  * [45] Shah, D., Eysenbach, B., Kahn, G., Rhinehart, N., Levine, S.: Ving: Learning open-world navigation with visual goals. In: 2021 IEEE International Conference on Robotics and Automation (ICRA). pp. 13215–13222. IEEE (2021)
  * [46] Stiffler, N.M., O’Kane, J.M.: Planning for robust visibility-based pursuit-evasion. In: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). pp. 6641–6648 (2020). https://doi.org/10.1109/IROS45743.2020.9341031
  * [47] Vladareanu, V., Tont, G., Vladareanu, L., Smarandache, F.: The navigation of mobile robots in non-stationary and non-structured environments. International Journal of Advanced Mechatronic Systems 5(4), 232–242 (2013)
  * [48] Wu, Q., Wang, J., Liang, J., Gong, X., Manocha, D.: Image-goal navigation in complex environments via modular learning. IEEE Robotics and Automation Letters 7(3), 6902–6909 (2022)
  * [49] Yadav, K., Ramrakhya, R., Ramakrishnan, S.K., Gervet, T., Turner, J., Gokaslan, A., Maestre, N., Chang, A.X., Batra, D., Savva, M., et al.: Habitat-matterport 3d semantics dataset. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 4927–4936 (2023)
  * [50] Yokoyama, N.H., Ha, S., Batra, D., Wang, J., Bucher, B.: Vlfm: Vision-language frontier maps for zero-shot semantic navigation. In: 2nd Workshop on Language and Robot Learning: Language as Grounding (2023)
  * [51] Yu, B., Kasaei, H., Cao, M.: L3mvn: Leveraging large language models for visual target navigation. In: 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE (Oct 2023). https://doi.org/10.1109/iros55552.2023.10342512, http://dx.doi.org/10.1109/IROS55552.2023.10342512
  * [52] Zhai, A.J., Wang, S.: Peanut: predicting and navigating to unseen targets. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 10926–10935 (2023)
  * [53] Zhao, X., Ding, W., An, Y., Du, Y., Yu, T., Li, M., Tang, M., Wang, J.: Fast segment anything (2023)
  * [54] Zhao, X., Ding, W., An, Y., Du, Y., Yu, T., Li, M., Tang, M., Wang, J.: Fast segment anything. arXiv preprint arXiv:2306.12156 (2023)
  * [55] Zheng, Y., Meng, Z., Hao, J., Zhang, Z., Yang, T., Fan, C.: A deep bayesian policy reuse approach against non-stationary agents. Advances in neural information processing systems 31 (2018)
  * [56] Zhou, G., Hong, Y., Wu, Q.: Navgpt: Explicit reasoning in vision-and-language navigation with large language models (2023)
  * [57] Zhou, K., Zheng, K., Pryor, C., Shen, Y., Jin, H., Getoor, L., Wang, X.E.: Esc: Exploration with soft commonsense constraints for zero-shot object navigation. arXiv preprint arXiv:2301.13166 (2023)
  * [58] Zhou, Y., Ho, H.W.: Online robot guidance and navigation in non-stationary environment with hybrid hierarchical reinforcement learning. Engineering Applications of Artificial Intelligence 114, 105152 (2022)


# Discovery of a damped Ly$\alpha$ galaxy at $z\sim 3$ towards the quasar SDSS
J$011852+040644$

Ravi Joshi  Kavli Institute for Astronomy and Astrophysics, Peking University,
Beijing 100871, China  Michele Fumagalli  Dipartimento di Fisica G.
Occhialini, Università degli Studi di Milano Bicocca, Piazza della Scienza 3,
20126 Milano, Italy  Institute for Computational Cosmology, Durham University,
South Road, Durham, DH1 3LE, UK  Centre for Extragalactic Astronomy, Durham
University, South Road, Durham, DH1 3LE, UK  Raghunathan Srianand  Inter-
University Centre for Astronomy and Astrophysics, Post Bag 4, Ganeshkhind,
Pune 411007, India  Pasquier Noterdaeme  Sorbonne Universitè, CNRS, UMR 7095,
Institut d’Astrophysique de Paris, 98 bis bd Arago, 75014 Paris, France
Patrick Petitjean  Sorbonne Universitè, CNRS, UMR 7095, Institut
d’Astrophysique de Paris, 98 bis bd Arago, 75014 Paris, France  Marc Rafelski
Space Telescope Science Institute, Baltimore, MD 21218, USA  Department of
Physics & Astronomy, Johns Hopkins University, Baltimore, MD 21218, USA  Ruari
Mackenzie  Centre for Extragalactic Astronomy, Durham University, South Road,
Durham, DH1 3LE, UK  Department of Physics, ETH Zurich, Wolfgang-Pauli-Strasse
27, 8093 Zurich, Switzerland  Qiong Li  Kavli Institute for Astronomy and
Astrophysics, Peking University, Beijing 100871, China  Department of
Astronomy, School of Physics, Peking University, Beijing 100871, China  Zheng
Cai  Department of Astronomy, Tsinghua University, Beijing 100084, China  D.
Christopher Martin  Cahill Center for Astrophysics, California Institute of
Technology, 1216 East California Boulevard, Mail Code 278-17, Pasadena,
California 91125, USA  Siwei Zou  Kavli Institute for Astronomy and
Astrophysics, Peking University, Beijing 100871, China  Xue-Bing Wu  Kavli
Institute for Astronomy and Astrophysics, Peking University, Beijing 100871,
China  Department of Astronomy, School of Physics, Peking University, Beijing
100871, China  Linhua Jiang  Kavli Institute for Astronomy and Astrophysics,
Peking University, Beijing 100871, China  Luis C. Ho  Kavli Institute for
Astronomy and Astrophysics, Peking University, Beijing 100871, China
Department of Astronomy, School of Physics, Peking University, Beijing 100871,
China Ravi Joshi<EMAIL_ADDRESS>
###### Abstract

