Sizhe<EMAIL_ADDRESS>Yapeng<EMAIL_ADDRESS>Chenliang<EMAIL_ADDRESS>Department of Computer Science
University of Rochester
Rochester, USA Sounding Object Localization in Videos

# Space-Time Memory Network for Sounding Object Localization in Videos

###### Abstract

Leveraging temporal synchronization and association within sight and sound is
an essential step towards robust localization of sounding objects. To this
end, we propose a space-time memory network for sounding object localization
in videos. It can simultaneously learn spatio-temporal attention over both
uni-modal and cross-modal representations from audio and visual modalities. We
show and analyze both quantitatively and qualitatively the effectiveness of
incorporating spatio-temporal learning in localizing audio-visual objects. We
demonstrate that our approach generalizes over various complex audio-visual
scenes and outperforms recent state-of-the-art methods. Code and data can be
found at https://sites.google.com/view/bmvc2021stm.

##  1 Introduction

Neurological evidence suggests that human understandings of scenes
predominantly rely on the integration of visual and auditory cues [Britton and
Arshad(2019)]. As humans, we form attention mechanisms to sounding sources by
leveraging the temporal, cross-modal alignments between vision and sound,
where understandings of the past tell us where and what to attend to next. For
computational models, although there have been several developed sound source
spatial localization frameworks [Tian et al.(2018)Tian, Shi, Li, Duan, and Xu,
Owens and Efros(2018), Qian et al.(2020)Qian, Di Hu, Wu, Xu, and Lin], how
much we gain from explicitly leveraging temporal correspondence that exists
naturally in both videos and audios is yet to be explored.

However, considerations of temporal coherence are required to facilitate
consistent understandings in complex scenes. Imagine a person playing a guitar
in front of a wall of not-in-use guitars. In order to figure out which guitar
is sounding and obtain stable localization results, we must take multiple
timesteps into account. Hence, it is worthwhile to explore if learning
temporal cues could benefit the localization of sounding objects in videos.

To localize visual objects associated with specific sound sources, most of the
previous works capture audio-visual spatial correspondence using similarities
between audio and visual modalities [Arandjelovic and Zisserman(2018), Owens
and Efros(2018), Hu et al.(2019)Hu, Nie, and Li], cross-modal attention
mechanisms [Senocak et al.(2018)Senocak, Oh, Kim, Yang, and Kweon, Tian et
al.(2018)Tian, Shi, Li, Duan, and Xu], and sounding class activation mapping
[Qian et al.(2020)Qian, Di Hu, Wu, Xu, and Lin]. Nevertheless, these methods
often identify sounding objects for static images, and audio-visual temporal
coherence in videos is commonly ignored.

Figure 1: Our space-time memory network learns to attend to objects that
currently sound by leveraging the temporal, cross-modal correspondence within
sight and sound from the past. Here, given the frames of the two objects in
motion and the sound of the horse walking, our model outputs stable and
accurate localization results.

Consequently, the essential question we must answer is how to design an
efficient multi-modal deep neural network architecture that exploits the
temporal coherence in visual frames and the corresponding audio segments. In
this paper, we propose a spatio-temporal attention-based memory module, that
can learn rich reference information from uni-modal as well as cross-modal
(audio-visual) representations. With temporal memory updates, our approach is
more robust against appearance and acoustic changes than the previous methods.
It yields more temporally consistent localization results and can handle the
absence of audio-visual events. In particular, to demonstrate the values of
multi-modal temporal learning in sounding object localization, we resort to an
easily affordable, weakly-supervised task in classifying the audio-visual
event category of a given video segment.

Herein, our main contributions are: (1) we propose a novel space-time memory
network that learns representations of sounding objects to promote robust
localization performance, as illustrated in Figure 1; (2) we validate the
effectiveness of temporal learning in localizing sounding objects both
quantitatively and qualitatively based on numerical benchmarks and visual
interpretations; (3) we demonstrate that our approach generalizes over various
complex audio-visual events and outperforms recent state-of-the-art methods.

##  2 Related Work

###  2.1 Sounding Object Localization

Sounding object localization refers to the task of localizing visual
objects/scenes associated with specific sounds in videos. Early works resorted
to mutual information [Hershey and Movellan(2000)] and canonical correlation
analysis [Kidron et al.(2005)Kidron, Schechner, and Elad] to perform
localization and segmentation on sounding pixels. Recent efforts have learned
deep audio-visual models to localize sounding pixels, using audio-visual
embedding similarities [Arandjelovic and Zisserman(2018), Owens and
Efros(2018), Hu et al.(2019)Hu, Nie, and Li], cross-modal attention mechanisms
[Senocak et al.(2018)Senocak, Oh, Kim, Yang, and Kweon, Tian et al.(2018)Tian,
Shi, Li, Duan, and Xu], vision-to-sound knowledge transfer [Gan et
al.(2019)Gan, Zhao, Chen, Cox, and Torralba], sounding class activation
mapping [Qian et al.(2020)Qian, Di Hu, Wu, Xu, and Lin, Hu et al.(2020)Hu,
Qian, Jiang, Tan, Wen, Ding, Lin, and Dou], and sounding object visual
grounding [Tian et al.(2021)Tian, Hu, and Xu]. While these methods work well
on a single sound source in the simple audio-visual scenes, they lack temporal
knowledge and predict audible regions solely based on the association of the
current video frame with the corresponding audio segment. Most recently,
Afouras et al. [Afouras et al.(2020)Afouras, Owens, Chung, and Zisserman]
compute audio-visual cross-modal attention to spatially localize sounding
regions. Moreover, they incorporate temporal learning into the visual
modality, where they propose to use optical flow that is separately learned to
aggregate information over time and group sound sources into audio-visual
objects. Their model also relies on speech-oriented tasks and scenes, assuming
objects (speakers) of fixed size. By contrast, not only does our spatio-
temporal attention mechanism consider both uni-modal and cross-modal
representations, but it is also learned in an end-to-end manner. Hence,
different from the previous methods, with learnable space-time memory modules,
our model can effectively leverage multi-modal contexts for localizing
sounding objects and thus is capable of handling diverse and complex audio-
visual objects.

###  2.2 Audio-Visual Video Understanding

The community has attracted an increasing amount of interest in recent years
since synchronized audio-visual scenes are widely available in videos. In
addition to localizing sound sources, a wide range of tasks have been
proposed, including audio-visual sound separation [Gao et al.(2018)Gao, Feris,
and Grauman, Zhao et al.(2018)Zhao, Gan, Rouditchenko, Vondrick, McDermott,
and Torralba, Zhao et al.(2019)Zhao, Gan, Ma, and Torralba, Gan et
al.(2020a)Gan, Huang, Zhao, Tenenbaum, and Torralba, Tian and Xu(2021)],
audio-visual action recognition [Gao et al.(2020)Gao, Oh, Grauman, and
Torresani, Kazakos et al.(2019)Kazakos, Nagrani, Zisserman, and Damen, Korbar
et al.(2019)Korbar, Tran, and Torresani, Tian et al.(2021)Tian, Hu, and Xu],
audio-visual event localization [Tian et al.(2018)Tian, Shi, Li, Duan, and Xu,
Wu et al.(2019)Wu, Zhu, Yan, and Yang], audio-visual video captioning [Rahman
et al.(2019)Rahman, Xu, and Sigal, Tian et al.(2019)Tian, Guan, Justin, Moore,
and Xu, Wang et al.(2018)Wang, Wang, and Wang], embodied audio-visual
navigation [Gan et al.(2020b)Gan, Zhang, Wu, Gong, and Tenenbaum, Chen et
al.(2020)Chen, Jain, Schissler, Gari, Al-Halah, Ithapu, Robinson, and
Grauman], audio-visual sound recognition [Fayek and Kumar(2020)], and audio-
visual video parsing [Tian et al.(2020)Tian, Li, and Xu]. Our framework
demonstrates that temporal learning facilitates better audio-visual
understanding, which explicitly and subsequently benefits the localization
performance.

##  3 Proposed Method

Figure 2: Overview of the Space-Time Memory Network: (1-2) extracts audio and
visual features from inputs. (3) constructs one cross-modal memory module and
two uni-modal memory modules. (4) first propagates the uni-modal memory, then
computes cross-modal attention, and propagates the cross-modal memory. (5)
computes spatial attention from the memory-propagated audio features to the
memory-propagated audio-visual features. The outputs is an attention map that
localizes sounding objects, which is used for downstream audio-visual event
classification during training.

Our framework (see Figure 2) consists of three modules: audio and visual
feature extraction, memory construction and propagation, and sounding object
localization. Given a video, it learns to attend to objects that sound in
video frames. The spatio-temporal attention mechanisms are designed to
leverage both uni-modal and cross-modal representations. It is supported by
the idea of accumulating the past evidence into memory, which is then
aggregated and propagated onto the current timestep. Through the task of
audio-visual event classification, our model facilitates audio-visual
understanding by learning spatio-temporal attention mechanisms that locate
sounding objects.

###  3.1 Audio-Visual Feature Extraction

Consider $T$ audio-visual pairs $\\{X_{v}^{t},X_{a}^{t}\\}^{T}_{t=1}$ as
inputs, where $X_{v}^{t}\in\mathbb{R}^{H\times W\times
3},X_{a}^{t}\in\mathbb{R}^{M\times N\times 1}$ denote the frame and its
corresponding audio log-mel spectrogram at timestep $t$ respectively. Let
$H,W,M,N$ denote the height, width, frequency, and time. For a given pair, we
employ two convolutional encoders to project uni-modal features into
C-dimensional joint audio-visual subspace for spatial-temporal attention in
memory. Let $h,w$ and $m,n$ denote the corresponding spatial dimensions of the
feature maps. We obtain visual feature map $x_{v}^{t}\in\mathbb{R}^{h\times
w\times C}$ and audio feature map $x_{a}^{t}\in\mathbb{R}^{m\times n\times
C}$. This is reflected by steps 1 to 2 in Figure 2.

###  3.2 Audio-Visual Memory Accumulation and Propagation

To efficiently accumulate and aggregate the temporal audio-visual evidence, we
propose to build memory modules for audio and visual representations both
separately and jointly, which we collectively refer to as the Spatio-Temporal
Memory Layer. Here, we introduce the attention mechanisms in space and time,
then describe how they form the memory layer.

