MUSEQuBES: Unveiling Cosmic Web Filaments at z ≈ 3.6 through Dual Absorption and Emission Line Analysis

MUSEQuBES: Unveiling Cosmic Web Filaments at z ≈ 3.6 through Dual Absorption and Emission Line Analysis

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  Draft version January10, 2025 Typeset using L A TEX twocolumn style in AASTeX631 MUSEQuBES: Unveiling Cosmic Web Filaments at z ≈ 3.6 through Dual Absorption and Emission Line Analysis Eshita Banerjee ,1 Sowgat Muzahid ,1 Joop Schaye,2 Sebastiano Cantalupo,3 and Sean D. Johnson4 1IUCAA, Post Bag 04, Ganeshkhind, Pune, India, 411007 2Leiden Observatory, Leiden University, P.O. Box 9513, NL-2300 AA Leiden, the Netherlands 3Department of Physics, University of Milan Bicocca, Piazza della Scienza 3, I-20126 Milano, Italy 4Department of Astronomy, University of Michigan, 1085 S. University, Ann Arbor, MI 48109, USA ABSTRACT According to modern cosmological models, galaxies are embedded within cosmic filaments, which sup- ply a continuous flow of pristine gas, fueling star formation and driving their evolution. However, due to their low density, the direct detection of diffuse gas in cosmic filaments remains elusive. Here, we report the discovery of an extremely metal-poor ([X/H] ≈ −3.7), low-density (log10 nH/cm−3 ≈ −4, corre- sponding to an overdensity of ≈ 5) partial Lyman limit system (pLLS) at z ≈ 3.577 along the quasar sightline Q1317–0507, probing cosmic filaments. Additionally, two other low-metallicity ([X/H]≲ −2) absorption systems are detected at similar redshifts, one of which is also a pLLS. VLT/MUSE obser- vations reveal a significant overdensity of Lyα emitters (LAEs) associated with these absorbers. The spatial distribution of the LAEs strongly suggests the presence of an underlying filamentary structure. This is further supported by the detection of a large Lyα emitting nebula with a surface brightness of ≥ 10−19 erg cm−2 s−1 arcsec−2 , with a maximum projected linear size of ≈ 260 pkpc extending along the LAEs. This is the first detection of giant Lyα emission tracing cosmic filaments, linked to normal galaxies and likely powered by in-situ recombination. Keywords: galaxies: evolution — galaxies: high-redshift — (galaxies:) quasars: absorption lines 1. INTRODUCTION In the current cosmological framework, galaxies emerge within the dense intersections of the cosmic web—a large-scale network of dark matter halos and filaments that span the universe. These structures chan- nel gas from the intergalactic medium (IGM) into dark matter halos, where it eventually cools, triggering star formation. However, detecting these emission from the gas in elusive filaments is challenging due to their low densities. Recent advancements in integral field units (IFUs) with large fields of view, like MUSE (Bacon et al. 2010), have revolutionized our ability to detect these filament- like structures, glowing in Lyα -emission at high red- shifts (Fumagalli et al. 2016b; Bacon et al. 2021; Johnson et al. 2022; Tornotti et al. 2024a). These observations Corresponding author: Eshita Banerjee, Sowgat Muzahid eshitaban18@iucaa.in, sowgat@iucaa.in offer new insights into gas flow from the IGM into galax- ies, particularly through “cold-mode accretion” (e.g., Kereš et al. 2005), where gas is funneled into galaxies via narrow, dense filaments. This process significantly contributes to the optically thick gas associated with Lyman-limit systems (LLSs: log10(NHi) > 17.2) (see, Fumagalli et al. 2011; van de Voort et al. 2012). Fumagalli et al. (2013) have shown that while gas in galaxy halos can account for all LLSs at z < 3, at z ≳ 3.5, the contribution of the IGM to LLSs be- comes pronounced, as the overdensities associated with these systems decrease (see, Schaye 2001) and the ex- tragalactic UV background (UVB) weakens, enhancing gas shielding. Consequently, LLSs are considered effec- tive tracers of cold-stream inflows onto galaxies, often identified by their low metallicity (e.g., Ribaudo et al. 2011; Crighton et al. 2013) or filamentary morphology (e.g., Fumagalli et al. 2016b). At z ≈ 3, only a small fraction (≈ 18%) of LLSs and partial-LLSs (pLLSs: 16.2 < log10(NHi) < 17.2) are extremely metal-poor, arXiv:2412.04546v2 [astro-ph.GA] 9 Jan 2025
