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The formation of dust in the dense circumstellar medium of the bright supernova 2010jl is at first rapid and produces very large grains, which resist destruction, whereas later the dust production rate increases, meaning its source is ejecta; this links early and late dust mass evolution in supernovae with dense circumstellar media. A supernova source for large dust grains Dust grains are found practically throughout the Universe and are crucial to galactic evolution, planet formation and much else. Yet it is still not clear where all this dust comes from and how it survives in the harsh environments of star-forming galaxies. Recent work suggested that dust might form in supernova remnants, although subsequent observations of the bright supernova SN 2010jl proved inconclusive. Christa Gall et al . now report spectrographic observations of SN 2010jl consistent with the rapid (40–240 days) formation of dust in its dense circumstellar medium. The wavelength-dependent extinction of this dust points to the presence of very large grains, greater than a micrometre in diameter, that resist destruction. At later times (around 500–900 days), near-infrared thermal emissions suggest an accelerated growth in dust mass. The origin of dust in galaxies is still a mystery 1 , 2 , 3 , 4 . The majority of the refractory elements are produced in supernova explosions, but it is unclear how and where dust grains condense and grow, and how they avoid destruction in the harsh environments of star-forming galaxies. The recent detection of 0.1 to 0.5 solar masses of dust in nearby supernova remnants 5 , 6 , 7 suggests in situ dust formation, while other observations reveal very little dust in supernovae in the first few years after explosion 1 , 8 , 9 , 10 . Observations of the spectral evolution of the bright SN 2010jl have been interpreted as pre-existing dust 11 , dust formation 12 , 13 or no dust at all 14 . Here we report the rapid (40 to 240 days) formation of dust in its dense circumstellar medium. The wavelength-dependent extinction of this dust reveals the presence of very large (exceeding one micrometre) grains, which resist destruction 15 . At later times (500 to 900 days), the near-infrared thermal emission shows an accelerated growth in dust mass, marking the transition of the dust source from the circumstellar medium to the ejecta. This provides the link between the early and late dust mass evolution in supernovae with dense circumstellar media.

The origin of dust in galaxies is still a mystery(1-4). The majority of the refractory elements are produced in supernova explosions, but it is unclear how and where dust grains condense and grow, and how they avoid destruction in the harsh environments of star-forming galaxies. The recent detection of 0.1 to 0.5 solar masses of dust in nearby supernova remnants(5-7) suggests in situ dust formation, while other observations reveal very little dust in supernovae in the first few years after explosion(1,8,10). Observations of the spectral evolution of the bright SN 2010j1 have been interpreted as pre-existing dust(11), dust formationlz(12,13) or no dust at all(14). Here we report the rapid (40 to 240 days) formation of dust in its dense circumstellar medium. The wavelength-dependent extinction of this dust reveals the presence of very large (exceeding one micrometre) grains, which resist destruction(15). At later times (500 to 900 days), the near-infrared thermal emission shows an accelerated growth in dust mass, marking the transition of the dust source from the circumstellar medium to the ejecta. This provides the link between the early and late dust mass evolution in supernovae with dense circumstellar media.

The Carnegie Supernova Project-II (CSP-II) was an NSF-funded, four-year program to obtain optical and near-infrared observations of a “Cosmology” sample of ∼100 Type Ia supernovae located in the smooth Hubble flow (0.03 ≲ z ≲ 0.10). Light curves were also obtained of a “Physics” sample composed of 90 nearby Type Ia supernovae at z ≤ 0.04 selected for near-infrared spectroscopic timeseries observations. The primary emphasis of the CSP-II is to use the combination of optical and near-infrared photometry to achieve a distance precision of better than 5%. In this paper, details of the supernova sample, the observational strategy, and the characteristics of the photometric data are provided. In a companion paper, the near-infrared spectroscopy component of the project is presented.