Cross-Modal Spatial Attention $f$. We measure the similarity between the
extracted uni-modal features $x_{v}^{t}$ and $x_{a}^{t}$ at the given timestep
$t$. We denote the cross-modal spatial attention function as
$f(x_{a}^{t},x_{v}^{t})=\alpha_{av}^{t}$, such that $f:\mathbb{R}^{m\times
n\times C}\times\mathbb{R}^{h\times w\times C}\mapsto\mathbb{R}^{h\times w}$.
For each position $(i,j)$ in $x_{v}^{t}$, the attention weight can be computed
by:

$\begin{split}\alpha_{av}^{t}(i,j)&=\frac{exp({x_{v}^{t}(i,j)}{\phi(x_{a}^{t})}^{\intercal})}{\sum_{i,j}exp({x_{v}^{t}(i,j)}{\phi(x_{a}^{t})}^{\intercal})}.\\\
\end{split}$ (1)

Here, we adopt dot product as a generic choice to compute the spatial
similarity and $\phi(\cdot)$ is a global pooling operation over the spatial
dimension of its input. We compute the cross-modal audio-visual features
$x_{av}^{t}\in\mathbb{R}^{h\times w\times C}$ by multiplying the learned
spatial attention map with every channel $c\in\\{1,...,C\\}$ in the visual
feature map. Specifically,
$x_{av}^{t}(i,j,c)=\alpha_{av}^{t}(i,j)*x_{v}^{t}(i,j,c)$. For every pixel in
a given frame $X_{v}^{t}$, we have thus found its affinity with the aligned
audio input $X_{a}^{t}$ via cross-modal spatial attention $f$ on the feature
level.

Multi-Modal Temporal Attention $g$. Our memory formulation is generic and can
be adapted to both uni-modal and cross-modal representations. Consider the
modality $m\in\\{a,v,av\\}$ and the previous timestep $k\in\\{1,...,K\\}$. We
seek to measure the importance of $x_{m}^{k}$ with respect to $x_{m}^{t}$ from
the current timestep. We denote the multi-modal temporal attention function as
$g(x_{m}^{t},x_{m}^{k})=\beta_{m}^{k,t}$, where $h_{m},w_{m}$ denote the
spatial dimensions of the feature maps of modality $m$, such that
$g:\mathbb{R}^{h_{m}\times w_{m}\times C}\times\mathbb{R}^{h_{m}\times
w_{m}\times C}\mapsto\mathbb{R}$.

We adopt the generic multi-head scaled dot-product attention from Vaswani et
al. [Vaswani et al.(2017)Vaswani, Shazeer, Parmar, Uszkoreit, Jones, Gomez,
Kaiser, and Polosukhin], where we view $x_{m}^{t}$ as queries and $x_{m}^{k}$
as keys and values. For each of the $L$ attention heads, we globally pool the
input feature maps into feature vectors, and compute the set of attention
weights over $K$ timesteps. Concretely, the multi-modal temporal attention
function is formulated as:

$\beta^{k,t}_{m}=\frac{1}{L}\sum_{l=1}^{L}A(\phi(x^{t}_{m})W^{query}_{l},\phi(x^{k}_{m})W^{key}_{l})\\\
$ (2)

where $A(I_{query},I_{key})=softmax(\frac{I_{query}I_{key}^{T}}{\sqrt{C}})$
denotes the attention function, $C$ corresponds to the number of dimensions
for keys and queries, and $W_{l}^{query},W_{l}^{key}$ are the learnable
projections.

Having computed the set of attention weights, we propagate the memory to the
current timestep $t$ to obtain
$\dot{x}_{m}^{t}=\beta_{m}^{t,t}*x_{m}^{t}+\sum_{k=1}^{K}{\beta_{m}^{k,t}*x_{m}^{k}}$.
Hence, the multi-modal temporal attention takes into account the importance of
the past $k$ to the present $t$.

Spatio-Temporal Memory Layer. We now describe the algorithm that combines the
two proposed attention mechanisms in space and time. Our memory layer first
leverages the uni-modal association in time by applying the temporal attention
$g$ to $x_{a}^{t}$, $x_{v}^{t}$, respectively. It then considers the cross-
modal association in space by applying the spatial attention $f$ to
$\dot{x}_{a}^{t},\dot{x}_{v}^{t}$. Finally, the memory layer derives the
cross-modal association in time by applying the temporal attention $g$ to
$\dot{x}_{av}^{t}$. This is reflected by steps 3 to 4 in Figure 2 and
Algorithm 1.

Algorithm 1 Spatio-Temporal Memory Layer Pseudocode

1:procedure
MemoryLayerForward($x_{a}^{t},x_{v}^{t},\\{(x_{a}^{k},x_{v}^{k},x_{av}^{k})\\}_{k=1}^{K}$)

2: $\dot{x}_{a}^{t}=TemporalAttention(x_{a}^{t},\\{x_{a}^{k}\\}_{k=1}^{K})$
$\triangleright$ Uni-Modal Temporal Attention

3: $\dot{x}_{v}^{t}=TemporalAttention(x_{v}^{t},\\{x_{v}^{k}\\}_{k=1}^{K})$
$\triangleright$ Uni-Modal Temporal Attention

4: $\dot{x}_{av}^{t}=SpatialAttention(\dot{x}_{a}^{t},\dot{x}_{v}^{t})$
$\triangleright$ Cross-Modal Spatial Attention

5:
$\ddot{x}_{av}^{t}=TemporalAttention(\dot{x}_{av}^{t},\\{x_{av}^{k}\\}_{k=1}^{K})$
$\triangleright$ Cross-Modal Temporal Attention

6: return $\ddot{x}_{av}^{t}$, $\dot{x}_{a}^{t}$

7:end procedure

###  3.3 Localizing sounding objects

To extract audio-visual objects from various audio-visual events, we cannot
impose an assumption on the sizes of the objects. We propose two post-
processing approaches, using contour detection and pre-trained object-
proposal-networks, respectively.

Given outputs $\dot{x}_{a}^{t},\ddot{x}_{av}^{t}$ from the memory layer, we
compute the cross-modal spatial attention map
$\ddot{\alpha}_{av}^{t}=f(\dot{x}_{a}^{t},\ddot{x}_{av}^{t})$, which we view
as the final sounding object localization map. This is reflected by step 5 in
Figure 2. We generate bounding boxes by applying Otsu’s threshold [Otsu(1979)]
and contour detection to the normalized spatial attention map. Alternatively,
the attention map $\ddot{\alpha}_{av}^{t}$ can also be incorporated into
robust object instances generated by out-of-the-box object proposal methods.
Given frame $X_{v}^{t}$, we extract $N$ object instances using a region-
proposal-network (RPN). We convert them into binary masks
$\\{m_{n}^{t}\\}_{n=1}^{N}$, with $1$ indicating the instance and $0$
otherwise. We calculate the individual score of each box $S_{n}^{t}$ as the
weighted sum between $m_{n}^{t}$ and $\ddot{\alpha}_{av}^{t}$, or
$S_{n}^{t}=\sum_{i,j}{m_{n}^{t}(i,j)*\ddot{\alpha}_{av}^{t}(i,j)}$, and apply
non-maximum suppression (NMS) to filter overlapping boxes.

###  3.4 Learning Spatio-Temporal Attention Mechanisms

We utilize the easily affordable, weakly-supervised classification task on
audio-visual event categories to learn the proposed spatio-temporal attention
mechanisms. We fuse the outputs from the memory layer, including the uni-modal
audio feature vector $\dot{x}_{a}^{t}$ and the cross-modal audio-visual
feature map $\ddot{x}_{av}^{t}$, to obtain a joint representation. In
particular, we sum $\ddot{x}_{av}^{t}$ over its spatial dimensions, since it
is already weighted by the spatial attention $f$, and concatenate the result
with $\dot{x}_{a}^{t}$. This gives us the final output of our network at
timestep $t$, denoted as $\mathcal{O}^{t}\in\mathbb{R}^{2C}$. Concretely,
$\mathcal{O}^{t}=[\sum_{i,j}\ddot{x}_{av}^{t}(i,j);\dot{x}_{a}^{t}]$, where
$[...;...]$ denotes concatenation. This joint audio-visual representation is
used to estimate the audio-visual event category for the given video segment
using a multilayer-perceptron (MLP) and the cross-entropy loss.

##  4 Experiments

###  4.1 Datasets

To carefully and temporally evaluate the localization performance, we
emphasize three aspects when tailoring dataset: the scope of the sounding
object categories covered, the scale of the testing examples contained, and
the number of frames per video densely annotated. Therefore, we use AVE
dataset [Tian et al.(2018)Tian, Shi, Li, Duan, and Xu] for scope and AudioSet-
Instrument dataset [Gemmeke et al.(2017)Gemmeke, Ellis, Freedman, Jansen,
Lawrence, Moore, Plakal, and Ritter] for scale. For both datasets, we densely
annotated frames in the test videos.

AVE: Audio-Visual Event Dataset. AVE dataset [Tian et al.(2018)Tian, Shi, Li,
Duan, and Xu] consists of 4143 10-second video clips that are labeled with the
audio-visual event, covering 28 categories. Although the dataset is also
temporally labeled with audio-visual event boundaries, to demonstrate the
effectiveness of our weakly-supervised learning framework, we include the
whole length of every video. We adopt the original data split, including 3339
videos for training, 402 for validation, and 402 for testing.

AudioSet-Instrument. AudioSet-Instrument dataset is a subset of AudioSet
[Gemmeke et al.(2017)Gemmeke, Ellis, Freedman, Jansen, Lawrence, Moore,
Plakal, and Ritter] that consists of 101076 10-second video clips, spanning
across 15 instrument categories. It contains challenging video clips, where
many are of poor quality with multiple sound sources. We use 100221/424/424
for training/validation/testing.

Annotations. We annotated test sets in AVE and AudioSet-Instrument, where we
created bounding boxes for sounding objects in the duration of the audio-
visual event. For reproducibility, we will release the annotations with source
code.

###  4.2 Implementation Details

Data Preprocessing. During training, we randomly sample one second and extract
its corresponding audio-visual pair $\\{(X_{v}^{t},X_{a}^{t})\\}^{T}_{t=1}$,
where $T$ denotes the number of frames, as well as the number of audio
segments, we sample per second. In practice, we use $T=4$. During the
evaluation, $T$ corresponds to the number of frames that are annotated in the
given video. Final input frames are of spatial dimensions $256\times 256$.
During training, this is achieved by resizing the image by a scale of $1.1$
and randomly crop it to the desired size. During the evaluation, every frame
is directly resized to the target dimensions. Audio inputs are re-sampled to
16 kHz mono and its corresponding spectrogram is computed through Short-Time
Fourier Transform with a Hann window size of 25ms and a hop length of 10ms.
For every input frame, we use the output spectrogram of its temporally-aligned
audio segment.

Encoder. We use ResNet-152 [He et al.(2016)He, Zhang, Ren, and Sun] to extract
2048-D per-frame visual features and VGGish [Hershey et al.(2017)Hershey,
Chaudhuri, Ellis, Gemmeke, Jansen, Moore, Plakal, Platt, Saurous, Seybold,
Slaney, Weiss, and Wilson] to extract 512-D per-segment audio features, they
are then each projected to a 256-D joint feature space by a stack of 1x1
convolutions with ReLU non-linearity.

Memory Module. We use Multi-head scaled dot product attention with 256-D
embedding dimension and 1 head.

Sounding Object Extraction. To localize objects that sound from the cross-
modal spatial attention map, we use bilinear interpolation to resize to
$256\times 256$. We then normalize the attention map by subtracting the
minimum and dividing by the maximum, resulting in a lower bound of $0$ and an
upper bound of $1$. We use Otsu’s threshold and contour detection to extract
bounding boxes for experimental comparison and ablation study. For the RPN-
based extraction method demonstrated in Figure 7, we used a Faster R-CNN model
with a ResNet-50-FPN backbone [Ren et al.(2017)Ren, He, Girshick, and Sun, He
et al.(2016)He, Zhang, Ren, and Sun], pretrained on COCO train2017.