We report the detection of the host galaxy of a damped Ly$\alpha$ system (DLA)
with log $N$(H i) $[\rm cm^{-2}]$ = $21.0\pm 0.10$ at $z\approx 3.0091$
towards the background quasar SDSS J$011852+040644$ using the Palomar Cosmic
Web Imager (PCWI) at the Hale (P200) telescope. We detect Ly$\alpha$ emission
in the dark core of the DLA trough at a 3.3$\sigma$ confidence level, with
Ly$\alpha$ luminosity of $L_{\rm Ly\alpha}$ $\rm=(3.8\pm 0.8)\times 10^{42}\
erg\ s^{-1}$, corresponding to a star formation rate of $\gtrsim 2\ \rm
M_{\odot}\ yr^{-1}$ (considering a lower limit on Ly$\alpha$ escape fraction
$f_{esc}^{Ly{\alpha}}\sim 2\%$) as typical for Lyman break galaxies at these
redshifts. The Ly$\alpha$ emission is blueshifted with respect to the systemic
redshift derived from metal absorption lines by $281\pm 43$ km s-1. The
associated galaxy is at very small impact parameter of $\lesssim 12\rm\ kpc$
from the background quasar, which is in line with the observed anticorrelation
between column density and impact parameter in spectroscopic searches tracing
the large-scale environments of DLA host galaxies.

quasars: absorption lines – galaxies: high-redshift – galaxies:ISM – galaxies:
star formation

##  1 Introduction

The evolution of galaxies is significantly influenced by the physical state of
gas in and around the central star-forming regions. Observations of local
galaxies indicate that the atomic and molecular hydrogen, which make up most
of the mass in the interstellar medium (ISM) of galaxies, closely trace the
star-formation rate and are the key elements that participate in inflows and
outflows (Bigiel et al., 2008; Genzel et al., 2010; Cortese et al., 2011;
Janowiecki et al., 2017).

Unfortunately, mapping neutral hydrogen (H i) gas in emission from galaxies is
difficult at even moderate redshifts (Kanekar et al., 2016). This low density
gas imprints, however, absorption lines on the spectra of an unrelated bright
background sources, which offers a powerful tool to study the physical and
chemical properties of the intervening gas in a luminosity independent manner
(see Wolfe et al., 2005, for a review). At high redshift, most of our
knowledge of H i gas primarily comes from a particular class of absorption
line systems, the damped Ly$\alpha$ absorbers (DLAs) seen in quasar spectra.
With an H i column density of $\geq 2\times 10^{20}~{}\rm cm^{-2}$, DLAs
account for the bulk ($>80\%$) of the neutral hydrogen in the early Universe
(Péroux et al., 2003; Prochaska & Wolfe, 2009; Noterdaeme et al., 2009, 2012).

Moreover, DLAs appear to be linked to star-forming regions, as evidenced by
the metallicity evolution of DLAs with redshift (Rafelski et al., 2012, 2014;
Jorgenson et al., 2013) and the velocity spread of low-ion absorption lines
(see Wolfe et al., 2005). The average properties of Ly$\alpha$ emission from
DLAs, inferred from the stacking experiment of hundreds of DLAs from the Sloan
Digital Sky Survey (SDSS), further indicate a connection between star
formation activity and outflows in DLA host galaxies (see also, Rahmani et
al., 2010; Noterdaeme et al., 2014; Joshi et al., 2017). Therefore,
establishing a direct association of the H i gas seen in absorption with
emission from galaxies is a useful way to probe the link between the H i gas
and star formation at high redshift.

Earlier efforts to detect DLA host galaxies either in continuum emission or
nebular line emission have been moderately successful in completely blind
surveys, with a detection rate of $\sim 10\%$ (Möller et al., 2004), with
several studies mostly resulting in non-detections (Kulkarni et al., 2000;
Christensen et al., 2009; Fumagalli et al., 2015). Leveraging the observed
correlation between luminosity and metallicity in galaxies (Tremonti et al.,
2004; Ledoux et al., 2006; Møller et al., 2013; Christensen et al., 2014),
recent campaigns have focused instead on metal-rich DLAs, resulting in a far
higher detection rate of $\approx 65\%$ (Fynbo et al., 2010, 2011; Krogager et
al., 2017; Ranjan et al., 2020).

In spite of these numerous attempts, however, only $\approx 20$ DLAs at
redshift $\gtrsim 2$ have been associated directly to counterparts in emission
(see Krogager et al., 2017). This low detection rate could be attributed
either to the faint nature of DLA galaxies (which become difficult to image at
close separation from bright background quasars), or to their dusty nature, or
to high H i column density, or yet again to the fact that only a fraction of
the DLA population is directly connected to active star-formation.

More recently, interferometers such as the Atacama Large
Millimeter/submillimeter Array (ALMA) have overcome the dust bias, with the
detection of $\sim$ 10 molecular gas-rich systems using CO rotational
transitions and the atomic [CII] line (Neeleman et al., 2016, 2018; Fynbo et
al., 2018; Klitsch et al., 2019). So far, these studies have focused on
tracing relatively high-metallicity systems, finding the DLA hosts at
relatively large impact parameters, $\sim 16-45\rm\ kpc$, and with high
molecular gas masses of $10^{10}-10^{11}M_{\odot}$. Following these successes,
efforts to detect more representative DLAs are ongoing.

Furthermore, the use of integral field spectrographs (IFSs) at 8-10m class
telescopes has proven to be a very efficient tool for searching DLA galaxies
and for characterizing their environment out to several hundreds of
kiloparsecs (Péroux et al., 2011, 2012; Fumagalli et al., 2017; Mackenzie et
al., 2019). For example, using the MUSE IFS at the VLT telescope, Fumagalli et
al. (2017) have detected a tantalizing example of extended Ly$\alpha$ emission
tracing gas in a region of about 50 kpc near a $z\approx 3$ DLA. This region
hosts multiple galaxies, possibly in a filament, with Ly$\alpha$ emission
induced by in-situ star formation likely triggered by interactions.

Moreover, in a recent MUSE survey of 6 DLAs at $z\sim 3$, Mackenzie et al.
(2019) have obtained a high detection rate of galaxies up to $\approx 80\%$
within 1000 km s-1 of the DLAs, with impact parameters ranging between 25 and
280 kpc. Notably, in contrast to previous searches, the blind survey of
Mackenzie et al. (2019) has yielded detections of multiple galaxies also for
low metallicity systems (see their figure 9), including a galaxy group
associated with a metal poor DLA ($Z/Z_{\odot}\approx-2.33$). With a small but
representative sample, using cosmological simulations, these authors have been
able to place constraints on the typical mass of halos that host DLAs in the
range $10^{11}-10^{12}M_{\odot}$.

