JuanCastillo29/space-mission-intelligence-data huggingface dataset:
, M. F., Vincent, J. B., et al.
E. 2016, The International Archives of the
2016, MNRAS, 462, S78,
Photogrammetry, Remote Sensing and Spatial
doi: 10.1093/mnras/stw2179
Information Sciences, 41, 357

Agarwal, J., Della Corte, V., Feldman, P.

D., et al. 2017, Monthly Notices of the Royal Astronomical Society, 469, s606 Barrington, M.

N., Birch, S.

P.

D., Jindal, A., et al. 2023, Journal of Geophysical Research: Planets, 128, e2022JE007723, doi: 10.1029/2022JE007723 Cambianica, P., Fulle, M., Cremonese, G., et al. 2020, A&A, 636, A91, doi: 10.1051/0004-6361/202037485 De Sanctis, M.

C., Capaccioni, F., Ciarniello, M., et al. 2015, Nature, 525, 500, doi: 10.1038/nature14869 El-Maarry, M.

R., Groussin, O., Thomas, N., et al. 2017, Science, 355, 1392 Fornasier, S., Hoang, V., Hasselmann, P., et al. 2019, Astronomy & Astrophysics, 630, A7 Fornasier, S., Hoang, V.

H., Hasselmann, P.

H., et al. 2019, A&A, 630, A7, doi: 10.1051/0004-6361/201833803 Gr¨un, E., Agarwal, J., Altobelli, N., et al. 2016, MNRAS, 462, S220, doi: 10.1093/mnras/stw2088 Hansen, K.

C., Altwegg, K., Berthelier, J.

J., et al. 2016, MNRAS, 462, S491, doi: 10.1093/mnras/stw2413 Hasselmann, P.

H., Barucci, M.

A., Fornasier, S., et al. 2019, Astronomy & Astrophysics, 630, A8 Hu, X., & Shi, X. 2021, The Astrophysical Journal, 910, 10, doi: 10.3847/1538-4357/abddbf Hu, X., Shi, X., Sierks, H., et al. 2017a, Astronomy & Astrophysics, 604, A114, doi: 10.1051/0004-6361/201629910 —. 2017b, Monthly Notices of the Royal Astronomical Society, 469, S295, doi: 10.1093/mnras/stx1607 Keller, H.

U., Barbieri, C., Lamy, P., et al. 2007, Space Science Reviews, 128, 433, doi: 10.1007/s11214-006-9128-4 Keller, H.

U., Mottola, S., Hviid, S.

F., et al. 2017, Monthly Notices of the Royal Astronomical Society, 469, S357 Knollenberg, J., Lin, Z.

Y., Hviid, S.

F., et al. 2016, Astronomy & Astrophysics, 596, A89, doi: 10.1051/0004-6361/201527744 Michikami, T., Honda, C., Miyamoto, H., et al. 2019, Icarus, 331, 179, doi: 10.1016/j.icarus.2019.05.019
19 Miyamoto, H., Yano, H., Scheeres, D.

J., et al. 2007, Science, 316, 1011, doi: 10.1126/science.1134390 M¨uller, D.

R., Altwegg, K., Berthelier, J.-J., et al. 2024, MNRAS, 529, 2763, doi: 10.1093/mnras/stae622 Nishiyama, G., Kawamura, T., Namiki, N., et al. 2021, Journal of Geophysical Research (Planets), 126, e06594, doi: 10.1029/2020JE006594 Oklay, N., Sunshine, J.
: 10.1126/science.1134390 M¨uller, D.

R., Altwegg, K., Berthelier, J.-J., et al. 2024, MNRAS, 529, 2763, doi: 10.1093/mnras/stae622 Nishiyama, G., Kawamura, T., Namiki, N., et al. 2021, Journal of Geophysical Research (Planets), 126, e06594, doi: 10.1029/2020JE006594 Oklay, N., Sunshine, J.