Hyperparameters. In our experiments, we use batch size 128 and 30 epochs. Our
framework is trained with Adam optimizer with the initial learning rate of
$10^{-4}$ on four NVIDIA 1080Ti GPU.

Figure 3: Qualitative Visualizations: We show localization results from the
two datasets. Top four rows contain results from the AVE dataset. Bottom four
rows contain results from the Audioset-Instrument dataset. Bounding boxes are
extracted from the cross-modal spatial attention maps, using Otsu’s threshold
and contour detection.

Evaluation Metrics. To evaluate the localization performance of our framework,
we employ Intersection over Union (IoU) for the box-level localization
performance and Consensus Intersection over Union (cIoU) proposed in [Senocak
et al.(2018)Senocak, Oh, Kim, Yang, and Kweon] for the pixel-level
localization performance. We now expand on details of cIoU calculations. Given
an annotated frame, we convert the ground truth bounding boxes
$\\{b_{j}\\}_{j=1}^{N}$ into a binary ground truth map $\mathbf{g}$, where $1$
indicates that a pixel is sounding and $0$ otherwise. Given predicted location
map $\alpha$, we define cIoU under threshold $\tau$ as:
$cIoU(\tau)=\frac{\sum_{i\in\mathcal{A}(\tau)}\mathbf{g}_{i}}{\sum_{i}{\mathbf{g}_{i}}+\sum_{i\in\mathcal{A}(\tau)-\mathcal{G}}{1}}$.
Here, $i$ is the pixel index of the map.
$\mathcal{A}(\tau)=\\{i|\alpha_{i}>\tau\\}$ denotes the set of pixels with
attention intensity higher than the threshold, and
$\mathcal{G}=\\{i|\mathbf{g}_{i}>0\\}$ represents the set of pixels annotated
as positive. We use 0.5 as the cIoU threshold in our experiments.

(a) Input Frames

(b) Tian et al. [Tian et al.(2018)Tian, Shi, Li, Duan, and Xu]

(c) Qian et al. [Qian et al.(2020)Qian, Di Hu, Wu, Xu, and Lin]

(d) Hu et al. [Hu et al.(2020)Hu, Qian, Jiang, Tan, Wen, Ding, Lin, and Dou]

(e) Our model

Figure 4: Experimental Comparison: We qualitatively compare the localization
performance of our framework with two recent methods by Tian et al. [Tian et
al.(2018)Tian, Shi, Li, Duan, and Xu] and Qian et al. [Qian et al.(2020)Qian,
Di Hu, Wu, Xu, and Lin].

Method | AVE | Audioset-Instrument
---|---|---
|<EMAIL_ADDRESS>|<EMAIL_ADDRESS>|<EMAIL_ADDRESS>|<EMAIL_ADDRESS>
Tian et al. [Tian et al.(2018)Tian, Shi, Li, Duan, and Xu] | 11.73 | 18.81 | 25.11 | 33.20
Owens et al. [Owens and Efros(2018)] | 13.96 | 22.64 | 21.79 | 38.08
Qian et al. [Qian et al.(2020)Qian, Di Hu, Wu, Xu, and Lin] | 24.16 | 16.82 | 20.16 | 31.31
Hu et al. [Hu et al.(2020)Hu, Qian, Jiang, Tan, Wen, Ding, Lin, and Dou] | 21.25 | 33.05 | 33.74 | 40.02
Ours | 37.78 | 37.50 | 51.06 | 56.49

Table 1: Localization results of different methods on AVE and Audioset-
Instrument datasets. All methods are evaluated by<EMAIL_ADDRESS>and<EMAIL_ADDRESS>The
top-1 result in each column is highlighted.

###  4.3 Results

In Figure 3, we illustrate sounding object localization results on AVE and
Audioset-Instrument datasets. We observe that our model is capable of
correctly discovering sounding regions for a wide range of sound sources in
challenging unconstrained videos.

Experimental Comparison. To further validate the effectiveness of our space-
time memory network, we compare it with three recent methods: Owens and Efros
[Owens and Efros(2018)], Tian et al. [Tian et al.(2018)Tian, Shi, Li, Duan,
and Xu], Qian et al. [Qian et al.(2020)Qian, Di Hu, Wu, Xu, and Lin], and Hu
et al. [Hu et al.(2020)Hu, Qian, Jiang, Tan, Wen, Ding, Lin, and
Dou]111Afouras et al. [Afouras et al.(2020)Afouras, Owens, Chung, and
Zisserman] was not compared since their framework is trained on speech-
oriented downstream tasks and imposes an assumption on the size of the
sounding object.. We demonstrate the quantitative results in Table 1 and the
qualitative results in Figure 4222More results can be found in our
supplementary material.. We find that our framework outperforms the compared
approaches on AVE and Audioset-Instrument datasets both quantitatively and
qualitatively, which substantiates the benefits of the proposed space-time
memory network in localizing dynamic audio-visual objects.

Ablation Study. To evaluate the effectiveness of our proposed memory module,
we conduct ablation study for the following models: (1) Cross-Modal Memory +
Uni-Modal Memory (C+U): we employ both uni-modal and cross-modal temporal
learning modules. (2) Uni-Modal Memory (U): we remove the cross-modal memory
module, propagating memory on a uni-modal level only. (3) Without Temporal: we
remove both uni-modal and cross-modal memory modules. Without temporal
learning, the baseline model associates frames and sounds on single timesteps.
We show the quantitative results in Table 2 and the qualitative results in
Figure 5. We find that (1) outperforms the other two ablative groups
numerically and demonstrates significantly more robust visualization results.

Method | AVE | Audioset-Instrument
---|---|---
|<EMAIL_ADDRESS>|<EMAIL_ADDRESS>|<EMAIL_ADDRESS>|<EMAIL_ADDRESS>
Without Temporal | 34.81 | 33.82 | 44.30 | 51.57
U | 36.41 | 35.23 | 48.96 | 53.19
C+U | 37.78 | 37.50 | 51.06 | 56.49

Table 2: Ablation Study: Localization results of the ablative groups on AVE
and Audioset-Instrument datasets. All methods are evaluated by<EMAIL_ADDRESS>and
<EMAIL_ADDRESS>The top-1 result in each column is highlighted. Figure 5: Ablation
Study: Visualizations of sounding object localization from the three ablative
groups. Here, only horse is making audible sounds.

Handling the absence of audio-visual events. Given that not all audio-visual
segments contain audio-visual events, we further demonstrate the robustness of
our model in a challenging example in Figure 6. While the cello performer has
lifted her bow up, it is impossible to tell whether the flute is sounding
merely from sight. Our framework identifies the non-sounding frames by
resorting to sound.

Figure 6: Handling the absence of audio-visual events: In this challenging
example (top), both performers stop playing between the third and the fourth
steps, and resume on the fifth step. We show our model performance (bottom),
where background class is predicted following an absence of audio-visual
events.

Audio-Visual Object Grounding. Following the RPN-based approach to extract
sounding objects, we demonstrate how our cross-modal attention map can be
incorporated to further refine localization performance in Figure 7.

(a) Attention Output

(b) Box Proposals

(c) AV Grounding

Figure 7: Audio-Visual Object Grounding: Using a pretrained RPN, our grounding
approach can further refine the localization performance of our framework.
Here, we show (a) the final model output followed by contour detection, (b)
the extracted box proposals, and (c) the audio-visual object grounding
results.

##  5 Conclusion

In this paper, we investigate the effectiveness of multi-modal temporal
learning in localizing audio-visual objects. We propose a novel space-time
memory framework to address the problem. Results from experimental comparison
and ablation study support our claim both objectively and subjectively that
multi-modal temporal learning is crucial for robust sounding object
localization performance.

Acknowledgements: We would like to thank the anonymous reviewers for the
constructive comments. This work was supported in part by NSF 1741472 and
1909912. The article solely reflects the opinions and conclusions of its
authors but not the funding agents.