The Carnegie Supernova Project-II (CSP-II) was an NSF-funded, four-year program to obtain optical and near-infrared observations of a \"Cosmology\" sample of ∼100 Type Ia supernovae located in the smooth Hubble flow (0.03 z 0.10). Light curves were also obtained of a \"Physics\" sample composed of 90 nearby Type Ia supernovae at z ≤ 0.04 selected for near-infrared spectroscopic timeseries observations. The primary emphasis of the CSP-II is to use the combination of optical and near-infrared photometry to achieve a distance precision of better than 5%. In this paper, details of the supernova sample, the observational strategy, and the characteristics of the photometric data are provided. In a companion paper, the near-infrared spectroscopy component of the project is presented.

The Carnegie Supernova Project-II (CSP-II) was an NSF-funded, four-year program to obtain optical and near-infrared observations of a \"Cosmology\" sample of ~100 Type Ia supernovae located in the smooth Hubble flow (0.03 $\\lesssim$ z $\\lesssim$ 0.10). Light curves were also obtained of a \"Physics\" sample composed of 90 nearby Type Ia supernovae at z ≤ 0.04 selected for near-infrared spectroscopic timeseries observations. The primary emphasis of the CSP-II is to use the combination of optical and near-infrared photometry to achieve a distance precision of better than 5%. Here in this paper, details of the supernova sample, the observational strategy, and the characteristics of the photometric data are provided. In a companion paper, the near-infrared spectroscopy component of the project is presented.

Motivated by recent observations suggesting that core-collapse supernovae may on average produce ~0.3 M_sun of dust, we explore a simple dust production scenario which applies to star-forming galaxies in the local environment (the Magellanic Clouds and possibly the Milky Way) as well as to high redshift (sub- millimeter, QSO, Lyman break) galaxies. We assume that the net dust destruction (due to supernova reverse shock, shocks in the interstellar medium, or astration) is negligible on a timescale of 1 Gyr, in which case the dust mass can be estimated as 0.004 times the star-formation rate (for a Chabrier IMF) multiplied by the duration of the star-formation episode. The model can account for observed dust masses over four orders of magnitude and across the redshift range 0-8.4, with dust production rates spanning five orders of magnitudes. This suggests that star-forming galaxies may be seen as maximally dusty, in the sense that a dominant fraction of the dust-forming elements forged in a supernova eventually will go into the solid phase. In turn, this indicates little destruction of supernova dust or almost complete replenishment, on a short time scale, of any dust that is destroyed.

Context. Determining properties of dust formed in and around supernovae from observations remains challenging. This may be due to either incomplete coverage of data in wavelength or time but also due to often inconspicuous signatures of dust in the observed data. Aims. Here we address this challenge using modern machine learning methods to determine the amount, composition and temperature of dust from a large set of simulated data. We aim to determine whether such methods are suitable to infer these properties from future observations of supernovae. Methods. We calculate spectral energy distributions (SEDs) of dusty shells around supernovae. We develop a neural network consisting of eight fully connected layers and an output layer with specified activation functions that allow us to predict the dust mass, temperature and composition and their respective uncertainties from each SED. We conduct a feature importance analysis via SHapley Additive exPlanations (SHAP) to find the minimum set of JWST filters required to accurately predict these properties. Results. We find that our neural network predicts dust masses and temperatures with a root-mean-square error (RMSE) of \\(\\sim\\) 0.12 dex and \\(\\sim\\) 38 K, respectively. Moreover, our neural network can well distinguish between the different dust species included in our work, reaching a classification accuracy of up to 95\\% for carbon and 99\\% for silicate dust. Conclusions. Our analysis shows that the JWST filters NIRCam F070W, F140M, F356W, F480M and MIRI F560W, F770W, F1000W, F1130W, F1500W, F1800W are likely the most important needed to determine the properties of dust formed in and around supernovae from future observations. We tested this on selected optical to infrared data of SN 1987A at 615 days past explosion and find good agreement with dust masses and temperatures inferred with standard fitting methods in the literature.

