Starburst galaxies are often found to be the result of galaxy mergers. As a
result, galaxy mergers are often believed to lie above the galaxy main
sequence: the tight correlation between stellar mass and star formation rate.
Here, we aim to test this claim. Deep learning techniques are applied to images
from the Sloan Digital Sky Survey to provide visual-like classifications for
over 340 000 objects between redshifts of 0.005 and 0.1. The aim of this
classification is to split the galaxy population into merger and non-merger
systems and we are currently achieving an accuracy of 91.5%. Stellar masses and
star formation rates are also estimated using panchromatic data for the entire
galaxy population. With these preliminary data, the mergers are placed onto the
full galaxy main sequence, where we find that merging systems lie across the
entire star formation rate - stellar mass plane.Comment: 4 pages, 1 figure. For Proceedings IAU Symposium No. 34

Pearson, William J.

Petrillo, Carlo E.

Trayford, James

van der Tak, Floris F. S.

Wang, Lingyu

English

arXiv

Starburst galaxies are often found to be the result of galaxy mergers. As a result, galaxy mergers are often believed to lie above the galaxy main sequence: the tight correlation between stellar mass and star formation rate. Here, we aim to test this claim

University of Groningen

   University of GroningenDeep learning for galaxy mergers in the galaxy main sequencePearson, William J.; Wang, Lingyu; Trayford, James; Petrillo, Carlo E.; van der Tak, Floris F.S.Published in:Challenges in Panchromatic Modelling withNext Generation FacilitiesDOI:10.1017/S1743921319002187IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite fromit. Please check the document version below.Document VersionPublisher's PDF, also known as Version of recordPublication date:2020Link to publication in University of Groningen/UMCG research databaseCitation for published version (APA):Pearson, W. J., Wang, L., Trayford, J., Petrillo, C. E., & van der Tak, F. F. S. (2020). Deep learning forgalaxy mergers in the galaxy main sequence. In M. Boquien, E. Lusso, C. Gruppioni , & P. Tissera (Eds.),Challenges in Panchromatic Modelling withNext Generation Facilities: Proceedings (Vol. 15, pp. 104-108).(Proceedings of the International Astronomical Union ; Vol. 15, No. S341). Cambridge University Press.https://doi.org/10.1017/S1743921319002187CopyrightOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of theauthor(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons).The publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license.More information can be found on the University of Groningen website: https://www.rug.nl/library/open-access/self-archiving-pure/taverne-amendment.Take-down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons thenumber of authors shown on this cover page is limited to 10 maximum.Download date: 29-04-2023Challenges in Panchromatic Modelling withNext Generation FacilitiesProceedings IAU Symposium No. 341, 2018M. Boquien, E. Lusso, C. Gruppioni & P. Tissera, eds.c© International Astronomical Union 2020doi:10.1017/S1743921319002187Deep learning for galaxy mergers in thegalaxy main sequenceWilliam J. Pearson1,2, Lingyu Wang1,2, James Trayford3,Carlo E. Petrillo2 and Floris F. S. van der Tak1,21SRON Netherlands Institute for Space ResearchLandleven 12, 9747 AD, Groningen, The Netherlandsemail: w.j.pearson@sron.nl2Kapteyn Astronomical Institute, University of GroningenPostbus 800, 9700 AV Groningen, The Netherlands3Leiden Observatory, Leiden University, P.O. Box 9513, 2300 RA Leiden, The NetherlandsAbstract. Starburst galaxies are often found to be the result of galaxy mergers. As a result,galaxy mergers are often believed to lie above the galaxy main sequence: the tight correlationbetween stellar mass and star formation rate. Here, we aim to test this claim.Deep learning techniques are applied to images from the Sloan Digital Sky Survey to providevisual-like classifications for over 340 000 objects between redshifts of 0.005 and 0.1. The aim ofthis classification is to split the galaxy population into merger and non-merger systems and weare currently achieving an accuracy of 92.5%. Stellar masses and star formation rates are alsoestimated using panchromatic data for the entire galaxy population. With these preliminarydata, the mergers are placed onto the full galaxy main sequence, where we find that mergingsystems lie across the entire star formation rate - stellar mass plane.Keywords. methods: data analysis, galaxies: evolution, galaxies: interactions, galaxies:starburst, infrared: galaxies1. IntroductionGalaxy mergers are highly important for fully understanding how galaxies form andevolve, underpinning hierarchical growth models. Also, galaxy mergers are believed tobe the driving force behind some of the brightest infrared objects known (Sanders et al.1996). With bright infrared emission often comes high star formation rates (SFRs), hencethe argument that most mergers go through a starburst phase (e.g. Schewizer 2005).More recent studies have begun to show that merging systems may not always causestarbursts. Knapen et al. (2015) have found that the increase in SFR in merging galaxiesis at most a factor of two in the local universe, with the majority of galaxies showing noevidence of an enhance SFR or showing evidence of merger driven quenching. However,mergers can still cause starbursts with the majority of starbursts being caused by galaxymergers (Luo et al. 2014; Knapen et al. 2015; Cortijo-Ferrero et al. 2017).The main sequence of star forming galaxies (MS) is a tight correlation between thestellar mass (M) and SFR of star forming galaxies (e.g. Brinchmann et al. 2004, Noeskeet al. 2007; Speagle et al. 2014). The MS has been shown to be present out to at leastz = 6, although the presence, or not, of a high mass turnover is still debated (e.g. Tomczaket al. 2016; Pearson et al. 2018). Whatever the form it takes, the majority of galaxies arefound to lie on the MS, with high SFR objects and starbursts above and quenched orquiescent objects below. Thus, if all merging galaxies are starbursts, all merger galaxiesshould lie above the MS, otherwise they will be scattered around the SFR-M plane.104https://www.cambridge.org/core/terms. https://doi.org/10.1017/S1743921319002187Downloaded from https://www.cambridge.org/core. University of Groningen, on 12 Apr 2021 at 08:34:14, subject to the Cambridge Core terms of use, available atGalaxy mergers and the main sequence 105Deep learning neural networks are a rapidly growing technique that can be used toclassify galaxies. These techniques use layers of non-linear mathematical functions thatare inspired by the functioning of biological networks of neurons. Once properly trained,a neural network can classify thousands of galaxies in a fraction of the time it wouldtake a human, or team of humans, to perform the same task. Deep neural networks areincreasingly used in astronomy to perform tasks such as determining galaxy morphology(e.g. Huertas-Company et al. 2015), detecting lensed systems (e.g. Petrillo et al. 2017)and detecting galaxy mergers (e.g. Ackermann et al. 2018).2. Stellar Masses and Star Formation RatesTo populate the SFR-M plane, we need SFRs and Ms for the objects. For the SDSSdata release 7 galaxies with spectroscopic redshifts between 0.005 and 0.1, the five SDSSbands (ugriz) were run through CIGALE (Noll et al. 2009; Boquien et al. 2018) usingthe same configuration as Pearson et al. (2018) but removing the active galactic nucleicomponent to generate M, and U-V and V-J colour estimates. The SFR estimates werederived from the SDSS Hα spectral lines but still need to be corrected for extinction.We intend to improve the M and SFR estimates, from CIGALE, over the region ofSDSS that coincides with the Galaxy And Mass Assembly survey (GAMA; Driver et al.2009). The GAMA region contains data from the ultra-violet (UV) to the far-infrared(FIR), allowing better constraints on the stellar mass and the thermal dust emissionthan just using the five SDSS bands. We will improve the FIR data in the GAMA fieldsby following Pearson et al. (2017) to de-blend the Herschel SPIRE (Griffin et al. 2010)data. This involves running CIGALE with the UV to near-infrared data to predict theflux density in the three SPIRE bands and using these predictions as a prior in XID+(Hurley et al. 2017) to de-blend the SPIRE images. With more reliable Herschel SPIREdata from this technique, we can generate more reliable SFR estimates.3. Identifying MergersTo