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  2 with metallicity being[X/H]< −3 (Lehner et al. 2016, 2022; Lofthouse et al. 2023). Interestingly, in our MUSEQuBES survey, we identi- fied an overdensity of Lyα emitters (LAEs) at z ≈ 3.577, consisting of seven LAEs arranged in an almost linear configuration. Suspecting a filament connecting these LAEs, we explored potential inflow signatures by mod- eling absorbers probed by a background quasar and searched for extended emission around this structure. This investigation revealed a low-metallicity absorption system and a coincident giant Lyα nebula. This letter is organized as follows: section 2 introduces our data; section 3 presents absorption measurements and model- ing, and finally, we summarize our study and discuss the results in section 4. We adopt a flat ΛCDM cosmology with H0 = 70 km s−1 Mpc−1 , ΩM = 0.3 and ΩΛ = 0.7. Metallicity is expressed as log10(Z/Z⊙) ≡[X/H], where Z⊙ is the solar metallicity (= 0.013; see Grevesse et al. (2012)). Distances are in physical kpc (hereafter, pkpc) unless specified otherwise. 2. DATA The LAE overdensity analyzed in this study is de- tected toward the quasar Q1317−0507, observed as part of the MUSEQuBES survey (Muzahid et al. 2020, 2021; Banerjee et al. 2023, 2024). We obtained 10 hours of on- source VLT/MUSE observations with an effective seeing of < 0.6 ′′ . The final data cube has a spatial sampling of 0.2 ′′ ×0.2 ′′ per pixel, and a spectral resolution of ≈ 3600 (FWHM ≈ 86 km s−1 ) in the optical range (4750–9350 Å). The data reduction process is comprehensively de- scribed in Muzahid et al. (2021). Complementary to the MUSE data, we utilized a high-resolution optical spectrum of the quasar from VLT/UVES (R ≈ 45, 000), sourced from the SQUAD database (Murphy et al. 2019). The coadded and continuum-normalized spectrum provides a median signal-to-noise ratio (SNR) of 35 within the Lyα -forest region and 80 redward of the quasar’s Lyα emission. Additionally, we incorporated near-infrared data from VLT/X-shooter, covering 1000-2480 nm with a spectral resolution of R ≈ 5300 and a median SNR of ≈ 35. This spectrum, along with its best-fitting continuum, were re- trieved from the ESO data archive (López et al. 2016). 3. ANALYSIS AND RESULTS Muzahid et al. (2020) identified 22 LAEs in the MUSE field centered on the background quasar Q1317−0507 (zqso = 3.7) in the redshift range 2.9 < z < 3.6. These LAEs were detected based on their Lyα emission lines, which typically show offsets of hundreds of km s−1 from the systemic redshifts (e.g., Steidel et al. 2010; Rakic 3.566 3.568 3.570 3.572 3.574 3.576 3.578 zLAE 0 2 Count 1 2 3 4 5 6 7 13h 20m 32s 31s 30s 29s 28s −5◦ 230 1500 3000 4500 240 0000 R.A. (J2000) Dec (J2000) G7 1 2 3 4 5 6 7 0.1 0.5 5.0 SB (×10 −18 erg s −1 cm −2 arcsec −2 ) Figure 1. The optimally extracted Lyα surface brightness maps of the 7 LAEs (G7) within the MUSE FOV centered on the quasar Q1317−0507 (marked by the “+” sign). The pixels within the 3D segmentation map for each LAE are combined and projected onto the image, with the gray con- tours representing the 5 and 25 σ from the mean flux levels of the continuum-bright objects. A Gaussian smoothing func- tion with σ = 0.2′′ (≡ 1 pixel) has been applied to enhance visual clarity of the SB map. The histogram in top panel dis- plays the redshift distribution of the LAEs. The object IDs are indicated beside each LAEs as well as in the histogram plot. et al. 2011; Shibuya et al. 2014; Verhamme et al. 2018). The Lyα redshifts were corrected using the empirical relation from Muzahid et al. (2020). A friends-of-friends algorithm, using a linking velocity1 of 500 km s−1 along the line of sight (LOS), identified a galaxy overdensity with 7 LAEs at z ≈ 3.57, making it the most LAE-rich system in the MUSEQuBES sample. Figure 1 shows the optimally extracted Lyα surface brightness (SB) map of this overdense region (hereafter, G7). The redshifts of the seven LAEs range from z ≈ 3.566 to 3.578. The LAE closest to the quasar-sightline is Id:2, at a transverse distance of 34 pkpc, followed by Id:3 at 91 pkpc. The other LAEs are located beyond 100 pkpc, with the farthest at 220 pkpc. The redshift histogram reveals that five of the seven LAEs (excluding 1 earlier, Muzahid et al. (2021) also used the similar velocity win- dow for defining galaxy-groups.
