# Supervising Remote Sensing Change Detection Models with
3D Surface Semantics

###### Abstract

Remote sensing change detection, identifying changes between scenes of the
same location, is an active area of research with a broad range of
applications. Recent advances in multimodal self-supervised pretraining have
resulted in state-of-the-art methods which surpass vision models trained
solely on optical imagery. In the remote sensing field, there is a wealth of
overlapping 2D and 3D modalities which can be exploited to supervise
representation learning in vision models. In this paper we propose Contrastive
Surface-Image Pretraining (CSIP) for joint learning using optical RGB and
above ground level (AGL) map pairs. We then evaluate these pretrained models
on several building segmentation and change detection datasets to show that
our method does, in fact, extract features relevant to downstream applications
where natural and artificial surface information is relevant.111Experimental
code is available at https://github.com/isaaccorley/contrastive-surface-image-
pretraining and model checkpoints are made available in the TorchGeo [1]
library at https://github.com/microsoft/torchgeo

Index Terms—  self-supervised learning, contrastive learning, remote sensing,
above ground level maps

##  1 Introduction

Fig. 1: Our Contrastive Surface-Image Pre-training (CSIP) model learns the
combined latent distribution, 3D surface semantics while performing similarity
matching of optical RGB and AGL map pairings.

Self-supervised learning methods have recently become the de facto pretraining
method due to their conceptual simplicity and ability to make use of large
unlabeled datasets. Due to the wealth of multiview and multimodal data, self-
supervision methods have shown dramatic improvements in the remote sensing and
earth observation fields. Manas et al. [2] showed improved performance while
pretraining using Sentinel-2 satellite imagery of the same location from
different seasons as multiple views. Heidler et al. [3] experimented with
pretraining on an audiovisual dataset of aerial image and audio pairs and
showed efficient learning and improved downstream performance compared to
transfer learning from ImageNet as well as other state-of-the-art self-
supervised learning methods.

Fig. 2: An overview of the CSIP architecture we propose. Using optical RGB and
AGL map pairs, the multimodal self-supervised pretext encoders contrastively
learns to extract features relevant to 3D surfaces, after which a downstream
model provides change detection segmentations based on the learned 3D surface
semantics.

Change detection is an important problem in remote sensing. The development of
large scale labeled change detection datasets has allowed for rapid
humanitarian assistance and disaster response (HADR) applications for natural
disaster relief. Improvements in performance of computer vision models for
change detection is critical for planning and accurate detection of areas
requiring assistance from the HADR organizations and communities.

The combination of self-supervised learning methods for change detection has
been explored recently using mainly using pre and post imagery as multiple
views of the same location. Leenstra et al. [4] explored using a large dataset
of Sentinel-2 imagery sampled over the same geographic location at different
times to train a model to predict whether a patch spatially overlaps another
patch as a pretext task. Chen et al. [5] used pre and post imagery to train
using a contrastive framework. However, to our knowledge, no other methods
have contrastively trained using overlapping remotely sensed imagery and 3D
surface maps as a pretext task.

Our contributions can be described as following:

  * •

We propose a contrastive multimodal framework for pretraining deep neural
networks to learn representations of remotely sensed imagery which extract
features relevant to 3D surface information.

  * •

We perform thorough investigation through extensive experiments on downstream
tasks to validate our hypothesis that our pretraining framework does in fact
improve performance on tasks which require surface or height extraction to
perform image segmentation.

##  2 Methods

Fig. 3: Visual comparisons of ImageNet and CSIP weight initializations to
ground truth on randomly sampled test set predictions.

Inspired by the Contrastive Language-Image Pretraining (CLIP) [6] framework,
we propose Contrastive Surface-Image Pretraining (CSIP) framework which
utilizes dual encoders trained using a contrastive loss to encode imagery and
above ground level (AGL) maps into similar latent space representations. Given
an image $x_{i}$ and AGL map $x_{a}$ pair, an image encoder network $f_{i}$,
and AGL encoder network $f_{a}$, we encode the image and AGL map into feature
vectors $z_{i}$ and $z_{a}$, respectively. Each encoder network is composed of
a backbone and a projection multi-layer perceptron (MLP) head. We then
minimize the Normalized Temperature-scaled Cross Entropy (NT-Xent) loss [7]
with a learned temperature parameter $\tau$ which is a variant of the InfoNCE
contrastive loss [8] and is described in Equation 1. By minimizing the NT-Xent
loss we seek to maximize the cosine similarity between latent RGB and AGL
vectors from the same pair relative to other latent vectors in a minibatch.
Within a minibatch, we utilize other randomly sampled pairs as negative
samples. An overview of the CSIP architecture is provided in Figure 1.

$\ell_{i,j}=-\log\frac{\exp(\mathrm{sim}(\bm{z}_{i},\bm{z}_{j})/\tau)}{\sum_{k=1}^{2N}\mathbbm{1}_{[k\neq
i]}\exp(\mathrm{sim}(\bm{z}_{i},\bm{z}_{k})/\tau)}~{},$ (1)

The end result of our framework is to take the pretrained image encoder
network, $f_{i}$, and use as a backbone or encoder in segmentation or change
detection networks. An overview of this is provided in Figure 2.

##  3 Experiments

To assess the value of the feature representations learned during pretraining,
we compare against backbones pretrained on ImageNet [9] on several downstream
tasks. In all experiments, we freeze the pretrained backbones and only fine-
tune the model heads for each downstream task. In all downstream experiments
we use a batch size of 16, 25 number of epochs, AdamW optimizer [10] with a
learning rate of $\alpha=0.001$, patch sizes of 512x512, and a
training/validation split of 0.2 for datasets which did not have a predefined
validation split. For measuring downstream change detection and segmentation
performance we evaluate using the mean Intersection-over-Union (mIoU),
F1-score, and Average Accuracy metrics.

###  3.1 Contrastive Pretraining

During pretraining, we use the Overhead Geopose Challenge dataset [11] which
is an extension to the Urban Semantic 3D (US3D) dataset [12] used in the 2019
IEEE GRSS Data Fusion Contest (DFC19) [13]. The dataset is composed of pairs
of 2048x2048 2D orthorectified RGB Maxar WorldView satellite imagery and 3D
above ground level (AGL) maps. Furthermore, the dataset consists of oblique or
significantly off-nadir imagery which adds additional complexity during
pretraining. We pretrain separate ResNet-18 [14] RGB and AGL encoders using
the contrastive framework proposed in Section 2 for 500 epochs. We then
utilize the RGB encoder as a backbone in the following experiments.

###  3.2 Change Detection

We perform experiments for change detection on the following datasets:

OSCD

The Onera Satellite Change Detection (OSCD) [15] dataset is a dataset for
building change detection. The dataset contains 24 multispectral image pairs
of various sizes taken by the Sentinel-2 satellites [16] along with binary
change masks representing building change between pre and post imagery.

xBD

The xBD [17] is a dataset for change detection and HADR applications utilized
in the xView2 Challenge. The labels consist of building polygons categorized
into 4 damage severities in imagery from before and after natural disasters.

S2Looking

The S2Looking [18] dataset is a building change detection dataset consisting
of 5,000 1024x1024 0.5-0.8 m resolution image pairs taken by the Gaofen,
SuperView, and BeiJing-2 satellites. The dataset contains imagery with
significantly off-nadir angles of rural locations along with labels for both
newly built and demolished buildings.

