4MOST: Project overview and information for the First Call for Proposals

Jong, Roelof S. de; Agertz, Oscar; Berbel, Alex Agudo; Aird, James; Alexander, D. M.; Amarsi, A. M.; Anders, F.; Andrae, R.; Ansarinejad, Behzad; Ansorge, Wolfgang R.; Antilogus, P.; Anwand-Heerwart, H.; Arentsen, Anke; Arnadottir, Anna; Asplund, Martin; Auger, Matthew W.; Azais, Nicolas; Baade, D.; Baker, G. Derwood; Baker, Susanne; Balbinot, E.; Baldry, I. K.; Banerji, M.; Barden, S.; Barklem, P. S.; Barthelemy-Mazot, Eleonore; Battistini, C.; Bauer, Svend M.; Bell, C.P.M.; Bellido-Tirado, Olga; Bellstedt, Sabine; Belokurov, V.; Bensby, T.; Bergemann, M.; Bestenlehner, J. M.; Bielby, R. M.; Bilicki, Maciej; Blake, Chris; Bland-Hawthorn, J.; Boeche, C.; Boland, Wilfried; Böller, Thomas; Bongard, S.; Bongiorno, Angelo; Bonifacio, P.; Boudon, Didier; Brooks, Donald B.; Brown, Malcolm J.; Brown, Ryan; Brüggen, M.; Brynnel, Joar; Brzeski, Jurek; Buchert, T.; Buschkamp, P.; Caffau, E.; Caillier, Patrick; Carrick, Jonathan; Casagrande, Luca; Case, Scott W.; Casey, Andrew R.; Cesarini, Isabella; Cescutti, G.; Chapuis, Diane; Chiappini, Cristina; Childress, M.; Christlieb, N.; Church, Ross P.; Cioni, M. R. L.; Cluver, Michelle; Colless, Matthew; Collett, Thomas E.; Comparat, Johan; Cooper, Andrew P.; Couch, Warrick J.; Courbin, F.; Croom, S. M.; Croton, Darren J.; Daguisé, Eric; Dalton, Gavin; Davies, Luke J. M.; Davis, Tamara M.; Laverny, P. de; Deason, Alis J.; Dionies, Frank; Disseau, K.; Doel, P.; Döscher, D.; Driver, Simon P.; Dwelly, T.; Eckert, D.; Edge, A. C.; Edvardsson, B.; Youssoufi, Dalal El; Elhaddad, Ahmed; Enke, H.; Erfanianfar, G.; Farrell, Tony; Fechner, Thomas; Feiz, C.; Feltzing, Sofia; Ferreras, Isabelle; Feuerstein, Dietrich; Feuillet, D.; Finoguenov, A.; Ford, David N.; Fotopoulou, S.; Fouesneau, M.; Frenk, Carlos S.; Frey, Steffen; Gäessler, Wolfgang; Geier, S.; Fusillo, Nicola Gentile; Gerhard, Ortwin; Giannantonio, T.; Giannone, Domenico; Gibson, Brad K.; Gillingham, Peter; González-Fernández, C.; González-Solares, E.; Göttloeber, S.; Gould, A.; Grebel, Eva K.; Gueguen, A.; Guiglion, G.; Haehnelt, Martin G.; Hahn, T.; Hansen, Christopher J.; Hartman, Henrik; Hauptner, Katja; Hawkins, Keith; Haynes, Dionne; Haynes, Rachel; Heiter, U.; Helmi, Amina; Aguayo, C. Hernandez; Hewett, Paul C.; Hinton, Scott; Hobbs, D.; Hoenig, S.; Hofman, Daniel; Hook, I. M.; Hopgood, James R.; Hopkins, A. M.; Hourihane, A.; Howes, L.; Howlett, Cullan; Huet, T. R.; Irwin, M. J.; Iwert, Olaf; Jablonka, P.; Jähn, Thomas; Jahnke, K.; Jarno, Aurélien; Jin, Sun; Jofré, P.; Johl, Diana; Jones, Damien; Jönsson, Henrik; Jordan, Colm; Karovicova, I.; Khalatyan, A.; Kelz, Andreas; Kennicutt, R. C.; King, David