To further understand the link between H i gas and star formation near the
peak of the cosmic star-formation activity, we have started a survey to search
for high-redshift ($z\gtrsim 3$) DLA host galaxy counterparts in Ly$\alpha$
emission using the Palomar Cosmic Web Imager (PCWI). In this article, we
present results from a pilot observation that traces the large scale
environment of a strong intervening DLA with $\log$ $N$(H i)
$\rm[cm^{-2}]=21.0\pm 0.10~{}$ at $z_{\rm abs}$$\approx 3.0091$ out to 80 kpc.
Our observations lead to the discovery of the host galaxy, revealing a direct
association of the absorbing gas with star-formation, with no other
counterpart within the field of view $20\arcsec\times 40\arcsec$. This paper
is organized as follows. Section 2 describes the sample selection. In Section
3, we present the observations and data reduction. In Section 4, we present
results of our analysis, followed by a discussion and conclusion in Section
5\. Throughout, we have assumed a flat Universe with $H_{0}=$ 70 km s-1 $\rm
Mpc^{-1}$, $\Omega_{\rm m}=0.3$ and $\Omega_{\rm\Lambda}=0.7$.

Figure 1: _Top panel:_ The Ly$\alpha$ absorption profile in the SDSS spectrum
(black histogram) in the velocity scale with respect to $z_{\rm abs}$$\sim$
3.0091. The estimated unabsorbed quasar continuum is shown as blue dashed
curve along with the error spectrum using the dot-dashed curve. The continuum
template modified by the damped Ly$\alpha$ absorption is shown with a red
solid line, marking the profile uncertainty corresponding to 1$\sigma$ error
in column density with a red shaded region. _Middle panel:_ 1D quasar spectrum
from the original PCWI data (gray histogram) and following resampling at the
SDSS resolution of 2.5Å(black histogram). A new model fit derived on PCWI
data, which is consistent with the SDSS estimate, is also shown. _Bottom
panel:_ 2D quasar spectrum constructed from PCWI data cube. The trace of the
quasar is shown as dashed line. Figure 2:  Ratio between the flux dispersion
in apertures of varying size ($\sigma_{\rm eff}$) and the error computed
propagating the pixel standard deviation ($\sigma_{\rm N}$), which is useful
to assess the impact of correlated noise within the PCWI data cube.

##  2 Sample selection

Using the compilation of thousands of DLAs from SDSS (Noterdaeme et al.,
2012), we have selected a subset having high H i column density, with log
$N$(H i)$\rm[cm^{-2}]$ $\geq 21~{}\rm$, which provides the favorable
environment for star formation and thus is likely to trace regions in close
proximity to star-forming galaxies (Krogager et al., 2012; Altay & Theuns,
2013). Moreover, at these high column densities, the Ly$\alpha$ absorption has
a dark (optically thick) core, which spreads over at least 7 times the average
full-width at half-maximum (FWHM, $\approx$ 160 $\rm km~{}s^{-1})$ of the
instrumental profile of the SDSS spectrograph. This makes it possible to
search for Ly$\alpha$ emission lines within the spectrum.

We consider only the DLAs detected in SDSS spectra with a median continuum-to-
noise ratio $>3$ which ensure an accurate determination of the H i column
density. In addition, we avoid DLAs that are proximate to the quasars by
considering only systems with velocity offsets of $>5000\ \rm km\ s^{-1}$ with
respect to the quasar emission redshift. We also exclude sightlines showing
broad absorption lines from quasar outflows. To avoid introducing a
metallicity bias, we do not preselect targets based on metal lines (e.g., Si
ii, Fe ii, C ii).

In order to maximise the detection rate of Ly$\alpha$ emission, we further
search the spectra to identify systems with tentative Ly$\alpha$ emission
(non-zero flux) within the absorption trough where the quasar continuum goes
to zero (i.e., the dark core). For this, we avoid the regions with bright sky
emission to exclude the false positives due to residuals of the sky
subtraction. Due to the finite fiber size of SDSS, this step introduces a
selection effect, that is detections are expected to primarily occur at small
impact parameters of $\lesssim 15~{}\rm kpc$ (see below). An example of system
selected in this way, which is also the target of our pilot observations, is
shown in the upper panel of Figure 1.

Following visual inspection to remove systems with clear sky residuals or
artifacts, this selection resulted in a unique set of 13 DLAs (out of 608)
with absorption redshifts $z\geq 2.9$. Among them, 10 systems lie at
declinations that Hale (P200) can reach and are suitable for P200
observations. As a further verification of the presence of possible Ly$\alpha$
emission, we have also examined the multi-epoch observations from SDSS, which
exists for 3 DLAs in our sample. Reassuringly, all 3 systems shows Ly$\alpha$
emission in spectra at different epochs, albeit with low signal-to-noise. In
order to be able to detect the minimum Ly$\alpha$ emission flux of $\sim
2\times 10^{-17}\rm erg\ s^{-1}\ cm^{-2}$ found in our sample, an integration
of $\sim 1$hr on source and $\sim 1$hr on sky would allow us to detect the
emission feature at more than $\sim 5\sigma$ level. In what follows, we
present the results for the first target successfully observed so far.

##  3 Observations and Data reduction

We have performed observations of the quasar SDSS J$011852+040644$ ($z_{\rm
em}$ $\approx 3.226$) with an intervening DLA system from our selection above
($z_{\rm abs}$ $\approx 3.0091$ and log $N$(H i)$\rm[cm^{-2}]=21.0\pm 0.1$)
using the Palomar Cosmic Web Imager (PCWI) instrument mounted on the Hale 5
meter telescope on Mt. Palomar. PCWI uses a $40{\arcsec}\times 60{\arcsec}$
reflective image slicer with 24 slices of dimension $40{\arcsec}\times
2.5{\arcsec}$ each. The observations have been conducted on the night UT
20180816 with a clear sky and with airmass ranging between 1 to 2. We have
used the Richardson (MedRez) gratings with a slit-limited spectral resolution
of $\Delta\lambda\sim 1$Å.