Dataset | Weight Init. | mIoU | F1 | Acc.
---|---|---|---|---
OSCD | ImageNet | 0.302 | 0.674 | 0.643
CSIP (ours) | 0.454 | 0.692 | 0.707
xView2 | ImageNet | 0.315 | 0.526 | 0.480
CSIP (ours) | 0.367 | 0.529 | 0.533
S2Looking | ImageNet | 0.415 | 0.602 | 0.609
CSIP (ours) | 0.490 | 0.568 | 0.657
Table 1:  Experimental results on three benchmark change detection datasets
comparing our CSIP method with ImageNet pretrained weights in the encoder of
FCSiamDiff. Best results are marked in bold.

During training we fine-tune the Fully-Convolutional Siamese Difference (FC-
Siam-Diff) [19] architecture which is based on the U-Net architecture with the
exception of taking the difference between skip connections after passing both
pre and post imagery through the U-Net encoder. To compare the learned
semantic surface features, we freeze the encoder of the network and only fine-
tune the decoder on each dataset.

###  3.3 Semantic Segmentation

To compare performance on tasks not focused solely on building change or
mapping, we conduct experiments on the LandCover.ai land cover semantic
segmentation dataset [20]. The dataset consists of 10,674 512x512 RGB aerial
images with 0.25-0.55 m spatial resolution. The imagery is annotated with
semantic masks of 4 land cover categories. For benchmarking purposes, we
utilize the original splits provided with the dataset. During training, we
fine-tune the U-Net [21] architecture with the pretrained ResNet backbone as
the encoder. Similarly to the change detection experiments, we freeze the
encoder of the network and only fine-tune the decoder on the dataset.

Weight Init. | mIoU | F1 | Acc.
---|---|---|---
ImageNet | 0.833 | 0.865 | 0.860
CSIP (ours) | 0.766 | 0.843 | 0.809
Table 2:  Experimental results on the LandCover.ai land cover semantic
segmentation dataset comparing our CSIP method with ImageNet pretrained
weights in the encoder of U-Net. Best results are marked in bold.

##  4 Discussion and Future Work

The experimental results in Table 1 provide evidence that pretraining using
the CSIP framework does in fact improve downstream performance for building
change detection datasets in comparison to ImageNet initialization.
Additionally, the results on the LandCover.ai segmentation dataset display a
limitation to the framework where surface and height information may not be as
important and ImageNet feature extraction has a distinct advantage where
texture and color is more important in the delineating between land cover
categories. Visual comparisons on randomly sampled test set predictions is
provided for all datasets in Figure 3.

While our experiments only explore pretraining of ResNet backbones, we note
that investigation of pretraining Vision Transformers (ViT) [22] would be a
direct next step in this research. ViTs have shown improved performance in
segmentation [23] and change detection [24] applications in comparison to
fully-convolutional based architectures. Furthermore, pretraining of ViT
backbones with self-supervision [25] has recently shown promising results.

##  5 Conclusion

In this paper, we propose a contrastive framework for supervising optical RGB
image feature extraction of 3D surface information using AGL maps. We then
presented experimental results supporting the hypothesis that pretraining
vision models with self-supervision from 3D surface maps would improve
downstream performance for building change detection which has direct
applications in HADR efforts.

## References

  * [1] Adam J Stewart, Caleb Robinson, Isaac A Corley, Anthony Ortiz, Juan M Lavista Ferres, and Arindam Banerjee,  “Torchgeo: deep learning with geospatial data,”  arXiv preprint arXiv:2111.08872, 2021.
  * [2] Oscar Mañas, Alexandre Lacoste, Xavier Giro-i Nieto, David Vazquez, and Pau Rodriguez,  “Seasonal contrast: Unsupervised pre-training from uncurated remote sensing data,”  in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 9414–9423.
  * [3] Konrad Heidler, Lichao Mou, Di Hu, Pu Jin, Guangyao Li, Chuang Gan, Ji-Rong Wen, and Xiao Xiang Zhu,  “Self-supervised audiovisual representation learning for remote sensing data,”  arXiv preprint arXiv:2108.00688, 2021.
  * [4] Marrit Leenstra, Diego Marcos, Francesca Bovolo, and Devis Tuia,  “Self-supervised pre-training enhances change detection in sentinel-2 imagery,”  in International Conference on Pattern Recognition. Springer, 2021, pp. 578–590.
  * [5] Yuxing Chen and Lorenzo Bruzzone,  “Self-supervised remote sensing images change detection at pixel-level,”  arXiv preprint arXiv:2105.08501, 2021.
  * [6] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al.,  “Learning transferable visual models from natural language supervision,”  arXiv preprint arXiv:2103.00020, 2021.
  * [7] Kihyuk Sohn,  “Improved deep metric learning with multi-class n-pair loss objective,”  Advances in neural information processing systems, vol. 29, 2016\.
  * [8] Aaron van den Oord, Yazhe Li, and Oriol Vinyals,  “Representation learning with contrastive predictive coding,”  arXiv preprint arXiv:1807.03748, 2018.
  * [9] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei,  “Imagenet: A large-scale hierarchical image database,”  in 2009 IEEE conference on computer vision and pattern recognition. Ieee, 2009, pp. 248–255.
  * [10] Ilya Loshchilov and Frank Hutter,  “Decoupled weight decay regularization,”  arXiv preprint arXiv:1711.05101, 2017.
  * [11] Gordon Christie, Rodrigo Rene Rai Munoz Abujder, Kevin Foster, Shea Hagstrom, Gregory D Hager, and Myron Z Brown,  “Learning geocentric object pose in oblique monocular images,”  in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 14512–14520.
  * [12] Kevin Foster, Gordon Christie, and Myron Brown,  “Urban semantic 3d dataset,” 2020.
  * [13] Bertrand Le Saux, Naoto Yokoya, Ronny Hänsch, and Myron Brown,  “Data fusion contest 2019 (dfc2019),” 2019.
  * [14] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun,  “Deep residual learning for image recognition,”  in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770–778.
  * [15] Rodrigo Caye Daudt, Bertr Le Saux, Alexandre Boulch, and Yann Gousseau,  “Urban change detection for multispectral earth observation using convolutional neural networks,”  in IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2018, pp. 2115–2118.
  * [16] Matthias Drusch, Umberto Del Bello, Sébastien Carlier, Olivier Colin, Veronica Fernandez, Ferran Gascon, Bianca Hoersch, Claudia Isola, Paolo Laberinti, Philippe Martimort, et al.,  “Sentinel-2: Esa’s optical high-resolution mission for gmes operational services,”  Remote sensing of Environment, vol. 120, pp. 25–36, 2012.
  * [17] Ritwik Gupta, Bryce Goodman, Nirav Patel, Ricky Hosfelt, Sandra Sajeev, Eric Heim, Jigar Doshi, Keane Lucas, Howie Choset, and Matthew Gaston,  “Creating xbd: A dataset for assessing building damage from satellite imagery,”  in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2019, pp. 10–17.
  * [18] Li Shen, Yao Lu, Hao Chen, Hao Wei, Donghai Xie, Jiabao Yue, Rui Chen, Shouye Lv, and Bitao Jiang,  “S2looking: A satellite side-looking dataset for building change detection,”  Remote Sensing, vol. 13, no. 24, pp. 5094, 2021.
  * [19] Rodrigo Caye Daudt, Bertr Le Saux, and Alexandre Boulch,  “Fully convolutional siamese networks for change detection,”  in 2018 25th IEEE International Conference on Image Processing (ICIP). IEEE, 2018, pp. 4063–4067.
  * [20] Adrian Boguszewski, Dominik Batorski, Natalia Ziemba-Jankowska, Tomasz Dziedzic, and Anna Zambrzycka,  “Landcover.ai: Dataset for automatic mapping of buildings, woodlands, water and roads from aerial imagery,”  in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, June 2021, pp. 1102–1110.
  * [21] Olaf Ronneberger, Philipp Fischer, and Thomas Brox,  “U-net: Convolutional networks for biomedical image segmentation,”  in International Conference on Medical image computing and computer-assisted intervention. Springer, 2015, pp. 234–241.
  * [22] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al.,  “An image is worth 16x16 words: Transformers for image recognition at scale,”  arXiv preprint arXiv:2010.11929, 2020.
  * [23] René Ranftl, Alexey Bochkovskiy, and Vladlen Koltun,  “Vision transformers for dense prediction,”  in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 12179–12188.
  * [24] Hao Chen, Zipeng Qi, and Zhenwei Shi,  “Remote sensing image change detection with transformers,”  IEEE Transactions on Geoscience and Remote Sensing, 2021.
  * [25] Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin,  “Emerging properties in self-supervised vision transformers,”  in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 9650–9660.