L.; Kitaura, Francisco-Shu; Klar, J.; Klauser, Urs; Kneib, Jean-Paul; Koch, A.; Koposov, S. E.; Kordopatis, G.; Korn, A. J.; Kosmalski, Johan; Kotak, R.; Kovalev, M.; Kreckel, Kathryn; Kripak, Yevgen; Krumpe, M.; Kuijken, Konrad; Kunder, Andrea; Kushniruk, Iryna; Lam, Man I; Lamer, G.; Laurent, Florence; Lawrence, Jon; Lehmitz, Michael; Lemasle, B.; Lewis, Johnathan; Li, B.; Lidman, C.; Lind, Karin; Liske, J.; Lizon, J. L.; Loveday, Jon; Ludwig, H.‐G.; McDermid, Richard M.; Maguire, K.; Mainieri, V.; Mali, Slavko; Mandel, H.; Mandel, Kaisey S.; Mannering, Liz; Martell, Sarah L.; Delgado, David Martínez; Matijevič, G.; McGregor, Helen; McMahon, R. G.; McMillan, Paul J.; Mena, Olga; Merloni, A.; Meyer, M.; Michel, C.; Micheva, Genoveva; Migniau, Jean-Emmanuel; Minchev, Ivan; Monari, Giacomo; Müller, Robert; Murphy, Douglas; Muthukrishna, Daniel; Nandra, K.; Navarro, Ramón; Ness, Melissa; Nichani, Vijay; Nichol, R.; Nicklas, H.; Niederhofer, Florian; Norberg, Peder; Obreschkow, Danail; Oliver, S.; Owers, Matt S.; Pai, Naveen; Pankratow, S.; Parkinson, David; Parry, Ian; Paschke, J.; Paterson, Richard; Pécontal, A.; Phillips, David F.; Pillepich, Annalisa; Pinard, L.; Pirard, Jean-François; Piskunov, Nikolai; Plank, V.; Plüschke, D.; Pons, E.; Popesso, Paola; Power, Chris; Pragt, J.; Pramskiy, A.; Pryer, Daniel; Quattri, M.; Queiroz, Anna Bárbara de Andrade; Quirrenbach, A.; Rahurkar, Swara; Raichoor, Anand; Ramstedt, S.; Rau, A. R. P.; Recio–Blanco, A.; Reiss, Roland; F., Renaud,; Revaz, Yves; Rhode, P.; Richard, Johan; Richter, Ansgar; Rix, H. W.; Robotham, Aaron S. G.; Roelfsema, R.; Romaniello, M.; Rosario, D. J.; Rothmaier, F.; Roukema, Boudewijn F.; Ruchti, Gregory; Rupprecht, G.; Rybizki, Jan; Ryde, Nils; Saar, A.; Sadler, E. M.; Sahlén, Martin; Salvato, M.; Sassolas, B.; Saunders, Will; Saviauk, Allar; Sbordone, L.; Schmidt, Terry C.; Schnurr, O.; Scholz, R.‐D.; Schwope, A. D.; Seifert, W.; Shanks, T.; Sheinis, Andrew; Sivov, T.; Skúladóttir, Ása; Smartt, S. J.; Smedley, Scott; Smith, Gretchen; Smith, Rebecca K.; Sorce, Jenny G.; Spitler, Lee; Starkenburg, Else; Steinmetz, Matthias; Stilz, Ingo; Storm, J.; Sullivan, M.; Sutherland, William J.; Swann, E.; Tamone, Amélie; Taylor, Edward N.; Teillon, Julien; Tempel, Elmo; Horst, Reiner; Thi, Wing-Fai; Tolstoy, Eline; Trager, S. C.; Traven, G.; Tremblay, P.‐E.; Tresse, L.; Valentini, M.; Weygaert, Rien van de; Ancker, M. E. van den; Veljanoski, J.; Venkatesan, Suki; Wagner, L; Wagner, K.; Walcher, C. J.; Waller, Lew; Walton, N. A.; Wang, L.; Winkler, Roland; Wisotzki, L.; Worley, C. C.; Worseck, G.; Xiang, Maosheng; Xu, Wei; Yong, David; Zhao, Cheng; Zheng, Rong; Zscheyge, Florian; Zucker, D. B.