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  3 Id:1 and Id:2)are tightly clustered at z ≈ 3.577, which is ≈ 8000 km s−1 or 20 pMpc from the background quasar. 3.1. Measurements of absorption lines associated with G7 Fig. 2 shows the velocity plot for the Lyman-series lines and metal transitions associated with the G7 sys- tem. The seven LAEs are marked by red stars, with ∆v = 0 corresponding to their median redshift of z = 3.577. To constrain the H i absorber parame- ters, we simultaneously fitted the Lyman-series lines, from Lyα to H i-λ916, using the Voigt profile fitting software vpfit (Carswell & Webb 2014). This soft- ware minimizes χ2 to determine the best-fitting red- shift (z), Doppler parameter (b), and column density (N) of the absorbers. Strong, un-fitted absorption in higher-order lines is contamination, as evident from the lack of stronger absorption in Lyα at similar veloci- ties. This underscores the need for simultaneous fit- ting of all Lyman-series lines. We identified over 30 H i components within ±1000 km s−1 , including two pLLSs at −60 and −300 km s−1 with H i column densities of log10 N/cm−2 = 16.7 and 16.3, respectively. Next, we searched for metal transitions associated with G7 within the same velocity range. We detected metal absorption corresponding to H i absorbers at ap- proximately −300, −60, and 300 km s−1 , which we la- beled as S1, S2, and S3, respectively. The highlighted velocity ranges used to associate aligned transitions were based on the structure of the detected metal absorption lines. C iv absorption was observed in all three systems, while Si iv was detected in S1 and S2. No other metal transitions were detected within this range. For non- detections, we calculated 3σ limiting column densities using the 3σ limiting equivalent width (Hellsten et al. 1998), assuming the linear part of the curve of growth. When fitting the aligned C iv and Si iv transitions, we tied their redshifts. However, the C iv1548 line for S2 and C iv1550 for S1 are contaminated by Mg ii ab- sorption from z = 1.52, while C iv1550 in S3 is affected by a z = 2.83 Al iii line. To accurately measure the metal absorption parameters, we fitted these contam- inating lines as well. The N and b of these blended components are reliably constrained because the corre- sponding unblended, unsaturated doublet lines provide accurate measurements. We also excluded transitions like C iii and Si iii due to heavy contamination from the Lyα forest. Among the metal transitions, we identified four pairs of components (three from S1 and one from S2) where C iv and Si iv are aligned in redshift. By analyz- ing the b-parameters of these components, we sepa- Table 1. Range of the parameters used for Cloudy Parameter Minimum Maximum Interval log10 NHI/cm−2 12.5 20.5 0.25 z 2.75 4 0.25 [X/H] -4.0 1.0 0.25 log10 nH/cm−3 -4.5 0.0 0.25 Note:– For the Bayesian inference code, we have used interpolation to obtain intermediate values. rated the contributions from temperature (T) and tur- bulent velocity (vturb) in the medium using the rela- tion: b2 = v2 turb + 2kBT mion . Here, mion is the mass of ion, kB is the Boltzmann constant and vturb is the turbu- lent velocity contributing to the non-thermal broaden- ing. The resulting median temperature of the medium is log10 (T/K) ≈ 4.7 ± 0.2. While this method can also be applied using a metal ion and its associated H i ab- sorber, it was not feasible here due to the presence of multiple metal absorption components associated with a single H i absorber. 