The individual exposures were acquired using the standard PCWI nod-and-shuffle
technique, where the central 1/3 of the CCD is used for recording the spectrum
while masking the outer 2/3 of the CCD, restricting the spectral bandpass to
$\sim$ 150 Å (see, Martin et al., 2014). Note that our DLA sample is
preselected based on the likely presence of Ly$\alpha$ emission within the 2
or 3 arcsec SDSS-III or SDSS-II fibre spectra. Thus, it is expected that the
DLA host galaxy lies at small impact parameters (i.e, $\sim$ 8 to 12 kpc).
However, to trace the large scale environment around the quasar, while
performing nod-and-shuffle, we offset the frame by 25 arcsec so that the
quasar remains within the frame at all time. We acquired a series of 1200s
exposures, totalling 1.6 h with the quasar at the center, and 1.6 h after the
offset. Combined, this technique doubles the total integration time for the
object, to 3.3 h. This strategy resulted in an effective field of view of
$20\arcsec\times 40\arcsec$ with the quasar at the center which allow us to
search for the DLA host galaxy and trace its large scale environment out to
$\approx 80~{}\rm kpc$ ($\approx 10$ arcsec at $z\sim 3$).

The data are reduced with the standard PCWI pipeline (Martin et al., 2014).
The flux calibration was performed using the standard star BD$+$28D4211,
observed on the same night, and the final datacube is combined by weighting
individual exposures according to their inverse variance. In addition, the
wavelengths are converted into their values in vacuum. The final cube has a
pixel size of $\sim 1.5$ arcsec in the spatial direction, and 0.55 Å in the
wavelength direction. In addition, the spatial resolution of PCWI cube is
seeing limited along the slices in the short direction, which at the time of
observations was $1.4$ arcsec, and slit limited ($\sim$ 2.5 arcsec) in the
long direction.

Figure 3: Velocity plots of absorption lines and the Ly$\alpha$ emission from
DLA host galaxy. The zero velocity is defined with respect to the DLA redshift
at $z_{\rm abs}$= $3.0091\pm 0.0001$. The bottom panel shows the Ly$\alpha$
emission extracted in an aperture radius of 2 arcsec together with the best
fitting Gaussian model. In the top panels, we plot absorption line profiles of
Al ii, C ii, and Fe ii detected in SDSS spectrum.

##  4 Results

With the final PCWI datacube in hand, we first extract the quasar spectrum in
an aperture with radius of 3 arcsec which is twice the FWHM and encompasses
the total quasar emission. In Fig. 1, we compare the DLA absorption profile in
the flux-calibrated 1D quasar spectrum from our moderate-resolution ($R\sim
5000$) PCWI data (middle panel) with the lower-resolution ($R\sim 2000$) SDSS
fiber spectrum (top panel). The best fit continuum is shown with a dashed
(blue) curve, which is modelled with a quasar composite template from Harris
et al. (2016) by adjusting the continuum power-law slope and normalisation to
fit the quasar continuum over the Ly$\alpha$ forest. It is clear that the DLA
absorption profile and quasar continuum level agree well in both spectra.
Next, we measure the H i column densities by fitting Voigt profiles to the
Ly$\alpha$ lines in the flux-calibrated SDSS and PCWI spectra, as shown in
Fig.1. The modelled absorption profile touches the unabsorbed region of the
spectrum, but also due to the low resolution, there are regions that are
absorbed by the forest. The derived $N$(H i) from the lower-resolution SDSS
spectrum is log$N$(H i)$\rm[cm^{-2}]=21.03\pm 0.10$, while our PCWI data
result in log$N$(H i)$\rm[cm^{-2}]=20.90\pm 0.13$. Both measurements are
consistent with each other and with the measurement of log$N$(H
i)$\rm[cm^{-2}]=21.0\pm 0.10$ presented in Noterdaeme et al. (2012).

Figure 4:  _Left Panel:_ The Ly$\alpha$ emission map integrated over a
velocity window from $v=-450$ km s-1 to $v=-187$ km s-1, comprising the
Ly$\alpha$ emission feature, reveal the presence of DLA host galaxy at an
impact parameter of $\lesssim 12$ kpc. The black contours mark the continuum
detected source (i.e. the quasar). The quasar and the DLA host galaxy centers
are marked with a diamond and a cross, respectively. _Right Panel:_ The white-
light image reconstructed from the PCWI data cube showing the quasar at the
center. The cyan contours mark the Ly$\alpha$ emission map at the flux levels
of 0.35, 0.45, 0.55 $\times 10^{-17}\rm erg\ s^{-1}cm^{-2}$. Figure 5:  H i
Column density versus impact parameter relation for DLA host galaxies at
$z>1.5$. The filled circles show the DLAs identified over 25 year in long-slit
based spectroscopic searches, compiled by Krogager et al. (2017) as well as 3
extremely strong (log$N$(H i) $\rm[cm^{-2}]\geq 21.7$) DLAs from Ranjan et al.
(2020). The open diamonds show the galaxy population near DLAs from the recent
MUSE survey by Mackenzie et al. (2019). The red star shows our PCWI DLA host
galaxy detected at an impact parameter of $\lesssim 12$ kpc.

In line with the pre-selection from SDSS, we clearly see an emission line
signature in the DLA core at the expected position based on the SDSS spectrum.
This is also evident from the reconstructed 2D spectrum from the PCWI datacube
(see lower panel of Fig 1). To quantifying the detection significance of the
Ly$\alpha$ emission, we first need to account for any correlated noise
introduced by the resampling of individual pixels in the final data cube. Such
correlated noise typically results in an underestimate of the effective noise
inside an aperture and, thus in an overestimate of the real $S/N$ of a source
(Gawiser et al., 2006; Fumagalli et al., 2014).

To model the noise variation as a function of aperture size, we compute the
effective noise $\sigma_{eff}$ as the standard deviation of fluxes by
considering only the regions free from the continuum detected sources across
the PCWI data cube over a cubic apertures of 4 spectral pixels ($\sim$ 4 Å)
and a variable aperture size in the spatial direction. Fig. 2 shows the ratio
between this effective noise ($\sigma_{eff}$) and the error computed by
propagating the variance ($\sigma_{N}$) as a function of apertures size.
Ratios above unity indicate that the pipeline noise is underestimated by a
factor of $\sim$ 50% for an aperture of $\sim 3$ arcsec. We account for this
effect throughout our analysis.