# Diverse protostellar evolutionary states in the young cluster AFGL961

Jonathan P. Williams1, Rita K. Mann1, Christopher N. Beaumont1, Jonathan J.
Swift1, Joseph D. Adams2, Joe Hora3, Marc Kassis4, Elizabeth A. Lada5, Carlos
G. Román-Zúñiga6 1 Institute for Astronomy, University of Hawaii, 2680
Woodlawn Drive, Honolulu, HI 96822<EMAIL_ADDRESS>2 Cornell University,
Department of Radiophysics Space Research, Ithaca NY 14853  3 Harvard-
Smithsonian, CfA, 60 Garden St., MS 65, Cambridge, MA 02138  4 Keck
Observatory 65-1120 Mamalahoa Hwy, Kamuela, HI 96743  5 Department of
Astronomy, University of Florida, Gainesville, FL 32611  6 Centro Astronómico
Hispano Alemán, Camino Bajo de Huétor 50, Granada 18008, Spain

###### Abstract

We present arcsecond resolution mid-infrared and millimeter observations of
the center of the young stellar cluster AFGL961 in the Rosette molecular
cloud. Within 0.2 pc of each other, we find an early B star embedded in a
dense core, a neighboring star of similar luminosity with no millimeter
counterpart, a protostar that has cleared out a cavity in the circumcluster
envelope, and two massive, dense cores with no infrared counterparts. An
outflow emanates from one of these cores, indicating a deeply embedded
protostar, but the other is starless, bound, and appears to be collapsing. The
diversity of states implies either that protostellar evolution is faster in
clusters than in isolation or that clusters form via quasi-static rather than
dynamic collapse. The existence of a pre-stellar core at the cluster center
shows that that some star formation continues after and in close proximity to
massive, ionizing stars.

circumstellar matter – stars: formation — stars: pre-main sequence — ISM:
structure

##  1 Introduction

The dominant mode of star formation is in groups. In this sense, understanding
cluster formation is a prerequisite for understanding the origin of most
stars, including all massive stars and, in all likelihood, our Sun. Some major
unanswered questions include the role of global and local processes, the
formation time-scale, and whether high mass stars shut down further cluster
growth.

Long wavelength, mid-infrared to millimeter, observations are required to
image the youngest, most deeply embedded protostars and their nascent cores.
The study of dense stellar groups is hampered by the low resolution at these
wavelengths and, consequently, less is known about their origins and evolution
than closer, isolated star forming systems. Technological developments
including large format mid-infrared arrays and sensitive millimeter
interferometers provide a new view of the most embedded regions of young
clusters. We report here observations of young protostars and molecular cores
in the AFGL961 cluster and the constraints that these place on models of
cluster formation.

AFGL961 owes its name to an Air Force infrared sky survey carried out on a
rocket-borne telescope in the early 1970s. The resolution of these data was
low and Cohen (1973) was the first to give a precise, ground-based, position.
Due to its high luminosity and location in the nearby Rosette molecular cloud,
AFGL961 has been the subject of many subsequent studies.

Bally & Predmore (1983) found a weak point-like 5 GHz source with the VLA and
interpreted it as a compact HII region around a B3 star. Based on the
definition of Zinnecker & Yorke (2007), AFGL961 can therefore be considered as
a (borderline) massive star forming region. Near-infrared imaging by Lenzen et
al. (1984) revealed a double source with the easternmost, redder component
coinciding with the VLA source. They also found a third object about 30”
further west near the most prominent optical emission, a fan-shaped reflection
nebula. Castelaz et al. (1985) confirmed the double source and showed that
both members are pre-main-sequence with the luminosity of B stars. Aspin
(1998) presented a wide field image of shocked H2 emission showing numerous
stellar sources and bow shock structures. Román-Zúñiga et al. (2008) mapped
the entire Rosette molecular cloud in the near-infrared and identified 10
embedded clusters of which AFGL961 is the brightest and the most heavily
obscured by nebulosity. This obscuration, however, prevented a detailed study
of the fainter and most embedded sources.

AFGL961 lies in a massive clump along a broad ridge of molecular emission
extending away from the Rosette nebula (Blitz & Thaddeus, 1980). Lada &
Gautier (1982) mapped a bipolar outflow from the cluster, most recently imaged
in CO 3–2 by Dent et al. (2009) who show a high velocity, collimated flow
extending over $5^{\prime}$.

Distance estimates to the Rosette cloud range from $1.4-1.7$ kpc (Ogura &
Ishida, 1981; Perez et al., 1987; Hensberge et al., 2000) and we adopt 1.6 kpc
here for consistency with most previous work. AFGL961 has an infrared
luminosity of $11400\,L_{\odot}$ (Cox et al., 1990) and is, by far, the
brightest embedded cluster in the cloud.

A large-scale view of AFGL961 is shown in Figure 1. The image is from the
observations of Román-Zúñiga et al. (2008) and shows the embedded protostars
and associated nebulosity at 2.2 $\mu$m. The contours show the emission at 850
$\mu$m from the dusty cluster envelope and were produced from archival SCUBA
data. The flux per beam at this wavelength is a direct measure of column
density where we have assumed a uniform temperature $T=20$ K and dust opacity
$\kappa_{\nu}=0.1(\nu/1200\,{\rm GHz})$ cm2 g-1 (Hildebrand, 1983). The
contours are in units of g cm-2 to best compare to theoretical calculations
and numerical simulations (see §4). The total flux of the map is 10 Jy which
converts to a mass $M=90\,M_{\odot}$. Three bright stars dominate the
luminosity of the cluster at this wavelength and through the mid-infrared
(Poulton et al., 2008). The dust peak is slightly elongated and offset from
the central double source and the western source at the center of the optical
fan-shaped nebulosity lies near the edge of the envelope.