M., Pajola, M., et al. 2016, Monthly Notices of the Royal Astronomical Society, 462, S394 O’Rourke, L., Heinisch, P., Blum, J., et al. 2020, Nature, 586, 697, doi: 10.1038/s41586-020-2834-3 Pajola, M., Vincent, J.-B., G¨uttler, C., et al. 2015, Astronomy & Astrophysics, 583, A37 Pajola, M., H¨ofner, S., Vincent, J.-B., et al. 2017, Nature Astronomy, 1, 0092 Pfeifer, M., Agarwal, J., & Schr¨oter, M. 2022, A&A, 659, A171, doi: 10.1051/0004-6361/202141953 Pommerol, A., Thomas, N., El-Maarry, M.

R., et al. 2015, Astronomy and Astrophysics, 583, A25, doi: 10.1051/0004-6361/201525977 Preusker, F., Scholten, F., Matz, K.-D., et al. 2017, Astronomy & Astrophysics, 607, L1, doi: 10.1051/0004-6361/201731798 Richardson, J.

E., Steckloff, J.

K., & Minton, D.

A. 2020, Icarus, 347, 113811, doi: 10.1016/j.icarus.2020.113811 Rubin, M., Altwegg, K., Berthelier, J.-J., et al. 2023, Monthly Notices of the Royal Astronomical Society, 526, 4209, doi: 10.1093/mnras/stad3005 Shi, X., Hu, X., Sierks, H., et al. 2016, Astronomy & Astrophysics, 586, A7 Shi, X., Hu, X., Mottola, S., et al. 2018, Nature Astronomy, 2, 562, doi: 10.1038/s41550-018-0481-5 Shi, X., Hu, X., Agarwal, J., et al. 2024, The Astrophysical Journal Letters, 961, L16 Sierks, H., Barbieri, C., Lamy, P.

L., et al. 2015, Science, 347, aaa1044, doi: 10.1126/science.aaa1044 Skorov, Y.

V., Rezac, L., Hartogh, P., Bazilevsky, A.

T., & Keller, H.

U. 2016, A&A, 593, A76, doi: 10.1051/0004-6361/201628365
al. 2024, The Astrophysical Journal Letters, 961, L16 Sierks, H., Barbieri, C., Lamy, P.

L., et al. 2015, Science, 347, aaa1044, doi: 10.1126/science.aaa1044 Skorov, Y.

V., Rezac, L., Hartogh, P., Bazilevsky, A.

T., & Keller, H.

U. 2016, A&A, 593, A76, doi: 10.1051/0004-6361/201628365

20 Taylor, M., Altobelli, N., Buratti, B., & Choukroun, M. 2017, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 375, 20160262 Thomas, N., Davidsson, B., El-Maarry, M. R., et al. 2015, A&A, 583, A17, doi: 10.1051/0004-6361/201526049 Thomas, N., El Maarry, M. R., Theologou, P., et al. 2018, Planet. Space Sci., 164, 19, doi: 10.1016/j.pss.2018.05.019
Tubiana, C., G¨uttler, C., Kovacs, G., et al. 2015, Astronomy & Astrophysics, 583, A46, doi: 10.1051/0004-6361/201525985 Vincent, J.-B., Birch, S., Hayes, A., et al. 2019, in EPSC-DPS Joint Meeting 2019, Vol. 2019, EPSC–DPS2019–502 Vincent, J.-B., Bodewits, D., Besse, S., et al. 2015, Nature, 523, 63 Vincent, J.-B., Oklay, N., Pajola, M., et al. 2016a, Astronomy & Astrophysics, 587, A14 Vincent, J.-B., A’Hearn, M. F., Lin, Z.-Y., et al. 2016b, Monthly Notices of the Royal Astronomical Society, 462, S184

APPENDIX

[Figure: Figure 11. Effective surface slope in the areas surrounding the migrating boulder. The effective surface slope was defined as the angle between the negative surface normal direction and the vector of surface acceleration that consists of both gravitational and rotational accelerations. (a) Surface slope in the Khonsu region. (b) The slope between the boulder’s original location and new location.]

[Figure: Figure 12. Original boulder position and mini-outburst projected onto the NAC image of 2016 June 11, UTC 05:11. Position of pre-migration boulder is represented in red dots created from vertices of its shape model, while the orange markers show the reprojection of night-time dust emission in 2015 October 3, UTC 19:30.]