## References

  * [Afouras et al.(2020)Afouras, Owens, Chung, and Zisserman] Triantafyllos Afouras, Andrew Owens, Joon Son Chung, and Andrew Zisserman.  Self-supervised learning of audio-visual objects from video.  In _ECCV_ , 2020.
  * [Arandjelovic and Zisserman(2018)] Relja Arandjelovic and Andrew Zisserman.  Objects that sound.  In _ECCV_ , 2018.
  * [Britton and Arshad(2019)] Zelie Britton and Qadeer Arshad.  Vestibular and multi-sensory influences upon self-motion perception and the consequences for human behavior.  _Frontiers in Neurology_ , 2019.
  * [Chen et al.(2020)Chen, Jain, Schissler, Gari, Al-Halah, Ithapu, Robinson, and Grauman] Changan Chen, Unnat Jain, Carl Schissler, Sebastia Vicenc Amengual Gari, Ziad Al-Halah, Vamsi Krishna Ithapu, Philip Robinson, and Kristen Grauman.  Soundspaces: Audio-visual navigaton in 3d environments.  In _ECCV_ , 2020.
  * [Fayek and Kumar(2020)] Haytham M. Fayek and Anurag Kumar.  Large scale audiovisual learning of sounds with weakly labeled data.  In _IJCAI_ , 2020.
  * [Gan et al.(2019)Gan, Zhao, Chen, Cox, and Torralba] Chuang Gan, Hang Zhao, Peihao Chen, David Cox, and Antonio Torralba.  Self-supervised moving vehicle tracking with stereo sound.  In _ICCV_ , 2019.
  * [Gan et al.(2020a)Gan, Huang, Zhao, Tenenbaum, and Torralba] Chuang Gan, Deng Huang, Hang Zhao, Joshua B. Tenenbaum, and Antonio Torralba.  Music gesture for visual sound separation.  In _CVPR_ , 2020a.
  * [Gan et al.(2020b)Gan, Zhang, Wu, Gong, and Tenenbaum] Chuang Gan, Yiwei Zhang, Jiajun Wu, Boqing Gong, and J. Tenenbaum.  Look, listen, and act: Towards audio-visual embodied navigation.  _ICRA_ , 2020b.
  * [Gao et al.(2018)Gao, Feris, and Grauman] Ruohan Gao, Rogerio Feris, and Kristen Grauman.  Learning to separate object sounds by watching unlabeled video.  In _ECCV_ , 2018.
  * [Gao et al.(2020)Gao, Oh, Grauman, and Torresani] Ruohan Gao, Tae-Hyun Oh, Kristen Grauman, and Lorenzo Torresani.  Listen to look: Action recognition by previewing audio.  In _CVPR_ , 2020.
  * [Gemmeke et al.(2017)Gemmeke, Ellis, Freedman, Jansen, Lawrence, Moore, Plakal, and Ritter] J. F. Gemmeke, D. P. W. Ellis, D. Freedman, A. Jansen, W. Lawrence, R. C. Moore, M. Plakal, and M. Ritter.  Audio set: An ontology and human-labeled dataset for audio events.  In _ICASSP_ , 2017.
  * [He et al.(2016)He, Zhang, Ren, and Sun] K. He, X. Zhang, S. Ren, and J. Sun.  Deep residual learning for image recognition.  In _CVPR_ , 2016.
  * [Hershey and Movellan(2000)] John Hershey and Javier Movellan.  Audio vision: Using audio-visual synchrony to locate sounds.  In _NIPS_ , 2000.
  * [Hershey et al.(2017)Hershey, Chaudhuri, Ellis, Gemmeke, Jansen, Moore, Plakal, Platt, Saurous, Seybold, Slaney, Weiss, and Wilson] S. Hershey, S. Chaudhuri, D. P. W. Ellis, J. F. Gemmeke, A. Jansen, R. C. Moore, M. Plakal, D. Platt, R. A. Saurous, B. Seybold, M. Slaney, R. J. Weiss, and K. Wilson.  Cnn architectures for large-scale audio classification.  In _ICASSP_ , 2017.
  * [Hu et al.(2019)Hu, Nie, and Li] Di Hu, Feiping Nie, and Xuelong Li.  Deep multimodal clustering for unsupervised audiovisual learning.  In _CVPR_ , 2019.
  * [Hu et al.(2020)Hu, Qian, Jiang, Tan, Wen, Ding, Lin, and Dou] Di Hu, Rui Qian, Minyue Jiang, Xiao Tan, Shilei Wen, Errui Ding, Weiyao Lin, and Dejing Dou.  Discriminative sounding objects localization via self-supervised audiovisual matching.  In _NeurIPS_ , 2020.
  * [Kazakos et al.(2019)Kazakos, Nagrani, Zisserman, and Damen] Evangelos Kazakos, Arsha Nagrani, Andrew Zisserman, and Dima Damen.  Epic-fusion: Audio-visual temporal binding for egocentric action recognition.  In _ICCV_ , 2019.
  * [Kidron et al.(2005)Kidron, Schechner, and Elad] E. Kidron, Y. Y. Schechner, and M. Elad.  Pixels that sound.  In _CVPR_ , 2005.
  * [Korbar et al.(2019)Korbar, Tran, and Torresani] Bruno Korbar, Du Tran, and Lorenzo Torresani.  Scsampler: Sampling salient clips from video for efficient action recognition.  In _ICCV_ , 2019.
  * [Otsu(1979)] Nobuyuki Otsu.  A threshold selection method from gray-level histograms.  _IEEE SMC_ , 1979.
  * [Owens and Efros(2018)] Andrew Owens and Alexei A. Efros.  Audio-visual scene analysis with self-supervised multisensory features.  In _ECCV_ , 2018.
  * [Qian et al.(2020)Qian, Di Hu, Wu, Xu, and Lin] Rui Qian, Heinrich Dinkel Di Hu, Mengyue Wu, Ning Xu, and Weiyao Lin.  Multiple sound sources localization from coarse to fine.  In _ECCV_ , 2020.
  * [Rahman et al.(2019)Rahman, Xu, and Sigal] Tanzila Rahman, Bicheng Xu, and Leonid Sigal.  Watch, listen and tell: Multi-modal weakly supervised dense event captioning.  In _ICCV_ , 2019.
  * [Ren et al.(2017)Ren, He, Girshick, and Sun] S. Ren, K. He, R. Girshick, and J. Sun.  Faster r-cnn: Towards real-time object detection with region proposal networks.  _TPAMI_ , 2017.
  * [Senocak et al.(2018)Senocak, Oh, Kim, Yang, and Kweon] Arda Senocak, Tae-Hyun Oh, Junsik Kim, Ming-Hsuan Yang, and In So Kweon.  Learning to localize sound source in visual scenes.  In _CVPR_ , 2018.
  * [Tian and Xu(2021)] Yapeng Tian and Chenliang Xu.  Can audio-visual integration strengthen robustness under multimodal attacks?  In _CVPR_ , June 2021.
  * [Tian et al.(2018)Tian, Shi, Li, Duan, and Xu] Yapeng Tian, Jing Shi, Bochen Li, Zhiyao Duan, and Chenliang Xu.  Audio-visual event localization in unconstrained videos.  In _ECCV_ , 2018.
  * [Tian et al.(2019)Tian, Guan, Justin, Moore, and Xu] Yapeng Tian, Chenxiao Guan, Goodman Justin, Marc Moore, and Chenliang Xu.  Audio-visual interpretable and controllable video captioning.  In _CVPR Workshops_ , 2019.
  * [Tian et al.(2020)Tian, Li, and Xu] Yapeng Tian, Dingzeyu Li, and Chenliang Xu.  Unified multisensory perception: Weakly-supervised audio-visual video parsing.  In _ECCV_ , 2020.
  * [Tian et al.(2021)Tian, Hu, and Xu] Yapeng Tian, Di Hu, and Chenliang Xu.  Cyclic co-learning of sounding object visual grounding and sound separation.  In _CVPR_ , June 2021.
  * [Vaswani et al.(2017)Vaswani, Shazeer, Parmar, Uszkoreit, Jones, Gomez, Kaiser, and Polosukhin] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin.  Attention is all you need.  In _NIPS_ , 2017.
  * [Wang et al.(2018)Wang, Wang, and Wang] Xin Wang, Yuan-Fang Wang, and William Yang Wang.  Watch, listen, and describe: Globally and locally aligned cross-modal attentions for video captioning.  In _NAACL-HLT_ , 2018.
  * [Wu et al.(2019)Wu, Zhu, Yan, and Yang] Y. Wu, L. Zhu, Y. Yan, and Y. Yang.  Dual attention matching for audio-visual event localization.  In _ICCV_ , 2019.
  * [Zhao et al.(2018)Zhao, Gan, Rouditchenko, Vondrick, McDermott, and Torralba] Hang Zhao, Chuang Gan, Andrew Rouditchenko, Carl Vondrick, Josh McDermott, and Antonio Torralba.  The sound of pixels.  In _ECCV_ , 2018.
  * [Zhao et al.(2019)Zhao, Gan, Ma, and Torralba] Hang Zhao, Chuang Gan, Wei-Chiu Ma, and Antonio Torralba.  The sound of motions.  In _ICCV_ , 2019.


# Two Luminous Post-AGB Stars in the Galactic Globular Cluster M19

Howard E. Bond Department of Astronomy & Astrophysics, Pennsylvania State
University, University Park, PA 16802, USA  Space Telescope Science Institute,
3700 San Martin Drive, Baltimore, MD 21218, USA  Visiting Astronomer, Cerro
Tololo Inter-American Observatory, National Optical Astronomy Observatory,
operated by the Association of Universities for Research in Astronomy under a
cooperative agreement with the National Science Foundation.  Brian D. Davis
Department of Astronomy & Astrophysics, Pennsylvania State University,
University Park, PA 16802, USA  Michael H. Siegel Department of Astronomy &
Astrophysics, Pennsylvania State University, University Park, PA 16802, USA
Robin Ciardullo Department of Astronomy & Astrophysics, Pennsylvania State
University, University Park, PA 16802, USA  Institute for Gravitation and the
Cosmos, Pennsylvania State University, University Park, PA 16802, USA

###### Abstract

We report the discovery of a luminous “yellow” post-asymptotic-giant-branch
(PAGB) star in the globular cluster (GC) M19 (NGC 6273), identified during our
uBVI survey of Galactic GCs. The uBVI photometric system is optimized to
detect stars with large Balmer discontinuities, indicating very low surface
gravities and high luminosities. The spectral-energy distribution (SED) of the
star is consistent with an effective temperature of about 6250 K and a surface
gravity of $\log g=0.5$. We use Gaia data to show that the star’s proper
motion and radial velocity are consistent with cluster membership. One aim of
our program is to test yellow PAGB stars as candidate Population II standard
candles for determining extragalactic distances. We derive a visual absolute
magnitude of $M_{V}=-3.39\pm 0.09$ for the M19 star. This is in close
agreement with the $M_{V}$ values found for yellow PAGB stars in the GCs
$\omega$ Cen, NGC 5986, and M79, indicating a very narrow luminosity function.
These objects are four magnitudes brighter than RR Lyrae variables, and they
can largely avoid the issues of interstellar extinction that are a problem for
Population I distance indicators. We also identified a second luminous PAGB
object in M19, this one a hotter “UV-bright” star. Its SED is consistent with
an effective temperature of about 11,750 K and $\log g=2.0$. The two objects
have nearly identical bolometric luminosities, $\log L/L_{\odot}=3.24$ and
3.22, respectively.

stars: AGB and post-AGB — globular clusters: individual (M19) — distance scale
— stars: evolution

††journal: AJ††facilities: CTIO 0.9m, Gaia, GALEX, 2MASS, Swift, HST, Spitzer,
WISE

##  1 Yellow Post-AGB Stars in Globular Clusters

The visually brightest objects in globular clusters (GCs) and old stellar
populations are stars in their final evolution from the tip of the asymptotic
giant branch (AGB) toward higher temperatures in the color-magnitude diagram
(CMD). As these post-AGB (PAGB) stars pass at nearly constant bolometric
luminosity through spectral types early G, F, and late A, they reach their
brightest visual absolute magnitudes, because of the dependence of the
bolometric correction upon effective temperature.

It has been suggested (Bond, 1997a, b) that these luminous “yellow” PAGB stars
may be useful Population II standard candles. In populations containing only
old stars there should be fairly sharp upper and lower limits to their
brightnesses. In addition, yellow PAGB stars have conspicuously large Balmer
discontinuities in their spectral-energy distributions (SEDs), making them
easy to recognize using a single set of suitable photometric observations.
They should be detectable in early-type galaxies that do not contain Cepheids,
and in the halos of spirals, where there are fewer issues of interstellar
extinction than there are for Population I distance indicators.

Because of the rapid evolutionary timescales for PAGB stars, however, these
objects are very rare. The previously known luminous yellow PAGB stars in the
Galactic GC system number only five: (1) HD 116745 (“Fehrenbach’s star,” ROA
24; Gonzalez & Wallerstein 1992 and references therein) and the RV Tauri
variable V1 in $\omega$ Centauri (Jones, 1968); (2) two luminous yellow PAGB
stars in NGC 5986 (Bond, 1977; Alves et al., 2001); and (3) a yellow PAGB star
in M79, discovered by Bond et al. (2016, hereafter B16). There are very few,
if any, yellow stars in the CMDs of Galactic GCs that lie above the horizontal
branch (HB) and within $\sim$1 magnitude of the brightness of these objects
(see Davis et al. 2021, hereafter D21). Analogs of the GC objects are also
known in the field; an example is the Galactic halo star BD+14∘3061, along
with a few other similar field objects (Bond 2020, hereafter B20, and
references therein).

In this paper, we report our discovery of a luminous yellow PAGB star in the
Galactic GC M19 (NGC 6273). As described in detail in D21, this object was the
only new luminous yellow PAGB star found in our photometric survey of nearly
the entire known sample of Galactic GCs. Thus, it is likely to be the final
member of this class in the Galactic GCs.