Focusing on the properties of the emission line next, a single component
Gaussian fit to the Ly$\alpha$ line gives an intrinsic FWHM (i.e. deconvolved
from instrumental effects) of $\approx 131$ km s-1 and a velocity dispersion
of $\sigma\approx 56$ km s-1. The Ly$\alpha$ emission is found to be
blueshifted from the systemic redshift of $z_{\rm abs}$$\approx 3.0091$
derived from metal absorption lines (see upper three panels of Fig. 3) by
$281\pm 43$ km s-1. The flux of the Ly$\alpha$ line is found to be $f_{\rm
Ly\alpha}=\rm(4.9\ \pm 0.9)\times 10^{-17}erg\ s^{-1}\ cm^{-2}$ detected at
5.3$\sigma$ level which reduces to 3.9$\sigma$ level after accounting for the
correlated noise (see Fig 2). This corresponds to a Ly$\alpha$ luminosity of
$L_{\rm Ly\alpha}$ $\rm=(3.8\pm 0.7)\times 10^{42}\ erg\ s^{-1}$ at the DLA
redshift. Although the detection significance is marginal from a statistical
point of view, the presence of emission feature exactly at the expected
location from the SDSS spectrum further strengthen the case for real
detection.

In order to identify the location of the galaxy responsible for Ly$\alpha$
emission, we generate a Ly$\alpha$ emission map integrated over a velocity
window from $v=-450$ km s-1 to $v=-187$ km s-1, comprising the Ly$\alpha$
emission feature. The left panel of Fig. 4 shows the Ly$\alpha$ emission map,
revealing the location of the DLA host galaxy. Given the poor spatial
resolution of PCWI, we could only place an upper limit on the extent of the
Ly$\alpha$ emission to be $<30$ kpc, at a surface brightness limit of
$\Sigma_{Ly\alpha}>10^{-17.5}\ \rm erg\ s^{-1}\ cm^{-2}\ arcsec^{-2}$. The
right panel of Fig. 4 shows instead the quasar image in the continuum after
collapsing the cube. The overlayed contours mark the Ly$\alpha$ emission map
at the flux levels of 0.35, 0.45, 0.55 $\times 10^{-17}\rm erg\
s^{-1}cm^{-2}$.

It is clear from the figure that the peak flux of the Ly$\alpha$ emission
(marked as cross symbol) is off centered from quasar (marked as diamond). More
quantitatively, we have calculated the separation between quasar and DLA host
galaxy based on light-weighted center, finding $0.6$ arcsec with a
corresponding projected distance of $5$ kpc. Given that the offset is less
than the pixel scale we consider this as a lower limit. In addition,
constrained by the pixel size we measure an upper limit on impact parameter of
12 kpc (see also, Fig.5).

##  5 Discussion and Conclusion

We report the detection of Ly$\alpha$ emission from the host galaxy of a DLA
with log$N$(H i) $\rm[cm^{-2}]=21.0\pm 0.10$ at $z_{\rm abs}$$=3.0091$ toward
the background quasar SDSS J$011852+040644$ at $z_{\rm em}$ = $3.226$. The DLA
host is detected, by selection, at a small impact parameter of $\lesssim 12$
kpc with Ly$\alpha$ luminosity of $L_{\rm Ly\alpha}$ $\rm=(3.8\pm 0.7)\times
10^{42}\ erg\ s^{-1}$, which is typical of the characteristic luminosity (log
L${}_{\star}\rm[erg\ s^{-1}]\ =\ 42.66$) of the Ly$\alpha$ emitter galaxies at
$z\sim 3$ (Herenz et al., 2019).

Given the resonant scattering nature of the Ly$\alpha$ line, the emergent
profile is modified and suppressed by many physical effects, e.g. H i content,
gas geometry and kinematics, and the dust content and distribution. For
instance, in an optically-thick static medium Ly$\alpha$ escapes through
successive resonance scattering leading to a double-humped profile, with the
position of the peaks determined by column density, temperature, and
kinematics of the medium (Neufeld, 1990; Dijkstra, 2014). In addition,
scattering through an inflowing (outflowing) medium leads to an overall
blueshift (redshift) of the Ly$\alpha$ profile with enhanced blue (red) peak
and suppressed red (blue) peak (Dijkstra et al., 2006, , see below). In a pure
static medium, the expected velocity offset of the Ly$\alpha$ emission is
$\sim$ 344 km s-1 if we assume H i gas temperatures of $10^{4}$ K (Dijkstra,
2014, see their eq. 21). For a column density of $10^{21}~{}\rm cm^{-2}$,
consistent with this DLA, a velocity of $\approx 300~{}\rm km~{}s^{-1}$ is
expected, in line with our observations ($\approx 281$ km s-1 with respect to
the systemic redshift derived from metal absorption lines).

However, a static configuration is perhaps unlikely in real systems, and the
blue offset seen for this DLA host may arise because of an inflowing gas
geometry (e.g., Rauch et al., 2008, 2011). Dijkstra et al. (2006) have
modelled the spectra and surface brightness distributions for the Ly$\alpha$
radiation from collapsing protogalaxies. They demonstrate that due to transfer
of energy from the collapsing gas to the Ly$\alpha$ photons, together with a
reduced escape probability for photons in there wing, causes the blue peak to
be significantly enhanced, which results in an effective blueshift of the
Ly$\alpha$ line. Furthermore, employing a three-dimensional Ly$\alpha$
radiative transfer code, Laursen & Sommer-Larsen (2007) have investigated the
properties of young Ly$\alpha$ emitting galaxies at high redshift (z $\sim$ 3)
from a cosmological galaxy formation simulation and found the dominant blue
peak showing the signature of infalling gas (see also, Laursen et al., 2009).
Such Ly$\alpha$ emission profiles with prominent blue peaks and suppressed red
peaks, with typical offset of a few hundred km s-1 as seen here, have been
observed in several Ly$\alpha$ blobs (Bower et al., 2004; Wilman et al.,
2005), LAEs (Bunker et al., 2003) as well as one high confidence DLA by
Mackenzie et al. (2019) in VLT-MUSE observations.

Absorption lines from C ii 1334Å, Fe ii 1608Å, and Al ii 1670Å ions are
detected in the SDSS spectrum with an equivalent width of $0.36\pm 0.04$Å,
$0.25\pm 0.08$Å, and $0.59\pm 0.09$Å, respectively. Based on the strong
correlation seen between the rest-frame equivalent width of Si ii$\lambda$1526
line and metallicity, we derive an upper limit on the metallicity of
log$Z/Z_{\odot}<-1.58$ from the observed $3\sigma$ upper limit of 0.34Å on Si
ii$\lambda$1526 equivalent width (Prochaska et al., 2008; Jorgenson et al.,
2013; Neeleman et al., 2013). In addition, we also derive a metallciity of
$\simeq-$2.13 based on C ii$\lambda$1334 line using the apparent optical depth
method. This limit also places the DLA just below the average metallicity of
the population at this redshift (Rafelski et al., 2012), and well below the
typical metallicity of DLAs chosen for targetted searches towards enriched
systems, which typically select DLAs with log $Z/Z_{\odot}>-1.0$ (see,
Krogager et al., 2017). To date, only one other DLA host at this low
metallicity, namely J$2358+0149$ with log Z/Z$\odot$ $\simeq-$1.7 derived
based on Si II absorption, is detected at small impact parameter of $<15$ kpc
(Srianand et al., 2016).