We have gathered data on AFGL961 over a period of several years with the
Infrared Telescope Facility111 The Infrared Telescope Facility, is operated by
the University of Hawaii under Cooperative Agreement no. NCC 5-538 with the
National Aeronautics and Space Administration, Science Mission Directorate,
Planetary Astronomy Program. (IRTF), Submillimeter Array222 The Submillimeter
Array is a joint project between the Smithsonian Astrophysical Observatory and
the Academia Sinica Institute of Astronomy and Astrophysics and is funded by
the Smithsonian Institution and the Academia Sinica. (SMA), and James Clerk
Maxwell Telescope333 The James Clerk Maxwell Telescope is operated by The
Joint Astronomy Centre on behalf of the Science and Technology Facilities
Council of the United Kingdom, the Netherlands Organisation for Scientific
Research, and the National Research Council of Canada. (JCMT) at the Mauna Kea
Observatory. Our observations, detailed in §2, are centered on the white box
in Figure 1. The data, presented in §3, reveal a surprising diversity of
evolutionary states at the cluster center, including the discovery of a Class
0 and starless core neighboring the known infrared sources. We discuss the
implications of this work for understanding cluster formation in §4 and
conclude in §5.

##  2 Observations

We used the MIRSI camera on the IRTF (Kassis et al., 2008) to map the mid-
infrared emission on December 12th 2003\. Images were taken in the M, N, and Q
filters centered at 4.9, 10.4, and 20.9 $\mu$m respectively using a
$5^{\prime\prime}\times 5^{\prime\prime}$ dither. The skies were clear, cold
and dry, and images were diffraction limited at
$0\hbox{$.\\!\\!^{{}^{\prime\prime}}$}5,0\hbox{$.\\!\\!^{{}^{\prime\prime}}$}9$,
and $1\hbox{$.\\!\\!^{{}^{\prime\prime}}$}7$ respectively. Sky subtraction was
obtained by chopping 30” North-South and nodding 40” East-West. This kept the
cluster on the array at all times and maximized the sensitivity in the
registered and co-added maps. Calibration was performed by observations of
$\alpha$ Canis Minor (Procyon) bracketing the AFGL961 observations. The final
maps were made using an IDL reduction pipeline written by support astronomer
E. Volquardsen. Three sources were detected (Figure 2) and all have
counterparts in the 2MASS catalog. We therefore used the latter to define the
astrometry.

The SMA observations were carried out under dry and stable skies on 13th
December 2005 in the compact configuration ($20-70$ m baselines) and in a
partial track on 4th February 2006 in the extended configuration ($70-240$ m
baselines). A mosaic of two Nyquist-spaced pointings was made to ensure
approximately uniform coverage over the MIRSI field. The receiver was tuned to
place the H2CO $3_{12}-2_{11}$ line at 225.7 GHz in the upper sideband and DCN
3–2 at 217.2 GHz in the lower sideband with a resolution of 0.81 MHz (1.1
${\rm km~{}s^{-1}}$) per channel. The time dependence of the gains was
measured via observations of 0530+135 interleaved with the source, the shape
of the passband was measured by deep observations of 3C454.3 and 3C111, and
observations of Uranus were used to set the flux scale. The visibilities were
calibrated with the MIR software package and maps were then produced using
standard MIRIAD routines. A continuum image centered at 1400 $\mu$m was
produced by combining the 2 GHz lower and upper sidebands from the compact and
extended configuration datasets and inverting with natural weighting. The
resulting resolution and noise is
$3\hbox{$.\\!\\!^{{}^{\prime\prime}}$}1\times
2\hbox{$.\\!\\!^{{}^{\prime\prime}}$}8$ and 3 mJy beam-1. We also produced a
higher resolution map, $1\hbox{$.\\!\\!^{{}^{\prime\prime}}$}4\times
0\hbox{$.\\!\\!^{{}^{\prime\prime}}$}9$, at the expense of increased noise, by
inverting with higher weights on the longer baselines. In this paper, we use
the former to measure the distribution and masses of the cores, and the latter
to measure core locations and determine their association (or lack thereof)
with mid-infrared sources. The lines were undetected on the long baselines and
maps were made using the compact configuration data only using natural
weighting. The resolution and noise in the H2CO map are
$3\hbox{$.\\!\\!^{{}^{\prime\prime}}$}7\times
3\hbox{$.\\!\\!^{{}^{\prime\prime}}$}4$ and 0.17 K. The corresponding numbers
for the DCN map are $3\hbox{$.\\!\\!^{{}^{\prime\prime}}$}8\times
3\hbox{$.\\!\\!^{{}^{\prime\prime}}$}6$ and 0.08 K.

As interferometers are unable to measure the flux at small spatial
frequencies, we used Receiver A on the JCMT to observe the same H2CO line as
the SMA observations. We mapped a $3^{\prime}\times 3^{\prime}$ region
centered on the peak of the SMA map on 19th March 2008. The map was made using
the on-the-fly mapping mode with a nearby reference position that was first
verified to be free of emission. Data reduction was carried out with the
STARLINK package. The JCMT map and SMA dirty map were weighted by their
respective beam areas, combined in the image plane, and cleaned with a
similarly combined beam following the method detailed in Stanimirovic (2002).
The resulting map retains the high resolution in the interferometer map and,
as the signal-to-noise ratio was very high in the JCMT data, the noise is
similar, 0.17 K per channel, as the SMA data.

##  3 Results

###  3.1 Morphology

The MIRSI maps are shown in Figure 2. The three infrared sources first seen by
Lenzen et al. (1984) are detected in each filter. To avoid confusion,
particularly for the varied “western” nomenclature, we label them AFGL961A, B
and C in order of infrared luminosity. The positions and fluxes of each source
are listed in Table 1. The fluxes of each source sharply rise with wavelength
indicating that they are deeply embedded. The infrared spectral energy
distributions (SEDs) are discussed in more detail in §4; we focus here on the
morphological differences. AFGL961C is slightly elongated in the N-band image
and the emission at Q-band is very extended. Aspin (1998) found H2 bow shock
features on either side of the star and Li et al. (2008) present a detailed
study of these features. The position angle of the elongated stucture in the
Q-band image lines up with these and the extended mid-infrared emission is
likely to be from hot dust filling a cavity that the star has blown out around
itself.

The differences between the sources are even more striking at 1400 $\mu$m.
Figure 3 shows the SMA continuum map in relation to the infrared sources. As
for the SCUBA map in Figure 1, the contour units are converted from flux per
beam to a mass surface density assuming $T=20$ K and a Hildebrand (1983) dust
opacity, $\kappa=0.018$ cm2 g-1. Three prominent sources, strung out along a
filament, are detected and labeled SMA1–3. Their positions and fluxes are
listed in Table 2. There is a negligible
$0\hbox{$.\\!\\!^{{}^{\prime\prime}}$}2$ offset between the peak of the SMA1
core and AFGL961A. The $1\hbox{$.\\!\\!^{{}^{\prime\prime}}$}6$ offset between
SMA2 and AFGL961B is significant, however. A closeup of the region with the
higher resolution continuum map is discussed in §3.2 and more clearly shows
the distinction between these two objects.

We therefore consider SMA1 to be the dusty envelope around the pre-main-
sequence B star AFGL961A and suggest that SMA2 is a distinct source in the
cluster, a dense core that lacks an infrared counterpart at the limits of
detection in the MIRSI map. SMA3 is the brightest source in the 1400 $\mu$m
map and also lacks an infrared counterpart. Both these sources are also
undetected in 2MASS images and the deeper JHK observations of Román-Zúñiga et
al. (2008).