As PAGB stars continue to evolve, they reach high effective temperatures, and
arrive at the top of the white-dwarf cooling sequence in the CMD. These hot,
luminous objects are conspicuous at short wavelengths, especially in the
ground- and space-based ultraviolet (UV). There is a substantial literature on
these prominent, but rare, “UV-bright” objects in GCs, beginning with Zinn et
al. (1972, hereafter ZNG), and summarized recently by Moehler et al. (2019),
who list about three dozen known or candidate members of the class. Our
observations of M19 also revealed a previously unrecognized luminous UV-bright
PAGB star, and we briefly discuss this object as well.

##  2 Observations

In the mid-1990s, H.E.B. and collaborators began to develop a ground-based
photometric system optimized for efficient discovery of luminous, low-gravity
A, F, and G-type stars having large Balmer jumps. This “uBVI” system combines
the Thuan & Gunn (1976) $u$ filter, whose bandpass lies almost entirely
shortward of the Balmer discontinuity,111The Thuan-Gunn $u$ bandpass is
distinct from, and shortward of, that of the $u^{\prime}$ filter of the Sloan
Digital Sky Survey, which partially overlaps the Balmer jump. with the broad-
band BVI filters of the standard Johnson-Kron-Cousins system. The
astrophysical motivations and design principles of the uBVI system were
presented by Bond (2005, hereafter Paper I). Siegel & Bond (2005, hereafter
Paper II) established a network of equatorial uBVI standard stars, based on
extensive CCD observations with 0.9-, 1.5-, and 4-m telescopes at Kitt Peak
National Observatory (KPNO) and Cerro Tololo Inter-American Observatory
(CTIO).

Over the years 1994 to 2001, H.E.B. made uBVI observations at KPNO and CTIO of
nearly the entire known sample of Galactic GCs. The primary aim of this survey
was to search systematically for yellow PAGB stars, with a goal of testing
their potential as standard candles and to establish a photometric zero-point.
A forthcoming paper (D21) will present a complete census of stars in the
Galactic GC system lying above the HB, bluer than the red-giant branch and
AGB, and redder than $(B-V)_{0}=-0.1$ in the cluster CMDs. Further papers will
describe the zero-point calibration for yellow PAGB stars, and the results of
searches for them in Local Group galaxies. This paper focuses on the luminous
yellow PAGB star that the survey revealed in M19, as well as a luminous hot
UV-bright member of the cluster.

uBVI survey observations of M19 were made with the CTIO 0.9-m telescope on
1998 April 21 ($2\times 2$ grid centered on the cluster; exposure times at
each pointing were $2\times 800$, 75, 40, and 45 s, respectively) and April 22
(cluster center; exposure times 400, 75, 40, and 45 s). The field of view of
the 0.9-m CCD camera was $13^{\prime}\times 13^{\prime}$. Exposure times in
the uBVI filters on the second night were chosen so as to reach a signal-to-
noise ratio of about 200–300 for stars about 2 mag brighter than the HB,
allowing for interstellar extinction. The first night was not photometric, and
the exposure times in $u$ were lengthened.

M19 is a populous GC, one of the 10 or so most massive clusters in the
Galactic GC system. Kruijssen et al. (2020), Pfeffer et al. (2020), and others
have argued that it is the remnant nuclear star cluster of a dwarf galaxy
(“Kraken”), which was disrupted and accreted by the Milky Way. M19 lies in the
Galactic bulge in Ophiuchus ($l=356\fdg 9,b=+9\fdg 4$), and is overlain by a
considerable number of field stars. Early work (e.g., Harris et al. 1976)
showed that M19 is affected by substantial differential reddening, and that it
has a very blue HB, consistent with low metallicity. The Harris (2010,
hereafter H10) catalog of GC parameters222Online version of 2010 December, at
http://physwww.mcmaster.ca/~harris/mwgc.dat gives a metal content of
$\rm[Fe/H]=-1.74$. A recent spectroscopic study (Johnson et al., 2017) of over
300 red giants and AGB stars in the cluster showed that there is a range of
iron contents among the members, from about $\rm[Fe/H]=-2$ to $-1$, but
concentrated around $-1.75$ and $-1.5$. These findings are indicative of
considerable self-enrichment in this massive GC.

##  3 Data Analysis and Selection of PAGB Candidates

The CCD frames were reduced as described in detail by B16 and D21, using
standard tasks in IRAF333IRAF was distributed by the National Optical
Astronomy Observatory, operated by the Association of Universities for
Research in Astronomy (AURA) under a cooperative agreement with the National
Science Foundation. for bias subtraction and flat-fielding. Instrumental
stellar magnitudes were measured on the frames with the ALLSTAR and DAOGROW
tasks in DAOPHOT (Stetson, 1987). These data were corrected for atmospheric
extinction, and then the 1998 April 22 frames were calibrated to the uBVI
system using measurements of standard fields obtained during this observing
run. We used the $u$ magnitudes of standard stars from Paper II, and $BVI$
magnitudes for the standards of Landolt (1992). Since the 1998 April 21 night
was not photometric, its zero-points were determined by scaling to the M19
frames obtained on the photometric night. Finally, all of the measurements
were combined into a single catalog of mean calibrated magnitudes. For the
bright PAGB candidates considered here, the photometry provides essentially
complete stellar samples into the cluster center (apart from rare cases of
badly blended bright stars; see D21 for further discussion of the sample
completeness for our GC survey).

We then searched for candidate yellow PAGB stars by choosing objects that are
simultaneously bright, have large Balmer jumps, and have spatial locations and
proper motions consistent with cluster membership. Figure 3 illustrates the
selection process. The top two frames show two CMDs for the cluster, after
correcting for a nominal cluster-averaged interstellar extinction of
$E(B-V)=0.38$ (from H10), and assuming $R_{V}=3.1$. (For the extinction
corrections in $V-I$, we used the formula of Dean et al. 1978.) The top left
panel plots $V_{0}$ vs. $(B-V)_{0}$, and the top right plots $V_{0}$ vs.
$(V-I)_{0}$.

As noted in §2, M19 lies in the Galactic bulge, and there are substantial
numbers of field stars in our CCD frames. To remove most of them from our uBVI
catalog, we applied three constraints, based on spatial location and on
astrometry of the stars given in the Gaia Data Release 2 (DR2; Gaia
Collaboration et al. 2016, 2018)444http://vizier.cfa.harvard.edu/viz-
bin/VizieR-3?-source=I/345/gaia2: (1) angular distance from the cluster center
less than three times the half-light radius (i.e., $3\times 79\farcs 2$, using
the half-light radius given by H10); (2) DR2 proper motions in right ascension
and declination within $1.1\rm\,mas\,yr^{-1}$ of the cluster mean of
$(\mu_{\alpha},\mu_{\delta})=(-3.22,+1.61)\rm\,mas\,yr^{-1}$ (Gaia
Collaboration et al., 2018); and (3) DR2 parallax less than 0.7 mas (to
exclude objects almost certainly in the foreground). The top panels in Figure
3 show that these criteria result in a nearly pure sample of cluster members,
especially brighter than $V_{0}\simeq 14$; but there do appear to be a few
remaining field stars below that level.

Also shown in the top left panel is the approximate location of the RR Lyrae,
Cepheid, and RV Tauri instability strip (e.g., Harris et al. 1983; D21). M19
contains seven objects listed in the Clement et al. (2001) catalog555Updated
version available online at
http://www.astro.utoronto.ca/~cclement/cat/listngc.html of variable stars in
GCs. However, two of them (V6 and V7) appear to be non-members, based on their
proper motions in Gaia DR2. The positions of the remaining five variables in
the CMDs in Figure 3, based on our photometry, are plotted as filled green
circles. They consist of four Type II Cepheids666Clement & Sawyer Hogg (1978)
point out that the only Galactic GCs having more Type II Cepheids than M19 are
$\omega$ Cen and M14. and one RR Lyrae variable. Our data are comprised of
only a few measurements on two successive nights, made at random pulsation
phases, so they should not be regarded as representing the time-averaged
locations of the variables in the CMDs. Nevertheless, the RR Lyr variable, and
three of the Cepheids, do lie inside, or on the border of, the schematic
instability strip.

The red-giant branch (RGB) and HB of the cluster’s CMD in Figure 3 have
appreciable width (compare with, for example, the very narrow RGB and HB of
the nearly unreddened GC M79 in our team’s data, shown in Figure 1 of B16).
This spread is partially due to substantial differential interstellar
reddening across the face of M19—as we noted above, and has been discussed in
several more recent studies of the cluster, including Piotto et al. (1999),
Alonso-García et al. (2012), Johnson et al. (2017), and references therein to
earlier literature.

Top panels: Color-magnitude diagrams for M19 in $V,B-V$ and $V,V-I$. Our
photometry has been corrected for foreground reddening of $E(B-V)=0.38$, and
Gaia DR2 proper motions and parallaxes have been used to reject field stars
(see text). The filled orange and blue circles show the locations of the two
luminous PAGB stars belonging to the cluster. Dashed lines indicate the
location of the pulsational instability strip. Filled green circles mark one
RR Lyrae variable (the faintest object) and four Type II Cepheids. Bottom
panel: Gravity-sensitive $(u-B)-(B-V)$ color index, corrected for extinction
and plotted against the temperature-sensitive $V-I$ color, for stars brighter
than $V_{0}=14$ (black points). The red curve is a polynomial fit to the
location of the HB in a sample of GCs with redder HBs than M19’s, taken from
D21. The yellow PAGB star (filled orange circle), and the Cepheids, stand out
from the HB locus because of their lower gravities and larger Balmer jumps.
The blue PAGB star (filled blue circle) is hotter, and lies in the regime
where the size of the Balmer jump depends on temperature rather than surface
gravity.

As our index of the strength of the Balmer discontinuity, we use the color
difference $(u-B)-(B-V)$, which is a broad-band analog of the $c_{1}$ Balmer-
jump index of the Strömgren $uvby$ system.777Use of a color difference has the
advantage of only a weak dependence on interstellar extinction; a formula for
the extinction correction as a function of $E(B-V)$ is given in Paper I. In
the bottom panel of Figure 3, we plot this color difference vs. the
$(V-I)_{0}$ temperature index, for the M19 members in the top panels that are
brighter than $V_{0}=14.0$. At these bright magnitudes, nearly all of the M19
stars are red giants or AGB stars. The selection criterion for yellow PAGB
stars is that they have a Balmer-jump index significantly larger (i.e.,
redder) than do HB stars at the same color, and that they are at least 3
magnitudes brighter than the HB at the same color. As the top panels show, the
HB in M19 is extremely blue, and almost entirely lacks stars on the cooler
“horizontal” locus of the HB. Therefore, as an indicator of the HB’s location,
we use a polynomial fit to the mean location of the HB in a sample of GCs with
redder HBs, taken from D21. This relation is plotted as a red line in the
bottom panel of Figure 3. The peak in this sequence on the left-hand side of
the diagram, at $(V-I)_{0}\simeq 0.1$, is due to the maximum size of the
Balmer jump at this color on the HB.