identify merging systems, a convolutional neural network (CNN) was employed.This type of neural network uses a series of kernels to identify features in the inputimage and create classifications, in this case if the galaxy is merging or not. The net-work used is based on the Dieleman et al. (2015) architecture with some changes to theparameters of the four convolutional layers and two fully connected layers, as well aschanging the input to be a 64×64 pixel, 3 colour channel image. To train the network,the 3003 Sloan Digital Sky Survey (SDSS; York et al. 2000) objects classified as mergingby Darg et al. (2010a,b) were used along with 3003 randomly selected galaxies that hadGalaxy Zoo merger classification rate of less than 0.2 and fell within the same redshiftrange (Lintott et al. 2011; Ackermann et al. 2018). After training, this resulted in anaccuracy (percentage of objects that are correctly classified) of 92.5%. The network wasthen applied to over 340 000 SDSS galaxies with redshifts between z = 0.005 and z = 0.1,categorising them in approximately two hours. Approximately 19% of these objects arepreliminarily classified as being merging systems, twice the merger fraction seen in otherobservational works (Man et al. 2016).4. The Main Sequence of Starforming GalaxiesTo find the trend of the MS for this data set, we follow Pearson et al. (2018). The qui-escent galaxies are split off using a UVJ colour cut and the 90% mass completeness limitdetermined empirically by following Pozzetti et al. (2010) and using predicted Ks-bandmagnitudes from CIGALE. A simple power law is fitted to the remaining objects usinga Markov Chain Monte Carlo routine to simultaneously fit the slope, normalisation andhttps://www.cambridge.org/core/terms. https://doi.org/10.1017/S1743921319002187Downloaded from https://www.cambridge.org/core. University of Groningen, on 12 Apr 2021 at 08:34:14, subject to the Cambridge Core terms of use, available at106 W. J. Pearson et al.(a)(b)Figure 1. M-SFR plane as a number density plot from low (light) to high (dark) with (a) allSDSS objects with detections (at any confidence level) of Hα and Hβ lines above the mass limit(vertical dashed line) and (b) only the objects from (a) that are identified as mergers. The solidline is the main sequence trend, with extrapolations to higher and lower mass as a dotted line.As can be seen, the merging systems closely follow the entire galaxy population, perhaps witha slight rise, and are not preferentially starbursting or quenched.scatter of the MS, taking into account the observational errors of both the SFR and M.The resulting trend is plotted in Fig. 1 as a solid line.To determine if all mergers are starbursts, we present two plots. Fig. 1a shows theSFR-M plane populated with all SDSS galaxies that have detections (at any confidencelevel) of Hα and Hβ lines above our mass completeness limit. As can be seen the MS trendruns through the high number density regions. In Fig. 1b we also plot the SFR-M planepopulated only objects in Fig. 1a that our CNN identifies as mergers. As can be seen,there is no excess of galaxies above the MS, as would be expected if every merger causeda starburst at the time of observation. Equally, there is no clumping of objects belowthe MS, showing no evidence that all mergers trigger quenching. Indeed, the mergingsystems follow the distribution of the entire galaxy population, perhaps with a slight risein SFR for the merger population. With this preliminary result, there is no evidence thatall merging systems are starbursts.5. Ongoing WorkWe are currently working to train a neural network with simulated SDSS images fromthe EAGLE simulation (Schaye et al. 2015). This network will be applied to the observedSDSS images, allowing us to test for possible biases and incompleteness in human clas-sification: galaxies that are classed by humans as merger or non-merger but are givenhttps://www.cambridge.org/core/terms. https://doi.org/10.1017/S1743921319002187Downloaded from https://www.cambridge.org/core. University of Groningen, on 12 Apr 2021 at 08:34:14, subject to the Cambridge Core terms of use, available atGalaxy mergers and the main sequence 107the opposite class by the EAGLE trained network. This also has applications for theupcoming large surveys, such as Euclid (Laureijs et al. 2011) and the Large SynopticSurvey Telescope (LSST Science Collaboration et al. 2009). If this technique proves tofunction as well as, or better than, an observationally trained network, it will reduce theneed to first create a sample of galaxies for humans to classify for training a network.The simulation trained network can immediately be applied to the data from the largesurveys and create data sets for science quickly.Similarly, the simulated images can be passed through the observation trained network.This will allow us to check that the simulation data are representative of the observeduniverse. Objects from simulations that are classified as mergers but the observationtrained network classifies as non-mergers can be identified. Understanding why theseobjects are rejected as mergers by observations could provide insight into shortcomingsof the simulations and inform future work simulating our Universe.ReferencesAckermann, S., Schawinski, K., Zhang, C., Weigel, A., & Turp, M. 2018, MNRAS, 479, 415Boquien, M., Burgarella, D., Roehlly, Y., et al. 2018, ArXiv arXiv:1811.03094Brinchmann, J., Charlot, S., White, S. D. M., et al. 2004, MNRAS, 351, 1151Cortijo-Ferrero, C., González Delgado, R. M., Pérez, E., et al. 2017, A&A, 607, A70Darg, D. W., Kaviraj, S., Lintott, C. J., et al. 2010a, MNRAS, 401, 1552Darg, D. W., Kaviraj, S., Lintott, C. J., et al. 2010b, MNRAS, 401, 1043Dieleman, S., Willett, K. W., & Dambre, J. 2015, MNRAS, 450, 1441Driver, S. P., Norberg, P., Baldry, I. K., et al. 2009, Astronomy and Geophysics, 50, 5.12Griffin, M. J., Abergel, A., Abreu, A., et al. 2010, A&A, 518, L3Huertas-Company, M., Gravet, R., Cabrera-Vives, G., et al. 2015, ApJS, 221, 8Hurley, P. D., Oliver, S., Betancourt, M., et al. 2017, MNRAS, 464, 885Knapen, J. H. & Cisternas, M. 2015, ApJ, 807, L16Knapen, J. H., Cisternas, M., & Querejeta, M. 2015, MNRAS, 454, 1742Laureijs, R., Amiaux, J., Arduini, S., et al. 2011, ArXiv arXiv:1110.3193Lintott, C., Schawinski, K., Bamford, S., et al. 2011, MNRAS, 410, 166LSST Science Collaboration, Abell, P. A., Allison, J., et al. 2009, ArXiv arXiv:0912.0201Luo, W., Yang, X., & Zhang, Y. 2014, ApJ, 789, L16Man, A. W. S., Zirm, A. W., & Toft, S. 2016, ApJ, 830, 89Noeske, K. G., Weiner, B. J., Faber, S. M., et al. 2007, ApJ, 660, L43Noll, S., Burgarella, D., Giovannoli, E., et al. 2009, A&A, 507, 1793Pearson, W. J., Wang, L., Hurley, P. D., et al. 2018, A&A, 615, A146Pearson, W. J., Wang, L., van der Tak, F. F. S., et al. 2017, A&A, 603, A102Petrillo, C. E., Tortora, C., Chatterjee, S., et al. 2017, MNRAS, 472, 1129Pozzetti, L., Bolzonella, M., Zucca, E., et al. 2010, A&A, 523, A13Sanders, D. B. & Mirabel, I. F. 1996, ARA&A, 34, 749Schaye, J., Crain, R. A., Bower, R. G., et al. 2015, MNRAS, 446, 521Schweizer, F. 2005, in Astrophysics and Space Science Library, Vol. 329, Starbursts: From 30Doradus to Lyman Break Galaxies, ed. R. de Grijs & R. M. González Delgado, 143Speagle, J. S., Steinhardt, C. L., Capak, P. L., & Silverman, J. D. 2014, ApJS, 214, 15Tomczak, A. R., Quadri, R. F., Tran, K.-V. H., et al. 2016, ApJ, 817, 118York, D. G., Adelman, J., Anderson, Jr., J. E., et al. 2000, AJ, 120, 1579DiscussionFabio Fontanot: What is the fraction of merging galaxies as a function of stellar mass?https://www.cambridge.org/core/terms. https://doi.org/10.1017/S1743921319002187Downloaded from https://www.cambridge.org/core. University of Groningen, on 12 Apr 2021 at 08:34:14, subject to the Cambridge Core terms of use, available at108 W. J. Pearson et al.William J. Pearson: This is not something we have looked at yet but it would beinteresting to see. (A quick look after the talk shows that the merger fraction decreasesas stellar mass increases).Minju Lee: It may be worth to separate the sample into two groups of gas-rich andgas-poor to test if gas-rich mergers cause SB while gas-poor don’t.William J. Pearson: Yes, it would indeed be interesting to split by gas-rich/poor tosee where they lie. I have not done this yet but would be interesting.https://www.cambridge.org/core/terms. https://doi.org/10.1017/S1743921319002187Downloaded from https://www.cambridge.org/core. University of Groningen, on 12 Apr 2021 at 08:34:14, subject to the Cambridge Core terms of use, available at