The MIRSI data show that any embedded object in SMA2 or SMA3 is more than 500
times fainter than AFGL961A from 4.9-20.9 $\mu$m but this does not rule out a
solar mass protostar. For the case of the relatively isolated SMA3 core, we
can place far more stringent limits on the infrared luminosity from the
Spitzer observations of Poulton et al. (2008). We plot the SMA continuum map
in contours over the IRAC 3.6 $\mu$m image in the lower panel of Figure 3. No
infrared source is apparent at the position of SMA3 in this image or in the
other IRAC bands. Comparing with faint stars in the cluster and taking into
account the point spread function and extended nebulosity, we estimate the
limits on the flux of any embedded object in SMA3 to be 0.4, 0.8, 1.2, and 2
mJy at 3.6, 4.5, 5.8, and 8.0 $\mu$m respectively. Many of the low mass,
moderately embedded protostars in Perseus (Jørgensen et al., 2006) would have
been detected at this level. SMA2 is lost in the glare around AFGL961A,B of
the IRAC images so these limits do not apply in that case.

The Q-band data are critical for ruling out very deeply embedded protostars.
Based on the SED models of Robitaille et al. (2006), we estimate that the most
luminous object consistent with our MIRSI upper limits in SMA2 and SMA3 is a
$300\,L_{\odot}$ protostar behind $\lower
2.15277pt\hbox{$\;\buildrel>\over{\sim}\;$}100$ magnitudes of visual
extinction. Unfortunately the Spitzer MIPS 24 $\mu$m image is heavily
saturated and the SMA2 and SMA3 cores lie in the point spread function wings
of AFGL961A,B. de Wit et al. (2009) recently imaged the AFGL961A,B pair at
24.5 $\mu$m with the 8 m Subaru telescope. Their map also shows no source
within SMA2 but the field-of-view is too small to include SMA3. The resolution
of these data is higher than our MIRSI Q-band image but the sensitivity does
not appear to be significantly greater.

At the other extreme of the MIRSI-SMA comparison, the bright infrared source
AFGL961C is not detected in the 1400 $\mu$m map. This indicates a lack of
compact, cool dust around it and, despite the similarities of the mid-infrared
spectral slopes, places it at a more advanced evolutionary state than AFGL961A
which is fully embedded in a cold molecular core.

The millimeter fluxes can be converted directly to a mass assuming a
temperature and dust grain opacity. Masses are listed in Table 2 under the
same prescription for the envelope mass calculation in §1. The three cores
have similar fluxes and, hence, similar inferred masses. However, we might
expect SMA1, which contains a B star, to be hotter than SMA2 and 3 and its
mass to be proportionately lower than listed here.

The principal finding from our examination of the IRTF and SMA maps is the
detection of 5 distinct pre- or proto-stellar sources within the central
regions of the cluster. AFGL961 A is embedded in a dense core, AFGL961 B and C
lack a similarly sized cold dusty envelope, and there are two moderately
massive cores, SMA2 and 3 that lack bright infrared sources.

###  3.2 Dynamics

Our SMA data provide spectroscopic information at $\sim 1$ ${\rm km~{}s^{-1}}$
resolution and allow us to study the dynamics of the dusty cores detected in
the continuum. We chose a tuning that placed 3–2 rotational transitions of
H2CO in the upper sideband and DCN in the lower sideband. The former molecule
is abundant in protostellar envelopes and a good tracer of infall motions
(Mardones et al., 1997), the latter, as a deuterated species, is well suited
for pinpointing the cold, potentially pre-stellar gas (Stark et al., 1999) and
measuring the systemic motions of the core. Figure 4 shows the integrated
intensity of H2CO $3_{12}-2_{11}$ and DCN 3–2 overlaid on the continuum. Both
lines follow the same filamentary structure as the dust. The H2CO map shows a
very strong peak toward SMA2 and a weaker peak toward SMA3. The small offsets
between the line and dust peaks is likely due to the high opacity of this
line. The DCN emission is weaker but more closely follows the dust morphology.
There is a single peak toward SMA2 but enhanced emission toward SMA1 and 3 is
also evident.

The close correspondence between the DCN and continuum maps shows that we can
use the former to estimate the velocity dispersion, $\sigma$, of the SMA1–3
cores. The central velocities, linewidths, $\Delta v=2.355\sigma$, and virial
masses, $M_{\rm vir}=3R\sigma^{2}/G$, are listed in Table 2. The latter
assumes spherical cores with an inverse square density profile (Bertoldi &
McKee, 1992). At the resolution of these data, it is hard to determine with
much certainty where the cores end and the filament begins from the continuum
map in Figure 3 but, depending on the intensity threshold used to define their
limits, we estimate core radii $\sim 3-4^{\prime\prime}$ from their projected
area indicating that the cores are barely resolved with deconvolved sizes
$\lower 2.15277pt\hbox{$\;\buildrel<\over{\sim}\;$}5000$ AU. We find that the
virial masses of SMA1 and 3 exceed the measured masses by about a factor of
two indicating an approximate balance between kinetic and potential energy in
these two cores. The large virial mass of SMA2, due its high linewidth,
indicates that this core is unbound.

The intense H2CO emission toward SMA2 is likely due to grain mantle sputtering
or evaporation and signposts an embedded protostar. Indeed, inspection of the
spectra and channel maps reveal an outflow centered on this core with line
wings discernible from $4-22$ ${\rm km~{}s^{-1}}$. Figure 5 zooms in on this
region and shows the most intense emission from the blue and redshifted sides
of the line. The two moment maps are offset from each other and on opposite
sides of the continuum emission. The discovery of this outflow explains the
high DCN linewidth, why the core is unbound, and confirms that SMA2 is a
physically distinct object from AFGL961B.

SMA3 also lacks a detectable infrared source but has a much smaller linewidth
and appears to be bound. The H2CO data does not show any evidence for an
outflow but the spectrum toward the core is asymmetric and shows a small dip
at the velocity of the DCN line (Figure 6). This is indicative of red-shifted
self-absorption and a signature of infall motion. Unfortunately the velocity
resolution of these data is poor and the self-absorption is seen in only one
spectra channel. We were consequently unable to successfully fit the spectra
using the radiative transfer models of De Vries & Myers (2005). As there is
some ambiguity in modeling the interferometric data alone without knowledge of
the larger scale emission, we observed the same line with the JCMT and made a
fully sampled H2CO datacube. The integrated intensity is shown as dotted
contours in Figure 4. The combined JCMT+SMA map show the large scale cluster
envelope emission, similar to the SCUBA map in Figure 1 and the individual
pre- and proto-stellar cores are not apparent. No self-absorption is seen in
the combined spectra across the cluster and toward SMA3 in particular. Hence
there is no evidence from these data for large scale collapse onto the
cluster.

##  4 Discussion

###  4.1 Evolutionary states

Our IRTF images from $5-20$ $\mu$m show the most deeply embedded, luminous
stars in the cluster. The sensitivity of these images is much lower than the
Spitzer data but the resolution is higher and the fidelity of the bright
sources better. We do not find any new sources that are not seen in existing
near-infrared images but we show that the three known members, AFGL961A, B,
and C, each have rising spectral indices in the mid-infrared. The SEDs are
plotted in Figure 7. We have fit the near- and mid-infrared data using the
precomputed models of Robitaille et al. (2006, 2007). As the model SEDs are
noisy at millimeter wavelengths we have not fit the SMA data point but show
the interpolation between model and 1400 $\mu$m flux. The model parameters
include a central source, disk, and envelope. In each case, most fits show the
sources are embedded under more than 20 magnitudes of extinction. The precise
amount is not well constrained (although background sources are ruled out).
Similarly, a wide range of disk and envelope parameters can fit the data and
their masses are not well determined. However, all the fits show that the
envelope dominates, typically by more than a factor of 100. The best
constrained parameters are the total luminosity and stellar mass of the
source. These are tabulated in Table 3 for the best fit and the mean and
standard deviation of the top 100 fits, weighted by the inverse of $\chi^{2}$.
The three sources are all massive stars but the morphological differences and
comparison with the millimeter map shows that they are in quite different
evolutionary states.

