# A rest-frame near-IR study of clumps in galaxies at $1<z<2$ using
JWST/NIRCam: connection to galaxy bulges

Boris S. Kalita Kavli Astrophysics Fellow  Kavli Institute for the Physics and
Mathematics of the Universe, The University of Tokyo, Kashiwa, 277-8583, Japan
Kavli Institute for Astronomy and Astrophysics, Peking University, Beijing
100871, People’s Republic of China  Center for Data-Driven Discovery, Kavli
IPMU (WPI), UTIAS, The University of Tokyo, Kashiwa, Chiba 277-8583, Japan
John D. Silverman Kavli Institute for the Physics and Mathematics of the
Universe, The University of Tokyo, Kashiwa, 277-8583, Japan  Department of
Astronomy, School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo,
Tokyo 113-0033, Japan  Center for Data-Driven Discovery, Kavli IPMU (WPI),
UTIAS, The University of Tokyo, Kashiwa, Chiba 277-8583, Japan  Emanuele Daddi
CEA, Irfu, DAp, AIM, Universitè Paris-Saclay, Universitè de Paris, CNRS,
F-91191 Gif-sur-Yvette, France  Connor Bottrell Kavli Institute for the
Physics and Mathematics of the Universe, The University of Tokyo, Kashiwa,
277-8583, Japan  Luis C. Ho Kavli Institute for Astronomy and Astrophysics,
Peking University, Beijing 100871, People's Republic of China  Department of
Astronomy, School of Physics, Peking University, Beijing 100871, People’s
Republic of China  Xuheng Ding Kavli Institute for the Physics and Mathematics
of the Universe, The University of Tokyo, Kashiwa, 277-8583, Japan  Lilan Yang
Kavli Institute for the Physics and Mathematics of the Universe, The
University of Tokyo, Kashiwa, 277-8583, Japan

###### Abstract

A key question in galaxy evolution has been the importance of the apparent
‘clumpiness’ of high redshift galaxies. Until now, this property has been
primarily investigated in rest-frame UV, limiting our understanding of their
relevance. Are they short-lived or are associated with more long-lived massive
structures that are part of the underlying stellar disks? We use JWST/NIRCam
imaging from CEERS to explore the connection between the presence of these
‘clumps’ in a galaxy and its overall stellar morphology, in a mass-complete
($log\,M_{*}/M_{\odot}>10.0$) sample of galaxies at $1.0<z<2.0$. Exploiting
the uninterrupted access to rest-frame optical and near-IR light, we
simultaneously map the clumps in galactic disks across our wavelength
coverage, along with measuring the distribution of stars among their bulges
and disks. Firstly, we find that the clumps are not limited to rest-frame UV
and optical, but are also apparent in near-IR with $\sim 60\,\%$ spatial
overlap. This rest-frame near-IR detection indicates that clumps would also
feature in the stellar-mass distribution of the galaxy. A secondary
consequence is that these will hence be expected to increase the dynamical
friction within galactic disks leading to gas inflow. We find a strong
negative correlation between how clumpy a galaxy is and strength of the bulge.
This firmly suggests an evolutionary connection, either through clumps driving
bulge growth, or the bulge stabilizing the galaxy against clump formation, or
a combination of the two. Finally, we find evidence of this correlation
differing from rest-frame optical to near-IR, which could suggest a
combination of varying formation modes for the clumps.

galaxies: evolution – galaxies: formation – galaxies: structure

##  1 Introduction

Over the last two decades, extremely deep high resolution data, courtesy
mainly of the Hubble Space telescope, revealed high redshift star-forming
galaxies to be much more clumpy than their low redshift counterparts
(Conselice et al., 2004; Elmegreen & Elmegreen, 2005; Elmegreen et al., 2008;
Förster Schreiber et al., 2011; Guo et al., 2015, 2018; Shibuya et al., 2016).
This has sparked a debate about the consequences of these structures on galaxy
evolution. Mainly observed in rest-frame UV and optical wavelengths, the
origin and evolution of these ‘clumps’ within star-forming galaxies are far
from well understood.

Properties such as stellar-masses, stellar-ages, sizes and gas (or dust)
content have been investigated in observational studies (Elmegreen et al.,
2007; Guo et al., 2012, 2018; Soto et al., 2017; Zanella et al., 2019;
Rujopakarn et al., 2023; Sattari et al., 2023) of these kpc-scale star-forming
complexes (resembling ‘localized star-bursts’; Wuyts et al., 2012, 2013;
Bournaud et al., 2015; Zanella et al., 2015; Mieda et al., 2016). In parallel,
simulations have been driving forward our theoretical understanding of their
origin, evolution and subsequent fate (e.g., Elmegreen et al., 2008; Dekel et
al., 2009; Bournaud et al., 2009; Ceverino et al., 2010, 2012; Mandelker et
al., 2017).

One of the most consequential questions, concerning the morphological
evolution of galaxies, is whether these are simply very low-mass features only
observed in rest-frame UV and optical, or massive enough to contribute to the
total stellar-mass tracing rest-frame near-IR light. Some simulations suggest
that they are disrupted on short timescales ($\lesssim 50\,\rm Myr$), thus
having minimal effect on the underlying stellar distribution (Murray et al.,
2010; Tamburello et al., 2015; Buck et al., 2017; Oklopčić et al., 2017).
However, other simulations suggest that clumps survive for much longer and are
expected to be crucial drivers of bulge growth through dynamical friction and
gravitational torques (Bournaud et al., 2011, 2014; Elmegreen et al., 2008;
Ceverino et al., 2010; Mandelker et al., 2014, 2017). They could also be
associated with more massive structures that HST-based rest-frame UV
observations miss (Faure et al., 2021).

Before the James Webb Space Telescope (JWST) era, observations were limited to
star-formation tracing rest-frame UV and optical light, mainly using HST
(e.g., Guo et al., 2015, 2018; Sattari et al., 2023). We did not have high
resolution capabilities in rest-frame near-IR at $z>1$ to map the underlying
stellar distribution on the relevant spatial scales ($\sim 1\,\rm kpc$). With
the highly sensitive and high resolution rest-frame optical (better than HST)
and near-IR capacities of JWST, we can finally undertake a relatively more
complete study. It is now possible to extend the resolved imaging of these
clumpy galaxies well into rest-frame near-IR, thereby accessing the bulk of
the stellar light.

In this work, we use the capabilities of JWST/NIRCam to make the first
resolved maps of clumps within galaxies in both the rest-frame optical and
near-IR for galaxies at $z>1$. It should be noted that the term ‘clumps’ has
until now been used to refer to marginally resolved or un-resolved structures
and are expected to be a result of gravitational instabilities. These
conclusions are based on in-depth investigations conducted primarily in rest-
frame UV. The structures detected in this work are at rest-frame optical and
near-IR, and albeit we expect them to be associated with the previously
studied UV-detected clumps (Sec. 4.1), the exact connection will be tackled in
a follow-up paper (Kalita et. al. in prep). Nevertheless, we still use the
same term ‘clumps’ in this work as a general reference to structures giving
galaxies a clumpy appearance. We are not implying that they necessarily share
the same properties as the structures previously studied.

To make a parallel assessment of the general stellar morphology of each
galaxy, we exploit the longest wavelength NIRCam band (F444W) to obtain a
bulge-to-disk flux ratio. This value closely traces the corresponding stellar-
mass distribution due to a minimally varying mass-to-light ratio at such high
a wavelength (Bell et al., 2003; Zibetti et al., 2009; Schombert et al.,
2019). Throughout, we adopt a concordance $\Lambda$CDM cosmology,
characterized by $\Omega_{m}=0.3$, $\Omega_{\Lambda}=0.7$, and $\rm H_{0}=70$
km s${}^{-1}\rm Mpc^{-1}$. We use a Chabrier initial mass function. All images
are oriented such that north is up and east is left.