doi:10.18727/0722-6691/5117

Cited by 251 publications

(30 citation statements)

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“…Although hypothetical, Section 3.2.2 of Howlett et al (2017c) demonstrates that the expected target density (∼ 900 deg −2 ) would be comparable to DESI BGS (Ruiz-Macias et al 2020). It is approximately one magnitude deeper than the above proposed 4MOST Hemisphere Survey, but could be considered as either a rough combination of this and other confirmed 4MOST Consortium surveys (de Jong et al 2019;Driver et al 2019;Finoguenov et al 2019;Merloni et al 2019;Richard et al 2019), or a possible extension to these towards the end of the LSST timeline. Overall, the authors predict that approximately 160, 000 SNe Ia could be detected by LSST that will have host redshifts from the overlapping redshift sample.…”

Section: Lsst and J < 19 Samplementioning

confidence: 71%

Using peculiar velocity surveys to constrain neutrino masses

Whitford,

Howlett,

Davis

2021

Preprint

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The presence of massive neutrinos in the early Universe is expected to have influenced the observed distribution of galaxies, and their observed motions. In this work, we explore whether measurements of galaxy peculiar velocities could allow us to improve upon neutrino mass constraints from galaxy redshift surveys alone. Using Fisher matrix forecasts, we show that the galaxy peculiar motions do contain information on the sum of the masses of neutrinos 𝑚 𝜈 , and that this information can be used to improve upon constraints that may be obtained from low-redshift galaxy surveys (𝑧 < 0.5) combined with Planck measurements of the Cosmic Microwave Background. Compared to the full constraining power offered by Planck and higher redshift DESI/Euclid data, we find that the benefit of including peculiar velocities only marginally improves neutrino mass constraints. However, when one is limited to modeling only quasi-linear scales, adds additional degrees of freedom via a varying number of effective neutrino species (𝑁 eff ), or does not include information from Planck, our results show that the inclusion of peculiar velocity measurements can still substantially improve upon the constraints. As such, we demonstrate that it may be possible to achieve upper bounds on 𝑚 𝜈 (𝜎 𝑚 𝜈 = 0.051 eV; 68% confidence limit) comparable to those from Planck, from spectroscopic galaxy surveys alone, as long as the peculiar velocity data is available. This could allow for an independent comparison between constraints on 𝑚 𝜈 from the early-and late-time Universe probes that are particularly timely given current cosmological parameter tensions.

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Section: Lsst and J < 19 Samplementioning

confidence: 71%

Using peculiar velocity surveys to constrain neutrino masses

Whitford,

Howlett,

Davis

2021

Preprint

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“…Before we can assess the potential success of our science goals, we have to first consider the practical capabilities of 4MOST. 4MOST-TiDES is one of ten consortium surveys (de Jong et al 2019), each with its own individual objectives. In the context of this paper, we particularly consider TiDES' science goals (i) spectroscopic classification of live transients (TiDES-SN) and (ii) spectroscopy of supernova host galaxies (TiDES-Hosts) (Swann et al 2019).…”

Section: Future Ground-based Supernova Surveysmentioning

confidence: 99%

“…The follow-up potential with 4MOST is determined by its survey overlap (both angular and temporal) with LSST's observing strategy, its cadence, and TiDES' allocated 250000 fibrehours. 4MOST will be conducting multiple surveys with different science goals simultaneously (de Jong et al 2019). TiDES will be 'piggy-backing' on other surveys and will not be driving where to point 4MOST or for how long.…”

Section: Introductionmentioning

confidence: 99%

Optimising a magnitude-limited spectroscopic training sample for photometric classification of supernovae