Deep learning for galaxy mergers in the galaxy main sequence

ARTS repository - University of Groningen

   University of GroningenDeep learning for galaxy mergers in the galaxy main sequencePearson, William J.; Wang, Lingyu; Trayford, James; Petrillo, Carlo E.; van der Tak, Floris F.S.Published in:Challenges in Panchromatic Modelling withNext Generation FacilitiesDOI:10.1017/S1743921319002187IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite fromit. Please check the document version below.Document VersionPublisher's PDF, also known as Version of recordPublication date:2020Link to publication in University of Groningen/UMCG research databaseCitation for published version (APA):Pearson, W. J., Wang, L., Trayford, J., Petrillo, C. E., & van der Tak, F. F. S. (2020). Deep learning forgalaxy mergers in the galaxy main sequence. In M. Boquien, E. Lusso, C. Gruppioni , & P. Tissera (Eds.),Challenges in Panchromatic Modelling withNext Generation Facilities: Proceedings (Vol. 15, pp. 104-108).(Proceedings of the International Astronomical Union ; Vol. 15, No. S341). Cambridge University Press.https://doi.org/10.1017/S1743921319002187CopyrightOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of theauthor(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons).The publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license.More information can be found on the University of Groningen website: https://www.rug.nl/library/open-access/self-archiving-pure/taverne-amendment.Take-down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons thenumber of authors shown on this cover page is limited to 10 maximum.Download date: 21-06-2022Challenges in Panchromatic Modelling withNext Generation FacilitiesProceedings IAU Symposium No. 341, 2018M. Boquien, E. Lusso, C. Gruppioni & P. Tissera, eds.c© International Astronomical Union 2020doi:10.1017/S1743921319002187Deep learning for galaxy mergers in thegalaxy main sequenceWilliam J. Pearson1,2, Lingyu Wang1,2, James Trayford3,Carlo E. Petrillo2 and Floris F. S. van der Tak1,21SRON Netherlands Institute for Space ResearchLandleven 12, 9747 AD, Groningen, The Netherlandsemail: w.j.pearson@sron.nl2Kapteyn Astronomical Institute, University of GroningenPostbus 800, 9700 AV Groningen, The Netherlands3Leiden Observatory, Leiden University, P.O. Box 9513, 2300 RA Leiden, The NetherlandsAbstract. Starburst galaxies are often found to be the result of galaxy mergers. As a result,galaxy mergers are often believed to lie above the galaxy main sequence: the tight correlationbetween stellar mass and star formation rate. Here, we aim to test this claim.Deep learning techniques are applied to images from the Sloan Digital Sky Survey to providevisual-like classifications for over 340 000 objects between redshifts of 0.005 and 0.1. The aim ofthis classification is to split the galaxy population into merger and non-merger systems and weare currently achieving an accuracy of 92.5%. Stellar masses and star formation rates are alsoestimated using panchromatic data for the entire galaxy population. With these preliminarydata, the mergers are placed onto the full galaxy main sequence, where we find that mergingsystems lie across the entire star formation rate - stellar mass plane.Keywords. methods: data analysis, galaxies: evolution, galaxies: interactions, galaxies:starburst, infrared: galaxies1. IntroductionGalaxy mergers are highly important for fully understanding how galaxies form andevolve, underpinning hierarchical growth models. Also, galaxy mergers are believed tobe the driving force behind some of the brightest infrared objects known (Sanders et al.1996). With bright infrared emission often comes high star formation rates (SFRs), hencethe argument that most mergers go through a starburst phase (e.g. Schewizer 2005).More recent studies have begun to show that merging systems may not always causestarbursts. Knapen et al. (2015) have found that the increase in SFR in merging galaxiesis at most a factor of two in the local universe, with the majority of galaxies showing noevidence of an enhance SFR or showing evidence of merger driven quenching. However,mergers can still cause starbursts with the majority of starbursts being caused by galaxymergers (Luo et al. 2014; Knapen et al. 2015; Cortijo-Ferrero et al. 2017).The main sequence of star forming galaxies (MS) is a tight correlation between thestellar mass (M) and SFR of star forming galaxies (e.g. Brinchmann et al. 2004, Noeskeet al. 2007; Speagle et al. 2014). The MS has been shown to be present out to at leastz = 6, although the presence, or not, of a high mass turnover is still debated (e.g. Tomczaket al. 2016; Pearson et al. 2018). Whatever the form it takes, the majority of galaxies arefound to lie on the MS, with high SFR objects and starbursts above and quenched orquiescent objects below. Thus, if all merging galaxies are starbursts, all merger galaxiesshould lie above the MS, otherwise they will be scattered around the SFR-M plane.104https://www.cambridge.org/core/terms. https://doi.org/10.1017/S1743921319002187Downloaded from https://www.cambridge.org/core. University of Groningen, on 12 Apr 2021 at 08:34:14, subject to the Cambridge Core terms of use, available atGalaxy mergers and the main sequence 105Deep learning neural networks are a rapidly growing technique that can be used toclassify galaxies. These techniques use layers of non-linear mathematical functions thatare inspired by the functioning of biological networks of neurons. Once properly trained,a neural network can classify thousands of galaxies in a fraction of the time it wouldtake a human, or team of humans, to perform the same task. Deep neural networks areincreasingly used in astronomy to perform tasks such as determining galaxy morphology(e.g. Huertas-Company et al. 2015), detecting lensed systems (e.g. Petrillo et al. 2017)and detecting galaxy mergers (e.g. Ackermann et al. 2018).2. Stellar Masses and Star Formation RatesTo populate the SFR-M plane, we need SFRs and Ms for the objects. For the SDSSdata release 7 galaxies with spectroscopic redshifts between 0.005 and 0.1, the five SDSSbands (ugriz) were run through CIGALE (Noll et al. 2009; Boquien et al. 2018) usingthe same configuration as Pearson et al. (2018) but removing the active galactic nucleicomponent to generate M, and U-V and V-J colour estimates. The SFR estimates werederived from the SDSS Hα spectral lines but still need to be corrected for extinction.We intend to improve the M and SFR estimates, from CIGALE, over the region ofSDSS that coincides with the Galaxy And Mass Assembly survey (GAMA; Driver et al.2009). The GAMA region contains data from the ultra-violet (UV) to the far-infrared(FIR), allowing better constraints on the stellar mass and the thermal dust emissionthan just using the five SDSS bands. We will improve the FIR data in the GAMA fieldsby following Pearson et al. (2017) to de-blend the Herschel SPIRE (Griffin et al. 2010)data. This involves running CIGALE with the UV to near-infrared data to predict theflux density in the three SPIRE bands and using these predictions as a prior in XID+(Hurley et al. 2017) to de-blend the SPIRE images. With more reliable Herschel SPIREdata from this technique, we can generate more reliable SFR estimates.3. Identifying MergersTo identify merging systems, a convolutional neural network (CNN) was employed.This type of neural network uses a series of kernels to identify features in the inputimage and create classifications, in this case if the galaxy is merging or not. The net-work used is based on the Dieleman et al. (2015) architecture with some changes to theparameters of the four convolutional layers and two fully connected layers, as well aschanging the input to be a 64×64 pixel, 3 colour channel image. To train the network,the 3003 Sloan Digital Sky Survey (SDSS; York et al. 2000) objects classified as mergingby Darg et al. (2010a,b) were used