The single RR Lyrae star in M19 (included in the bottom panel of Figure 3 in
spite of being fainter than $V_{0}=14.0$) does lie almost exactly on the mean
HB locus. The four Cepheids fall above the HB relation, consistent with their
higher luminosities, lower surface gravities, and consequent higher
$(u-B)-(B-V)$ color differences. There also appears to be an above-the-HB
(AHB) member of M19 at $V_{0}\simeq 12.7$ with a high value of the color
difference; curiously, although this star apparently lies within the
instability strip, it is not a known variable. This object, and other AHB
stars in Galactic GCs, will be discussed in D21.

The most conspicuous star in the three panels of Figure 3 is the very bright
object plotted as an orange filled circle. It has a Balmer jump larger than
those of HB stars, as shown in the bottom panel, making it a strong candidate
for a yellow PAGB star.

Also conspicuous in the Figure 3 CMD is a bright, very blue star. For stars
this hot, the $(u-B)-(B-V)$ color difference is no longer sensitive to surface
gravity (see Paper I, Figures 4 and 5), becoming instead a temperature index.
This star is a candidate luminous blue PAGB star.

Both objects had been marked as candidate UV-bright stars in the classical ZNG
study; our yellow PAGB star is M19 ZNG 4, and the blue one is M19 ZNG 2. To
our knowledge, the present study is the first to present evidence that both of
them are luminous members of the cluster, rather than foreground objects.

The left panel in Figure 2 presents a finding chart for the yellow and blue
PAGB stars in M19, made from one of our $B$-band frames. To illustrate how
conspicuous these two stars are in the ground-based UV, the right panel in
Figure 3 shows a $u$-band frame. The two PAGB stars dominate this image. (The
bright star to the NW of the blue PAGB star is the brightest Type II Cepheid
in M19, V1, which was marked as the UV-bright object ZNG 3 in the ZNG study.
The bright star to the NE of the blue PAGB star is ZNG 1, a foreground field
object, as indicated by its Gaia parallax and proper motion. Two more UV-
bright candidates, further from the cluster center, designated ZNG 5 and 6,
are also non-members, according to Gaia astrometry.)

Left panel: finding chart for the yellow PAGB star (orange circle) and the
blue PAGB star (blue circle) in the globular cluster M19. Made from a $B$-band
CCD frame obtained with the CTIO 0.9-m telescope. North is at the top, east on
the left; frame is $2\farcm 5$ high. Right panel: $u$-band frame from same
telescope, height $4\farcm 8$, illustrating how conspicuous both stars are in
the ground-based ultraviolet. ( The bright star to the NE of the blue PAGB
star is a foreground object, ZNG 1. The bright star to the NW is the Type II
Cepheid V1 = ZNG 3.)

Table 1 presents basic data for our two PAGB candidates. The Gaia Early Data
Release 3 (EDR3; Gaia Collaboration et al. 2020) became available as we were
completing this paper, and is the source for the astrometry given in rows 1
through 5 of the table. The radial velocity (RV) for the yellow PAGB star in
row 6 is from Gaia DR2. Rows 7 through 10 give our uBVI photometry. The
interstellar reddenings in row 11 are taken from the high-resolution M19
extinction maps of Alonso-García et al. (2012) and Johnson et al. (2017). The
former map is based on stellar photometry with the Magellan Telescope, and the
latter on stellar photometry using Hubble Space Telescope (HST) images.888The
Alonso-Garcia et al. reddening map is presented pictorially in their Figure
13, and in tabular form at https://vizier.u-strasbg.fr/viz-bin/VizieR-4. C.
Johnson kindly sent us a numerical table giving the extinction map depicted in
Figure 2 of Johnson et al. (2017). Unfortunately the existing HST frames
cannot be used for optical photometry of the bright PAGB stars because their
images are saturated. The reddening values from these two maps agree well [the
differences in $E(B-V)$ at the locations of the two stars are 0.006 and 0.013
mag, respectively], and we give their means in Table 1. However, it should be
noted that there are appreciable variations in $E(B-V)$ from cell to cell in
both extinction maps, and it is difficult to estimate the uncertainty in the
reddening for an individual star. We adopt $\pm$0.02 mag as a reasonable
guess.

Table 1: Basic Data for the Luminous Post-AGB Stars in M19  Parameter | “Yellow” PAGB | “Blue” PAGB | SourceaaSources: (1) Gaia EDR3; (2) Gaia DR2; (3) This paper (not corrected for extinction); note that the zero-point for the $u$ magnitude is such that $u=1.0$ for Vega; (4) From the reddening maps of Alonso-García et al. (2012) and Johnson et al. (2017) (see the text, §3); (5) This paper, calculated from data in this table, $R_{V}=3.1$, and a distance modulus of $(m-M)_{0}=14.84\pm 0.06$ mag (see the text, §4); (6) This paper, calculated from data in this table, and bolometric corrections from Castelli & Kurucz (2004) (see the text, §7).
---|---|---|---
| (M19 ZNG 4) | (M19 ZNG 2) |
R.A. [J2000] | 17:02:35.186 | 17:02:39.155 | (1)
Dec. [J2000] |  $-26$:15:24.14 |  $-26$:15:29.36 | (1)
Parallax [mas] | $+0.119\pm 0.016$ | $+0.158\pm 0.024$ | (1)
R.A. proper motion [$\rm\,mas\,yr^{-1}$] | $-2.878\pm 0.019$ | $-2.990\pm 0.025$ | (1)
Dec. proper motion [$\rm\,mas\,yr^{-1}$] | $+1.146\pm 0.012$ | $+1.454\pm 0.018$ | (1)
Radial velocity [${\>\rm km\>s^{-1}}$] | $+142.3\pm 1.4$ | $\dots$ | (2)
$V$ | $12.512\pm 0.006$ | $13.291\pm 0.006$ | (3)
$u-B$ | $1.471\pm 0.012$ | $0.608\pm 0.015$ | (3)
$B-V$ | $0.795\pm 0.010$ | $0.211\pm 0.010$ | (3)
$V-I$ | $1.021\pm 0.011$ | $0.390\pm 0.010$ | (3)
Reddening, $E(B-V)$ | $0.344\pm 0.020$ | $0.342\pm 0.020$ | (4)
Absolute magnitude, $M_{V}$ | $-3.39\pm 0.09$ | $-2.61\pm 0.09$ | (5)
Absolute luminosity, $\log L/L_{\odot}$ | 3.24 | 3.22 | (6)

##  4 Cluster Distance and Visual Absolute Magnitudes

To convert the apparent magnitudes in row 7 of Table 1 to absolute magnitudes,
we need the distance to the cluster. For M19, distance determinations from
photometric methods are unusually problematic, for at least two reasons.
First, the relatively large and spatially variable reddening makes the
correction for extinction difficult. Second, distance methods based on the HB
suffer because, in the case of M19 (as shown in Figure 3), the cluster’s
“horizontal” branch is nearly vertical in CMDs made at optical wavelengths.

With the recent availability of Gaia EDR3, it is also possible to obtain a
direct geometric distance estimate. We considered the sample of 202 RGB
cluster members brighter than $V_{0}=14$, which we had chosen for the bottom
panel in Figure 3. The mean EDR3 parallax for this sample, and its standard
error, are $0.0934\pm 0.0062$ mas. To this we applied the global zero-point
offset of +0.017 mas from the analysis of Lindegren et al. (2020).

In Table 2 we list six distance determinations, made using a variety of
essentially independent techniques, as summarized in the notes to the table.
We see no compelling reason to adopt one of these determinations, so we will
use a weighted mean of the four determinations for which uncertainties are
given (and which, as it happens, span the range of distances found by these
studies). This results in our adopted distance modulus of $(m-M)_{0}=14.84\pm
0.06$ ($d=9.29\pm 0.26$ kpc).

Row 12 in Table 1 gives the visual absolute magnitudes of both stars,
calculated from our photometry, the interstellar extinction given in row 11
(using $R_{V}=3.1$), and the distance modulus adopted in this section. The
stated formal uncertainties are likely somewhat optimistic, given the
possibilities of systematic errors in the extinction and distance.

Table 2: M19 Distance Determinations  Distance [kpc] | $(m-M)_{0}$ | Source
---|---|---
$8.99\pm 0.83$ | $14.77\pm 0.20$ |  Piotto et al. (1999)aaDistance determined from luminosity of red-giant bump in HST-based $BV$ color-magnitude diagram.
$9.95\pm 0.37$ | $14.99\pm 0.08$ |  Recio-Blanco et al. (2005)bbDistance calculated by us by subtracting $3.1\,E(B-V)$ from the value of $(m-M)_{\rm F555W}=16.04\pm 0.08$, and adopting $E(B-V)=0.34$, as given by Recio-Blanco et al. (2005).
8.24 | 14.58 |  Valenti et al. (2007)ccDistance determined from near-IR photometry of stars on the red-giant branch. Uncertainty not given.
8.79 | 14.72 |  Harris (2010)ddDistance calculated by us by subtracting $3.1\,E(B-V)$ from the value of $(m-M)_{V}=15.90$, and adopting $E(B-V)=0.38$, as given by Harris (2010). Uncertainty not given.
$8.13\pm 0.47$ | $14.55\pm 0.13$ |  Baumgardt et al. (2019)ee“Kinematic” distance derived from cluster-member proper-motion and radial-velocity dispersions and $N$-body modeling.
$9.06\pm 0.51$ | $14.78\pm 0.12$ | This paper, Gaia EDR3ffCalculated by us based on the mean Gaia EDR3 parallax of a sample of cluster red giants, as described in §4.

##  5 Cluster-Membership Tests

Both of the PAGB candidates lie well within the spatial boundaries of the
cluster (see Figure 3): the yellow and blue stars fall $50^{\prime\prime}$ and
$36^{\prime\prime}$ from the cluster center, respectively, which are within
the half-light radius of $79\farcs 2$. Moreover, they have uBVI colors that
are extremely unusual for field stars. Thus it is already highly probable that
they are members of the cluster.

Two further membership tests are possible: (1) RV, and (2) proper motion.
Parallax is a less useful criterion for individual stars at the large adopted
distance of M19, which corresponds to a parallax of only 0.108 mas.
Nevertheless, the Gaia EDR3 parallaxes for both stars, given in row 3 of Table
1, corrected for the +0.017 zero-point offset (Lindegren et al., 2020), agree
with the nominal value to within 1.8 and 2.8$\sigma$, respectively.

Gaia EDR3 did not give RVs for either star, but the earlier DR2 listed a RV of
$+142.3\pm 1.4\,{\>\rm km\>s^{-1}}$ for the yellow PAGB star. This agrees
extremely well with the mean cluster RV of $+145.54\pm 0.59\,{\>\rm
km\>s^{-1}}$ (Baumgardt et al., 2019); the velocity dispersion in the cluster
is $11.0{\>\rm km\>s^{-1}}$ (Baumgardt & Hilker, 2018). Gaia DR2 and EDR3 did
not list a RV for the early-type blue PAGB candidate.