##  2 Sample selection

We use of the Cosmic Evolution and Epoch of Reionization Survey (CEERS; ERS
1345, PI: S Finkelstein), a JWST Early Release Science programs. CEERS
observed a section of the Extended Goth Strip Hubble Space Telescope (EGS-HST)
field using NIRCam Wide-band imaging. The reduced images (made available by
the CEERS collaboration; Bagley et al., 2023) span a large wavelength window
from $1\,\mu$m to $5\,\mu$m, covered by six filters (with average $5\,\sigma$
depths): F115W (29.1 mag), F150W (29.0 mag), F200W (29.2 mag), F277W (29.2
mag), F356W (29.2 mag) and F444W (28.6 mag)111We do not incorporate the F410M
data into this study due to it being shallower by a factor of $\sim 2$ than
the rest of the data. F444W also has a similar issue, due to which we shall be
excluding measurements of clumpiness in this filter later. .

We require simultaneous coverage of rest-frame optical as well as near-IR
light, along with similar physical to angular scales (to not introduce
systematic biases) across our sample. Moreover, the galaxies should
preferentially be at $z>0.5$, where most studies in literature begin finding a
significant population of clumpy galaxies (Guo et al., 2015). To satisfy each
of these requirements, we settle on a redshift range of $1.0<z<2.0$.

For the sample selection itself, we use the catalogue for the EGS-HST field
(Stefanon et al., 2017, S17) created with an extremely large wavelength
coverage ($0.4-8.0\,\mu$m). Its stellar-mass completeness including a maximum
dust attenuation value, $A_{v}=3.0\,$mag, is set at $log(M_{*}/M_{\odot})=9.5$
for $1.0<z<1.5$ and $log(M_{*}/M_{\odot})=10.0$ for $1.5<z<2.0$. For the sake
of convenience, we limit our analysis to galaxies above
$log(M_{*}/M_{\odot})=10.0$ over the whole redshift range of our sample. This
method leaves us with a total of galaxies 412 galaxies. However, we do provide
the results for galaxies with $log(M_{*}/M_{\odot})=9.5-10.0$ at $1.0<z<1.5$
in the Appendix.

##  3 Methodology

###  3.1 Stellar morphology: bulges and disks

We begin with an assessment of the stellar light variation by measuring the
bulge-to-disk flux ratio of each galaxy. To ensure that this reflects the
underlying stellar-mass distribution, we use the longest wavelength NIRCam
filter available to us, F444W. For our redshift range, we expect a
corresponding variation of mass-to-light ratio $\lesssim 25-30\%$ (Schombert
et al., 2019; Zibetti et al., 2009; Bell et al., 2003) with color. This would
have increased had we used shorter wavelengths, thereby complicating the
interpretation of our results. Therefore, this decision to use the longest
wavelengths is aimed at using an ‘almost’ color-invariant tracer of the
stellar distribution. An additional advantage of this approach is the
sensitivity to highly obscured cores/bulges that we expect to find at high-z
(e.g., Kalita et al., 2022), since attenuation is minimal222to quote a
generous upper limit, assuming an $\rm A_{V}=3.0$, we get an attenuation of
0.85 in F444W for a typical Cardelli et al. (1989) Milky Way extinction curve
in rest-frame near-IR.

We measure the flux of the bulge and the rest of the galaxy by fitting our
sample with a dual component model: two Sérsic profiles with fixed indices of
$n=1$ for the disk and $n=4$ for the bulge. While we use this parameter
settings to obtain the results presented throughout this work, we use also
redo our measurements with a Sérsic index of $2$ (associated with a pseudo-
bulge; Gadotti, 2009) in place of $4$ for the (classical) bulge and find only
negligible differences (well within the uncertainties). Moreover, the use of
$n=4$ is driven by our choice to not obtain a perfect fit on an individual
galaxy level but rather achieve a uniform determination of the flux across our
sample (this being a widely used approach, e.g., Simard et al., 2011; Meert et
al., 2015; Bottrell et al., 2017a, b, 2019). Finally, it also allows us to
broadly separate the disk and bulge.

For each object, we make cutouts of dimensions $101\times 101$ pixels
($3^{\prime\prime}\times 3^{\prime\prime}$) of the background subtracted F444W
images. We also create corresponding cutouts of the available weight maps from
CEERS, that is used as the noise maps in the fitting procedures. Finally, the
PSF for the fitting is generated by median-stacking 7 unsaturated stars we
found within the field-of-view of the observations. The fitting is done using
a python-based package GALIGHT 333https://github.com/dartoon/galight (Ding et
al., 2022), which in turn implements the forward-modelling galaxy image
fitting tool LENSTRONOMY (Birrer & Amara, 2018). The approach allows us access
to the full posterior distribution of each fitted parameter. This fitting is
then optimised through a two-stage Particle Swarm Optimizer (Kennedy &
Eberhart, 1995, PSO), which is finally fed into a Markov Chain Monte Carlo
(MCMC) fitting as the initial condition. We are then left with the best-fit
parameter as well as their respective $1\sigma$ confidence intervals.

Finally, we obtain three morphological flux ratios from this analysis: the
bulge-to-disk (using the fluxes of the two components), bulge-to-total (where
the total flux is given by the sum of the disk and residual flux) and disk-to-
total. We will mainly be using the bulge-to-disk ratio throughout this work,
but will refer to the other two to in order to further our understanding of
the results (in Sec. 4.2)

###  3.2 Quantifying the clumpiness

Figure 1: The clump detection: (top-left) An F115W image of a galaxy within our sample (with intermediate levels of clumpiness) with an equivalent PSF as that for F444W. The $2\,\sigma$ segmentation map for the galaxy at F444 has a red color-map to distinguish it from other sources. (Top-middle) This image is smoothed using a Gaussian filter of $\sigma=4$ pixels and then subtracted from the original image to get the contrast image. The core is masked during this detection algorithm and is shown with an overlaid blackened mesh. The remaining ‘red region’ gives the net galaxy flux without the bulge. (Top-right) The source detection algorithm discussed in Sec. 3.2 results in the collection of confirmed clumps highlighted in blue. The net flux in these regions is divided by the disk flux to get the clumpiness. (Middle and bottom) The lower panels show F115W images and their corresponding contrast images (with highlighted regions of clumps) of other objects within our sample. The S17 IDs for each object is also provided. Figure 2: Contrast images similar to those in Fig. 1, but of galaxies which had no clumps detected. Figure 3: Examples of galaxies with detected clumps. For each, the rest-frame optical (F115W) and rest-frame near-IR (F356W) images are shown. The detected clumps using the method shown in Fig.1 are shown with a blue color scheme. It should be noted that the the clumps may not always be clearly detected by eye on these original images, for which one would rather require the respective contrast maps. Table 1: Stellar-mass complete ($10.0<log(M_{*}/M_{\odot})<11.0$) sample used in this work.  | Total Galaxies | Bulge+disk measurements | clumpiness detected
---|---|---|---
|  |  | ($>68\,\%\,\rm confidence$)
|  |  | F115W F150W F200W F277W F356W
$1.0<z<1.5$ ($>10^{10.0}\,M_{\odot}$) | 213 | 156 | 99 94 86 88 82
$1.0<z<1.5$ ($10^{10.0}-10^{10.5}\,M_{\odot}$) | 114 | 79 | 48 48 45 42 41
$1.0<z<1.5$ ($10^{10.5}-10^{11.0}\,M_{\odot}$) | 99 | 77 | 51 46 41 46 41
$1.5<z<2.0$ ($>10^{10.0}\,M_{\odot}$) | 199 | 136 | 86 83 81 77 69
$1.5<z<2.0$ ($10^{10.0}-10^{10.5}\,M_{\odot}$) | 101 | 69 | 41 38 37 36 34
$1.5<z<2.0$ ($10^{10.5}-10^{11.0}\,M_{\odot}$) | 98 | 67 | 45 45 44 41 35
Figure 4: (Top) Histogram showing the bulge-to-disk ratio distribution of all
galaxies that have both significant bulge and disk flux measurements. (in
grey, which makes up a fraction ‘ftot’ of all galaxies in S17 within our mass
and redshift brackets). The black histogram is made up of the subset of
sources that have clumpiness detected at rest-frame wavelength $\sim 1\,\mu$m
(f200W and f277W for the two redshift windows), which makes up a fraction
‘fclumpy’ of all the S17 galaxies. The probabilty of the ftot and ‘fclumpy’
samples being from the same distribution (the p-value) is also shown. (Middle
and bottom) The same histogram but limited to the stellar-mass ranges
$log(M_{*}/M_{\odot})=10.0-10.5$ and $log(M_{*}/M_{\odot})=10.0-11.0$
respectively. The ‘fclumpy/bin’ is the ‘fclumpy’ counted for the individual
stellar-mass windows. The two columns correspond to the redshift ranges
$1.0-1.5$ and $1.5-2.0$.