along with 3003 randomly selected galaxies that hadGalaxy Zoo merger classification rate of less than 0.2 and fell within the same redshiftrange (Lintott et al. 2011; Ackermann et al. 2018). After training, this resulted in anaccuracy (percentage of objects that are correctly classified) of 92.5%. The network wasthen applied to over 340 000 SDSS galaxies with redshifts between z = 0.005 and z = 0.1,categorising them in approximately two hours. Approximately 19% of these objects arepreliminarily classified as being merging systems, twice the merger fraction seen in otherobservational works (Man et al. 2016).4. The Main Sequence of Starforming GalaxiesTo find the trend of the MS for this data set, we follow Pearson et al. (2018). The qui-escent galaxies are split off using a UVJ colour cut and the 90% mass completeness limitdetermined empirically by following Pozzetti et al. (2010) and using predicted Ks-bandmagnitudes from CIGALE. A simple power law is fitted to the remaining objects usinga Markov Chain Monte Carlo routine to simultaneously fit the slope, normalisation andhttps://www.cambridge.org/core/terms. https://doi.org/10.1017/S1743921319002187Downloaded from https://www.cambridge.org/core. University of Groningen, on 12 Apr 2021 at 08:34:14, subject to the Cambridge Core terms of use, available at106 W. J. Pearson et al.(a)(b)Figure 1. M-SFR plane as a number density plot from low (light) to high (dark) with (a) allSDSS objects with detections (at any confidence level) of Hα and Hβ lines above the mass limit(vertical dashed line) and (b) only the objects from (a) that are identified as mergers. The solidline is the main sequence trend, with extrapolations to higher and lower mass as a dotted line.As can be seen, the merging systems closely follow the entire galaxy population, perhaps witha slight rise, and are not preferentially starbursting or quenched.scatter of the MS, taking into account the observational errors of both the SFR and M.The resulting trend is plotted in Fig. 1 as a solid line.To determine if all mergers are starbursts, we present two plots. Fig. 1a shows theSFR-M plane populated with all SDSS galaxies that have detections (at any confidencelevel) of Hα and Hβ lines above our mass completeness limit. As can be seen the MS trendruns through the high number density regions. In Fig. 1b we also plot the SFR-M planepopulated only objects in Fig. 1a that our CNN identifies as mergers. As can be seen,there is no excess of galaxies above the MS, as would be expected if every merger causeda starburst at the time of observation. Equally, there is no clumping of objects belowthe MS, showing no evidence that all mergers trigger quenching. Indeed, the mergingsystems follow the distribution of the entire galaxy population, perhaps with a slight risein SFR for the merger population. With this preliminary result, there is no evidence thatall merging systems are starbursts.5. Ongoing WorkWe are currently working to train a neural network with simulated SDSS images fromthe EAGLE simulation (Schaye et al. 2015). This network will be applied to the observedSDSS images, allowing us to test for possible biases and incompleteness in human clas-sification: galaxies that are classed by humans as merger or non-merger but are givenhttps://www.cambridge.org/core/terms. https://doi.org/10.1017/S1743921319002187Downloaded from https://www.cambridge.org/core. University of Groningen, on 12 Apr 2021 at 08:34:14, subject to the Cambridge Core terms of use, available atGalaxy mergers and the main sequence 107the opposite class by the EAGLE trained network. This also has applications for theupcoming large surveys, such as Euclid (Laureijs et al. 2011) and the Large SynopticSurvey Telescope (LSST Science Collaboration et al. 2009). If this technique proves tofunction as well as, or better than, an observationally trained network, it will reduce theneed to first create a sample of galaxies for humans to classify for training a network.The simulation trained network can immediately be applied to the data from the largesurveys and create data sets for science quickly.Similarly, the simulated images can be passed through the observation trained network.This will allow us to check that the simulation data are representative of the observeduniverse. Objects from simulations that are classified as mergers but the observationtrained network classifies as non-mergers can be identified. Understanding why theseobjects are rejected as mergers by observations could provide insight into shortcomingsof the simulations and inform future work simulating our Universe.ReferencesAckermann, S., Schawinski, K., Zhang, C., Weigel, A., & Turp, M. 2018, MNRAS, 479, 415Boquien, M., Burgarella, D., Roehlly, Y., et al. 2018, ArXiv arXiv:1811.03094Brinchmann, J., Charlot, S., White, S. D. M., et al. 2004, MNRAS, 351, 1151Cortijo-Ferrero, C., González Delgado, R. M., Pérez, E., et al. 2017, A&A, 607, A70Darg, D. W., Kaviraj, S., Lintott, C. J., et al. 2010a, MNRAS, 401, 1552Darg, D. W., Kaviraj, S., Lintott, C. J., et al. 2010b, MNRAS, 401, 1043Dieleman, S., Willett, K. W., & Dambre, J. 2015, MNRAS, 450, 1441Driver, S. P., Norberg, P., Baldry, I. K., et al. 2009, Astronomy and Geophysics, 50, 5.12Griffin, M. J., Abergel, A., Abreu, A., et al. 2010, A&A, 518, L3Huertas-Company, M., Gravet, R., Cabrera-Vives, G., et al. 2015, ApJS, 221, 8Hurley, P. D., Oliver, S., Betancourt, M., et al. 2017, MNRAS, 464, 885Knapen, J. H. & Cisternas, M. 2015, ApJ, 807, L16Knapen, J. H., Cisternas, M., & Querejeta, M. 2015, MNRAS, 454, 1742Laureijs, R., Amiaux, J., Arduini, S., et al. 2011, ArXiv arXiv:1110.3193Lintott, C., Schawinski, K., Bamford, S., et al. 2011, MNRAS, 410, 166LSST Science Collaboration, Abell, P. A., Allison, J., et al. 2009, ArXiv arXiv:0912.0201Luo, W., Yang, X., & Zhang, Y. 2014, ApJ, 789, L16Man, A. W. S., Zirm, A. W., & Toft, S. 2016, ApJ, 830, 89Noeske, K. G., Weiner, B. J., Faber, S. M., et al. 2007, ApJ, 660, L43Noll, S., Burgarella, D., Giovannoli, E., et al. 2009, A&A, 507, 1793Pearson, W. J., Wang, L., Hurley, P. D., et al. 2018, A&A, 615, A146Pearson, W. J., Wang, L., van der Tak, F. F. S., et al. 2017, A&A, 603, A102Petrillo, C. E., Tortora, C., Chatterjee, S., et al. 2017, MNRAS, 472, 1129Pozzetti, L., Bolzonella, M., Zucca, E., et al. 2010, A&A, 523, A13Sanders, D. B. & Mirabel, I. F. 1996, ARA&A, 34, 749Schaye, J., Crain, R. A., Bower, R. G., et al. 2015, MNRAS, 446, 521Schweizer, F. 2005, in Astrophysics and Space Science Library, Vol. 329, Starbursts: From 30Doradus to Lyman Break Galaxies, ed. R. de Grijs & R. M. González Delgado, 143Speagle, J. S., Steinhardt, C. L., Capak, P. L., & Silverman, J. D. 2014, ApJS, 214, 15Tomczak, A. R., Quadri, R. F., Tran, K.-V. H., et al. 2016, ApJ, 817, 118York, D. G., Adelman, J., Anderson, Jr., J. E., et al. 2000, AJ, 120, 1579DiscussionFabio Fontanot: What is the fraction of merging galaxies as a function of stellar mass?https://www.cambridge.org/core/terms. https://doi.org/10.1017/S1743921319002187Downloaded from https://www.cambridge.org/core. University of Groningen, on 12 Apr 2021 at 08:34:14, subject to the Cambridge Core terms of use, available at108 W. J. Pearson et al.William J. Pearson: This is not something we have looked at yet but it would beinteresting to see. (A quick look after the talk shows that the merger fraction decreasesas stellar mass increases).Minju Lee: It may be worth to separate the sample into two groups of gas-rich andgas-poor to test if gas-rich mergers cause SB while gas-poor don’t.William J. Pearson: Yes, it would indeed be interesting to split by gas-rich/poor tosee where they lie. I have not done this yet but would be interesting.https://www.cambridge.org/core/terms. https://doi.org/10.1017/S1743921319002187Downloaded from https://www.cambridge.org/core. University of Groningen, on 12 Apr 2021 at 08:34:14, subject to the Cambridge Core terms of use, available at