AFGL961A,B have been considered a binary system based on their close proximity
to each other (e.g. Aspin, 1998). However, their properties at 1400 $\mu$m are
quite different: source A is embedded in the dense $6\,M_{\odot}$ SMA1 core
but there is no millimeter emission centered on source B. Using the higher
resolution continuum map (Figure 5) we can place a $3\sigma$ limit on the 1400
$\mu$m flux toward source B of 9 mJy ($0.3\,M_{\odot}$) at $\sim 2000$ AU
scales. AFGL961A,B did not form from a common core, therefore, and are not a
binary pair but simply neighboring protostars, in different evolutionary
states, in a dense cluster.

AFGL961C is different in its own way. It is a point source in the M-band
image, noticeably elongated at N-band, and a large cavity of hot dust is seen
in the Q-band image. This star is clearing out its surrounding material, as
also shown by the shocked H2 emission image of Aspin (1998). Li et al. (2008)
postulated that the hourglass shape of the infrared nebula is due to polar
winds from a very young protostar punching a hole in its surrounding core. No
core emission is detected at 1400 $\mu$m, however, and the same
$0.3\,M_{\odot}$ limit at $\sim 2000$ AU scales applies as for source B.

The ratio of stellar to envelope mass, $M_{*}/M_{\rm env}$, is an effective
measure of protostellar evolutionary state (Andre et al., 1993). There is
certainly some uncertainty in determining each of these quantities but it is
clear that there is a large range in the ratio. $M_{*}/M_{\rm env}\approx 2$
for AFGL961A but is greater than 30 for AFGL961B and greater than 15 for
AFGL961C.

The outflow toward SMA2 indicates an embedded protostar and the non-detection
in the infrared limit the bolometric luminosity to less than $300\,L_{\odot}$.
Depending on its age, this constrains the stellar mass to no more than
$6\,M_{\odot}$ and probably much lower (Siess et al., 2000). Therefore
$M_{*}/M_{\rm env}\lower 2.15277pt\hbox{$\;\buildrel<\over{\sim}\;$}1$ and
this is a Class 0 object. Given its youth and the bipolar H2CO structure we
see toward it, we further suggest that this source is the driving source of
the energetic CO outflow from the cluster (Lada & Gautier, 1982; Dent et al.,
2009) and not the more luminous infrared sources.

SMA3 is far enough offset from the bright cluster center for the Spitzer/IRAC
data to provide the most stringent limits on any embedded object and it
appears to be truly starless. As it is gravitationally bound and very dense
with a free-fall time, $t_{\rm ff}\sim 10^{4}$ yr, it is likely to be on the
brink of star formation. The interferometric H2CO spectrum shows evidence that
the core is collapsing but higher spectral resolution data are required to
confirm this, and to allow modeling and a determination of the infall speed.
The difference between the peak and self-absorption dip is an approximate
measure and suggests $v_{\rm in}\sim 1$ ${\rm km~{}s^{-1}}$. Adding in the
short spacing information from the JCMT swamps the self-absorption showing
that any inward motions are on small scales. That is, the core is collapsing
on itself rather than growing through the accretion of envelope material. With
a mass of $6\,M_{\odot}$, SMA3 is more massive than starless cores in isolated
star forming regions such as Taurus (Shirley et al., 2000). Presumably it will
form a correspondingly more massive star but probably not comparable to
AFGL961A.

A mix of early evolutionary states in clusters is not uncommon. The first core
identified as a Class 0 object, VLA1623 in $\rho$ Ophiuchus, sits on the edge
of a small group of pre-stellar cores (Andre et al., 1993). Williams & Myers
(1999) found a collapsing starless core adjacent to a Class I protostar in the
Serpens cluster and Swift & Welch (2008) found a range of young stellar
objects, from Class 0 to III, in L1551. These are all low mass objects in low
mass star forming regions. Radio observations of the W3 and W75 N massive star
forming regions show a range of HII region morphologies and spectral indices
indicating different evolutionary states (Tieftrunk et al., 1997; Shepherd et
al., 2004, respectively). In particular, Shepherd et al. (2004) were able to
estimate an age spread of at least 1 Myr between a cluster of five early B
stars based on Strömgren sphere expansion. The detection of cold, dense pre-
stellar cores and young lower mass protostars require observations at
millimeter wavelengths. Interferometer maps by Hunter et al. (2006) and Rodón
et al. (2008) show tight groups of dusty cores in NGC6334 I and W3 IRS5
respectively. Core separations are even smaller and masses higher than we have
found here in the lower luminosity AFGL961 cluster. The cores have a range of
infrared properties and some power outflows and they likely span a range of
evolutionary states. There does not appear, however, to be a clear counterpart
to the pre-stellar core, AFGL961-SMA3, with its combination of low limit to
the luminosity of any embedded source and lack of outflow.

###  4.2 Dynamic or equilibrium cluster formation?

Theoretical models of cluster formation divide into two camps: a global
collapse of a massive molecular clump or piecemeal growth from the formation
of individual protostars in a more local process. The former occurs on short,
dynamical time-scales (Bate et al., 2003) but the latter is more gradual and
the cluster forming clump is in quasi-equilibrium (Tan et al., 2006).

Based on the SCUBA image in Figure 1, the cluster envelope has a mass
$M\approx 90\,M_{\odot}$ within a radius $R\approx 0.25$ pc. The free-fall
timescale for this region, based on the inferred average density, $\rho\approx
10^{-19}$ g cm-3, is $t_{\rm ff}=(3\pi/32G\rho)^{1/2}\approx 0.2$ Myr. Note
that material on larger scales, for example in the surrounding CO clump
(Williams et al., 1995), would have a longer collapse timescale.

Our observations of a filamentary structure at high surface densities,
$\Sigma\approx 0.1-1$ g cm-2, in a roughly spherical envelope at
$\Sigma\approx 0.025-0.25$ g cm-2 are quite similar to the numerical
simulations by Bate et al. (2003) and Bonnell et al. (2003). In particular,
the initial conditions of Bate et al. (2003) with a $M=50\,M_{\odot},R=0.19$
pc clump are closest to the properties of AFGL961. In these simulations, the
clump collapses on the free-fall timscale, fragments, and produces stars in
two short bursts of about $0.02$ Myr duration spread by about 0.2 Myr. The
addition of radiative feedback (Bate, 2009) does not change these numbers
significantly.

Kurosawa et al. (2004) calculated the radiative transfer for the Bate et al.
(2003) simulation and showed that the infrared classification of young stellar
objects varied from Class 0 to III. Under this scenario, a protostar’s
evolutionary state is dependent more on its dynamical history than its age and
we would interpret the lack of circumstellar material around AFGL961B,C as due
to their ejection from the dense star forming filament.