The aim of the second segment of our analysis is to quantify the flux from
clumps that one can observe by visual inspection in the highly sensitive
JWST/NIRCam images of the sample galaxies. As we shall be making comparisons
across the wide wavelength coverage of the data (from F115W to F444W), we
begin by matching the PSF sizes to that of the longest wavelength and
therefore the lowest resolution filter (F444W). We obtain the PSFs of each
filter as discussed in Sec. 3.1. A Gaussian-model fitting gives us the PSF
parameters which are used to calculate the effective $\sigma$ of the Gaussian
kernel that each image needs to be convolved with to match the resolution of
the F444W data.

We build an automated clump detection algorithm (Fig. 1) based on the method
widely used for detecting clumps in galaxies in HST UV and optical bands
(Conselice et al., 2003; Guo et al., 2015; Calabrò et al., 2019). We first
smooth the measurement image (from F115W to F444W, but with a PSF equivalent
to that of F444W), using a Gaussian filter of $\sigma=4$ pixels.444 We try a
range of values for $\sigma=3-6$ pixels, a range based on the values used in
previous works. Albeit the results of this paper do not change beyond the
already estimated errors, we use $\sigma=4$ pixels as it picks the finest
clumps we detect through a visual inspection of the JWST images. This smoothed
image is subtracted from the original image, leaving behind a contrast map
showing structures varying at scales similar to the difference of the F444W
PSF and the convolution of it with the Gaussian used for smoothing. This
difference is found to be $\sim 0.07^{\prime\prime}$. It should be noted that
this is not the size of the clumps. We then make cutouts from the measurement
images corresponding to each source of dimensions $3^{\prime\prime}\times
3^{\prime\prime}$ as done in the previous section.

We then run a source detection using the python package PHOTUTILS (Bradley et
al., 2022), also included in GALIGHT, with a threshold555There are two reasons
we use a $5\,\sigma$ detection threshold with PHOTUTILS: Using thresholds that
are lower results in large sections of the galaxies rather the clumps.
Moreover, the robustness check carried out for each detection leads to the
rejection of more than $\gtrsim 60\%$ of the sources at $3\,\sigma$ and
$\gtrsim 45\%$ at $4\,\sigma$. At $5\,\sigma$ however, this rejection rate is
around 20-30%. Above this threshold our sample starts shrinking considerably.
Hence we settle at the $5\,\sigma$ threshold. at $5\,\sigma$/pixel. This
method selects regions with peaked emission associated with clumps within the
galaxies (Fig. 1). We only consider clumps within the extent of each galaxy,
which is determined by a source detection on the stellar-mass sensitive F444W
image with a threshold of $2\,\sigma$ (the red color map region in Fig. 1).
Moreover, it is critical to not include the central bulge as a clump since
both are interpreted as small-scale structures by our algorithm. Hence, we
mask the central region666The central mask is determined by the extent of the
central clump (using the contrast map) as seen in the segmentation map for
F444W. The central position is determined by finding the point of minimum
asymmetry in the F444W band, where the central bulge is the most dominant
object. This is always found to be coincident with the bulge location in the
bulge-disk decomposition fit. as shown in the top panel of Fig. 1.

To ensure that our detection is robust, we carry out an additional test. For
every single clump (associated with a continuous segmentation map source
detected at $5\,\sigma$), we artificially replicate it at another random
position (by replacing the flux originally there) within the galaxy image
while ensuring that it is not positioned within the core or outside the galaxy
edge. Then we re-run our clump detection algorithm and check if this new
(fake) clump is detected. We repeat this process a 1000 times, and only allow
a clump to be included in our final measurement for the galaxy if it is
detected $>68\%$ times. This method removes $\sim 20\%$ of the original
detections. The reason for the rejection includes the clumps being marginally
detected, and not leading to a strong enough fluctuation within the underlying
disks to be detected repeatedly. Finally, once we have the robustly detected
clump map, we simply measure the net flux within them and divide it by the
flux of the whole galaxy in the same filter (the region for which uses the
previously used $2\,\sigma$ threshold map from the F444W image, shown in red
color map in Fig. 1), after masking the core.777The reason we remove the core
for the net flux measurement, is to remove any possible correlation between
the clump flux fraction and the bulge measurements in Sec. 3.1. This method is
different from how ‘clumpiness’ is measured in most previous works, where they
do not remove it. We do check however if our results change if we include the
core, and we find that they do not. This gives us the fractional flux
contained in clumps, with respect to the rest of the galaxy without the core.
We define this ratio as the clumpiness.

$\rm Clumpiness=\frac{\Sigma\,\rm Clump\,\,flux}{\rm
Galaxy\,\,Flux\,\,without\,\,core}$

The uncertainty in the clumpiness is determined from the error in estimation
of the flux of each detection during the artificial replication process. In
cases where no clumps are detected, we add a point source within the galaxy
with varying levels of fractional flux ($-4.0$ to $0.0$ in log scale) compared
to the net flux of the galaxy. For each fractional flux value, we repeat the
process a 1000 times. This step allows us to estimate an upper limit based on
the flux level below which we begin having no robust detections ($<68\%$).

This process is repeated for each filter separately. Some of the results for
F115W and F356W, approximately representing rest-frame optical and near-IR are
shown in Fig. 3.

##  4 Results

###  4.1 Clumps in optical and near-IR

We focus our study to galaxies that have detectable bulges and disks (i.e.
those having robust bulge and disk measurements with $<50\%$ uncertainty in
both components). This choice removes any galaxy that is composed exclusively
of a disk or of a spheroidal bulge888Note that all clumpy galaxies are
retained within our sample in spite of this, as discussed in Sec. 4.2. We also
ensure a disk axis-ratio $>0.3$. The latter condition is applied to prevent
any biases due to high dust column density in a galaxy observed perfectly
edge-on. Our selected sample constitutes $73\%$ and $67\%$ of the S17 galaxies
($10.0<log(M_{*}/M_{\odot})<11.0$) at redshift ranges $1.0-1.5$ and $1.5-2.0$
respectively (also evident from Table 1 and ftot in Fig. 4). This is in line
with previous expectation of disk-galaxy fractions in massive galaxy samples
up to $z=2$ (van der Wel et al., 2014)999To check if we are indeed studying
almost all disk galaxies appearing at these redshifts, we make a selection of
all S17 galaxies within 0.3 dex of the star-forming main sequence and above
$log(M_{*}/M_{\odot})>10.5$. We find that the galaxies we select, albeit after
removing the disk axis-ratio constraint, make up $\gtrsim 80\,\%$ of the
sample, in line with expectations for massive star-forming galaxies up to
$z=2$ in van der Wel et al. (2014)..

At $z>1$, studies have usually concentrated on studying clumpiness in UV bands
(below the $4000\rm\AA$ break; Guo et al., 2015). We however concentrate on
the rest-frame optical and near-IR bands to study the underlying stellar
distribution, as all the JWST/NIRCam bands we use in our study are above the
$4000\rm\AA$ break (barring F115W for $1.5<z<2.0$, the results for which we
separately discuss later).

We first present the results at rest-frame $\sim 1\,\mu$m as representative of
the whole wavelength range covered in our study. We find that out of all
galaxies in our study, $40\,\%$ and $41\,\%$ of them at redshift ranges
$1.0-1.5$ (using F200W) and $1.5-2.0$ (F277W) respectively show at least some
level of clumpiness within our detection limits. This feature is represented
as a fraction $f_{\rm clumpy}$ in Fig. 4. Our fractions are of course
dependent on the detection threshold we set, and using a lower value might
result in a higher fraction. Nevertheless as mentioned earlier, doing so gives
us less reliable clumpiness. Moreover, this somewhat strict detection still
gives us results in agreement with the expected percentage of galaxies
featuring UV-clumps (Guo et al., 2015) at the respective mass and redshift
ranges. Hence our conditions do not hinder the scientific relevance of our
sample within the current literature framework.