   University of GroningenDeep learning for galaxy mergers in the galaxy main sequencePearson, William J.; Wang, Lingyu; Trayford, James; Petrillo, Carlo E.; van der Tak, Floris F.S.Published in:Challenges in Panchromatic Modelling withNext Generation FacilitiesDOI:10.1017/S1743921319002187IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite fromit. Please check the document version below.Document VersionPublisher's PDF, also known as Version of recordPublication date:2020Link to publication in University of Groningen/UMCG research databaseCitation for published version (APA):Pearson, W. J., Wang, L., Trayford, J., Petrillo, C. E., & van der Tak, F. F. S. (2020). Deep learning forgalaxy mergers in the galaxy main sequence. In M. Boquien, E. Lusso, C. Gruppioni , & P. Tissera (Eds.),Challenges in Panchromatic Modelling withNext Generation Facilities: Proceedings (Vol. 15, pp. 104-108).(Proceedings of the International Astronomical Union ; Vol. 15, No. S341). Cambridge University Press.https://doi.org/10.1017/S1743921319002187CopyrightOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of theauthor(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons).The publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license.More information can be found on the University of Groningen website: https://www.rug.nl/library/open-access/self-archiving-pure/taverne-amendment.Take-down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons thenumber of authors shown on this cover page is limited to 10 maximum.Download date: 19-11-2022Challenges in Panchromatic Modelling withNext Generation FacilitiesProceedings IAU Symposium No. 341, 2018M. Boquien, E. Lusso, C. Gruppioni & P. Tissera, eds.c© International Astronomical Union 2020doi:10.1017/S1743921319002187Deep learning for galaxy mergers in thegalaxy main sequenceWilliam J. Pearson1,2, Lingyu Wang1,2, James Trayford3,Carlo E. Petrillo2 and Floris F. S. van der Tak1,21SRON Netherlands Institute for Space ResearchLandleven 12, 9747 AD, Groningen, The Netherlandsemail: w.j.pearson@sron.nl2Kapteyn Astronomical Institute, University of GroningenPostbus 800, 9700 AV Groningen, The Netherlands3Leiden Observatory, Leiden University, P.O. Box 9513, 2300 RA Leiden, The NetherlandsAbstract. Starburst galaxies are often found to be the result of galaxy mergers. As a result,galaxy mergers are often believed to lie above the galaxy main sequence: the tight correlationbetween stellar mass and star formation rate. Here, we aim to test this claim.Deep learning techniques are applied to images from the Sloan Digital Sky Survey to providevisual-like classifications for over 340 000 objects between redshifts of 0.005 and 0.1. The aim ofthis classification is to split the galaxy population into merger and non-merger systems and weare currently achieving an accuracy of 92.5%. Stellar masses and star formation rates are alsoestimated using panchromatic data for the entire galaxy population. With these preliminarydata, the mergers are placed onto the full galaxy main sequence, where we find that mergingsystems lie across the entire star formation rate - stellar mass plane.Keywords. methods: data analysis, galaxies: evolution, galaxies: interactions, galaxies:starburst, infrared: galaxies1. IntroductionGalaxy mergers are highly important for fully understanding how galaxies form andevolve, underpinning hierarchical growth models. Also, galaxy mergers are believed tobe the driving force behind some of the brightest infrared objects known (Sanders et al.1996). With bright infrared emission often comes high star formation rates (SFRs), hencethe argument that most mergers go through a starburst phase (e.g. Schewizer 2005).More recent studies have begun to show that merging systems may not always causestarbursts. Knapen et al. (2015) have found that the increase in SFR in merging galaxiesis at most a factor of two in the local universe, with the majority of galaxies showing noevidence of an enhance SFR or showing evidence of merger driven quenching. However,mergers can still cause starbursts with the majority of starbursts being caused by galaxymergers (Luo et al. 2014; Knapen et al. 2015; Cortijo-Ferrero et al. 2017).The main sequence of star forming galaxies (MS) is a tight correlation between thestellar mass (M) and SFR of star forming galaxies (e.g. Brinchmann et al. 2004, Noeskeet al. 2007; Speagle et al. 2014). The MS has been shown to be present out to at leastz = 6, although the presence, or not, of a high mass turnover is still debated (e.g. Tomczaket al. 2016; Pearson et al. 2018). Whatever the form it takes, the majority of galaxies arefound to lie on the MS, with high SFR objects and starbursts above and quenched orquiescent objects below. Thus, if all merging galaxies are starbursts, all merger galaxiesshould lie above the MS, otherwise they will be scattered around the SFR-M plane.104https://www.cambridge.org/core/terms. https://doi.org/10.1017/S1743921319002187Downloaded from https://www.cambridge.org/core. University of Groningen, on 12 Apr 2021 at 08:34:14, subject to the Cambridge Core terms of use, available atGalaxy mergers and the main sequence 105Deep learning neural networks are a rapidly growing technique that can be used toclassify galaxies. These techniques use layers of non-linear mathematical functions thatare inspired by the functioning of biological networks of neurons. Once properly trained,a neural network can classify thousands of galaxies in a fraction of the time it wouldtake a human, or team of humans, to perform the same task. Deep neural networks areincreasingly used in astronomy to perform tasks such as determining galaxy morphology(e.g. Huertas-Company et al. 2015), detecting lensed systems (e.g. Petrillo et al. 2017)and detecting galaxy mergers (e.g. Ackermann et al. 2018).2. Stellar Masses and Star Formation RatesTo populate the SFR-M plane, we need SFRs and Ms for the objects. For the SDSSdata release 7 galaxies with spectroscopic redshifts between 0.005 and 0.1, the five SDSSbands (ugriz) were run through CIGALE (Noll et al. 2009; Boquien et al. 2018) usingthe same configuration as Pearson et al. (2018) but removing the active galactic nucleicomponent to generate M, and U-V and V-J colour estimates. The SFR estimates werederived from the SDSS Hα spectral lines but still need to be corrected for extinction.We intend to improve the M and SFR estimates, from CIGALE, over the region ofSDSS that coincides with the Galaxy And Mass Assembly survey (GAMA; Driver et al.2009). The GAMA region contains data from the ultra-violet (UV) to the far-infrared(FIR), allowing better constraints on the stellar mass and the thermal dust emissionthan just using the five SDSS bands. We will improve the FIR data in the GAMA fieldsby following Pearson et al. (2017) to de-blend the Herschel SPIRE (Griffin et al. 2010)data. This involves running CIGALE with the UV to near-infrared data to predict theflux density in the three SPIRE bands and using these predictions as a prior in XID+(Hurley et al. 2017) to de-blend the SPIRE images. With more reliable Herschel SPIREdata from this technique, we can generate more reliable SFR estimates.3. Identifying MergersTo identify merging systems, a convolutional neural network (CNN) was employed.This type of neural network uses a series of kernels to identify features in the inputimage and create classifications, in this case if the galaxy is merging or not. The net-work used is based on the Dieleman et al. (2015) architecture with some changes to theparameters of the four convolutional layers and two fully connected layers, as well aschanging the input to be a 64×64 pixel, 3 colour channel image. To train the network,the 3003 Sloan Digital Sky Survey (SDSS; York et al. 2000) objects classified as mergingby Darg et al. (2010a,b) were used along with 3003 randomly selected galaxies that