Besides the detection of emission coincident with the DLA position, our PCWI
search has allowed us to trace the environment of this DLA out to 80 kpc.
However, apart from the detection of a fairly bright DLA host, no other
galaxies are seen in the field, to a luminosity limit of $5.3\times 10^{41}\rm
erg\ s^{-1}$. This is a somewhat rare occurrence compared to previous studies
that have examined the large-scale environment of DLAs. For instance, efforts
to directly image the DLA host galaxies show a very small probability of DLAs
being associated with bright Lyman-break galaxies at distances $<10-20~{}\rm
kpc$. These studies favour instead associations with either faint, possibly
isolated, star-forming galaxies or dwarf galaxies which are clustered with
more massive LBGs (see, Fumagalli et al., 2015). Moreover, in a recent MUSE
survey of 6 high redshift ($z>3$) quasar sightlines with H i column density
ranging between 20.3 $\leq$ log$N$(H i)$\rm[cm^{-2}]\leq 21.15$ Mackenzie et
al. (2019) have traced the environment of DLA host galaxies out to 250 kpc,
detecting 5 high-confidence Ly$\alpha$ emitting galaxies associated with three
DLAs and 9 lower-significance Ly$\alpha$ emission objects in five sightlines.
The MUSE detections are typically found at relatively large impact parameters
of $>50$ kpc, implying that DLAs generally trace the neutral gas in a wide
variety of rich environments, including overdense structures with multiple
members.

In Fig.5, we explore in more detail the known anticorrelation between the
impact parameters versus $N$(H i). Considering only the high-confidence DLA
associations from the literature and the IFU-based searches probing large
scale environments, a clear trend seems to emerge, although with large
scatter. Indeed, high $N$(H i) systems are observed at preferentially small
impact parameters, with a Pearson’s correlation coefficient of $-0.544$ and a
$p$ value of 0.029 (Zwaan et al., 2005; Péroux et al., 2011; Rubin et al.,
2015; Krogager et al., 2017). It should however be noted that some of these
detections rely on long-slit spectroscopic measurements, for which only small
impact parameters are accessible. Larger samples studied with large format
IFUs are needed to confirm the significance of this relation.

As a final point, we infer a value for the _in-situ_ star formation rate (SFR,
$(\dot{M}_{\rm SF})$) of this DLA host, by assuming that the Ly$\alpha$
photons mainly originate from H ii regions around massive stars embedded in
the DLAs. Assuming case-B recombination (Osterbrock & Ferland, 2006),

$L_{\rm Ly\alpha}=0.68h\nu_{\alpha}(1-f_{esc})N_{\gamma}\dot{M}_{\rm SF},$ (1)

where $h\nu_{\alpha}$=10.2 eV [$\rm erg\ s^{-1}$] is the energy of the
Ly$\alpha$ photons, $f_{esc}$ is the fraction of ionising photons that escape
before giving rise to ionisation and $N_{\gamma}$ represents the number of
ionising photons released per baryon of star-formation.

At the redshift of interest of this work, the escape fraction of Lyman
continuum photons is found to vary over a range of $0\lesssim f_{esc}\lesssim
0.29$ with an average value of $f_{esc}\sim 0.09$, as inferred by the Keck
Lyman Continuum Spectroscopic Survey of star-forming galaxies at $z\sim 3$
(Steidel et al., 2018). We further assume $N_{\gamma}=9870$, corresponding to
the average metallicity, i.e. log$Z/Z_{\odot}=-1.5$, of high redshift DLA
absorbers and a Salpeter initial mass function with $\alpha=2.35$, as given in
Rahmani et al. (2010, and references therein). The observed Ly$\alpha$
luminosity also depends on the escape fraction ($f_{esc}^{Ly{\alpha}}$) of
Ly$\alpha$ photons, and it is related to the emitted Ly$\alpha$ luminosity
($\rm L_{Ly{\alpha}}$) as
$L_{Ly{\alpha}}^{obs}=f_{esc}^{Ly{\alpha}}L_{Ly{\alpha}}$. The Ly$\alpha$
escape fraction increases smoothly and monotonically out to $z\sim 6$ and
strongly depends on the dust content (Hayes et al., 2011). At the redshift of
our interest the $f_{esc}^{Ly{\alpha}}\approx 5\pm 3\%$, as is estimated for
the high-redshift ($z\sim 3$) star forming galaxies by Hayes et al. (2010,
2011, see their table 1).

Following this method, we infer that the DLA host galaxy is forming stars at
$\dot{M}_{\rm SF}\approx 21M_{\odot}$ yr-1 for an average $f_{esc}\approx 9\%$
and $f_{esc}^{Ly{\alpha}}\approx 5\%$. However, this value is highly uncertain
and ranges between $2\leq\dot{M}_{\rm SF}\leq 53$ [M⊙ yr-1] if we account for
the large uncertainty in the Ly$\alpha$ escape fraction from 70% to 2%,
respectively (see also, Kimm et al., 2019). A similar SFR of $\sim 2M_{\odot}$
yr-1 is also inferred from the star formation rate calibration for $H\alpha$
luminosity [i.e. $L_{H\alpha}$[erg/s] = 1041.27 * SFR $M_{\odot}$ yr-1] from
Kennicutt & Evans (2012) and the intrinsic $Ly\alpha/H\alpha$ ratio of 8-10.
This is comparable with the typical SFR of Lyman break galaxies at the similar
redshifts (Kornei et al., 2010).

In conclusion, our search for DLA hosts of high column density (log $N$ (H i)
$\geq 21$) systems, with no metallicity pre-selection but identified on the
basis of likely presence of Ly$\alpha$ emission in the SDSS fiber, appears to
be effective in uncovering the gas-galaxy connection in an interesting region
of parameter space, where we expect a direct link between gas in absorption
and star formation in emission (Rafelski et al., 2012, 2016). Therefore,
future IFU observations (e.g., PCWI, MUSE, KCWI) of our sample are likely to
yield additional bright DLA host galaxies at small impact parameters, with
which we can start to investigate more systematically both the galaxy
population on large scales, and how neutral gas relates directly to star
formation on smaller scales.