To make proper-motion membership tests for the two stars, we selected nearly
pure samples of M19 members from Gaia EDR3. We chose stars lying within
$30^{\prime\prime}$ of each object, brighter than magnitude $G=18$, redder
than $BP-RP=1.2$, and having a parallax less than 1 mas. The proper motions
for these samples are plotted as black points in the two panels in Figure 5,
with the proper motions of the PAGB candidates themselves (rows 4 and 5 in
Table 1) marked with orange and blue open circles. The Gaia CMDs of these two
samples indicate that a large majority of the stars are cluster members lying
on the RGB. In both cases, the motions of the PAGB candidates are well within
the distributions of the cluster members. To illustrate the proper-motion
distribution in the surrounding field, we selected stars from EDR3 in a nearby
field, with a radius of $200^{\prime\prime}$ and the same criteria. These are
plotted as red points in the two figures; they show a wide range of proper
motions, most of them in fact outside the range plotted.

In summary, the available evidence strongly confirms that both stars are
physical members of M19.

Left panel: Proper motions from Gaia EDR3 for stars within $30^{\prime\prime}$
of the yellow PAGB star in M19, with parallax less than 1 mas, brighter than
$G=18$, and $BP-RP$ color redder than 1.2 (black points). The color-magnitude
diagram of these stars (not shown) indicates that nearly all of them are
cluster members on the red-giant branch. The proper motion of the yellow PAGB
star is marked with an orange circle. Right panel: Proper motions from EDR3
for stars within $30^{\prime\prime}$ of the blue PAGB star in M19, selected
with the same criteria (black points). The proper motion of the blue PAGB star
is marked with a blue circle. In both panels the red points show the proper
motions of stars in a nearby field, selected using the same criteria. The
proper motions of both stars are well within the distributions of cluster
members.

##  6 Variability

As shown in the top left panel in Figure 3, the M19 yellow PAGB star lies
close to the instability strip in the CMD. Several field analogs of the yellow
PAGB stars in GCs are known to be low-amplitude semi-regular variables,
including HD 46703 and BD+14∘3061\. As discussed in B20 (and references
therein), both field stars have typical pulsation periods of $\sim$29–32 days,
and peak-to-peak amplitudes of $\sim$0.1–0.3 mag. The pulsation amplitudes are
variable; in the case of BD+14∘3061, the variations can actually drop below
detectability for extended intervals.

The most recent extensive search for variable stars in M19 of which we are
aware is the photographic study by Clement & Sawyer Hogg (1978); this
investigation did not mark the yellow PAGB star as variable. Our own uBVI data
are of limited value, since we only have the two epochs in 1998 April
separated by one day, plus an earlier lower-quality CCD observation obtained
with the CTIO 1.5-m in 1995, which we did not include in our calibrated
photometric reductions. We see no convincing evidence for variability in this
limited material at a level of more than a few hundredths of a magnitude. The
available data from current all-sky monitoring programs with small telescopes
are generally of limited use, because of the crowding within the cluster. The
uncertainty given for the $G$ magnitude in Gaia EDR3 is very small, consistent
with little or no variability.

Thus there is no evidence that the yellow PAGB star is variable, but
additional monitoring observations with sufficient spatial resolution would be
useful. (There is also no evidence that the blue PAGB star is variable, based
on the same material.)

##  7 Spectral-Energy Distributions

We determined SEDs for both PAGB stars by combining our optical photometry
from Table 1 with public data from the following sources:

(1) The Galaxy Evolution Explorer (GALEX) (Morrissey et al., 2007) imaged M19
in its all-sky UV survey,999https://galex.stsci.edu/GalexView/ but only in its
far-UV (FUV) bandpass. Although our blue PAGB star is prominent at FUV
wavelengths, it is not contained in the GALEX source catalog (presumably
because of crowding near the cluster center). We obtained the FUV image and,
because the blue PAGB is so much brighter than its neighboring objects at FUV
wavelengths, we measured its brightness using simple aperture photometry. We
then translated these raw magnitudes into the standard flux system by scaling
our measurements to similar aperture photometry of nearby isolated field stars
with magnitudes given in the GALEX source catalog. The yellow PAGB star was
too faint and blended to be measured in the FUV.

(2) The Hubble Space Telescope (HST) has imaged M19 on a few
occasions,101010https://archive.stsci.edu/ but in nearly all of the frames the
images of both PAGB stars are heavily saturated. We found an exposure of the
blue PAGB star obtained with the Wide Field Planetary Camera 2 (WFPC2) in the
near-UV (NUV) F255W filter (program GO-8718; PI G. Piotto), in which only the
central pixel was saturated; we performed aperture photometry to obtain a
lower limit on the star’s flux. For the yellow PAGB star, there are 10 WFPC2
frames in F255W (GO-10815; PI T. Brown) with unsaturated images, on which we
also performed aperture photometry. We used the PHOTFLAM keyword in the image
headers to convert the counts to absolute fluxes, and applied the standard
0.10 mag correction to an infinite aperture. The HST images are virtually
unaffected by source crowding.

(3) The UV/Optical Telescope (UVOT) on the Neil Gehrels Swift Observatory
obtained images111111https://archive.stsci.edu/swiftuvot/ of M19 in 2009 and
2010 in its three NUV/FUV bandpasses, $uvw1$, $uvm2$, and $uvw2$. We combined
these images, and then performed PSF-fitting photometry of both PAGB stars
using DAOPHOT. Corrections for time-dependent sensitivity loss, coincidence
losses, and exposure time were applied, as detailed in Siegel et al. (2014);
the data were then calibrated to the absolute photometric system of Breeveld
et al. (2011). The $uvw1$ and $uvw2$ filters have substantial red leaks, which
are problematic for cool stars; because of this, exacerbated by the
considerable interstellar reddening, we did not include the observations of
the yellow PAGB star in these two bandpasses in our analysis.

(4) The 2MASS near-infrared (NIR) sky survey (Skrutskie et al., 2006) obtained
images of M19 in $J$, $H$, and $K_{s}$. Photometry of our PAGB stars is
contained in the 2MASS source catalog,121212https://irsa.ipac.caltech.edu/cgi-
bin/Gator/nph-dd but is clearly affected by the stellar crowding near the
cluster center. We therefore downloaded the 2MASS frames, and performed
DAOPHOT PSF photometry on the images, calibrating to the 2MASS photometric
zero-point using measurements of nearby isolated stars contained in the source
catalog. HST images in the $I$ (F814W) band show that the yellow PAGB star, at
the 2MASS resolution, is blended with four nearby red giants. We attempted to
de-blend the images, using the HST frame as a prior to define the precise
locations of the PAGB stars in the 2MASS frames, but with limited success.

(5) Images of M19 were obtained in 2013 with the warm Spitzer Space Telescope
and its Infrared Array Camera (IRAC), in the 3.6 and $4.5\,\mu$m bandpasses;
the observations were part of a program aimed at RR Lyrae stars (PI W.
Freedman). We downloaded a selection of these
images131313https://sha.ipac.caltech.edu/applications/Spitzer/SHA/ and
performed DAOPHOT photometry to de-blend the stellar images, using PSF fitting
radii of the order of the FWHM. Aperture corrections were then applied, based
on the total magnitudes of isolated field stars in the frames.

(6) We examined NIR and far-IR images of M19 from the Wide-Field Infrared
Survey Explorer (WISE) sky survey, which we downloaded from
SkyView.141414https://skyview.gsfc.nasa.gov/current/cgi/query.pl However, the
spatial resolution of these frames is far too poor to provide useful
photometry for our targets lying in very crowded regions of the cluster.

We then corrected all of the measured fluxes for interstellar extinction,
using the values of $E(B-V)$ in Table 1, and for most bandpasses applying the
formulae of Cardelli et al. (1989), calculated at the effective wavelength of
each bandpass, and assuming $R_{V}=3.1$. For the UVOT photometry, because of
the complex structure of the extinction curve around 2200 Å, we determined the
extinction corrections by integrating the Cardelli et al. formula convolved
with the system throughput curves and a blackbody function having the
approximate temperature of each star. For the two IRAC bandpasses, whose
wavelengths are beyond the range of the Cardelli et al. formulae, we
determined the extinction at the $K_{s}$ band, and scaled it to [3.6] and
[4.5] according to the relations given by Indebetouw et al. (2005). The
resulting extinction-corrected fluxes must be regarded as only approximate,
given the very large correction factors in the UV, the uncertainty whether
$R_{V}=3.1$ is appropriate for this line of sight, and the source crowding in
many of the bandpasses.

The two panels in Figure 7 plot the SEDs for the two PAGB stars. We superpose
model-atmosphere SEDs selected from the ATLAS9
grid151515http://wwwuser.oats.inaf.it/castelli/grids.html of Castelli & Kurucz
(2004). The metal abundances for these SEDs are $\rm[M/H]=-1.5$, with
$\alpha$-element enhancements. We adopted surface gravities of $\log g=0.5$
and 2.0 for the yellow and blue PAGB stars, respectively. The best fits to the
SEDs are found for effective temperatures of 6250 and 11750 K, respectively,
but we caution that there is some degeneracy between the adopted $T_{\rm eff}$
values and reddenings.

Using the bolometric corrections for the two Castelli & Kurucz models, we find
the absolute luminosities given in the final row of Table 1, $\log
L/L_{\odot}=3.24$ and 3.22 for the yellow and blue PAGB stars, respectively.
Thus their luminosities are nearly identical, and they are likely to be on
very similar post-AGB evolutionary tracks.

Left panel: Spectral-energy distribution for the M19 yellow PAGB star (filled
black circles), corrected for interstellar reddening of $E(B-V)=0.344$ as
described in the text. The orange curve is a model-atmosphere SED for a star
with the parameters indicated in the figure. Note the conspicuously large
Balmer discontinuity. Right panel: SED for the M19 blue PAGB star (filled
black circles), corrected for $E(B-V)=0.342$. The HST observation at F255W
(cyan plotting point and label) is a lower limit (see text). The blue curve is
a model-atmosphere SED with parameters indicated in the figure. See the text
for details of the various public sky surveys used to assemble these SEDs, and
caveats about uncertainties due to blending and the extinction corrections.

A primary aim in investigating the SEDs was to search for evidence of
circumstellar dust, ejected during the AGB phase. In general, circumstellar
dust has not been found to be prominent in yellow and blue PAGB stars in old
populations, such as those in GCs (e.g., B16 and B20), possibly because the
PAGB evolutionary timescales are slow enough for any ejecta to have
dissipated, combined with the difficulty of forming dust in a low-metallicity
environment. As Figure 7 shows, we likewise see no evidence for warm
circumstellar dust in the two M19 PAGB stars. Unfortunately, the available
observations only go out to the Spitzer [4.5] bandpass; the stellar crowding
in the cluster precludes photometry at longer wavelengths with instruments
such as WISE. Thus the constraints on dust are not very tight.

##  8 Discussion and Future Studies

###  8.1 Yellow PAGB Stars as Standard Candles

The main goal of our uBVI survey was to search the Galactic GC system for
yellow PAGB stars and to test them as potential Population II standard
candles. The yellow PAGB star in M19 reported in this paper joins the small
number of known objects of this class. We find it to have a visual absolute
magnitude of $M_{V}=-3.39\pm 0.09$, based on several recent determinations of
the distance to M19. The main contributors to the error are the uncertainties
in the cluster’s distance, and in the foreground extinction.