hadGalaxy Zoo merger classification rate of less than 0.2 and fell within the same redshiftrange (Lintott et al. 2011; Ackermann et al. 2018). After training, this resulted in anaccuracy (percentage of objects that are correctly classified) of 92.5%. The network wasthen applied to over 340 000 SDSS galaxies with redshifts between z = 0.005 and z = 0.1,categorising them in approximately two hours. Approximately 19% of these objects arepreliminarily classified as being merging systems, twice the merger fraction seen in otherobservational works (Man et al. 2016).4. The Main Sequence of Starforming GalaxiesTo find the trend of the MS for this data set, we follow Pearson et al. (2018). The qui-escent galaxies are split off using a UVJ colour cut and the 90% mass completeness limitdetermined empirically by following Pozzetti et al. (2010) and using predicted Ks-bandmagnitudes from CIGALE. A simple power law is fitted to the remaining objects usinga Markov Chain Monte Carlo routine to simultaneously fit the slope, normalisation andhttps://www.cambridge.org/core/terms. https://doi.org/10.1017/S1743921319002187Downloaded from https://www.cambridge.org/core. University of Groningen, on 12 Apr 2021 at 08:34:14, subject to the Cambridge Core terms of use, available at106 W. J. Pearson et al.(a)(b)Figure 1. M-SFR plane as a number density plot from low (light) to high (dark) with (a) allSDSS objects with detections (at any confidence level) of Hα and Hβ lines above the mass limit(vertical dashed line) and (b) only the objects from (a) that are identified as mergers. The solidline is the main sequence trend, with extrapolations to higher and lower mass as a dotted line.As can be seen, the merging systems closely follow the entire galaxy population, perhaps witha slight rise, and are not preferentially starbursting or quenched.scatter of the MS, taking into account the observational errors of both the SFR and M.The resulting trend is plotted in Fig. 1 as a solid line.To determine if all mergers are starbursts, we present two plots. Fig. 1a shows theSFR-M plane populated with all SDSS galaxies that have detections (at any confidencelevel) of Hα and Hβ lines above our mass completeness limit. As can be seen the MS trendruns through the high number density regions. In Fig. 1b we also plot the SFR-M planepopulated only objects in Fig. 1a that our CNN identifies as mergers. As can be seen,there is no excess of galaxies above the MS, as would be expected if every merger causeda starburst at the time of observation. Equally, there is no clumping of objects belowthe MS, showing no evidence that all mergers trigger quenching. Indeed, the mergingsystems follow the distribution of the entire galaxy population, perhaps with a slight risein SFR for the merger population. With this preliminary result, there is no evidence thatall merging systems are starbursts.5. Ongoing WorkWe are currently working to train a neural network with simulated SDSS images fromthe EAGLE simulation (Schaye et al. 2015). This network will be applied to the observedSDSS images, allowing us to test for possible biases and incompleteness in human clas-sification: galaxies that are classed by humans as merger or non-merger but are givenhttps://www.cambridge.org/core/terms. https://doi.org/10.1017/S1743921319002187Downloaded from https://www.cambridge.org/core. University of Groningen, on 12 Apr 2021 at 08:34:14, subject to the Cambridge Core terms of use, available atGalaxy mergers and the main sequence 107the opposite class by the EAGLE trained network. This also has applications for theupcoming large surveys, such as Euclid (Laureijs et al. 2011) and the Large SynopticSurvey Telescope (LSST Science Collaboration et al. 2009). If this technique proves tofunction as well as, or better than, an observationally trained network, it will reduce theneed to first create a sample of galaxies for humans to classify for training a network.The simulation trained network can immediately be applied to the data from the largesurveys and create data sets for science quickly.Similarly, the simulated images can be passed through the observation trained network.This will allow us to check that the simulation data are representative of the observeduniverse. Objects from simulations that are classified as mergers but the observationtrained network classifies as non-mergers can be identified. Understanding why theseobjects are rejected as mergers by observations could provide insight into shortcomingsof the simulations and inform future work simulating our Universe.ReferencesAckermann, S., Schawinski, K., Zhang, C., Weigel, A., & Turp, M. 2018, MNRAS, 479, 415Boquien, M., Burgarella, D., Roehlly, Y., et al. 2018, ArXiv arXiv:1811.03094Brinchmann, J., Charlot, S., White, S. D. M., et al. 2004, MNRAS, 351, 1151Cortijo-Ferrero, C., González Delgado, R. M., Pérez, E., et al. 2017, A&A, 607, A70Darg, D. W., Kaviraj, S., Lintott, C. J., et al. 2010a, MNRAS, 401, 1552Darg, D. W., Kaviraj, S., Lintott, C. J., et al. 2010b, MNRAS, 401, 1043Dieleman, S., Willett, K. W., & Dambre, J. 2015, MNRAS, 450, 1441Driver, S. P., Norberg, P., Baldry, I. K., et al. 2009, Astronomy and Geophysics, 50, 5.12Griffin, M. J., Abergel, A., Abreu, A., et al. 2010, A&A, 518, L3Huertas-Company, M., Gravet, R., Cabrera-Vives, G., et al. 2015, ApJS, 221, 8Hurley, P. D., Oliver, S., Betancourt, M., et al. 2017, MNRAS, 464, 885Knapen, J. H. & Cisternas, M. 2015, ApJ, 807, L16Knapen, J. H., Cisternas, M., & Querejeta, M. 2015, MNRAS, 454, 1742Laureijs, R., Amiaux, J., Arduini, S., et al. 2011, ArXiv arXiv:1110.3193Lintott, C., Schawinski, K., Bamford, S., et al. 2011, MNRAS, 410, 166LSST Science Collaboration, Abell, P. A., Allison, J., et al. 2009, ArXiv arXiv:0912.0201Luo, W., Yang, X., & Zhang, Y. 2014, ApJ, 789, L16Man, A. W. S., Zirm, A. W., & Toft, S. 2016, ApJ, 830, 89Noeske, K. G., Weiner, B. J., Faber, S. M., et al. 2007, ApJ, 660, L43Noll, S., Burgarella, D., Giovannoli, E., et al. 2009, A&A, 507, 1793Pearson, W. J., Wang, L., Hurley, P. D., et al. 2018, A&A, 615, A146Pearson, W. J., Wang, L., van der Tak, F. F. S., et al. 2017, A&A, 603, A102Petrillo, C. E., Tortora, C., Chatterjee, S., et al. 2017, MNRAS, 472, 1129Pozzetti, L., Bolzonella, M., Zucca, E., et al. 2010, A&A, 523, A13Sanders, D. B. & Mirabel, I. F. 1996, ARA&A, 34, 749Schaye, J., Crain, R. A., Bower, R. G., et al. 2015, MNRAS, 446, 521Schweizer, F. 2005, in Astrophysics and Space Science Library, Vol. 329, Starbursts: From 30Doradus to Lyman Break Galaxies, ed. R. de Grijs & R. M. González Delgado, 143Speagle, J. S., Steinhardt, C. L., Capak, P. L., & Silverman, J. D. 2014, ApJS, 214, 15Tomczak, A. R., Quadri, R. F., Tran, K.-V. H., et al. 2016, ApJ, 817, 118York, D. G., Adelman, J., Anderson, Jr., J. E., et al. 2000, AJ, 120, 1579DiscussionFabio Fontanot: What is the fraction of merging galaxies as a function of stellar mass?https://www.cambridge.org/core/terms. https://doi.org/10.1017/S1743921319002187Downloaded from https://www.cambridge.org/core. University of Groningen, on 12 Apr 2021 at 08:34:14, subject to the Cambridge Core terms of use, available at108 W. J. Pearson et al.William J. Pearson: This is not something we have looked at yet but it would beinteresting to see. (A quick look after the talk shows that the merger fraction decreasesas stellar mass increases).Minju Lee: It may be worth to separate the sample into two groups of gas-rich andgas-poor to test if gas-rich mergers cause SB while gas-poor don’t.William J. Pearson: Yes, it would indeed be interesting to split by gas-rich/poor tosee where they lie. I have not done this yet but would be interesting.https://www.cambridge.org/core/terms. https://doi.org/10.1017/S1743921319002187Downloaded from https://www.cambridge.org/core. University of Groningen, on 12 Apr 2021 at 08:34:14, subject to the Cambridge Core terms of use, available at