As can be deduced from Table 1, similar fraction of clumpy galaxies can be
obtained across all filters spanning rest-frame optical and near-IR. We do
note however that there is a drastic fall in the numbers for the F444W filter
(not shown in Table 1). But it is most likely due to this being a factor $\sim
2$ shallower than the others, in addition to the clumps being fainter at long
wavelengths. We therefore drop this from our clumpiness estimations and limit
our analysis up to F356W, still maintaining coverage of exclusively rest-frame
near-IR light in at least one filter across our redshift range.

The similarity of fraction of galaxies showing clumpiness across bands do not
however inform us whether the same structures are being detected in each
galaxy. Thus, we apply a method of quantification of the overlap between the
clumpiness maps in rest frame optical and near-IR bands. We do so by finding
the net area of all pixels that are detected in both the shortest (F115W) and
longest (F356W) wavelength band used in our detection algorithm. This value is
then normalised by the area of detected clumps in either of the two bands to
give a percentage of associations for each. We find a value of $0.6\pm 0.2$
and $0.7\pm 0.2$ for rest-frame optical (F115W) and near-IR (F356W). This
result strongly suggests that we are not looking at mutually exclusive clumps
in separate bands.

Our results indicate that a large fraction of the galaxies showing clumpiness
in the optical and near-IR bands would also show UV-clumps, given the
similarity of the fraction of galaxies showing clumpiness in our study and
that in Guo et al. (2015). However, we do not attempt to make a comparison to
ancillary HST/acs CANDELS data due to the images being a factor of $\sim 2$
shallower than the JWST/NIRCam images we are using (F115W to F356W).
Nevertheless, we observe that UV-clumps do appear in the F115W filter at
$1.5<z<2.0$, which traces UV light at this redshift range, for all galaxies
showing optical and near-IR clumps.

Finally, a Kolmogorov-Smirnov test on the complete sample of galaxies and
those with non-zero clumpiness gives only a $4\,\%$ and $7\,\%$ cumulative
probablilty (p-value) of being from the same distribution. However, if we
remove the clumpy galaxies from the full sample and remeasure the probabilty,
we obtain p-values well below $1\,\%$. We therefore interpret this result as
an indication of clumpy galaxies being largely independent of galaxies with no
clumps, in regards to their bulge-to-disk ratios.

###  4.2 Bulge-to-disk ratio and clumpiness

Figure 5: Bulge-to-disk ratio vs clumpiness at rest-frame wavelength $\sim
1\,\mu$m for the complete sample. For the redshift window $1.0-1.5$ we use
F200, while for $1.5-2.0$ we use F277. The Pearson correlation coefficient
($r$) for the plotted data is provided in the bottom-right corner. The grey
points mark the upper-limit values for all sources without clumpiness but have
robust bulge-to-disk ratio. These are not included in the estimation of $r$.
Figure 6: Bulge-to-disk ratio vs clumpiness at rest-frame wavelength $\sim
1\,\mu$m from Fig.5, split into two redshift windows $1.0-1.5$ and $1.5-2.0$.
The three rows from top to bottom are the same as in Fig. 4, which correspond
to the full mass range (above the mass-completeness limit of
$log(M_{*}/M_{\odot})=10.0$), $log(M_{*}/M_{\odot})=10.0-10.5$ and
$log(M_{*}/M_{\odot})=10.5-11.0$ respectively. The thick grey line represents
the average of the data-points and the Pearson correlation coefficient ($r$)
for the plotted data is provided in the bottom-right corner in each panel.
Finally, the grey points mark the upper-limit values for all sources without
clumpiness but have robust bulge-to-disk ratio.

This work exploits the ability to simultaneously map the clumps in rest-frame
optical and near-IR, along with the near-IR bulge-to-disk flux ratio (that
closely traces the stellar-mass ratio between these two components as
discussed in Sec. 3.1). As done in Sec. 4.1, we only show the relation between
these two components at $\sim 1\,\mu$m rest-frame as a representation of our
sample (Fig. 5 and 6). For all other filters, which are found to corroborate
our conclusions, please refer to the Appendix.

Firstly, all galaxies with non-zero clumpiness values appear within our sample
with robust bulge and disk measurements. Fig. 5 reveals clumpiness to decrease
with increasing bulge-to-disk flux ratio. This negative correlation, as
determined by the Pearson correlation coefficient101010The uncertainty for the
coefficient is measured through bootstrapping. We randomize the pairs of
values for clumpiness and bulge-to-disk ratio, and re-measure the coefficient.
This process is done a total of 1000 times and the standard deviation of the
distribution of the coefficient is used as the uncertainty., is found to be
moderate to strong, with a value of $-0.50\pm 0.05$ for the whole sample. This
strength of correlation is also found to mostly hold if the sample is divided
into two bins in redshift as well as stellar-mass (Fig. 6 along with Figs. 10
and 11 in the appendix), with a few exceptions likely due to low number
statistics. We also note that the position of the clumpiness upper limits (in
both Fig.5 and 6) follow the same trend, with almost all of them appearing at
the high bulge-to-disk end of the distribution. Finally, there does not seem
to be any obvious evolution of this negative correlation with redshift in our
sample within the current levels of uncertainty.

We inspect if this trend is induced by the measurement method rather than an
intrinsic correlation. As discussed in Sec. 3.1, we already ensure that the
bulge flux is not incorporated in any way in the measurement of the clumpiness
111111To further check this empirically, we measure the upper limits of disk
clumpiness even in galaxies where we have non-zero clumpiness values. This is
done by following the same procedure discussed in Sec. 3.2, but by masking the
currently present clumps. We find that there is no correlation of this limit
on the bulge-to-disk ratio. We do find that using the net-flux of the galaxy
to normalise the clump flux, we still end up with a similar correlation. We
also ensure that the disk axis-ratio (above the cut-off of 0.3) is not
correlated to either of the two plotted parameters. Making the axis-ratio cut-
off stricter ($>0.6$), which removes $50\%$ of our sample, actually improves
the negative correlation (the coefficients become more negative by $\sim 40\%$
in Fig. 5). We further check if this correlation is rather driven by a
dependence on the total stellar-mass of the galaxy. While the bulge-to-disk
ratio is found to be associated with the stellar-mass, the correlation is much
weaker ($r\sim 0.14$) than that with the clumpiness. We therefore claim that
this is possibly a second-order effect. We also do not find any detectable
correlation between clumpiness and stellar-mass ($r\sim 0.05$).

Finally, we investigate a dependence on integrated star-formation over the
whole galaxy using the star-formation rate (SFR) measurements from spectral
fitting in S17. We compare them to the expected SFR for each galaxy had it
been exactly on the star-forming main-sequence (Leslie et al., 2020). We re-
create Fig. 5, with only the galaxies having measured SFR in S17 using a
constant star-formation history model and with values within or above a 0.3
dex scatter of the main-sequence. This filtering is to ensure only confirmed
star-forming galaxies are included. The results show no change in the observed
correlation (r = $-0.52\pm 0.08$), and no dependence is observed with the
distance of the galaxy from the main sequence (the ratio of SFR observed and
that estimated based on the main-sequence). The corresponding figure in shown
in the Appendix (Fig. 12).

Although we primarily discuss the bulge-to-disk ratio here, replacing it with
bulge-to-total ratio maintains the negative correlation, although saturating
at $\sim 1$ as expected. When we check the same for the disk-to-total ratio,
we observe that there is a very weak positive correlation ($r\sim 0.10$). Both
these results suggest that the strong relation observed in Fig. 5 is mainly
driven by the relation between the bulge dominance and clumpiness, with weak
contributions from a parallel decline of the disk strength.

###  4.3 Bulge-to-disk and clumpiness optical/near-IR ratios

We further investigate the ratio of the clumpiness detected in rest-frame
optical (F115W) and near-IR (F356W) as a tracer of an equivalent integrated
color of the clumps observed in a galaxy. This value will be associated, but
not identical to the color of individual clumps.

Figure 7: The bulge-to-disk ratio vs the ratio of clumpiness measured in rest-
frame optical (f115W) and near-IR (f356w) for redshift $1.0-1.5$.