Carrick,

Hook,

Swann

et al. 2020

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In preparation for photometric classification of transients from the Legacy Survey of Space and Time (LSST) we run tests with different training data sets. Using estimates of the depth to which the 4-metre Multi-Object Spectroscopic Telescope (4MOST) Time Domain Extragalactic Survey (TiDES) can classify transients, we simulate a magnitude-limited sample reaching 𝑟 AB ≈ 22.5 mag. We run our simulations with the software , a photometric classification pipeline using machine learning. The machine-learning algorithms struggle to classify supernovae when the training sample is magnitude-limited, in contrast to representative training samples. Classification performance noticeably improves when we simulate adding just a few additional fainter supernovae to the magnitude-limited training sample; average area under ROC curve (AUC) score over 10 runs increases from 0.554 to 0.760 for a k-nearest neighbours (KNN) algorithm. By creating new, artificial light curves using the augmentation software , we achieve a purity in our classified sample of 95 per cent using an artificial neural network, with completeness ≈ 0.4 in 9 out of 10 runs. We also reach a highest average AUC score of 0.962 with KNN. Our results are a proof of concept that augmentation is a crucial requirement in optimisation of a 4MOST spectroscopic training sample. However, to create the optimal training sample and achieve the best classification results, it is necessary to have at least a few 'true' faint supernovae to complement our magnitude-limited sample before augmenting.

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“…As a typical disc galaxy, the Milky Way (MW) is an ideal laboratory to test galaxy formation and evolution scenarios with the advantage that the individual stars in the MW can be well resolved. A number of completed or ongoing largescale spectroscopic surveys, e.g., RAVE (Steinmetz et al 2006), SEGUE (Yanny et al 2009), LAMOST Experiment for Galactic Understanding and Exploration (LEGUE; Deng et al 2012;Liu et al 2014;Zhao et al 2012), Gaia-ESO (Gilmore et al 2012), Galactic Archaeology with HERMES (GALAH; De Silva et al 2015), Apache Point Observatory Galactic Evolution Experiment (APOGEE; Majewski et al 2017), Gaia Radial Velocity Spectrometer (Cropper et al 2018), and the MMT H3 survey (Conroy et al 2019) -as well as the upcoming surveys such as SDSS-V (Kollmeier et al 2017), 4MOST (Feltzing et al 2018;de Jong et al 2019), and WEAVE (Dalton et al 2014), have propelled studies of the structure, stellar populations and the chemical and dynamic evolution of the MW. Stellar atmospheric parameters and elemental abundances of stars could be accurately derived from high-to-low resolution spectra collected by those massive spectroscopic surveys.…”

Section: Introductionmentioning

confidence: 99%

The value-added catalogue for LAMOST DR8 low-resolution spectra

Wang,

Huang,

Yuan

et al. 2022

Preprint

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We present a value-added catalog containing stellar parameters estimated from 7.10 million lowresolution spectra for 5.16 million unique stars with spectral signal-to-noise ratios (SNRs) higher than 10 obtained by the Large Sky Area Multi-Object Fibre Spectroscopic Telescope (LAMOST) Galactic spectroscopic surveys. The catalog presents values of stellar atmospheric parameters (effective temperature T eff , surface gravity log g, metallicity [Fe/H]/[M/H]), α-element to metal abundance ratio [α/M], carbon and nitrogen to iron abundance ratios [C/Fe] and [N/Fe] and 14 bands' absolute magnitudes deduced from LAMOST spectra using the method of neural network. The spectro-photometric distances of those stars are also provided based on the distance modulus. For stars with spectral SNRs larger than 50, precisions of T eff , log g, [Fe/H], [M/H], [C/Fe], [N/Fe] and [α/M] are 85 K, 0.098 dex, 0.05 dex, 0.05 dex, 0.052 dex, 0.082 dex and 0.027 dex, respectively. The errors of 14 band's absolute magnitudes are 0.16-0.22 mag for stars with spectral SNRs larger than 50. The spectro-photometric distance is accurate to 8.5% for stars with spectral SNRs larger than 50, and is more accurate than geometrical distance for stars with distance larger than 2.0 kpc. Our estimates of [Fe/H] are reliable down to [Fe/H] ∼ −3.5 dex, significantly better than previous results. The catalog provide 26,868 unique very metal poor star candidates ([Fe/H] ≤ −2.0). The catalog would be a valuable data set to study the structure and evolution of the Galaxy, especially the solar-neighbourhood and the outer disc.

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4MOST: Project overview and information for the First Call for Proposals

Cited by 251 publications

References 0 publications

Using peculiar velocity surveys to constrain neutrino masses

Using peculiar velocity surveys to constrain neutrino masses

Optimising a magnitude-limited spectroscopic training sample for photometric classification of supernovae

The value-added catalogue for LAMOST DR8 low-resolution spectra

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