Evans et al. (2009) shows that circumstellar material around isolated low mass
stars in nearby star forming regions is lost in about 0.5 Myr. If the same
timescale applies to the more luminous sources in the crowded environs of
AFGL961, then sources B and C are older than the cluster free-fall time
suggesting a quasi-equilibrium collapse. In this scenario, evolution
correlates with age and we would infer that the massive star AFGL961A formed
after B and C, and that collapse continued even around this compact HII region
to form dense cores SMA 2 and 3.

These observations alone cannot distinguish between dynamic or equilibrium
models of cluster formation. However, similar high resolution mid-infrared
through millimeter observations of other young clusters of varied luminosity
and protostellar density will add to the classification statistics. In this
way, we might hope to decipher the relative effect of environment and time on
protostellar evolution.

##  5 Conclusions

We have carried out a high resolution mid-infrared and millimeter study of the
central region of the young stellar cluster AFGL961. Our observations show the
most deeply embedded bright protostars in the cluster and the filamentary
distribution of the highest column density gas. We find five sources within
0.2 pc of each other, each with distinct properties unlike the others. The
brightest infrared source is an early B star that powers a compact HII region
and lies within a dense molecular core. The core mass is a substantial
fraction of the stellar mass indicative of a Class I protostar. The two other
infrared sources are not detected at millimeter wavelengths and the ratio of
circumstellar to stellar mass is very low, suggesting that these sources are
most similar to Class II objects. Further, one has cleared out a cavity in the
circumcluster envelope. The SMA data also reveal two millimeter cores with no
infrared counterparts. One is a strong source of line emission, drives an
outflow, and has the characteristics of a deeply embedded Class 0 protostar.
The other core is massive, starless, and appears to be collapsing.

The dense mixture of diverse protostellar evolutionary states suggests either
that circumstellar matter is removed rapidly through dynamical interactions
with other cluster members or that clusters build up gradually over several
free-fall timescales. Regardless of its history, however, the discovery of a
massive, collapsing core shows that AFGL961 continues to grow even after the
birth of a massive star.

J.P.W. acknowledges the NSF for support through grants AST-0324328 and
AST-0607710 and BHO for his support of the NSF. We thank Matthew Bate and Ian
Bonnell for comments. 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, and the facilities of the
Canadian Astronomy Data Centre operated by the National Research Council of
Canada with the support of the Canadian Space Agency.

## References

  * Andre et al. (1993) Andre, P., Ward-Thompson, D., & Barsony, M. 1993, ApJ, 406, 122
  * Aspin (1998) Aspin, C. 1998, A&A, 335, 1040
  * Bally & Predmore (1983) Bally, J., & Predmore, R. 1983, ApJ, 265, 778
  * Bate et al. (2003) Bate, M. R., Bonnell, I. A., & Bromm, V. 2003, MNRAS, 339, 577
  * Bate (2009) Bate, M. R. 2009, MNRAS, 392, 1363
  * Bertoldi & McKee (1992) Bertoldi, F., & McKee, C. F. 1992, ApJ, 395, 140
  * Blitz & Thaddeus (1980) Blitz, L., & Thaddeus, P. 1980, ApJ, 241, 676
  * Bonnell et al. (2003) Bonnell, I. A., Bate, M. R., & Vine, S. G. 2003, MNRAS, 343, 413
  * Castelaz et al. (1985) Castelaz, M. W., Grasdalen, G. L., Hackwell, J. A., Capps, R. W., & Thompson, D. 1985, AJ, 90, 1113
  * Cohen (1973) Cohen, M. 1973, ApJ, 185, L75
  * Cox et al. (1990) Cox, P., Deharveng, L., & Leene, A. 1990, A&A, 230, 181
  * Dent et al. (2009) Dent, W. R. F., et al. 2009, arXiv:0902.4138
  * De Vries & Myers (2005) De Vries, C. H., & Myers, P. C. 2005, ApJ, 620, 800
  * de Wit et al. (2009) de Wit, W. J., et al. 2009, A&A, 494, 157
  * Evans et al. (2009) Evans, N. J., et al. 2009, ApJS, 181, 321
  * Hildebrand (1983) Hildebrand, R. H. 1983, QJRAS, 24, 267
  * Hensberge et al. (2000) Hensberge, H., Pavlovski, K., & Verschueren, W. 2000, A&A, 358, 553
  * Hunter et al. (2006) Hunter, T. R., Brogan, C. L., Megeath, S. T., Menten, K. M., Beuther, H., & Thorwirth, S. 2006, ApJ, 649, 888
  * Jørgensen et al. (2006) Jørgensen, J. K., et al. 2006, ApJ, 645, 1246
  * Kassis et al. (2008) Kassis, M., Adams, J. D., Hora, J. L., Deutsch, L. K., & Tollestrup, E. V. 2008, PASP, 120, 1271
  * Kurosawa et al. (2004) Kurosawa, R., Harries, T. J., Bate, M. R., & Symington, N. H. 2004, MNRAS, 351, 1134
  * Lada & Gautier (1982) Lada, C. J., & Gautier, T. N., III 1982, ApJ, 261, 161
  * Lenzen et al. (1984) Lenzen, R., Hodapp, K.-W., & Reddmann, T. 1984, A&A, 137, 365
  * Li et al. (2008) Li, J. Z., Smith, M. D., Gredel, R., Davis, C. J., & Rector, T. A. 2008, ApJ, 679, L101
  * Mardones et al. (1997) Mardones, D., Myers, P. C., Tafalla, M., Wilner, D. J., Bachiller, R., & Garay, G. 1997, ApJ, 489, 719
  * Ogura & Ishida (1981) Ogura, K., & Ishida, K. 1981, PASJ, 33, 149
  * Perez et al. (1987) Perez, M. R., The, P. S., & Westerlund, B. E. 1987, PASP, 99, 1050
  * Poulton et al. (2008) Poulton, C. J., Robitaille, T. P., Greaves, J. S., Bonnell, I. A., Williams, J. P., & Heyer, M. H. 2008, MNRAS, 384, 1249
  * Robitaille et al. (2006) Robitaille, T. P., Whitney, B. A., Indebetouw, R., Wood, K., & Denzmore, P. 2006, ApJS, 167, 256
  * Robitaille et al. (2007) Robitaille, T. P., Whitney, B. A., Indebetouw, R., & Wood, K. 2007, ApJS, 169, 328
  * Rodón et al. (2008) Rodón, J. A., Beuther, H., Megeath, S. T., & van der Tak, F. F. S. 2008, A&A, 490, 213
  * Román-Zúñiga et al. (2008) Román-Zúñiga, C. G., Elston, R., Ferreira, B., & Lada, E. A. 2008, ApJ, 672, 861
  * Shepherd et al. (2004) Shepherd, D. S., Kurtz, S. E., & Testi, L. 2004, ApJ, 601, 952
  * Shirley et al. (2000) Shirley, Y. L., Evans, N. J., II, Rawlings, J. M. C., & Gregersen, E. M. 2000, ApJS, 131, 249
  * Siess et al. (2000) Siess, L., Dufour, E., & Forestini, M. 2000, A&A, 358, 593
  * Stark et al. (1999) Stark, R., van der Tak, F. F. S., & van Dishoeck, E. F. 1999, ApJ, 521, L67
  * Stanimirovic (2002) Stanimirovic, S. 2002, Single-Dish Radio Astronomy: Techniques and Applications, 278, 375
  * Swift & Welch (2008) Swift, J. J., & Welch, W. J. 2008, ApJS, 174, 202
  * Tan et al. (2006) Tan, J. C., Krumholz, M. R., & McKee, C. F. 2006, ApJ, 641, L121
  * Tieftrunk et al. (1997) Tieftrunk, A. R., Gaume, R. A., Claussen, M. J., Wilson, T. L., & Johnston, K. J. 1997, A&A, 318, 931
  * Williams et al. (1995) Williams, J. P., Blitz, L., & Stark, A. A. 1995, ApJ, 451, 252
  * Williams & Myers (1999) Williams, J. P., & Myers, P. C. 1999, ApJ, 518, L37
  * Zinnecker & Yorke (2007) Zinnecker, H., & Yorke, H. W. 2007, ARA&A, 45, 481