We limit this to the $1.0<z<1.5$ range to maximise the wavelength difference
between the filters, while ensuring that F115W still traces the optical light
and F356W probes the stellar-mass tracing near-IR flux. The latter would not
be true at $z>1.5$. We observe that the clumpiness ratio shows a moderate
correlation with the bulge-to-disk ratio ($r=0.40\pm 0.09$, Fig. 7). In other
words, for a specific clumpiness in near-IR light sensitive to stellar-mass,
those showing higher clumpiness in optical wavelengths show higher bulge-to-
disk ratio and vice-versa. However, it should be also be noted that majority
of our sample has a clumpiness ratio $>1$, suggesting that most galaxies have
more prominent clumps in shorter bands tracing younger stars and star-
formation. We provide an interpretation of this correlation in Sec. 5.2.

##  5 Discussion

###  5.1 Clumpy galaxy fractions

Our work showcases that clumpiness in high redshift ($z>1$) galaxies is
present across rest-frame UV to near-IR. We also find that there is $60\pm
20\,\%$ spatial overlap between the clumpy maps between these wavelengths.
Especially given their presence in the stellar-mass tracing near-IR, we can
conclude that these features do play a role in the morphological evolution of
galaxies. It is therefore unlikely that we are only dealing with young short
lived star-forming objects termed as UV-clumps, demonstrating the need of a
more generalised classification of clumps. This also needs to accommodate the
possibility of the clumps being associated with early stages of structures
like spiral arms as suggested by the intensity maps in Fig. 1 and 3
(corroborated by detection of ‘clumpy spirals’ at $z>1$, e.g., Elmegreen et
al., 2009; Margalef-Bentabol et al., 2022).

We first discuss possible mechanisms of the formation of these structures in
lieu of the fraction of galaxies featuring non-zero clumpiness. Our $\gtrsim
40\%$ fractions are much higher than the fraction of galaxies expected to be
undergoing major-mergers. The gas-rich major-merger fraction is a factor of 2
lower based on expectations from López-Sanjuan et al. (2013). Similarly, the
same fraction from Lotz et al. (2011) is found to be lower (by a factor $>2$
up to $z=1.5$) for merger observability timescales of $\leq 2\,\rm Gyr$ and
only comparable if the effects of mergers are observable over a $\sim 3\,\rm
Gyr$ interval. We also compare to major-merger fractions from TNG50 and TNG100
simulations (Nelson et al., 2019) and find them to also be a factor $>1.5$
lower that the our clumpy galaxy fraction, across the stellar-mass range and
up to a merger detectability timescale of $1.5\,\rm Gyr$.

However, the fractions based on minor-mergers (Lotz et al., 2011) and violent
disk instabilites (Cacciato et al., 2012) do agree with our estimation of
galaxies showing clumpiness (for expectations from each scenario, check Guo et
al., 2015). This suggests that a large fraction of the clumpy galaxies are
likely experiencing violent disk instabilities and/or minor-mergers.

###  5.2 Physical interpretation based on simulations

The negative correlation observed between the clumpiness and bulge-to-disk
flux ratio (Fig. 5) may indicate an underlying physical connection. This trend
is especially important since we expect this ratio to closely trace the
associated mass-ratio due to a minimally varying mass-to-light ratio (Sec.
3.1). Furthermore, given that the bulges of galaxies are known to be redder
than the disks, either due to high dust obscuration or older stellar
populations, the negative correlation would actually get stronger if we
considered higher than expected mass-to-light ratio variations.

We now compare this to theoretical expectations to suggest a physical
interpretation of this negative correlation. Multiple simulation studies have
claimed clumps in disk galaxies enable the funneling of gas to the centre to
form the galactic bulge (see Bournaud, 2016, for a review). Our results
feature the first observational sample indicating how such a link could
manifest. The observed trend in Fig. 6 is consistent with an evolutionary
trajectory beginning with the appearance of clumps, which leads to the
dynamical friction and torques that drive gas (possibly also some the clumps
themselves) to the centre. As mentioned in Sec. 4.2, there is only a mild
contribution to the negative correlation from a decline of the galaxy disks in
our sample. Hence we expect the disk to mainly survive this possible scenario.
Therefore the clumpiness decreasing with increase in bulge-to-disk ratio would
be under the combined effects of migration as well as destruction by stellar
feedback (Elmegreen et al., 2008; Ceverino et al., 2010; Bournaud et al.,
2011, 2014; Mandelker et al., 2014, 2017).

On the other hand, our results also agree with the scenario of bulges leading
to the stabilization of the gas in disks (Martig et al., 2009; Agertz et al.,
2009; Ceverino et al., 2010; Hopkins et al., 2023). This process in turn would
prevent the formation of clumps resulting in lower clumpiness in galaxies with
high bulge-to-disk ratio. Assuming that galaxies without a dominant bulge
would be clumpy, this scenario would independently explain the negative
correlation in Fig. 5. Nevertheless, the dynamical effect of the clumps cannot
be disregarded. Hence, even if we reject the possibility of the individual
clumps migrating to the core, one could still expect the first scenario to be
contributing simply through driving gas inwards. This is especially true given
that we detect these clumps in the stellar-mass tracing near-IR which will
inevitably add to the dynamical friction experienced by the gas within the
disks (e.g., Bournaud et al., 2014).

Finally, Fig. 7 suggests that the underlying negative slope of the correlation
in Fig. 6 increases as we go from rest-frame near-IR to optical. It should be
noted that this is observed only if one combines the whole sample at
$1.0<z<1.5$ with galaxies showing clumps in both F115W and F356W filters. It
is not as evident separately in the two mass bins shown in Fig. 10 and 11 in
the appendix. This correlation can be interpreted as the ratio of color of
clumps and the color of the underlying disks, since the latter is the
denominator in the calculation of clumpiness. Therefore, Fig. 7 suggests that
galaxies with lower bulge-to-disk ratio have clumps and their parent disks
with similar colors. However those with more dominant bulges have clumps that
are bluer than their disks.This could be due to contributions from two
separate populations resulting from in-situ as well as ex-situ (accreted
clumps/minor-mergers) formation (Mandelker et al., 2014, 2017; Zanella et al.,
2019). It is expected that in-situ clumps are younger than their parent
galaxies, whereas clumps that fall into the galaxy may introduce older stellar
populations. However, this interpretation ignores contributions from dust
attenuation resulting in reddening. A definitive conclusion requires studying
the spectral energy distribution of individual clumps and placing them within
the framework of this study.

##  6 Conclusions

Our investigation aims to simultaneously map clumps in a mass-complete sample
of galaxies at $z=1-2$ along with their underlying stellar morphology. It has
been made possible by the high-resolution rest-frame optical and near-IR
capabilities of JWST/NIRCam. We find clumps to not only be limited to optical
wavelengths sensitive to young stellar populations (as suggested previously by
detection of UV-bright clumps), but also almost equally in near-IR light
suggesting an imprint on the stellar distribution. We find a statistically
significant correlation between bulge-to-disk ratio, determined in rest frame
near-IR, and the clumpiness in individual galaxies. We find a moderate to
strong negative correlation between the two: as the clumpiness decreases, the
bulge becomes more prominent. This result strongly suggests that the central
bulge is evolutionarily linked to clumps. Finally, we find that this
correlation is steeper in optical than in near-IR, suggesting multiple
formation mechanisms of the observed clumps.

We would like to express our gratitude to the anonymous referee who made
valuable comments that considerably improved the quality of this work. LCH was
supported by the National Science Foundation of China (11721303, 11991052,
12011540375, 12233001), the National Key R&D Program of China
(2022YFF0503401), and the China Manned Space Project (CMS-CSST-2021-A04, CMS-
CSST-2021-A06). XD is supported by JSPS KAKENHI Grant Number JP22K14071. BSK
would like to thank Benjamin Magnelli and Carlos Gómez-Guijarro for their
brilliant insights into this work and valuable suggestions.