Pearson, W.J.

Wang, L.

Trayford, J.W.

Petrillo, C.E.

Tak, F.F.S. van der

Leiden University Scholary Publications

arXiv:1901.07266v1  [astro-ph.GA]  22 Jan 2019PanModel2018 : Challenges in Panchromatic Galaxy Modellingwith Next Generation FacilitiesProceedings IAU Symposium No. 341, 2018M. Boquien, E. Lusso, C. Gruppioni & P. Tisserac© 2018 International Astronomical UnionDOI: 00.0000/X000000000000000XDeep Learning for Galaxy Mergers in theGalaxy Main SequenceWilliam J. Pearson1,2, Lingyu Wang1,2, James Trayford3, Carlo E.Petrillo2, and Floris F.S. van der Tak1,21SRON Netherlands Institute for Space ResearchLandleven 12, 9747 AD, Groningen, The Netherlandsemail: w.j.pearson@sron.nl2Kapteyn Astronomical Institute, University of GroningenPostbus 800, 9700 AV Groningen, The Netherlands3Leiden Observatory, Leiden University, P.O. Box 9513, 2300 RA Leiden, The NetherlandsAbstract. Starburst galaxies are often found to be the result of galaxy mergers. As a result,galaxy mergers are often believed to lie above the galaxy main sequence: the tight correlationbetween stellar mass and star formation rate. Here, we aim to test this claim.Deep learning techniques are applied to images from the Sloan Digital Sky Survey to providevisual-like classifications for over 340 000 objects between redshifts of 0.005 and 0.1. The aim ofthis classification is to split the galaxy population into merger and non-merger systems and weare currently achieving an accuracy of 92.5%. Stellar masses and star formation rates are alsoestimated using panchromatic data for the entire galaxy population. With these preliminarydata, the mergers are placed onto the full galaxy main sequence, where we find that mergingsystems lie across the entire star formation rate - stellar mass plane.Keywords. methods: data analysis, galaxies: evolution, galaxies: interactions, galaxies: star-burst, infrared: galaxies1. IntroductionGalaxy mergers are highly important for fully understanding how galaxies form andevolve, underpinning hierarchical growth models. Also, galaxy mergers are believed tobe the driving force behind some of the brightest infrared objects known (Sanders et al.1996). With bright infrared emission often comes high star formation rates (SFRs), hencethe argument that most mergers go through a starburst phase (e.g. Schewizer 2005).More recent studies have begun to show that merging systems may not always causestarbursts. Knapen et al. (2015) have found that the increase in SFR in merging galaxiesis at most a factor of two in the local universe, with the majority of galaxies showing noevidence of an enhance SFR or showing evidence of merger driven quenching. However,mergers can still cause starbursts with the majority of starbursts being caused by galaxymergers (Luo et al. 2014; Knapen et al. 2015; Cortijo-Ferrero et al. 2017).The main sequence of star forming galaxies (MS) is a tight correlation between the stel-lar mass (M⋆) and SFR of star forming galaxies (e.g. Brinchmann et al. 2004; Noeske et al.2007; Speagle et al. 2014). The MS has been shown to be present out to at least z = 6, al-though the presence, or not, of a high mass turnover is still debated (e.g. Tomczak et al.2016; Pearson et al. 2018). Whatever the form it takes, the majority of galaxies arefound to lie on the MS, with high SFR objects and starbursts above and quenched orquiescent objects below. Thus, if all merging galaxies are starbursts, all merger galaxiesshould lie above the MS, otherwise they will be scattered around the SFR-M⋆ plane.12 William J. Pearson et al.Deep learning neural networks are a rapidly growing technique that can be used toclassify galaxies. These techniques use layers of non-linear mathematical functions thatare inspired by the functioning of biological networks of neurons. Once properly trained,a neural network can classify thousands of galaxies in a fraction of the time it wouldtake a human, or team of humans, to perform the same task. Deep neural networks areincreasingly used in astronomy to perform tasks such as determining galaxy morphology(e.g. Huertas-Company et al. 2015), detecting lensed systems (e.g. Petrillo et al. 2017)and detecting galaxy mergers (e.g. Ackermann et al. 2018).2. Stellar Masses and Star Formation RatesTo populate the SFR-M⋆ plane, we need SFRs and M⋆s for the objects. For the SDSSdata release 7 galaxies with spectroscopic redshifts between 0.005 and 0.1, the five SDSSbands (ugriz) were run through CIGALE (Noll et al. 2009; Boquien et al. 2018) usingthe same configuration as Pearson et al. (2018) but removing the active galactic nucleicomponent to generate M⋆, and U-V and V-J colour estimates. The SFR estimates werederived from the SDSS Hα spectral lines but still need to be corrected for extinction.We intend to improve the M⋆ and SFR estimates, from CIGALE, over the region ofSDSS that coincides with the Galaxy And Mass Assembly survey (GAMA; Driver et al.2009). The GAMA region contains data from the ultra-violet (UV) to the far-infrared(FIR), allowing better constraints on the stellar mass and the thermal dust emissionthan just using the five SDSS bands. We will improve the FIR data in the GAMA fieldsby following Pearson et al. (2017) to de-blend the Herschel SPIRE (Griffin et al. 2010)data. This involves running CIGALE with the UV to near-infrared data to predict theflux density in the three SPIRE bands and using these predictions as a prior in XID+(Hurley et al. 2017) to de-blend the SPIRE images. With more reliable Herschel SPIREdata from this technique, we can generate more reliable SFR estimates.3. Identifying MergersTo identify merging systems, a convolutional neural network (CNN) was employed.This type of neural network uses a series of kernels to identify features in the inputimage and create classifications, in this case if the galaxy is merging or not. The net-work used is based on the Dieleman et al. (2015) architecture with some changes to theparameters of the four convolutional layers and two fully connected layers, as well aschanging the input to be a 64×64 pixel, 3 colour channel image. To train the network,the 3003 Sloan Digital Sky Survey (SDSS; York et al. 2000) objects classified as mergingby Darg et al. (2010a,b) were used along with 3003 randomly selected galaxies that hadGalaxy Zoo merger classification rate of less than 0.2 and fell within the same redshiftrange (Lintott et al. 2011; Ackermann et al. 2018). After training, this resulted in anaccuracy (percentage of objects that are correctly classified) of 92.5%. The network wasthen applied to over 340 000 SDSS galaxies with redshifts between z = 0.005 and z = 0.1,categorising them in approximately two hours. Approximately 19% of these objects arepreliminarily classified as being merging systems, twice the merger fraction seen in otherobservational works (Man et al. 2016).4. The Main Sequence of Starforming GalaxiesTo find the trend of the MS for this data set, we follow Pearson et al. (2018). Thequiescent galaxies are split off using a UVJ colour cut and the 90% mass completenessGalaxy Mergers and the Main Sequence 3−2−101234log(SFR / M⊙yr−1⊙(a⊙50100150200250300350N mber of Galaxies8.0 8.5 9.0 9.5 10.0 10.5 11.0 11.5 12.0 12.5log(M⋆ / M⊙⊙−3−2−10123log(SFR / M⊙yr−1⊙(b⊙1020304050⋆07080N mber of MergersFigure 1. M⋆-SFR plane as a number density plot from low (light) to high (dark) with (a) allSDSS objects with detections (at any confidence level) of Hα and Hβ lines above the mass limit(vertical dashed line) and (b) only the objects from (a) that are identified as mergers. The solidline is the main sequence trend, with extrapolations to higher and lower mass as a dotted line.As can be seen, the merging systems closely follow the entire galaxy population, perhaps witha slight rise, and are not preferentially starbursting or quenched.limit determined empirically by following Pozzetti et al. (2010) and using predicted Ks-band magnitudes from CIGALE. A simple power law is fitted to the remaining objectsusing a Markov Chain Monte Carlo routine to simultaneously fit the slope, normalisationand scatter of the MS, taking into account the observational errors of both the SFR andM⋆. The resulting trend is plotted in Fig. 1 as a solid line.To determine if all mergers are starbursts, we present two plots. Fig. 1a shows theSFR-M⋆ plane populated with all SDSS galaxies that have detections (at any confidencelevel) of Hα and Hβ lines above our mass completeness limit. As can be seen the MS trendruns through the high number density regions. In Fig. 1b we also plot the SFR-M⋆ planepopulated only objects in Fig. 1a that our CNN identifies as mergers. As can be seen,there is no excess of galaxies above the MS, as would be expected if every merger causeda starburst at the time of observation. Equally, there is no clumping of objects belowthe MS, showing no evidence that all mergers trigger quenching. Indeed, the mergingsystems follow the distribution of the entire galaxy population, perhaps with a slight risein SFR for the merger population. With this preliminary result, there is no evidence thatall merging systems are starbursts.4 William J. Pearson et al.5. Ongoing WorkWe are currently working to train a neural network with simulated SDSS images fromthe EAGLE simulation (Schaye et al. 2015). This network will be applied to the ob-served SDSS images, allowing us to test for possible biases and incompleteness in humanclassification: galaxies that are classed by humans as merger or non-merger but are giventhe opposite class by the EAGLE trained network. This also has applications for theupcoming large surveys, such as Euclid (Laureijs et al. 2011) and the Large SynopticSurvey Telescope (LSST Science Collaboration et al. 2009). If this technique proves tofunction as well as, or better than, an observationally trained network, it will reduce theneed to first create a sample of galaxies for humans to classify for training a network.The simulation trained network can immediately be applied to the data from the largesurveys and create data sets for science quickly.Similarly, the simulated images can be passed through the observation trained network.This will allow us to check that the simulation data are representative of the observeduniverse. Objects from simulations that are classified as mergers but the observationtrained network classifies as non-mergers can be identified. Understanding why theseobjects are rejected as mergers by observations could provide insight into shortcomingsof the simulations and inform future work simulating our Universe.ReferencesAckermann, S., Schawinski, K., Zhang, C., Weigel, A. & Turp, M. 2018, MNRAS, 479, 415Boquien, M., Burgarella, D., Roehlly, Y., et al. 2018, ArXiv arXiv1811.03094Brinchmann, J., Charlot, S., White, S. D. M., et al. 2004, MNRAS, 351, 1151Cortijo-Ferrero, C., González Delgado, R. M., Pérez, E., et al. 2017, A&A, 607, A70Darg, D. W., Kaviraj, S., Lintott, C. J., et al. 2010a, MNRAS, 401, 1552Darg, D. W., Kaviraj, S., Lintott, C. J., et al. 2010b, MNRAS, 401, 1043Dieleman, S., Willett, K. W., & Dambre, J. 2015, MNRAS, 450, 1441Driver, S. P., Norberg, P., Baldry, I. K., et al. 2009, Astronomy and Geophysics, 50, 5.12Griffin, M. J., Abergel, A., Abreu, A., et al. 2010, A&A, 518, L3Huertas-Company, M., Gravet, R., Cabrera-Vives, G., et al. 2015, ApJS, 221, 8Hurley, P. D., Oliver, S., Betancourt, M., et al. 2017, MNRAS, 464, 885Knapen, J. H. & Cisternas, M. 2015, ApJ, 807, L16Knapen, J. H., Cisternas, M., & Querejeta, M. 2015, MNRAS, 454, 1742Laureijs, R., Amiaux, J., Arduini, S., et al. 2011, ArXiv arXiv1110.3193Lintott, C., Schawinski, K., Bamford, S., et al. 2011, MNRAS, 410, 166LSST Science Collaboration, Abell, P. A., Allison, J., et al. 2009, ArXiv arXiv0912.0201Luo, W., Yang, X., & Zhang, Y. 2014, ApJ, 789, L16Man, A. W. S., Zirm, A. W., & Toft, S. 2016, ApJ, 830, 89Noeske, K. G., Weiner, B. J., Faber, S. M., et al. 2007, ApJ, 660, L43Noll, S., Burgarella, D., Giovannoli, E., et al. 2009, A&A, 507, 1793Pearson, W. J., Wang, L., Hurley, P. D., et al. 2018, A&A, 615, A146Pearson, W. J., Wang, L., van der Tak, F. F. S., et al. 2017, A&A, 603, A102Petrillo, C. E., Tortora, C., Chatterjee, S., et al. 2017, MNRAS, 472, 1129Pozzetti, L., Bolzonella, M., Zucca, E., et al. 2010, A&A, 523, A13Sanders, D. B. & Mirabel, I. F. 1996, ARA&A, 34, 749Schaye, J., Crain, R. A., Bower, R. G., et al. 2015, MNRAS, 446, 521Schweizer, F. 2005, in Astrophysics and Space Science Library, Vol. 329, Starbursts: From 30Doradus to Lyman Break Galaxies, ed. R. de Grijs & R. M. González Delgado, 143Speagle, J. S., Steinhardt, C. L., Capak, P. L., & Silverman, J. D. 2014, ApJS, 214, 15Tomczak, A. R., Quadri, R. F., Tran, K.-V. H., et al. 2016, ApJ, 817, 118York, D. G., Adelman, J., Anderson, Jr., J. E., et al. 2000, AJ, 120, 1579Galaxy Mergers and the Main Sequence 5DiscussionFabio Fontanot: What is the fraction of merging galaxies as a function of stellar mass?William J. Pearson: This is not something we have looked at yet but it would beinteresting to see. (A quick look after the talk shows that the merger fraction decreasesas stellar mass increases).Minju Lee: It may be worth to separate the sample into two groups of gas-rich andgas-poor to test if gas-rich mergers cause SB while gas-poor don’t.William J. Pearson: Yes, it would indeed be interesting to split by gas-rich/poor tosee where they lie. I have not done this yet but would be interesting.