3.2. Photoionization model High column density H i absorbers, such as pLLS and LLS, are typically photoionized across redshifts (Crighton et al. 2015; Fumagalli et al. 2016b; Prochaska et al. 2017; Lehner et al. 2018, 2022). We employed Cloudy (v-C17, Ferland et al. 2013) to compute ionization corrections, assuming a uniform slab of gas with a constant hydrogen density (nH) and solar elemental abundances (Asplund et al. 2009) is in ther- mal and ionization equilibrium. The incident radiation is assumed to be the redshift-dependent UV background (UVB) given by the Haardt & Madau (2001, hereafter, HM05) model, along with the cosmic microwave back- ground (CMB). It is important to note that for z > 3, the UVB models (e.g., HM05, HM12 Haardt & Madau (2012) or KS18 (Khaire & Srianand 2019)) show mini- mal variation in the relevant energy range. The model iterated until it reached the neutral hydrogen column density (NHi). We did not include dust or grains, as- suming all elements are in the gas phase. We applied a Bayesian approach to compare the col- umn densities and errors of each ion with a grid of pho- toionization models to derive the metallicities and den- sities. This method, commonly used in previous studies (e.g., Fumagalli et al. 2011; Crighton et al. 2015; Lehner et al. 2018, 2022), is effective in producing robust confi- dence intervals from posterior probability distributions. We employed likelihood functions similar to Fumagalli et al. (2016b). Our model parameters are: (i) neutral hydrogen column density (NHi), (ii) redshift (z), (iii)
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  4 −1000 −750 −500−250 0 250 500 750 1000 S1 S2 S3 1 2 34 5 6 7 // // HI 1215 HI 1025 HI 972 HI 949 HI 937 HI 930 HI 926 HI 923 HI 920 HI 919 HI 918 HI 917 HI 916 CIV 1548 CIV 1550 SiIV 1393 SiIV 1402 LOS velocity (km s−1 ) Normalized flux + offset Figure 2. Velocity plot of the G7 system, showing several Lyman-series lines (arranged in descending order of wavelength from bottom to top) along with metal lines detected in the system. The observed spectrum is shown in blue, with the best-fitting model curve from vpfit in red. Fits for contaminating transitions are displayed in green. The black, orange and red ticks mark the positions of individual H i, C iv and Si iv components, respectively. For visual clarity, the normalized fluxes of each line are shifted vertically. The positions of the seven LAEs are marked by red stars at the bottom and their respective IDs are written on top, with ∆v = 0 corresponding to their median redshift of z = 3.577. The total column densities measured within the three pink shaded regions (S1, S2 and S3) are used to construct photoionization models.