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Figure 8: Expansion of Fig. 4 for $1.0<z<1.5$. Please note the addition of the
mass-bin $log(M_{*}/M_{\odot})=9.5-10.0$, which has not been added in the
primary work for consistency with the completeness limit at $z>1.5$.
Nevertheless, S17 has a $90\,\%$ mass-completeness at
$log(M_{*}/M_{\odot})=9.5$ at $1.0<z<1.5$. Figure 9: Expansion of Fig. 4 for
$1.5<z<2.0$ Figure 10: Expansion of Fig. 6 for $1.0<z<1.5$. Please note the
addition of the mass-bin $log(M_{*}/M_{\odot})=9.5-10.0$, which has not been
added in the primary work for consistency with the completeness limit at
$z>1.5$. Nevertheless, S17 has a $90\,\%$ mass-completeness at
$log(M_{*}/M_{\odot})=9.5$ at $1.0<z<1.5$. Figure 11: Expansion of Fig. 6 for
$1.5<z<2.0$ Figure 12: Bulge-to-disk ratio vs clumpiness at rest-frame
wavelength $\sim 1\,\mu$m from Fig.5. Only the galaxies with measured SFR in
S17 and with values within or above a 0.3 dex scatter of the star-forming
galaxy main sequence (Leslie et al., 2020) for the respective stellar mass and
redshift of the galaxies. The colorbar represents the distance from the main
sequence, defined as the log of the ratio between the measured SFR and that
expected for the galaxy had it been exactly on the main sequence.


[orcid=0000-0002-6027-5132]

<Credit authorship details>

1]organization=Department of Physics, University of Michigan, city=Ann Arbor,
state=MI, postcode=48109, country=USA

[orcid=0000-0002-3617-290X]

[orcid=0000-0001-5660-2690]

[orcid=0000-0002-0034-6576]

[orcid=0000-0002-5278-2855]

[1]Corresponding author

# Development and test of a mini-Data Acquisition system for the High-
Luminosity LHC upgrade of the ATLAS Monitored Drift Tube detector

Yuxiang Guo<EMAIL_ADDRESS>[  Xueye Hu  Thomas Schwarz  Bing Zhou  Junjie
Zhu<EMAIL_ADDRESS>
###### Abstract

New front-end electronics including ASICs and FPGA boards are under
development for the ATLAS Monitored Drift Tube (MDT) detector to handle the
large data rates and harsh environment expected at high-luminosity LHC runs. A
mobile Data Acquisition (miniDAQ) system is designed to perform integration
tests of these front-end electronics. In addition, it will be used for surface
commissioning of 96 small-radius MDT (sMDT) chambers and for integration and
commissioning of new front-end electronics on the present ATLAS MDT chambers.
Details of the miniDAQ hardware and firmware are described in this article.
The miniDAQ system is also used to read out new front-end electronics on an
sMDT prototype chamber using cosmic muons and results obtained are shown.

###### keywords:

HL-LHC ATLAS MDT miniDAQ

##  1 Introduction

The ATLAS detector [1] is a general-purpose particle detector designed to
study proton-proton interactions at the Large Hadron Collider (LHC) at CERN
[2]. Its muon spectrometer is designed to trigger and identify muons produced
in collisions and to measure their momenta [3]. Resistive Plate Chambers (RPC)
[4] and Thin Gap Chambers (TGC) [5] are used as fast trigger chambers in the
barrel ($|\eta|<1.05$) 111ATLAS uses a right-handed coordinate system with its
origin at the nominal interaction point (IP) in the center of the detector and
the $z$-axis along the beam pipe. The $x$-axis points from the IP to the
center of the LHC ring, and the $y$-axis points upwards. Cylindrical
coordinates ($r$, $\phi$) are used in the transverse plane. The pseudorapidity
is defined in terms of the polar angle $\theta$ as $\eta=-\ln\tan(\theta/2)$.
and endcap ($1.05<|\eta|<2.4$) regions, respectively. Three (two) stations of
Monitored Drift Tube (MDT) chambers in the barrel (endcap) regions are used as
precision-tracking chambers. The endcap inner station has a new detector
composed of eight layers of Micromegas (MM) and eight layers of small-strip
TGC (sTGC) installed in 2021 [6].

The ATLAS muon spectrometer is designed to provide a standalone momentum
measurement with a relative resolution better than 3% over a wide $p_{T}$
range and 10% at $p_{T}=1$ TeV [1], where $p_{T}$ is the muon transverse
momentum. The momentum in the muon spectrometer is measured from the
deflection of the muon trajectory in the magnetic field generated by a system
of air-core toroid coils. The MDT chambers, together with MM and sTGC, perform
precision charged particle tracking in the transverse plane. Each MDT tube has
an average spatial resolution of $\sim 100$ $\mu$m.

The current MDT readout system was designed to cope with the original LHC
design luminosity of $10^{34}$ cm-2 s-1 with a first-level trigger acceptance
rate of 100 kHz and a latency of 2.5 $\mu$s [7]. Hits are stored in buffered
memories of front-end electronics waiting for the ATLAS first-level trigger
acceptance signal, while the first-level muon trigger is exclusively based on
RPC/TGC trigger detectors. The high-luminosity LHC (HL-LHC) will have the
instantaneous luminosity increased by a factor of $5-7.5$ and the integrated
luminosity increased by a factor of $\sim 10$. The targeted ATLAS first-level
trigger acceptance rate and latency are 1 MHz and 10 $\mu$s, respectively [8].
However, each MDT tube has a diameter of 3 cm and ionized electrons near the
tube wall can take up to 750 ns to reach the anode wire, far longer than the
LHC bunch crossing time interval of 25 ns. This long drift time makes the
current MDT readout scheme inappropriate for future HL-LHC runs since a hit is
likely to be read out multiple times at 1 MHz. A triggerless mode to send all
muon hits off chambers to new trigger and readout circuitry is preferred. In
addition, the relatively long trigger latency allows ATLAS to include MDT
chamber data at the first trigger level to improve the trigger muon momentum
resolution and to reject low momentum muons [9].

In addition to upgrading all MDT front-end and back-end electronics, ATLAS
also plans to replace part of the barrel inner station MDT chambers with
small-radius MDT (sMDT) chambers. These MDT chambers will have the tube radius
reduced by a factor of 2, to gain space to add an additional layer of RPC
chambers in that station to increase the overall RPC detector coverage [10].
In total 96 new sMDT chambers will be constructed and installed later [11].

Extensive developments are ongoing for the MDT front-end and back-end
electronics for the HL-LHC upgrade. A mobile data acquisition (miniDAQ) system
is needed to integrate various front-end electronics prototypes. This system
will also be critical for surface commissioning of these 96 sMDT chambers and
for integration and commissioning of new front-end electronics on the present
MDT chambers inside the ATLAS collision hall.

##  2 The miniDAQ system

###  2.1 Introduction of the MDT front-end and back-end electronics

Each MDT tube has a diameter of 3 cm and a wall thickness of 400 $\mu$m. The
central tungsten wire has a diameter of 50 $\mu$m. Tubes are operated with a
gas mixture of Ar/CO2 (93%/7%) at 3 bar absolute pressure. For each track, the
electrons from primary ionization clusters drift to the central wire along
radial lines. The induced signal propagates along the wire where it is read
out by the MDT front-end electronics. The difference between the earliest
arrival time of the signal at the wire and the reference time provided by
trigger chambers gives the drift time of the muon hit, and this drift time is
used to determine the drift radius.

The signal from each tube is first processed by a custom-designed
Amplifier/Shaper/Discriminator (ASD) ASIC [12, 13]. A discriminator is used to
determine the signal arrival time, the time when the signal crosses a
predefined threshold. This time depends on the signal pulse height which
results in a degradation of the time resolution. The resolution degradation
can partially be recovered by applying a time skew correction using the
integrated charge of the signal pulse. A Wilkinson analog-to-digital converter
(ADC) is introduced inside the ASD to integrate the signal pulse over a
predefined integration window of $\sim 20$ ns. The total collected charge is
measured by the discharge time of a capacitor by a rundown current. The signal
arrival time (also called the leading edge time) and the discharge time (also
called the trailing edge time) are converted into ADC counts using a time-to-
digital converter (TDC) ASIC with a bin size of 0.78 ns [14, 15, 16]. To avoid
multiple hits from multiple threshold crossings of a single signal, the ASD
ASIC can be programmed with a dead time of $\sim 1~{}\mu$s. After the
detection of the earliest arrival signal, there are no additional time
measurements performed within this dead time.