[Figure: Figure 13. The sequence MTP021 STP076 OUTBURST 003, observations for the prolonged activity after migration event in October 4.]

[Figure: Figure 14. Mini-outburst on 2015 October 4, and its possible correlation with the boulder. (a) Actual observation taken at UTC 19:16 with long exposure time to reveal dust activity and possible outbursts on 67P. (b) Enlarged view of part of the image in the red frame in (a), showing a mini-outburst from the unilluminated smooth area near the “pancake” feature. Red circle indicates the possible source of the minioutburst being its brightest spot. (c) Synthetic image showing the same part as (b). Artificial lighting is applied to reveal all surface features. The red circle is at the same position as in (b), which coincides with the position of the boulder after migration.]

[Figure: Figure 15. OSIRIS NAC image taken on 2016 September 20, showing a ∼60 m boulder in Hatmehit region. On top of this boulder, several smaller meter-scale boulders are observed, and two of which are indicated by arrows.]
aInstitute for Astronomy, University of Edinburgh,, Royal Observatory, Edinburgh, EH9 3HJ, United Kingdom bSpace Science and Technology Centre, Curtin University, GPO Box U1987, Perth, 6845, WA, Australia cInternational Centre for Radio Astronomy Research, Curtin University, GPO Box U1987, Perth, 6845, WA, Australia dPhysics Department, United States Naval Academy, Annapolis, 21402, MD, United States eFacultad di Ingenier´ıa y Ciencias, Universidad Diego Portales, Av. Ej´ercito 441, Santiago, Chile fPlanetary Science Institute, 1700 E Fort Lowell Rd STE 106, Tucson, 85719, AZ, United States
We present VLT/MUSE observations of comet 67P/Churyumov-Gerasimenko during its 2021 perihelion passage, from which we generated simultaneous maps of dust, [OI], C2, NH2, and CN comae across 12 pre- and post-perihelion epochs. These maps reveal the evolutionary and compositional trends of 67P’s coma and further enrich the context and findings of ESA’s Rosetta mission. Dust and gas species displayed distinct structures, where NH2 and CN signals were uniquely associated with known dust fans, raising the question of possible correlation to the dust and contributions of extended sources. Localised fitted NH2 scale lengths were 1.5-1.9× larger than those fitted for the rest of the coma, which is consistent with an extended source component for northern pre-perihelion emissions. In the southern hemisphere, CN was correlated with a prominent and sharp dust structure, potentially revealing an extended source origin via larger dust particles that preserve the CN parent species, as evidenced by higher spectral slopes in the region. Gas maps
Email address: brian.murphy@ed.ac.uk (Brian P. Murphy)
Preprint submitted to Planetary and Space Science July 21, 2025 depicted two distinct evolutionary regimes:

