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Context: Sulphur is one of the most abundant elements in the Universe (S/H ∼ 1.3 × 10 -5 ) and plays a crucial role in biological systems on Earth. The understanding of its chemistry is therefore of major importance., Aims: Our goal is to complete the inventory of S-bearing molecules and their abundances in the prototypical photodissociation region (PDR) the Horsehead nebula to gain insight into sulphur chemistry in UV irradiated regions. Based on the WHISPER (Wide-band High-resolution Iram-30m Surveys at two positions with Emir Receivers) millimeter (mm) line survey, our goal is to provide an improved and more accurate description of sulphur species and their abundances towards the core and PDR positions in the Horsehead., Methods: The Monte Carlo Markov Chain (MCMC) methodology and the molecular excitation and radiative transfer code RADEX were used to explore the parameter space and determine physical conditions and beam-averaged molecular abundances., Results: A total of 13 S-bearing species (CS, SO, SO 2 , OCS, H 2 CS - both ortho and para - HDCS, C 2 S, HCS + , SO + , H 2 S, S 2 H, NS and NS + ) have been detected in the two targeted positions. This is the first detection of SO + in the Horsehead and the first detection of NS + in any PDR. We find a differentiated chemical behaviour between C-S and O-S bearing species within the nebula. The C-S bearing species C 2 S and o-H 2 CS present fractional abundances a factor of > two higher in the core than in the PDR. In contrast, the O-S bearing molecules SO, SO 2 , and OCS present similar abundances towards both positions. A few molecules, SO + , NS, and NS + , are more abundant towards the PDR than towards the core, and could be considered as PDR tracers., Conclusions: This is the first complete study of S-bearing species towards a PDR. Our study shows that CS, SO, and H 2 S are the most abundant S-bearing molecules in the PDR with abundances of ∼ a few 10 -9 . We recall that SH, SH + , S, and S + are not observable at the wavelengths covered by the WHISPER survey. At the spatial scale of our observations, the total abundance of S atoms locked in the detected species is < 10 -8 , only ∼0.1% of the cosmic sulphur abundance.

GEMS is an IRAM 30m Large Program whose aim is determining the elemental depletions and the ionization fraction in a set of prototypical star-forming regions. This paper presents the first results from the prototypical dark cloud TMC 1. Extensive millimeter observations have been carried out with the IRAM 30m telescope (3 mm and 2 mm) and the 40m Yebes telescope (1.3 cm and 7 mm) to determine the fractional abundances of CO, HCO + , HCN, CS, SO, HCS + , and N 2 H + in three cuts which intersect the dense filament at the well-known positions TMC 1-CP, TMC 1-NH3, and TMC 1-C, covering a visual extinction range from A V ~ 3 to ~20 mag. Two phases with differentiated chemistry can be distinguished: i) the translucent envelope with molecular hydrogen densities of 1-5×10 3 cm -3 ; and ii) the dense phase, located at A V > 10 mag, with molecular hydrogen densities >10 4 cm -3 . Observations and modeling show that the gas phase abundances of C and O progressively decrease along the C + /C/CO transition zone (A V ~ 3 mag) where C/H ~ 8×10 -5 and C/O~0.8-1, until the beginning of the dense phase at A V ~ 10 mag. This is consistent with the grain temperatures being below the CO evaporation temperature in this region. In the case of sulfur, a strong depletion should occur before the translucent phase where we estimate a S/H ~ (0.4 - 2.2) ×10 -6 , an abundance ~7-40 times lower than the solar value. A second strong depletion must be present during the formation of the thick icy mantles to achieve the values of S/H measured in the dense cold cores (S/H ~8×10 -8 ). Based on our chemical modeling, we constrain the value of ζ H 2 to ~ (0.5 - 1.8) ×10 -16 s -1 in the translucent cloud.

Context: Previous attempts at segmenting molecular line maps of molecular clouds have focused on using position-position-velocity data cubes of a single molecular line to separate the spatial components of the cloud. In contrast, wide field spectral imaging over a large spectral bandwidth in the (sub)mm domain now allows one to combine multiple molecular tracers to understand the different physical and chemical phases that constitute giant molecular clouds (GMCs)., Aims: We aim at using multiple tracers (sensitive to different physical processes and conditions) to segment a molecular cloud into physically/chemically similar regions (rather than spatially connected components), thus disentangling the different physical/chemical phases present in the cloud., Methods: We use a machine learning clustering method, namely the Meanshift algorithm, to cluster pixels with similar molecular emission, ignoring spatial information. Clusters are defined around each maximum of the multidimensional Probability Density Function (PDF) of the line integrated intensities. Simple radiative transfer models were used to interpret the astrophysical information uncovered by the clustering analysis., Results: A clustering analysis based only on the J = 1 - 0 lines of three isotopologues of CO proves suffcient to reveal distinct density/column density regimes ( n H ~ 100 cm -3 , ~ 500 cm -3 , and > 1000 cm -3 ), closely related to the usual definitions of diffuse, translucent and high-column-density regions. Adding two UV-sensitive tracers, the J = 1 - 0 line of HCO + and the N = 1 - 0 line of CN, allows us to distinguish two clearly distinct chemical regimes, characteristic of UV-illuminated and UV-shielded gas. The UV-illuminated regime shows overbright HCO + and CN emission, which we relate to a photochemical enrichment effect. We also find a tail of high CN/HCO + intensity ratio in UV-illuminated regions. Finer distinctions in density classes ( n H ~ 7 × 10 3 cm -3 ~ 4 × 10 4 cm -3 ) for the densest regions are also identified, likely related to the higher critical density of the CN and HCO + (1 - 0) lines. These distinctions are only possible because the high-density regions are spatially resolved., Conclusions: Molecules are versatile tracers of GMCs because their line intensities bear the signature of the physics and chemistry at play in the gas. The association of simultaneous multi-line, wide-field mapping and powerful machine learning methods such as the Meanshift clustering algorithm reveals how to decode the complex information available in these molecular tracers.