Each ASD ASIC can handle 8 tubes and each TDC can handle discriminated signals
from three ASDs. A mezzanine card with three ASDs and one TDC thus handles 24
tubes. A Chamber Service Module (CSM) multiplexes data from up to 18 mezzanine
cards and sends these data via an optical module (VTRx+) [17] to the MDT
Trigger Processor [18], where the relevant hits are extracted out of the raw
data stream. Pattern recognition, segment-finding, and track-fitting
algorithms are then applied to determine the muon momentum at the first
trigger level. Hit data are stored for transmission to a network called Front
End LInk eXchange (FELIX) [19] after receiving the first-level trigger
acceptance signal.

###  2.2 Introduction of the miniDAQ system

Due to the new MDT trigger and readout scheme, all front-end and back-end
electronics need to be redesigned. Both ASD and TDC designs have been
finished. All ASD chips have been produced, while all TDC chips are expected
to be produced in 2022. The designs for both mezzanine cards and CSM are close
to be final and minor modifications to the current prototypes are expected.
The MDT Data Processor is still under development.

It is critical to design a miniDAQ system to integrate these prototype ASICs
and boards together and to demonstrate that the new front-end electronics can
run in the triggerless mode to send out all hits. The miniDAQ system is a
lightweight version of the MDT Data Processor. It will send out all matched
hits to a PC for storage and pattern recognition, segment-finding, and track-
fitting algorithms will be performed offline. As a result, a low-cost FPGA can
be used and a FELIX system is not needed. The miniDAQ system is expected to be
mobile and can be used to study the performance of new sMDT chambers. It is
also expected to be used for the integration and commissioning of new front-
end electronics on the present MDT chambers inside the ATLAS collision hall.

Figure 1 shows the overall miniDAQ readout system planned for a single (s)MDT
chamber. Due to the smaller tube radius, an sMDT chamber can have more than
500 tubes, thus two CSMs are needed to read out all tubes. The miniDAQ system
is designed to handle at least 108 ASD and 36 TDC ASICs on 36 mezzanine cards.
All TDCs can be configured to run in either the triggerless or the trigger
mode, and the default mode is the triggerless mode [15]. The miniDAQ system
also receives data from at least two CSMs. Matched hits with the arrival time
within the trigger time window are sent to a PC using an ethernet port.

The requirements of the miniDAQ system are the following: 1) configure all
ASICs and boards connected; 2) distribute Trigger, Timing, and Control (TTC)
signals to each CSM via a downlink fiber at a line rate of 2.56 Gbps; 3)
receive the CSM output optical signals, and each CSM has two serial optical
uplink fibers running at a line rate of 10.24 Gbps; 4) receive the trigger
signal (can be either the external coincidence trigger signal or a programmed
trigger logic signal) and perform trigger matching to only send matched hit
data for offline storage; (5) monitor voltages and temperatures of all
mezzanine cards connected; and 6) monitor detector data in real time.

Fig. 1: The miniDAQ readout system used to read out data from two CSMs. Each
CSM will handle up to 18 mezzanine cards, and each mezzanine card has three
ASD ASICs and one TDC ASIC. Trigger signals can be either the external
coincidence trigger signals or programmed trigger logic signals. The output
data will be sent to a PC for storage.

###  2.3 The miniDAQ hardware

Figure 2 shows the connections of the miniDAQ system. A miniDAQ board is
designed to communicate between front-end electronics and a PC. Figure 3 shows
a picture of the actual miniDAQ board.

Fig. 2: Connections between the front-end boards, the miniDAQ board, and a PC.
The red box indicates major components on the miniDAQ board. The central part
of the board is a Xilinx FPGA. Fig. 3: Picture of an miniDAQ board. Major
components and connectors are indicated.

The central part of the miniDAQ board is a Xilinx Ultrascale KU035-1FBVA676C
FPGA. It contains about 444k logic cells, 19 Mb Block RAM, and 16 GTH
transceivers with a maximum line rate of 12.5 Gbps each. In addition, the FPGA
has 10 Clock Management tiles, 104 high-range I/Os, and 208 high-performance
I/Os.

Since each CSM uses two uplink fibers and one downlink fiber, in total four
SFP+ modules are needed to handle two CSMs. Four additional SFP+ modules are
reserved to handle two additional CSMs if needed. A 400 I/O FPGA Mezzanine
Card (FMC) High-pin count (HPC) connector is placed on the lower left corner
of the board and is connected to four FPGA GTH transceivers. This connector
provides flexibility for extra devices (such as an additional SFP+ adapter
board to integrate two extra CSMs).

Four SMA connectors are used to receive trigger signals, as shown in Fig. 4.
Each channel contains a high-speed comparator (ADCMP604) and the comparator
outputs are connected to the on-board FPGA. The trigger signal can be either
the analog signals from photomultipliers (PMTs) connnected to plastic
scintillators or the final coincidence signal of these analog signals. When
multiple PMTs are used, the coincidence can be made inside the FPGA firmware.
The coincidence signal is then digitized with a TDC implemented in the FPGA
firmware (FPGA-TDC). After a programmable delay, the digitized signal queues
in the first-in first-out buffer (FIFO) to start the trigger matching process.

Fig. 4: Trigger option of the miniDAQ board. An adjustable threshold is
applied to all comparators. Threshold-crossing time after the coincidence
logic is digitized by the FPGA-TDC and then used in the trigger matching
process after a programmable delay.

The miniDAQ board features an on-board 320 MHz oscillator, providing the
reference clock for the GTH transceivers. External clock can also be fed into
for synchronization if multiple miniDAQ boards are used. A flash memory is
used to boot the firmware into the FPGA when the board is powered on.

The board exchanges commands and front-end electronics monitoring information
with a PC via a USB-UART interface, and the detector data is sent out for
offline storage through the gigabit ethernet interface. Four miniSAS
connectors are reserved for joint test with a TDC test board. DC-DC converters
and low-dropout regulators are used to provide power to the FPGA and all other
on-board devices.

###  2.4 The miniDAQ firmware

Figure 5 shows the functional block diagram of the miniDAQ system. The FPGA
firmware can handle uplink and downlink data of the CSMs, digitize the
external trigger from scintillators, and communicate with a PC for offline
data storage and command exchange. The uplink data is passed through the
multi-stage decoding, the trigger matching, and sent out in packets to a PC
via the ethernet interface. The front-end configuration commands are exchanged
with the PC, then encoded and serialized as the downlink data. A JTAG debug
core is also used for online debugging.

Fig. 5: Functional block diagram of the miniDAQ system. Main FPGA firmware
modules are shown in the red box and the arrows indicate data-flow directions.

The key to successfully store the detector timing data for offline analysis is
to reduce the overall data bandwidth and select events of interest. When two
CSMs are connected to the miniDAQ system (shown in Fig. 1), the line rate of
the uplink data adds up to 40.96 Gbps. While this rate suits the maximum data
rate requirement of the HL-LHC upgrade, data filtering and trigger matching
are a must for the offline data storage as either background noises or idle
words occupy more than 90% of the uplink bandwidth. The miniDAQ firmware runs
data decoding and trigger matching in parallel for each uplink.

Multi-stage data decoding for one uplink is processed as illustrated in Fig.
6. The 10.24 Gbps serial data is firstly sampled and de-serialized by the
Xilinx GTH data interface, yielding a 32-bit word at 320 MHz. Secondly, the
lpGBT-FPGA IP further de-interleaves, decodes, and de-scrambles the uplink
data to a 230-bit word at 40 MHz according to the GBT protocol. This 230-bit
word contains 160-bit TDC data from 10 mezzanine cards and 70-bit data from
voltage and temperature sensors. The TDC data are then grouped into 10 slots
corresponding to its original mezzanine number and are decoded individually.

Fig. 6: Decoding scheme of the miniDAQ system for one uplink. Serial data from
one uplink goes through different de-serial and decoding stages to form data
for each individual TDC and data for voltage/temperature monitoring.

The MDT TDC utilizes two e-links running at 320 Mbps for its 8b/10b-encoded
output data, one for even bits and one for odd bits. When there are no hits to
be sent out, the TDC generates idle words continuously, which are useful for
data alignment when decoding at the receiver end. Even and odd bits are
aligned, concatenated and decoded by the miniDAQ system. At this stage, idle
words are thrown out and hits are buffered in on-chip random-access memories
(RAMs). While the throughput data rate is reduced by removing idle words, a
trigger matching process is implemented to select interesting hits from the
background noise.