## Acknowledgments

This work was supported by the National Key R&D Program of China
(2016YFA0400702, 2016YFA0400703), the National Science Foundation of China
(11473002, 11721303, 11533001) and China Postdoctoral Science Foundation
Grants (2018M630024, 2019T120011). MF acknowledges support by the Science and
Technology Facilities Council [grant number ST/P000541/1]. This project has
received funding from the European Research Council (ERC) under the European
Union’s Horizon 2020 research and innovation programme (grant agreement No
757535). This project has received funding from the European Research Council
(ERC) under the European Union’s Horizon 2020 research and innovation
programme (grant agreement No 757535). This work has been supported by
Fondazione Cariplo, grant No 2018-2329. PN acknowledges support from the
French Agence Nationale de la Recherche under grant no ANR-17-CE31-0011-01.
Observations obtained with the Hale Telescope at Palomar Observatory were
obtained as part of an agreement between the National Astronomical
Observatories, Chinese Academy of Sciences, and the California Institute of
Technology. Funding for the Sloan Digital Sky Survey IV has been provided by
the Alfred P. Sloan Foundation, the U.S. Department of Energy Office of
Science, and the Participating Institutions. SDSS-IV acknowledges support and
resources from the Center for High-Performance Computing at the University of
Utah. The SDSS web site is www.sdss.org. SDSS-IV is managed by the
Astrophysical Research Consortium for the Participating Institutions of the
SDSS Collaboration including the Brazilian Participation Group, the Carnegie
Institution for Science, Carnegie Mellon University, the Chilean Participation
Group, the French Participation Group, Harvard-Smithsonian Center for
Astrophysics, Instituto de Astrofísica de Canarias, The Johns Hopkins
University, Kavli Institute for the Physics and Mathematics of the Universe
(IPMU) / University of Tokyo, Lawrence Berkeley National Laboratory, Leibniz
Institut für Astrophysik Potsdam (AIP), Max-Planck-Institut für Astronomie
(MPIA Heidelberg), Max-Planck-Institut für Astrophysik (MPA Garching), Max-
Planck-Institut für Extraterrestrische Physik (MPE), National Astronomical
Observatories of China, New Mexico State University, New York University,
University of Notre Dame, Observatário Nacional / MCTI, The Ohio State
University, Pennsylvania State University, Shanghai Astronomical Observatory,
United Kingdom Participation Group, Universidad Nacional Autónoma de México,
University of Arizona, University of Colorado Boulder, University of Oxford,
University of Portsmouth, University of Utah, University of Virginia,
University of Washington, University of Wisconsin, Vanderbilt University, and
Yale University.