(1) evolving H2O ([OI]1D) and C2 emissions driven by nucleus sublimation and subsolar insolation, and (2) stable NH2 and CN emissions associated with seasonal dynamics and possible distributed sources. Dust spectral slope maps revealed spectral slope trends consistent with Rosetta findings, while green/red [OI] ratios generally indicate a coma dominated by H2O.
Keywords: Comets, Comet Coma, Comet Dust, IFU Spectroscopy, Ground-based Observations
Comets are among the most well-preserved remnants of the early Solar System, and encapsulate the primordial conditions present 4.56 billion years ago. These icy bodies, composed of refractory solids, volatile ices, and organic compounds, can therefore provide crucial insights into the primordial solar nebula and the evolution of the Solar System until present. From 2014-2016, the European Space Agency’s Rosetta Orbiter investigated the Jupiter Family Comet (JFC) 67P/Churyumov-Gerasimenko, hereafter referred to as 67P, and sought to unravel this formation history. Rosetta operated in close proximity to the comet (10s-100s of kilometres), and probed the volatile and refractory inventories, bulk composition, diurnal and seasonal evolution, nucleus topography, inner coma chemistry and composition, and inner coma morphologies across roughly a third of 67P’s 2015 apparition (Taylor et al., 2017). The orbiter carried 11 instruments and the Philae lander, which itself carried another 9 independent instruments for surface and subsurface investigation. Most important to this work are the orbiter instruments that pertained to coma composition and dust particle classification. Rosetta detected vast reservoirs of volatile parent species such as H2O, CO, CO2, NH3, C2H2, C2H6, CH4, ammoniated salts and more, see Table 2 of Rubin et al. (2019); Altwegg et al. (2020); Poch et al. (2020) and references therein, in proximity to 67P using Alice (Stern et al., 2007), ROSINA (Rosetta Orbiter Spectrometer for Ion and Neutral Analysis) (Balsiger et al., 2007), VIRTIS (Visible and Thermal Infrared Thermal Imaging Spectrometer) (Coradini et al., 2007), and OSIRIS (Optical, Spectroscopic, and Infrared Remote Imaging System) (Keller et al., 2007) instruments. Additional classification of several major dust regimes and their compositions were determined (G¨uttler et al., 2019) through the GIADA (Grain Impact Analyser and Dust Accumulator)
icantly with heliocentric distance (Raghuram and Bhardwaj, 2014). Direct
relations between HCN (parent) and CN (daughter) have been established
by Combi and Delsemme (1980); Fray et al. (2005), however, recent work in
H¨anni et al. (2020) demonstrate that HCN sublimating directly from the nu-
cleus cannot be responsible for all the CN signal observed by Rosetta, hinting
however, recent studies have suggested more complex photochemical path-
ways for its production that are currently not well-defined (Weiler, 2012). Ad-
CHON particles, or volatile-bearing dusts. Finally, Rosetta revealed that the nucleus of 67P is distinctly anisotropic and comprised of two unique hemispheres and two diverse lobes, joined by a volatile-rich neck (Thomas et al., 2015). Sierks et al. (2015) showed that the majority of H2O activity around perihelion originated from this neck region, designated Hapi. However, dispersed activity sources were detected across the northern hemisphere pre-perihelion, and do sustain substantial hyper-volatile sublimation post-perihelion (Le Roy et al., 2015). Seasonal effects were observed to have one of the greatest influences on surface activity and coma composition, due to the 58◦rotational obliquity and the rapid onset of an intense southern summer in the 6-months bounding perihelion. This short and highly energetic southern summer has been identified as the likely cause of the pronounced compositional and topographical dichotomy between the northern and southern hemispheres (Filacchione et al., 2022). The southern summer is also accepted as the driver of global mass transport and resurfacing processes across the northern hemisphere (Keller et al., 2017), and is responsible for large-scale chemical evolution in the coma, such as the enhanced southern production of CN post-equinox (Opitom et al., 2017). Understanding these seasonal dynamics is essential for interpreting the evolution of the coma around perihelion. In this study, we present the results derived from our MUSE dataset in the context of the Rosetta mission, focusing on the isolation of simultaneous spectral features within the coma of comet 67P. These include dust particle regimes, the C2 d3Πg–a3Πu (0,0) Swan band (∼5100 ˚A), NH2 ˜A2A1
– ˜X2B1 bands (∼6000-7400 ˚A), the 5577.339, 6300.304, 6363.776 ˚A [OI] lines, and CN A2Π–X2Σ+ (1,0) Red band (∼9100-9300 ˚A) in our datacubes. Utilizing these concurrent molecular maps, we investigated the spatial distribution within the middle and outer coma, identified evolutionary trends pre- and post-perihelion, and uncovered novel relationships and correlations that enhance the understanding of 67P and results of the Rosetta mission.
[Table: Table 1: Observing Conditions throughout the 67P 2016 and 2021 Apparitions VLT/MUSE Campaigns]