Table 1: Infrared sources |  $\Delta\alpha$aa$\alpha_{2000}=06^{\rm h}34^{\rm m}37.74^{\rm s},\delta_{2000}=04^{\circ}\,12^{\prime}\,44.2^{\prime\prime}$ |  $\Delta\delta$aa$\alpha_{2000}=06^{\rm h}34^{\rm m}37.74^{\rm s},\delta_{2000}=04^{\circ}\,12^{\prime}\,44.2^{\prime\prime}$ | $J$ | $H$ | $K$ | $M$ | $N$ | $Q$
---|---|---|---|---|---|---|---|---
Source | (′′) | (′′) | (Jy) | (Jy) | (Jy) | (Jy) | (Jy) | (Jy)
AFGL961A | 0.0 | 0.0 | 0.006 | 0.023 | 0.009 | 18.7 | 37.2 | 159
AFGL961B | $-4.9$ | $-1.7$ | 0.026 | 0.147 | 0.029 | 3.98 | 12.3 | 74.0
AFGL961C | $-30.8$ | 1.3 | 0.539 | 0.598 | 0.085 | 0.55 | 2.05 | 17.0
Table 2: Millimeter cores Source | $\Delta\alpha$ | $\Delta\delta$ | $S_{\rm 1400}$ |  $M_{\rm core}$aa$T=20$ K, $\kappa_{\nu}=0.19$ cm2 g-1 | $v_{\rm lsr}$ | $\Delta v$ |  $M_{\rm vir}$bb$R=5000$ AU
---|---|---|---|---|---|---|---
| (′′) | (′′) | (mJy) | ($M_{\odot}$) | (${\rm km~{}s^{-1}}$) | (${\rm km~{}s^{-1}}$) | ($M_{\odot}$)
SMA1 | 0.2 | 0.0 | 215 | 6.3 | 15.0 | 2.1 | 13
SMA2 | $-5.0$ | $-0.1$ | 184 | 5.4 | 14.2 | 4.3 | 56
SMA3 | $-16.8$ | 4.0 | 210 | 6.2 | 12.6 | 1.8 | 9.8
Table 3: SED fits | $M_{\ast}~{}(M_{\odot})$ | $L~{}(L_{\odot})$
---|---|---
Source | Best | Mean | Best | Mean
AFGL961A | 11.0 | $11.3\pm 1.8$ | 4600 | $6000\pm 2500$
AFGL961B | 9.1 | $9.1\pm 1.3$ | 1200 | $2500\pm 1300$
AFGL961C | 5.3 | $6.4\pm 1.1$ | 370 | $480\pm 200$

Figure 1: Large scale view of the AFGL961 cluster. The background is the
K-band image from Román-Zúñiga et al. (2008) on a log scale showing the
embedded stars and associated nebulosity. The contours of the SCUBA 850 $\mu$m
emission from the cold, dusty cluster envelope show surface densities at
$\Sigma=0.025\times(1,2,3...)$ g cm-2. The rectangle outlines the region shown
in the MIRSI images in Figure 2.

Figure 2: MIRSI maps of the center of AFGL961 at M, N, and Q bands
($4.9,10.4$, and $20.9\,\hbox{$\mu$m}$ respectively). For each band, the scale
is logarithmic with a dynamic range of 300 and the contour levels are at 1, 3,
9, 27, 81% of the peak intensity. The axes are arcsecond offsets from
AFGL961A.

Figure 3: The 1400 $\mu$m continuum emission from the SMA data showing the
cool dust condensations in the cluster. The top panel overlays the MIRSI
M-band image in logarithmic contours on the 1400 $\mu$m continuum map. The
scale ranges linearly from 5 to 75 mJy beam-1, corresponding to surface
densities $\Sigma=0.05$ to 0.75 g cm-2. The three prominent millimeter peaks
are labeled SMA1–3 and their properties listed in Table 2. The bottom panel
overlays the 1400 $\mu$m continuum map in contours on the Spitzer 3.6 $\mu$m
map in log scale. The contours are at surface densities
$\Sigma=0.1\times(1,2,3...)$ g cm-2. The stars show the locations of the MIRSI
sources AFGL961A,B,C. The Spitzer image is saturated at the positions of these
sources but provides stringent limits on the luminosity of any embedded source
in SMA3. The $3\hbox{$.\\!\\!^{{}^{\prime\prime}}$}1\times
2\hbox{$.\\!\\!^{{}^{\prime\prime}}$}8$ SMA beam is shown in the lower left
corner.

Figure 4: Integrated intensity maps of H2CO $3_{12}-2_{11}$ (top panel) and
DCN $3-2$ (bottom panel). The background image in each case is the 1400 $\mu$m
continuum map. The velocity range of integration was 8 to 20 ${\rm
km~{}s^{-1}}$. For the H2CO map, the SMA data are shown in solid contours
beginning at and in increments of 0.4 K ${\rm km~{}s^{-1}}$. Dashed contours
show negative levels. The combined JCMT+SMA map is shown in dotted contours at
4 K ${\rm km~{}s^{-1}}$. The DCN contours are at 0.2 K ${\rm km~{}s^{-1}}$.
The stars shows the location of AFGL961A,B, and C. The resolution of the
integrated intensity maps is shown in the lower left corner of each panel.

Figure 5: Red and blueshifted H2CO emission revealing a molecular outflow from
an (undetected) embedded protostar in SMA2. The background image is the high
resolution ($1\hbox{$.\\!\\!^{{}^{\prime\prime}}$}4\times
0\hbox{$.\\!\\!^{{}^{\prime\prime}}$}9$) 1400 $\mu$m continuum map and ranges
from 15 to 30 mJy beam-1. The solid or red contours show the intensity
integrated over $13-25$ ${\rm km~{}s^{-1}}$. and the dashed or blue contours
show the intensity integrated over $0-13$ ${\rm km~{}s^{-1}}$. Contour levels
are 30, 40, 50,… K ${\rm km~{}s^{-1}}$, and show only the most intense
emission above the cloud background. The stars show the location of AFGL961A,
B and the crosses locate the peak of the millimeter cores SMA1, 2.

Figure 6: Spectra toward SMA3 showing infall at small scales, possibly due to
core collapse. The thick solid line is the SMA H2CO spectrum and shows a
slight dip at the same velocity as the peak of the DCN spectrum shown as a
thin solid line. The dotted line is the combined JCMT+SMA H2CO spectrum which
is more symmetric and does not show any self-absorption features.

Figure 7: Spectral energy distributions and best fit model for the three
infrared sources AFGL961A,B,C. The dotted line linearly interpolates between
the model at 70 $\mu$m and SMA data point at 1400 $\mu$m.