B16 listed (their Table 4) the visual absolute magnitudes of the four other
known non-variable yellow PAGB stars in Galactic GCs; they range from
$M_{V}=-3.10$ to $-3.46$. Adding our new result for the M19 star, and
including also the absolute magnitude for the field yellow PAGB star
BD+14∘3061 from B20, we find that these six objects have a mean absolute
magnitude of $M_{V}=-3.35\pm 0.06$, with a standard deviation of 0.14 mag.
This is a very narrow luminosity function, compared for example to that of
Population I Cepheids at a given pulsation period (e.g., Clementini et al.
2019). And the discovery and measurement of yellow PAGB stars requires only a
single observation epoch. Thus, we believe that these stars continue to be
potential extragalactic distance indicators.161616If we had used its yellow
PAGB star to estimate the distance to M19, using the zero-point of
$M_{V}=-3.38\pm 0.05$ found earlier by B16, we would have obtained
$(m-M)_{0}=14.83\pm 0.08$. This agrees extremely well with the mean distance
modulus from several independent methods of $(m-M)_{0}=14.84\pm 0.06$, which
we discussed in §4. In several forthcoming papers, we will further explore
this possibility.

We also note that the five yellow PAGB stars in Galactic GCs all belong to
clusters with “intermediate” metallicities, of $\rm[Fe/H]=-1.59$ (NGC 5986),
$-1.60$ (M79), $-1.74$ (M19), and $-1.53$ ($\omega$ Cen), as tabulated by H10.
(However, M19 [see §2] and $\omega$ Cen have fairly wide ranges of [Fe/H]
among their members.) Moreover, all four clusters contain very blue HB stars.
We will discuss these and other clues to the evolutionary status of these
stars in more detail in papers now in preparation.

###  8.2 Future Work

There are several desirable future investigations of these two new and
relatively bright PAGB stars in M19. First, moderate-resolution spectra should
be obtained in order to confirm even more definitively that both stars are
indeed low-metallicity cluster members, consistent with the interpretations in
this paper. High-resolution spectroscopic abundance studies of both of them
would be of considerable interest, given the peculiarities seen in other PAGB
stars. For example, Şahin & Lambert (2009) found an anomalously low iron
abundance in the yellow PAGB star in M79, and several PAGB stars in younger
populations show extreme depletions of refractory chemical elements with high
condensation temperatures (see Oomen et al. 2019 and references therein).
Several field PAGB stars appear to be long-period spectroscopic binaries (B20
and references therein), so RV monitoring would be useful to investigate
whether binary interactions play a role in the evolution of these objects.
Effective temperatures of the stars determined from model-atmosphere fitting,
compared to the observed colors, would help refine the estimates of reddening,
and thus of their absolute magnitudes. High-precision photometric monitoring
would be useful to test whether the yellow PAGB star is a low-amplitude
pulsating variable. High-spatial-resolution MIR photometry would provide
tighter limits on circumstellar dust than was possible in the study reported
here.

The observational work in 1998 was partially supported by NASA grant NAG
5-6821 under the “UV, Visible, and Gravitational Astrophysics Research and
Analysis” program, and by the Director’s Discretionary Research Fund at STScI.
H.E.B. thanks the staff at Cerro Tololo for their support over the years. This
work has made use of data from the European Space Agency (ESA) mission Gaia
(https://www.cosmos.esa.int/gaia), processed by the Gaia Data Processing and
Analysis Consortium (DPAC,
https://www.cosmos.esa.int/web/gaia/dpac/consortium). Funding for the DPAC has
been provided by national institutions, in particular the institutions
participating in the Gaia Multilateral Agreement. Based in part on
observations made with the NASA Galaxy Evolution Explorer. GALEX was operated
for NASA by the California Institute of Technology under NASA contract NAS
5-98034. Based in part on observations made with the NASA/ESA Hubble Space
Telescope, obtained from the data archive at the Space Telescope Science
Institute. STScI is operated by the Association of Universities for Research
in Astronomy, Inc. under NASA contract NAS 5-26555. We acknowledge the use of
public data from the Neil Gehrels Swift Observatory data archive. This
publication makes use of data products from the Two Micron All Sky Survey,
which is a joint project of the University of Massachusetts and the Infrared
Processing and Analysis Center/California Institute of Technology, funded by
the National Aeronautics and Space Administration and the National Science
Foundation. This work is based in part on observations made with the Spitzer
Space Telescope, operated by the Jet Propulsion Laboratory, California
Institute of Technology under a contract with NASA. It also makes use of data
products from the Wide-field Infrared Survey Explorer, which is a joint
project of the University of California, Los Angeles, and the Jet Propulsion
Laboratory/California Institute of Technology, funded by the National
Aeronautics and Space Administration.

## References

  * (1)
  * Alonso-García et al. (2012) Alonso-García, J., Mateo, M., Sen, B., et al. 2012, AJ, 143, 70
  * Alves et al. (2001) Alves, D. R., Bond, H. E., & Onken, C. 2001, AJ, 121, 318
  * Baumgardt & Hilker (2018) Baumgardt, H. & Hilker, M. 2018, MNRAS, 478, 1520
  * Baumgardt et al. (2019) Baumgardt, H., Hilker, M., Sollima, A., et al. 2019, MNRAS, 482, 5138
  * Bond (1977) Bond, H. E. 1977, BAAS, 9, 601
  * Bond (1997a) Bond, H. E. 1997a, in IAU Symp. 180, Planetary Nebulae, ed. H. J. Habing & H. J. G. L. M. Lamers (Dordrecht: Kluwer), 460
  * Bond (1997b) Bond, H. E. 1997b, in The Extragalactic Distance Scale, ed. M. Livio, M. Donahue, & N. Panagia (Cambridge: Cambridge Univ. Press), 224
  * Bond (2005) Bond, H. E. 2005, AJ, 129, 2914 (Paper I)
  * Bond (2020) Bond, H. E. 2020, AJ, 160, 274 (B20)
  * Bond et al. (2016) Bond, H. E., Ciardullo, R., & Siegel, M. H. 2016, AJ, 151, 40 (B16)
  * Breeveld et al. (2011) Breeveld, A. A., Landsman, W., Holland, S. T., et al. 2011, in AIP Conf. Proc. 1358, Gamma Ray Bursts, ed. J. E. McEnery, J. L. Racusin, & N. Gehrels (Melville, NY: AIP), 373
  * Cardelli et al. (1989) Cardelli, J. A., Clayton, G. C., & Mathis, J. S. 1989, ApJ, 345, 245
  * Castelli & Kurucz (2004) Castelli, F., & Kurucz, R. L. 2004, arXiv:astro-ph/0405087
  * Clement et al. (2001) Clement, C. M., Muzzin, A., Dufton, Q., et al. 2001, AJ, 122, 2587
  * Clement & Sawyer Hogg (1978) Clement, C. C., & Sawyer Hogg, H. 1978, AJ, 83, 167
  * Clementini et al. (2019) Clementini, G., Ripepi, V., Molinaro, R., et al. 2019, A&A, 622, A60
  * Davis et al. (2021) Davis, B. D., Bond, H. E., Siegel, M. H., & Ciardullo, R. 2021, in preparation (D21)
  * Dean et al. (1978) Dean, J. F., Warren, P. R., & Cousins, A. W. J. 1978, MNRAS, 183, 569
  * Gaia Collaboration et al. (2018) Gaia Collaboration, Brown, A. G. A., Vallenari, A., et al. 2018, A&A, 616, A1
  * Gaia Collaboration et al. (2020) Gaia Collaboration, Brown, A. G. A., Vallenari, A., et al. 2020, arXiv:2012.01533
  * Gaia Collaboration et al. (2018) Gaia Collaboration, Helmi, A., van Leeuwen, F., et al. 2018, A&A, 616, A12
  * Gaia Collaboration et al. (2016) Gaia Collaboration, Prusti, T., de Bruijne, J. H. J., et al. 2016, A&A, 595, A1
  * Gonzalez & Wallerstein (1992) Gonzalez, G., & Wallerstein, G. 1992, MNRAS, 254, 343
  * Harris (2010) Harris, W. E. 2010, arXiv:1012.3224 (H10)
  * Harris et al. (1983) Harris, H. C., Nemec, J. M., & Hesser, J. E. 1983, PASP, 95, 256
  * Harris et al. (1976) Harris, W. E., Racine, R., & de Roux, J. 1976, ApJS, 31, 13
  * Indebetouw et al. (2005) Indebetouw, R., Mathis, J. S., Babler, B. L., et al. 2005, ApJ, 619, 931
  * Johnson et al. (2017) Johnson, C. I., Caldwell, N., Rich, R. M., et al. 2017, ApJ, 836, 168
  * Jones (1968) Jones, D. H. P. 1968, MNRAS, 140, 265
  * Kruijssen et al. (2020) Kruijssen, J. M. D., Pfeffer, J. L., Chevance, M., et al. 2020, MNRAS, 498, 2472
  * Landolt (1992) Landolt, A. U. 1992, AJ, 104, 340
  * Lindegren et al. (2020) Lindegren, L., Bastian, U., Biermann, M., et al. 2020, arXiv:2012.01742
  * Moehler et al. (2019) Moehler, S., Landsman, W. B., Lanz, T., et al. 2019, A&A, 627, A34
  * Morrissey et al. (2007) Morrissey, P., Conrow, T., Barlow, T. A., et al. 2007, ApJS, 173, 682
  * Oomen et al. (2019) Oomen, G.-M., Van Winckel, H., Pols, O., et al. 2019, A&A, 629, A49
  * Pfeffer et al. (2020) Pfeffer, J., Lardo, C., Bastian, N., et al. 2020, MNRAS, in press
  * Piotto et al. (1999) Piotto, G., Zoccali, M., King, I. R., et al. 1999, AJ, 118, 1727
  * Recio-Blanco et al. (2005) Recio-Blanco, A., Piotto, G., de Angeli, F., et al. 2005, A&A, 432, 851
  * Şahin & Lambert (2009) Şahin, T., & Lambert, D. L. 2009, MNRAS, 398, 1730
  * Siegel & Bond (2005) Siegel, M. H. & Bond, H. E. 2005, AJ, 129, 2924 (Paper II)
  * Siegel et al. (2014) Siegel, M. H., Porterfield, B. L., Linevsky, J. S., et al. 2014, AJ, 148, 131
  * Skrutskie et al. (2006) Skrutskie, M. F., Cutri, R. M., Stiening, R., et al. 2006, AJ, 131, 1163
  * Stetson (1987) Stetson, P. B. 1987, PASP, 99, 191
  * Thuan & Gunn (1976) Thuan, T. X., & Gunn, J. E. 1976, PASP, 88, 543
  * Valenti et al. (2007) Valenti, E., Ferraro, F. R., & Origlia, L. 2007, AJ, 133, 1287
  * Zinn et al. (1972) Zinn, R. J., Newell, E. B., & Gibson, J. B. 1972, A&A, 18, 390 (ZNG)