We observed 67P with MUSE on 13 epochs from 15 May 2021 to 09 March 2022. MUSE collected three-dimensional spatio-spectral datacubes, which spanned two spatial (x,y) and one spectral (λ) dimensions. We also retrieved archival MUSE observations of 67P from 03 to 07 March 2016, observed while the Rosetta mission was still active at 67P. We utilised MUSE in the wide field mode (WFM) without adaptive optics, covering 4750 to 9350 ˚A with an average resolving power of R ∼3000 (Bacon et al., 2010). For 2021 observations of 67P, we exposed MUSE for 600 seconds using non-sidereal tracking, which ensured proper centring and a sufficient signal-to-noise ratio (SNR). We grouped the 600-second exposures into roughly one-hour-long observational blocks, consisting of an Object-Sky-Object-Object-Sky-Object pattern. Occasionally, the observational sequence was interrupted due to poor conditions. The second block of 27 September 2021 was interrupted, so only an additional partial Object-Sky-Object sequence was collected. Similarly, poor conditions interrupted the sequence on 10 January 2022, with only a single Object exposure being recorded. We included the partial sequence from 27 September in our analysis, however, rejected the 10 January observation due to low quality and low signal-to-noise. The sky observations were also exposed for 120 seconds and were positioned 5 to 10 arcminutes away from 67P to limit contamination from the extended coma, and were also coadded into 4-minute-long blocks. To ensure comprehensive coverage, we dithered and rotated by 90◦counter-clockwise between exposures, which minimised instrumental artefacts. Finally, the observatory staff conducted standard star observations used in the data reduction pipeline, which were exposed for only 120 seconds because of their higher intrinsic flux. The 2016 67P MUSE observations were conducted similarly; however, no rotations were applied between exposures and no sky observations were performed (Opitom et al., 2020). Conditions for both observational periods can be found in Table 1. The 2021-2022 MUSE observations were executed as a filler programme, therefore the observing conditions were suboptimal. For some analyses, these conditions (lunar distance, lunar illumination, thick clouds, etc.) caused significant contamination to the cubes, which reduced signal-to-noise and influenced spectral slope. We do not include these observations in our analysis.
2.1. Data Reduction We reduced the raw datacubes using the standard ESO Reflex automated data reduction pipeline, defined in Freudling et al. (2013) and Weilbacher 7 et al.
from the dust coma, which dominated the observed flux. The dust is non-radiative in the visible regime, so the bulk dust continuum originates from reflected solar light. This reflected solar light still carries signatures intrinsic to the Sun, such as a broad blackbody peak around 6000 ˚A and solar absorption features like the well-documented Fraunhofer lines (Fraunhofer, 1817; Kirchhoff and Bunsen, 1861). To correct for this dust and solar continuum, and isolate the 9

CN
C2 [OI]1S [OI]1D NH2

[Figure: Figure 2: The workflow from initial spectrum to final molecular species isolation and extraction. Top panel: illustration of the telluric correction processing, where the grey line is the raw input spectrum, extracted in ρ=10000 km aperture for this figure on 30 September 2021, the red line is the atmospheric transmission model spectrum, and the black line is the final telluric corrected spectrum. Middle panel: continuum isolation and subtraction processing. The grey line is the telluric correct spectrum of 67P from the upper panel. In practice, this process was completed for each of the ∼96,000 spectra across 48 datacubes, resulting in 4.6 million corrected gaseous spectra. The black line is the final continuum-subtracted spectrum, in which notable gas emission remains. Bottom panels: the residual gas emissions following the dust and solar continuum subtraction, increasing in wavelength from left to right. Red line denotes an ideal subtraction, meaning that if no gas were present the continuum-subtracted spectrum would be the same as this line. Black line is the gas and sky emissions. Highlighted regions correspond to the gas flux summed to create the molecular species maps. The colour of the highlight roughly corresponds to the colour of the species maps, in Figures 5 through 8.]

gas emission lines and bands, we implement an updated correction workflow that was first outlined in (Opitom et al., 2020). Therein, Opitom et al. (2020) analysed MUSE data from the 2016 67P apparition, and attempted to extract gas emissions from the datacubes. The observations were conducted at a heliocentric distance of 2.5 au, and no notable gas emissions were detected above the noise level present, so the spectrum is solely representative of the dust coma and solar reflectance. We leveraged this 2016 reference dust spectrum to isolate the gas emissions in the updated dust and solar continuum subtraction process, outlined below.
we were still over- and under-subtracting the dust and solar contributions by ∼1-5%, so we pivoted to a new technique for the C2 Swan band and Red CN bands. We created new datacubes that corresponded to 5000-5300 ˚A (C2 Swan), and 9000-9300 ˚A (Red CN), 11 selected two gas-free regions on either side of the bands, fit a 1D polynomial to extract the shape and slope, subtracted the dust and solar continuum, and extracted the residual C2 and CN emissions, separately.