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  5 12 14 16 log 10 (N/cm −2 ) [X/H] = −1.98+0.14 −0.16 log10(nH/cm−3 ) = −2.65+0.11 −0.10 Model Data: S1 HI CIV SiIV CII SiII AlIII MgII −1 0 1 ∆ 10 12 14 16 log 10 (N/cm −2 ) [X/H] = −3.69+0.08 −0.08 log10 (nH/cm−3 ) = −3.95+0.19 −0.21 Model Data: S2 HI CIV SiIV CII SiII AlIII MgII 0.0 2.5 ∆ 8 10 12 14 log 10 (N/cm −2 ) [X/H] = −2.58+0.13 −0.07 log10 (nH/cm−3 ) = −3.81+0.24 −0.31 Model Data: S3 HI CIV CII SiIV SiII AlIII MgII 0 5 ∆ Figure 3. Measured column densities of different transitions associated with S1, S2 and S3 (top to bottom) are shown in red, with upper limits indicated by downward arrows. Blue squares represent the predicted column densities based on the median values of the model parameters from their re- spective posterior PDFs. The median values of metallicity and nH along with their 16th -84th percentile ranges are dis- played at the top. The bottom panel shows the residuals (∆ ≡ log10 N − log10 Nmodel). metallicity ([X/H]), and (iv) the total hydrogen number density (both neutral and ionized), nH = nγ/U, where nγ is the hydrogen ionizing photon density and U is ion- ization parameter. The parameter ranges are shown in table 1. We used the nested sampling Monte Carlo algorithm MLFriends implemented in the UltraNest2 package of Python to obtain the posterior probability density func- tions (PDFs) of the modeling parameters. Gaussian pri- ors were adopted for NHi and z based on vpfit con- straints, while flat priors were used for metallicity and nH across the grid’s parameter space. 3.3. Results of Photoionization modeling Fig. 3 compares the observed column densities with the best-fitting values derived from the medians of the posterior distributions of model parameters for systems S1, S2, and S3. System S1 is closely aligned in velocity with LAE Id 2, the galaxy nearest to the quasar sightline. Classified as a pLLS (log10 N/cm−2 = 16.3), it shows C iv and Si iv detections, with upper limits on C ii, Si ii, Al iii, and Mg ii. Bayesian analysis with HM05 (KS18) UVB indi- cates low metallicity, [X/H]= −1.98+0.14 −0.16 (−2.22+0.17 −0.15) and density log10 nH/cm−3 = −2.65+0.11 −0.10 (−2.94+0.13 −0.11). System S2 lies near the redshift of the clustered LAEs and has the highest log10 N/cm−2 = 16.7, also classi- fied as a pLLS. It shows C iv and weak Si iv detections. The metallicity of S2 is extremely low, with [X/H]= −3.69+0.08 −0.08 (HM05) or [X/H]= −4.08+0.08 −0.08 (KS18). The density is log10 nH/cm−3 = −3.95+0.2 −0.2 (−4.27+0.18 −0.14) for HM05 (KS18). System S3 has only C iv detected in addition to H i. Hence, instead of using flat priors, a Gaussian prior on density with log10 nH/cm−3 ≈ −3.5 ± 0.5, was ap- plied. This corresponds to the maximum C iv ion- fraction for the given NHi for the metallicities and red- shift range included in our grid. This system is also metal-poor, with [X/H]= −2.58+0.13 −0.07 (−2.94+0.16 −0.13) and log10 nH/cm−3 = −3.81+0.24 −0.32 (−4.02+0.17 −0.27) using HM05 (KS18) UVB. 4. DISCUSSION AND CONCLUSION 4.1. The G7 system as a tracer of filamentary structure To assess the overdensity of the G7 system, we used the LAE luminosity function (LF) to estimate the ex- pected number of LAEs in a cosmological volume cor- responding to that of the G7 system. The LF of Drake et al. (2017) (Herenz et al. 2019) predicts only 0.6 (0.8) 2 https://johannesbuchner.github.io/UltraNest/
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  6 LAEs with log10(LLyα/erg s−1 ) ≥ 41.4, which is the lowest detected luminosity in the G7 system. Detecting seven LAEs thus corresponds to a Poisson probability of 3 × 10−6 (2 × 10−5 for a mean of 0.8), confirming that this is a highly overdense region. The projected spatial distribution of G7 member LAEs is notably non-random, forming a near-linear structure (see Fig. 1). Using a Monte Carlo toy model, we estimated the chance probability of this alignment. By fitting the pixel coordinates of LAEs with a straight line3 , we measured the maximum perpendicular distance (ϵ) from the line. Randomly placing