The arrival time of a trigger signal is digitized by the FPGA-TDC. The FPGA-
TDC utilizes multiple phases of the reference clock, with the help of the FPGA
built-in clock manager modules to achieve the same time bin size as the front-
end TDC ASIC. After a pre-set latency during when all expected hits are being
collected in the RAMs, the trigger matching process compares the leading edge
time information between the trigger signal and the hit data stored in the
RAMs. Hits within a fixed time window relative to the trigger signal are
packed with the trigger data as an valid event and sent out through the
gigabit ethernet. An internal counter monitors the hits in the RAM and rejects
the outdated ones, with a programmable rejection window set based on the
trigger latency and trigger matching window.

The miniDAQ system sends front-end ASIC configuration bits and the encoded
control (ENC) signals for bunch count reset and system reset through the
downlink data, as indicated in Fig. 7. The two lpGBT ASICs [20], which work in
the master/slave mode, are configured directly through their serial control
interfaces. The configuration of the lpGBT ASICs enables the clock
distribution and the data sampling of the mazzanine cards.

The configuration of the mezzanine cards is achieved by utilizing the JTAG
master in the GBT-SCA ASIC [21], which converts the serial downlink data into
JTAG signals and provides a configurable clock rate. The converted JTAG
signals in the GBT-SCA, along with the ENC signals directly from the downlink,
are distributed to all mezzanine cards through the fan-out FPGA on the CSM
board.

Fig. 7: The uplink (red) and downlink (blue) data flow of the front-end
electronics. Chamber data, as well as voltage/temperature monitoring data, are
sent by uplinks. Configuration and TTC signals are sent by downlinks.

##  3 Results from cosmic-ray studies

The miniDAQ system is used to read out an sMDT prototype chamber built at the
University of Michigan. Figure 8 illustrates the setup of the detector and the
miniDAQ system. The sMDT prototype chamber has eight layers of drift tubes
with 70 tubes per layer and a tube diameter of 1.5 cm. Tubes are filled with a
gas mixture of 93% Argon and 7% CO2 at 3-bar absolute pressure. A high voltage
of 2,730 volts is applied on the central wire. Due to the availability of
prototype boards, four three-layer stacked mezzanine card designed by the
ATLAS group at Max Planck Institute for Physics at Munich are used to read out
96 tubes. Each set of stacked card has three ASD ASICs and one TDCv2 ASIC
mounted. A new CSM board is used to collect output data from these four TDCs.
A Raspberry Pi board is also used to configure two lpGBT ASICs into ready
status on the CSM board. The lpGBT output is converted to optical signal and
sent by VTRx+ to the miniDAQ system via optical fibers. Trigger and reference
time information are generated by a 1 m $\times$ 2 m scintillator placed on
the top of the chamber. The signals from two PMTs placed at both ends of the
scintillator are discriminated and a coincidence trigger signal is formed. The
resulting trigger signal is sent to the miniDAQ board and all matched hits are
transmitted to a PC via the Ethernet port.

Fig. 8: Setup of the cosmic-ray test station with an sMDT chamber. Detector
signals are processed and digitized by ASD and TDC ASICs on the mezzanine
card. All digitized hit data are sent to the CSM and then to the miniDAQ
board. The miniDAQ board also receives the coincidence signal from two PMTs
connected to a large area plastic scintillator, and only matched hits within
the allowed time window are sent out to a PC via an Ethernet port. A Raspberry
Pi board is used to configure two lpGBT ASICs on the CSM.

Cosmic-ray data has been taken to study the performance of both the detector
and front-end electronics. Figure 9 shows the occupancy of the tubes that are
read out by the new electronics. In total 96 tubes (48 on the top multilayer
and 48 on the bottom multilayer) are connected to the four mezzanine cards. A
muon track with at least six tubes being hit is required to be reconstructed
in each event. That explains low occupancy for tubes around the edges since
most muon tracks passing through them are dropped during the reconstruction.

Fig. 9: Occupancy of tubes that are read out. Only 96 tubes are read out by
the new electronics. A muon track with at least 6 tubes being hit is required
for each event.

Figure 10 shows the measured signal arrival time and pulse height spectra for
cosmic-ray muons for a single tube. The width of the signal arrival time
spectrum is $\sim 200$ ns, which corresponds to the time needed for ionized
electrons produced close to the tube wall to drift to the central wire. The
spectra measured with the TDCv2 prototype ASIC are also compared with the
results obtained using a mezzanine board with a CERN-designed HPTDC ASIC [22].
Good agreement is observed.

(a)

(b)

Fig. 10: Comparisons between the TDCv2 prototype and the CERN-designed HPTDC
ASIC for (a) the signal arrival time spectrum, and (b) the pulse height
spectrum for a single channel. Cosmic muon data are used.

Detailed studies are performed on the data collected to obtain the spatial
resolution and detection efficiency. The drift-distance-to-drift-time relation
(also called the $r(t)$ function) is measured and used to obtain the drift
radius for each fired tube. A straight line is then used to fit these draft
radii and both biased and unbiased tracking residuals are obtained. The
residual is the difference between the drift radius and the radius predicted
by the straight line fit. Biased residuals are those from fits using all tube
hits, whereas unbiased residuals are found by refitting a track multiple times
by removing one hit from the fit and finding the residual of the hit removed
from the fit. More details about the track reconstruction can be found in Ref.
[23].

Figure 11 shows the biased and unbiased residual distributions. Two Gaussian
distributions with the same mean value are used to fit the residual
distributions. The average width of the fit ($\sigma_{b}$ for the biased
residual and $\sigma_{u}$ for the unbiased residual) is defined as the two
fitted Gaussian widths weighted by the amplitude of each Gaussian and is found
to be $\sigma_{b}=95.41\pm 0.54$ $\mu$m and $\sigma_{b}=163.23\pm 3.09$
$\mu$m. The spatial resolution $\sigma$, defined as
$\sqrt{\sigma_{b}\sigma_{u}}$, is $124.8\pm 1.2$ $\mu$m. It has to be noted
that the numbers presented are before subtracting the contribution of the
multiple scattering. Due to low energies of cosmic muons detected, multiple
scattering effects need to be subtracted. A detailed Monte Carlo simulation is
performed [23] and the spatial resolution as a function of the drift radius
with multiple scattering effects removed is shown in Fig. 12(a). The best fit
curve observed in the Run 2 MDT resolution study [24] is also shown for
comparison purpose.

(a)

(b)

Fig. 11: Residual distribution of the cosmic muon data for (a) biased
residual, and (b) unbiased residual. The red lines indicate the results from
the double Gaussian fit.

The muon detection efficiency as a function of the drift radius is shown in
Fig. 12(b). To evaluate whether a track passes through a tube volume, that
tube is excluded from the track fit to not bias the fit to be closer to the
tube. Hits greater than $5~{}\sigma$ away from a track are not counted towards
the efficiency [23]. The region near the tube wall is responsible for most of
the inefficiency of the chamber.

(a)

(b)

Fig. 12: Results from cosmic muon studies for (a) spatial resolution as a
function of the drift radius (the solid line shows the resolution measured for
the current ATLAS MDT detector installed inside the collision hall), and (b)
detection efficiency as a function of the drift radius.

##  4 Conclusions

We present the design of a miniDAQ system for the HL-LHC upgrade of the ATLAS
MDT detector. This miniDAQ system can configure all front-end ASIC and board
prototypes, distribute TTC signals, and collect the CSM output data. It can
also receive external trigger signals and perform trigger matching to select
matched MDT hits for offline storage. Voltages and temperatures of all
mezzanine cards are monitored, and detector data is monitored in real time.
The miniDAQ system has been used successfully to read out 96 tubes from an
sMDT prototype chamber using cosmic muons. Tube spatial resolution and
detection efficiency are measured and are found to be consistent with results
obtained from previous studies using the legacy ATLAS MDT electronics. This
system is expected to be used for surface commissioning of 96 sMDT chambers to
be built for the ATLAS HL-LHC upgrade. It will also be critical for future
commissioning of new front-end electronics on the present MDT chambers inside
the ATLAS collision hall.

##  5 Acknowledgement

The work is supported by the US National Science Foundation (NSF) and the US
Department of Energy (DOE) under contracts PHY1948993 (NSF) and DE-SC007859
(DOE).

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