Removing cold interstellar medium (ISM) from a galaxy is central to quenching star formation. However, the exact mechanism of this process remains unclear. The objective of this work is to find the mechanism responsible for dust and gas removal in simulated early-type galaxies (ETGs). A statistically significant sample of massive (M_*>\\(10^{10}\\)M\\(_\\odot\\)), simulated ETG in a redshift range of 0.02--0.32 is studied in the context of its ISM properties. In particular, we investigate the cold dust and gas removal timescales, the cold gas inflows, and their relation with black hole (BH) mass. We also investigate the evolution of galaxies in the dust vs. star formation rate (SFR) plane and the influence of merger events. We find agreement with previous observational works considering the timescales of dust and HI removal from ETGs. When considering the dust-to-stellar mass ratio as a function of time in simulations, we recovered a similar decline as in the observational sample as a function of stellar age, validating its use for timing the ISM decline. Moreover, we recover the observed relation between dust mass and SFR for actively star-forming galaxies as well as for passive ETGs. We also show that starburst galaxies form their own sequence on the dust vs. SFR plot in a form \\(\\log(M_{\\rm dust, SB})= 0.913\\times \\log({\\rm SFR}) + 6.533\\) with \\(2\\sigma\\) scatter of 0.32. Finally, we find that type II supernova reverse shocks dominate the dust destruction at the early stages of ETG evolution, while at later times stellar feedback becomes more important. We show that merger events lead to morphological transformations by increasing the bulge-to-total stellar mass ratio followed by an increase in BH masses. The BH feedback resulting from radio mode accretion prevents the hot halo gas from cooling, indirectly leading to a decrease in the SFR.

The mechanism by which galaxies stop forming stars and get rid of their interstellar medium (ISM) remains elusive. Here, we study a sample of more than two thousand elliptical galaxies in which dust emission has been detected. This is the largest sample of such galaxies ever analysed. We infer the timescale for removal of dust in these galaxies and investigate its dependency on physical and environmental properties. We obtain a dust removal timescale in elliptical galaxies of \\(\\tau\\) = 2.26 \\(\\pm\\) 0.18 Gyr, corresponding to a half-life time of 1.57 \\(\\pm\\) 0.12 Gyr. This timescale does not depend on environment, stellar mass or redshift. We observe a departure of dusty elliptical galaxies from the star formation rate vs. dust mass relation. This is caused by the star-formation rates declining faster than the dust masses and indicates that there exists an internal mechanism, which affects star formation, but leaves the ISM intact. Morphological quenching together with ionisation or outflows caused by older stellar populations (supernova type Ia or planetary nebulae) are consistent with these observations.

Strongly lensed supernovae can be detected as multiply imaged or highly magnified transients. In order to compare the performances of these two observational strategies, we calculate expected discovery rates as a function of survey depth in five grizy filters and for different classes of supernovae (Ia, IIP, IIL, Ibc and IIn). We find that detections via magnification is the only effective strategy for relatively shallow pre-LSST surveys. For survey depths about the LSST capacity, both strategies yield comparable numbers of lensed supernovae. Supernova samples from the two methods are to a large extent independent and combining them increases detection rates by about 50 per cent. While the number of lensed supernovae detectable via magnification saturates at the limiting magnitudes of LSST, detection rates of multiply imaged supernova still go up drastically at increasing survey depth. Comparing potential discovery spaces, we find that lensed supernovae found via image multiplicity exhibit longer time delays and larger image separations making them more suitable for cosmological constraints than their counterparts found via magnification. We find that the ZTF will find about 2 type Ia and 4 core-collapse lensed supernovae per year at a limiting magnitude of 20.6 in the r band. Applying a hybrid method which combines searching for highly magnified or multiply imaged transients, we find that LSST will detect 89 type Ia and 254 core-collapse lensed supernovae per year. In all cases, lensed core-collapsed supernovae will be dominated by type IIn supernovae contributing to 80 per cent of the total counts, although this prediction relies quite strongly on the adopted spectral templates for this class of supernovae. Revisiting the case of the lensed supernova iPTF16geu, we find that it is consistent within the 2\\sigma contours of predicted redshifts and magnifications for the iPTF survey.