seven points in the 324 × 324 spaxel2 MUSE FoV, we repeated this process to compute ϵi for 1000 realizations. The probability of ϵi ≤ ϵ was found to be ≈ 0.3%, indicating that such alignments are extremely rare. Five of the seven G7 LAEs (excluding Ids-1 and 2) are clustered in LOS velocity at z ≈ 3.577, closely matching the velocity (∆v ≈ −60 km s−1 ) of the extremely metal- poor system S2 ([X/H]= −3.69). These LAEs lie at a projected distance of 100 − 200 pkpc from the quasar- sightline. S2, with log10 nH/cm−3 = −4.0 (correspond- ing to an overdensity of δ ≈ 5; Schaye 2001), likely orig- inates from cosmic filaments rather than the CGM (e.g., Crighton et al. 2013; Fumagalli et al. 2016a,b; Mackenzie et al. 2019). We therefore investigated whether there are any traces of extended Lyα emission around this LAE overdensity. We reanalyzed the MUSE data using CubEx (Can- talupo et al. 2019) on the quasar’s point spread func- tion (PSF) and continuum subtracted cube, focusing on 5535–5600 Å (±2000 km s−1 from z = 3.577). We searched for sources with > 3500 connected voxels with SNR ≥ 1.8. To enhance sensitivity to low-SB sources, we applied a 4-pixel (0.8′′ ) Gaussian spatial smoothing. This analysis revealed a large extended structure com- prising > 10000 connected voxels, with a projected lin- ear size of ≈ 260 pkpc. We confirmed that the structure spans 16 distinct wavelength layers to ensure the detec- tion is not spurious. Fig. 4 displays the SB map (top) of the detected struc- ture, with the 7 LAEs marked by green squares and (bottom) the SNR map of the same, overlaid on a sin- gle wavelength layer associated to the extended emis- sion. The contours in the middle panel highlights the SB level of 10−19 erg s−1 cm−2 arcsec−2 , while that on bottom denotes SNR = 2. Although faint, the detection is significant as it aligns closely with the LAE positions. Two LAEs (Ids: 1 and 2) lie directly within the contour, 3 using LinearRegression class from Scikit-learn. 13h 20m 31s 30s 29s −5◦ 230 1500 3000 4500 pos.eq.ra Dec (J2000) G7 50 kpc 1 2 3 4 5 6 7 0.1 0.5 5.0 SB (×10 −18 c.g.s.) 13h 20m 31s 30s 29s −5◦ 230 1500 3000 4500 R.A. (J2000) Dec (J2000) 0.1 2.0 5.0 SNR Figure 4. Top: The surface brightness (SB) profile of the extended filament like structure as well as the associ- ated LAEs of the G7 system. The contour indicates the SB = 10−19 erg s−1 cm−2 arcsec−2 . Bottom: The SNR map of the extended emission and the associated LAEs, plotted on top of a single wavelength layer associated to the extended emission. The white contours correspond to the SNR of 2. In both panel, the positions of the seven LAEs are highlighted by the squares, while the quasar position is marked by a “x” symbol. Maximum projected length of this structure is ≈ 260 pkpc. Refer to the text for the details about white dashed and dotted lines. while two others (Ids: 4 and 7) are just outside it. The white dashed and dotted lines drawn on top of the SB- map indicate the best-fit linear alignment of the LAEs and their maximum deviation, ϵ. The figure shows ex- cellent correspondence between the extended emission and the filamentary structure traced by the LAEs. The absence of emission at the quasar’s location (marked by the “x”) is likely due to enhanced noise from PSF subtraction. However, this background source allows direct measurement of the filament’s nH and U. Bayesian analysis shows log10 nH/cm−3 ranges be- tween −4.0 and −2.6 (see Section 3.3), corresponding to log10 U of −0.8 to −2.2 (for HM05). LAE Id: 2, located