## References

  * Altay & Theuns (2013) Altay, G., & Theuns, T. 2013, MNRAS, 434, 748
  * Bigiel et al. (2008) Bigiel, F., Leroy, A., Walter, F., et al. 2008, AJ, 136, 2846
  * Bower et al. (2004) Bower, R. G., Morris, S. L., Bacon, R., et al. 2004, MNRAS, 351, 63
  * Bunker et al. (2003) Bunker, A. J., Stanway, E. R., Ellis, R. S., McMahon, R. G., & McCarthy, P. J. 2003, MNRAS, 342, L47
  * Christensen et al. (2014) Christensen, L., Møller, P., Fynbo, J. P. U., & Zafar, T. 2014, MNRAS, 445, 225
  * Christensen et al. (2009) Christensen, L., Noterdaeme, P., Petitjean, P., Ledoux, C., & Fynbo, J. P. U. 2009, A&A, 505, 1007
  * Cortese et al. (2011) Cortese, L., Catinella, B., Boissier, S., Boselli, A., & Heinis, S. 2011, MNRAS, 415, 1797
  * Dijkstra (2014) Dijkstra, M. 2014, PASA, 31, 40
  * Dijkstra et al. (2006) Dijkstra, M., Haiman, Z., & Spaans, M. 2006, ApJ, 649, 14
  * Fumagalli et al. (2014) Fumagalli, M., O’Meara, J. M., Prochaska, J. X., Kanekar, N., & Wolfe, A. M. 2014, MNRAS, 444, 1282
  * Fumagalli et al. (2015) Fumagalli, M., O’Meara, J. M., Prochaska, J. X., Rafelski, M., & Kanekar, N. 2015, MNRAS, 446, 3178
  * Fumagalli et al. (2017) Fumagalli, M., Mackenzie, R., Trayford, J., et al. 2017, MNRAS, 471, 3686
  * Fynbo et al. (2010) Fynbo, J. P. U., Laursen, P., Ledoux, C., et al. 2010, MNRAS, 408, 2128
  * Fynbo et al. (2011) Fynbo, J. P. U., Ledoux, C., Noterdaeme, P., et al. 2011, MNRAS, 413, 2481
  * Fynbo et al. (2018) Fynbo, J. P. U., Heintz, K. E., Neeleman, M., et al. 2018, MNRAS, 479, 2126
  * Gawiser et al. (2006) Gawiser, E., van Dokkum, P. G., Herrera, D., et al. 2006, ApJS, 162, 1
  * Genzel et al. (2010) Genzel, R., Tacconi, L. J., Gracia-Carpio, J., et al. 2010, MNRAS, 407, 2091
  * Harris et al. (2016) Harris, D. W., Jensen, T. W., Suzuki, N., et al. 2016, AJ, 151, 155
  * Hayes et al. (2011) Hayes, M., Schaerer, D., Östlin, G., et al. 2011, ApJ, 730, 8
  * Hayes et al. (2010) Hayes, M., Östlin, G., Schaerer, D., et al. 2010, Nature, 464, 562
  * Herenz et al. (2019) Herenz, E. C., Wisotzki, L., Saust, R., et al. 2019, A&A, 621, A107
  * Janowiecki et al. (2017) Janowiecki, S., Catinella, B., Cortese, L., et al. 2017, MNRAS, 466, 4795
  * Jorgenson et al. (2013) Jorgenson, R. A., Murphy, M. T., & Thompson, R. 2013, MNRAS, 435, 482
  * Joshi et al. (2017) Joshi, R., Srianand, R., Noterdaeme, P., & Petitjean, P. 2017, MNRAS, 465, 701
  * Kanekar et al. (2016) Kanekar, N., Sethi, S., & Dwarakanath, K. S. 2016, ApJ, 818, L28
  * Kennicutt & Evans (2012) Kennicutt, R. C., & Evans, N. J. 2012, ARA&A, 50, 531
  * Kimm et al. (2019) Kimm, T., Blaizot, J., Garel, T., et al. 2019, MNRAS, 486, 2215
  * Klitsch et al. (2019) Klitsch, A., Zwaan, M. A., Péroux, C., et al. 2019, MNRAS, 482, L65
  * Kornei et al. (2010) Kornei, K. A., Shapley, A. E., Erb, D. K., et al. 2010, ApJ, 711, 693
  * Krogager et al. (2012) Krogager, J.-K., Fynbo, J. P. U., Møller, P., et al. 2012, MNRAS, 424, L1
  * Krogager et al. (2017) Krogager, J. K., Møller, P., Fynbo, J. P. U., & Noterdaeme, P. 2017, MNRAS, 469, 2959
  * Kulkarni et al. (2000) Kulkarni, V. P., Hill, J. M., Schneider, G., et al. 2000, ApJ, 536, 36
  * Laursen & Sommer-Larsen (2007) Laursen, P., & Sommer-Larsen, J. 2007, ApJ, 657, L69
  * Laursen et al. (2009) Laursen, P., Sommer-Larsen, J., & Andersen, A. C. 2009, ApJ, 704, 1640
  * Ledoux et al. (2006) Ledoux, C., Petitjean, P., Fynbo, J. P. U., Møller, P., & Srianand, R. 2006, A&A, 457, 71
  * Mackenzie et al. (2019) Mackenzie, R., Fumagalli, M., Theuns, T., et al. 2019, MNRAS, 1435
  * Martin et al. (2014) Martin, D. C., Chang, D., Matuszewski, M., et al. 2014, ApJ, 786, 106
  * Möller et al. (2004) Möller, P., Fynbo, J. P. U., & Fall, S. M. 2004, A&A, 422, L33
  * Møller et al. (2013) Møller, P., Fynbo, J. P. U., Ledoux, C., & Nilsson, K. K. 2013, MNRAS, 430, 2680
  * Neeleman et al. (2018) Neeleman, M., Kanekar, N., Prochaska, J. X., et al. 2018, ApJ, 856, L12
  * Neeleman et al. (2013) Neeleman, M., Wolfe, A. M., Prochaska, J. X., & Rafelski, M. 2013, ApJ, 769, 54
  * Neeleman et al. (2016) Neeleman, M., Prochaska, J. X., Zwaan, M. A., et al. 2016, ApJ, 820, L39
  * Neufeld (1990) Neufeld, D. A. 1990, ApJ, 350, 216
  * Noterdaeme et al. (2009) Noterdaeme, P., Petitjean, P., Ledoux, C., & Srianand, R. 2009, A&A, 505, 1087
  * Noterdaeme et al. (2014) Noterdaeme, P., Petitjean, P., Pâris, I., et al. 2014, A&A, 566, A24
  * Noterdaeme et al. (2012) Noterdaeme, P., Petitjean, P., Carithers, W. C., et al. 2012, A&A, 547, L1
  * Osterbrock & Ferland (2006) Osterbrock, D. E., & Ferland, G. J. 2006, Astrophysics of gaseous nebulae and active galactic nuclei, ed. Osterbrock, D. E. & Ferland, G. J.
  * Péroux et al. (2011) Péroux, C., Bouché, N., Kulkarni, V. P., York, D. G., & Vladilo, G. 2011, MNRAS, 410, 2251
  * Péroux et al. (2012) —. 2012, MNRAS, 419, 3060
  * Péroux et al. (2003) Péroux, C., McMahon, R. G., Storrie-Lombardi, L. J., & Irwin, M. J. 2003, MNRAS, 346, 1103
  * Prochaska et al. (2008) Prochaska, J. X., Chen, H.-W., Wolfe, A. M., Dessauges-Zavadsky, M., & Bloom, J. S. 2008, ApJ, 672, 59
  * Prochaska & Wolfe (2009) Prochaska, J. X., & Wolfe, A. M. 2009, ApJ, 696, 1543
  * Rafelski et al. (2016) Rafelski, M., Gardner, J. P., Fumagalli, M., et al. 2016, ApJ, 825, 87
  * Rafelski et al. (2014) Rafelski, M., Neeleman, M., Fumagalli, M., Wolfe, A. M., & Prochaska, J. X. 2014, ApJ, 782, L29
  * Rafelski et al. (2012) Rafelski, M., Wolfe, A. M., Prochaska, J. X., Neeleman, M., & Mendez, A. J. 2012, ApJ, 755, 89
  * Rahmani et al. (2010) Rahmani, H., Srianand, R., Noterdaeme, P., & Petitjean, P. 2010, MNRAS, 409, L59
  * Ranjan et al. (2020) Ranjan, A., Noterdaeme, P., Krogager, J. K., et al. 2020, A&A, 633, A125
  * Rauch et al. (2011) Rauch, M., Becker, G. D., Haehnelt, M. G., et al. 2011, MNRAS, 418, 1115
  * Rauch et al. (2008) Rauch, M., Haehnelt, M., Bunker, A., et al. 2008, ApJ, 681, 856
  * Rubin et al. (2015) Rubin, K. H. R., Hennawi, J. F., Prochaska, J. X., et al. 2015, ApJ, 808, 38
  * Srianand et al. (2016) Srianand, R., Hussain, T., Noterdaeme, P., et al. 2016, MNRAS, 460, 634
  * Steidel et al. (2018) Steidel, C. C., Bogosavljević, M., Shapley, A. E., et al. 2018, ApJ, 869, 123
  * Tremonti et al. (2004) Tremonti, C. A., Heckman, T. M., Kauffmann, G., et al. 2004, ApJ, 613, 898
  * Wilman et al. (2005) Wilman, R. J., Gerssen, J., Bower, R. G., et al. 2005, Nature, 436, 227
  * Wolfe et al. (2005) Wolfe, A. M., Gawiser, E., & Prochaska, J. X. 2005, ARA&A, 43, 861
  * Zwaan et al. (2005) Zwaan, M. A., van der Hulst, J. M., Briggs, F. H., Verheijen, M. A. W., & Ryan-Weber, E. V. 2005, MNRAS, 364, 1467