This resulted in a near-perfect subtraction, and was also used for the 5577.339, 6300.304, and 6363.776 ˚A [OI] lines. To probe the environment of the dust coma, and the relative size of particles, we sought to extract maps of the spectral slope from each dust continuum datacube (devoid of gas emission). We first rebinned the λ axis (or dispersion axis) by 50 ˚A to decrease the impact of localised deviations, cropped the spectrum to 5000-7000 ˚A, and fit a 1D polynomial to each spectrum in each datacube. We took the fitted slope value, converted to units of %/1000 ˚A, and cross-checked the value with the slope calculated from the extrema of the rebinned spectrum, using the equation (1) below:
S′ = (F2 −F1) (7000 −5000) ∗ 1
∗10000 (1)
F6000
where S’ is the spectral slope, F2 and F1 are the median fluxes in a ± 200 ˚A window at 7000 ˚A and 5000 ˚A respectively, F6000 is the median flux in a ± 200 ˚A window around 6000 ˚A, and 10000 is the conversion factor to %/1000 ˚A (Jewitt and Meech, 1986). If both slope measurements were consistent, then the fitted slope was saved into the final slope map. If not, the slope was rejected and the pixel was masked. Finally, to increase our SNR in the gas emission datacubes, we rebinned them by a factor of 3 to 8 spatially to enhance the detections of C2, NH2, and CN before extracting spatial maps, described in subsection 2.3. The dust and [OI] datacubes remained at their original sampling. The spectral slope maps were also rebinned by a factor of 3 before extraction.
2.3. Image and Map Processing This section describes the image extraction and processing techniques implemented in the original data cubes, gas emission data cubes, and molecular species maps. To isolate each of the molecular species and dust, we collapsed the gas emission datacubes and original datacubes around specific wavelengths, respectively. For the gas emissions, we summed C2 from 5125-5167 ˚A, NH2 peaks at 5973-5982 ˚A, 5991-6001 ˚A, and 6016-6026 ˚A, and CN peaks at 9138.16-9147.7 ˚A, 9165.18-9174.7 ˚A, and 9185.1-9194.76 ˚A. Similarly, we summed the 5577.339 ˚A [OI] line from 5574.9-5581.2 ˚A, 6300.304 ˚A deviations from the summed radial profile that are caused by underlying dust features.
We present the morphological and spectroscopic measurements, calculations, and results, as well as the methods used to perform the requisite analyses, in the following sections.
3.1. Morphology of Dust and Gas Using methods most recently described in Murphy et al. (2023b) and Murphy et al. (2023a), we tracked the evolving dust and gas morphology throughout the entire observational dataset.
3.1.1. Dust Morphology In our first enhanced observations, on 15 May 2021, we found a strong dust signature that extended from a central condensation to around ρ=10000 kilometres in the sunward direction (Northeast), seen in panel (I) of Figure 3. We denote this structure as the first fan of our observations, Fan A. To the West (anti-sunward direction) a long tail was prominent and extended beyond our FoV of ρ=70000 km. The northern profile of the tail was very diffuse with a sharp and distinct southern boundary. The primary axial component of this fan was between the sky-projected northern pole of 67P and sunward angle, around 55◦NE from reference frame north (RFN).

I. II. III.
IV. V. VI.
VII. VIII. IX.
X. XI. XII.

[Figure: Figure 3: Enhanced dust maps corresponding to the 7080-7120 ˚A wavelength range, from 15 May 2021 to 09 March 2022. Red arrow denotes sky projection of the Northern pole orientation (2016 Rosetta solution). Lime arrow is the velocity vector. Yellow arrow is the Sun angle. North is up and East is left, denoted by white arrows. Concentric dashed rings denote projected cometocentric distance, ρ, as projected onto the sky, in units of 103 kilometres. No rebinning has been applied.]