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  7 at 34 pkpcof the quasar sightline and ≈ 300 km s−1 along the LOS, could enhance the local radiation field, raising the density estimate by 0.33 ± 0.49 dex (Fuma- galli et al. 2016b). Even with this correction, nH remains low. 4.2. Origin(s) of the Lyα nebula tracing the cosmic web The projected linear size of approximately 260 pkpc classifies this extended emission as “giant” Lyα nebula. Such giant Lyα nebulae are generally detected around high-z quasars (Cantalupo et al. 2014; Borisova et al. 2016) and between quasar pairs (Tornotti et al. 2024a; Herwig et al. 2024). However this is the first detec- tion of a giant Lyα emission tracing cosmic filaments and associated with normal Lyman-α emitting galaxies (see Tornotti et al. 2024b, for another recent example). Note that none of these LAEs shows He ii λ1640 or any other emission line (such as C iv). The low Lyα emis- sion equivalent width (≈ 50 Å in rest frame) and faint continuum also suggest the lack of AGN activity in these LAEs. The primary source of this extended-Lyα emission might be the “in-situ” recombination radiation follow- ing photoionization by the UV-photons. The Lyα SB expected from a LLS illuminated by the HM05 UVB at z ≈ 3.5 is 2.2 × 10−20 erg s−1 cm−2 arcsec−2 (see e.g., Cantalupo et al. 2005), which is 5 times less than what we detect. Note, however, that the G7 system is ≈ 10 times overdense compared to typical regions at this red- shift. Consequently, UV photons from the seven LAEs likely contribute significantly to the emission. Their combined SFR, derived from UV continuum flux, is about four times higher than expected from the cos- mic SFR density at this redshift (Madau & Dickinson 2014), within their comoving volume. This excess radia- tion field could explain the observed surface brightness. Without considering this excess radiation in over- dense fields, Bacon et al. (2021) proposed that UV- faint galaxies could significantly contribute to the ex- tended Lyα emission. Their figure 14 suggests that a LF with a slope of ≲ −1.8 integrated down to a Lyα luminosity of 0 or ≈ 1037 erg s−1 , can result in a SB of ≈ 10−19 erg s−1 cm−2 arcsec−2 . Similarly, Guo et al. (2024) proposed that high-z LAEs often have multiple satellite companions that might be the sources of the extended emission ≥ 50 pkpc. Although in our case a boost of the UV background by a factor similar to the galaxy overdensity is sufficient to explain the extended emission, we cannot exclude a contribution from fainter galaxies below the detection limit as this depends on the assumptions about the unknown faint end of the LAE luminosity function in such environments. Deeper observations and broader sky coverage with MUSE are essential for uncovering further insights into this intrigu- ing cosmic structure. ACKNOWLEDGEMENT We thank the anonymous referee for their useful sug- gestions. We thank Marijke Segers, Lorrie Straka, and Monica Turner for their early contributions to the MUSEQuBES project. EB and SM thank Raghunathan Srianand for useful suggestions. This work has used IU- CAA HPC facilities. We gratefully acknowledge the Eu- ropean Research Council (ERC) for funding this project through the Indo-Italian grant. We thank Vikram Khaire and Abhisek Mohapatra for useful discussions. This paper uses the following software: NumPy (Har- ris et al. 2020), SciPy (Virtanen et al. 2020), Mat- plotlib (Hunter 2007) and AstroPy (Astropy Collabo- ration et al. 2013). REFERENCES Asplund, M., Grevesse, N., Sauval, A. J., & Scott, P. 2009, ARA&A, 47, 481, doi: 10.1146/annurev.astro.46.060407.145222 Astropy Collaboration, Robitaille, T. P., Tollerud, E. J., et al. 2013, A&A, 558, A33, doi: 10.1051/0004-6361/201322068 Bacon, R., Accardo, M., Adjali, L., et al. 2010, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 7735, Ground-based and Airborne Instrumentation for Astronomy III, ed. I. S. McLean, S. K. Ramsay, & H. Takami, 773508, doi: 10.1117/12.856027 Bacon, R., Mary, D., Garel, T., et al. 2021, A&A, 647, A107, doi: 10.1051/0004-6361/202039887 Banerjee, E., Muzahid, S., Schaye, J., Johnson, S. D., & Cantalupo, S. 2023, MNRAS, 524, 5148, doi: 10.1093/mnras/stad2022 Banerjee, E., Muzahid, S., Schaye, J., et al. 2024, arXiv e-prints, arXiv:2411.11959. https://arxiv.org/abs/2411.11959 Borisova, E., Cantalupo, S., Lilly, S. J., et al. 2016, ApJ, 831, 39, doi: 10.3847/0004-637X/831/1/39 Cantalupo, S., Arrigoni-Battaia, F., Prochaska, J. X., Hennawi, J. F., & Madau, P. 2014, Nature, 506, 63, doi: 10.1038/nature12898
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