   University of GroningenDeep learning for galaxy mergers in the galaxy main sequencePearson, William J.; Wang, Lingyu; Trayford, James; Petrillo, Carlo E.; van der Tak, Floris F.S.Published in:Challenges in Panchromatic Modelling withNext Generation FacilitiesDOI:10.1017/S1743921319002187IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite fromit. Please check the document version below.Document VersionPublisher's PDF, also known as Version of recordPublication date:2020Link to publication in University of Groningen/UMCG research databaseCitation for published version (APA):Pearson, W. J., Wang, L., Trayford, J., Petrillo, C. E., & van der Tak, F. F. S. (2020). Deep learning forgalaxy mergers in the galaxy main sequence. In M. Boquien, E. Lusso, C. Gruppioni , & P. Tissera (Eds.),Challenges in Panchromatic Modelling withNext Generation Facilities: Proceedings (Vol. 15, pp. 104-108).(Proceedings of the International Astronomical Union ; Vol. 15, No. S341). Cambridge University Press.https://doi.org/10.1017/S1743921319002187CopyrightOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of theauthor(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons).The publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license.More information can be found on the University of Groningen website: https://www.rug.nl/library/open-access/self-archiving-pure/taverne-amendment.Take-down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons thenumber of authors shown on this cover page is limited to 10 maximum.Download date: 01-11-2023Challenges in Panchromatic Modelling withNext Generation FacilitiesProceedings IAU Symposium No. 341, 2018M. Boquien, E. Lusso, C. Gruppioni & P. Tissera, eds.c© International Astronomical Union 2020doi:10.1017/S1743921319002187Deep learning for galaxy mergers in thegalaxy main sequenceWilliam J. Pearson1,2, Lingyu Wang1,2, James Trayford3,Carlo E. Petrillo2 and Floris F. S. van der Tak1,21SRON Netherlands Institute for Space ResearchLandleven 12, 9747 AD, Groningen, The Netherlandsemail: w.j.pearson@sron.nl2Kapteyn Astronomical Institute, University of GroningenPostbus 800, 9700 AV Groningen, The Netherlands3Leiden Observatory, Leiden University, P.O. Box 9513, 2300 RA Leiden, The NetherlandsAbstract. Starburst galaxies are often found to be the result of galaxy mergers. As a result,galaxy mergers are often believed to lie above the galaxy main sequence: the tight correlationbetween stellar mass and star formation rate. Here, we aim to test this claim.Deep learning techniques are applied to images from the Sloan Digital Sky Survey to providevisual-like classifications for over 340 000 objects between redshifts of 0.005 and 0.1. The aim ofthis classification is to split the galaxy population into merger and non-merger systems and weare currently achieving an accuracy of 92.5%. Stellar masses and star formation rates are alsoestimated using panchromatic data for the entire galaxy population. With these preliminarydata, the mergers are placed onto the full galaxy main sequence, where we find that mergingsystems lie across the entire star formation rate - stellar mass plane.Keywords. methods: data analysis, galaxies: evolution, galaxies: interactions, galaxies:starburst, infrared: galaxies1. IntroductionGalaxy mergers are highly important for fully understanding how galaxies form andevolve, underpinning hierarchical growth models. Also, galaxy mergers are believed tobe the driving force behind some of the brightest infrared objects known (Sanders et al.1996). With bright infrared emission often comes high star formation rates (SFRs), hencethe argument that most mergers go through a starburst phase (e.g. Schewizer 2005).More recent studies have begun to show that merging systems may not always causestarbursts. Knapen et al. (2015) have found that the increase in SFR in merging galaxiesis at most a factor of two in the local universe, with the majority of galaxies showing noevidence of an enhance SFR or showing evidence of merger driven quenching. However,mergers can still cause starbursts with the majority of starbursts being caused by galaxymergers (Luo et al. 2014; Knapen et al. 2015; Cortijo-Ferrero et al. 2017).The main sequence of star forming galaxies (MS) is a tight correlation between thestellar mass (M) and SFR of star forming galaxies (e.g. Brinchmann et al. 2004, Noeskeet al. 2007; Speagle et al. 2014). The MS has been shown to be present out to at leastz = 6, although the presence, or not, of a high mass turnover is still debated (e.g. Tomczaket al. 2016; Pearson et al. 2018). Whatever the form it takes, the majority of galaxies arefound to lie on the MS, with high SFR objects and starbursts above and quenched orquiescent objects below. Thus, if all merging galaxies are starbursts, all merger galaxiesshould lie above the MS, otherwise they will be scattered around the SFR-M plane.104https://www.cambridge.org/core/terms. https://doi.org/10.1017/S1743921319002187Downloaded from https://www.cambridge.org/core. University of Groningen, on 12 Apr 2021 at 08:34:14, subject to the Cambridge Core terms of use, available atGalaxy mergers and the main sequence 105Deep learning neural networks are a rapidly growing technique that can be used toclassify galaxies. These techniques use layers of non-linear mathematical functions thatare inspired by the functioning of biological networks of neurons. Once properly trained,a neural network can classify thousands of galaxies in a fraction of the time it wouldtake a human, or team of humans, to perform the same task. Deep neural networks areincreasingly used in astronomy to perform tasks such as determining galaxy morphology(e.g. Huertas-Company et al. 2015), detecting lensed systems (e.g. Petrillo et al. 2017)and detecting galaxy mergers (e.g. Ackermann et al. 2018).2. Stellar Masses and Star Formation RatesTo populate the SFR-M plane, we need SFRs and Ms for the objects. For the SDSSdata release 7 galaxies with spectroscopic redshifts between 0.005 and 0.1, the five SDSSbands (ugriz) were run through CIGALE (Noll et al. 2009; Boquien et al. 2018) usingthe same configuration as Pearson et al. (2018) but removing the active galactic nucleicomponent to generate M, and U-V and V-J colour estimates. The SFR estimates werederived from the SDSS Hα spectral lines but still need to be corrected for extinction.We intend to improve the M and SFR estimates, from CIGALE, over the region ofSDSS that coincides with the Galaxy And Mass Assembly survey (GAMA; Driver et al.2009). The GAMA region contains data from the ultra-violet (UV) to the far-infrared(FIR), allowing better constraints on the stellar mass and the thermal dust emissionthan just using the five SDSS bands. We will improve the FIR data in the GAMA fieldsby following Pearson et al. (2017) to de-blend the Herschel SPIRE (Griffin et al. 2010)data. This involves running CIGALE with the UV to near-infrared data to predict theflux density in the three SPIRE bands and using these predictions as a prior in XID+(Hurley et al. 2017) to de-blend the SPIRE images. With more reliable Herschel SPIREdata from this technique, we can generate more reliable SFR estimates.3. Identifying MergersTo identify merging systems, a convolutional neural network (CNN) was employed.This type of neural network uses a series of kernels to identify features in the inputimage and create classifications, in this case if the galaxy is merging or not. The net-work used is based on the Dieleman et al. (2015) architecture with some changes to theparameters of the four convolutional layers and two fully connected layers, as well aschanging the input to be a 64×64 pixel, 3 colour channel image. To train the network,the 3003 Sloan Digital Sky Survey (SDSS; York et al. 2000) objects classified as mergingby Darg et al. (2010a,b) were used along with 3003 randomly selected galaxies that hadGalaxy Zoo merger classification rate of less than 0.2 and fell within the same redshiftrange (Lintott et al. 2011; Ackermann et al. 2018). After training, this resulted in anaccuracy (percentage of objects that are correctly classified) of 92.5%. The network wasthen applied to over 340 000 SDSS galaxies with redshifts between z = 0.005 and z = 0.1,categorising them in approximately two hours. Approximately 19% of these objects arepreliminarily classified as being merging systems, twice the merger fraction seen in otherobservational works (Man et al. 2016).4. The Main Sequence of Starforming GalaxiesTo find the trend of the MS for this data set, we follow Pearson et al. (2018). The qui-escent galaxies are split off using a UVJ colour cut and the 90% mass completeness limitdetermined empirically by following Pozzetti et al. (2010) and using predicted Ks-bandmagnitudes from CIGALE. A simple power law is fitted to the remaining objects usinga Markov Chain Monte Carlo routine to simultaneously fit the slope, normalisation andhttps://www.cambridge.org/core/terms. https://doi.org/10.1017/S1743921319002187Downloaded from https://www.cambridge.org/core. University of Groningen, on 12 Apr 2021 at 08:34:14, subject to the Cambridge Core terms of use, available at106 W. J. Pearson et al.(a)(b)Figure 1. M-SFR plane as a number density plot from low (light) to high (dark) with (a) allSDSS objects with detections (at any confidence level) of Hα and Hβ lines above the mass limit(vertical dashed line) and (b) only the objects from (a) that are identified as mergers. The solidline is the main sequence trend, with extrapolations to higher and lower mass as a dotted line.As can be seen, the merging systems closely follow the entire galaxy population, perhaps witha slight rise, and are not preferentially starbursting or quenched.scatter of the MS, taking into account the observational errors of both the SFR and M.The resulting trend is plotted in Fig. 1 as a solid line.To determine if all mergers are starbursts, we present two plots. Fig. 1a shows theSFR-M plane populated with all SDSS galaxies that have detections (at any confidencelevel) of Hα and Hβ lines above our mass completeness limit. As can be seen the MS trendruns through the high number density regions. In Fig. 1b we also plot the SFR-M planepopulated only objects in Fig. 1a that our CNN identifies as mergers. As can be seen,there is no excess of galaxies above the MS, as would be expected if every merger causeda starburst at the time of observation. Equally, there is no clumping of objects belowthe MS, showing no evidence that all mergers trigger quenching. Indeed, the mergingsystems follow the distribution of the entire galaxy population, perhaps with a slight risein SFR for the merger population. With this preliminary result, there is no evidence thatall merging systems are starbursts.5. Ongoing WorkWe are currently working to train a neural network with simulated SDSS images fromthe EAGLE simulation (Schaye et al. 2015). This network will be applied to the observedSDSS images, allowing us to test for possible biases and incompleteness in human clas-sification: galaxies that are classed by humans as merger or non-merger but are givenhttps://www.cambridge.org/core/terms. https://doi.org/10.1017/S1743921319002187Downloaded from https://www.cambridge.org/core. University of Groningen, on 12 Apr 2021 at 08:34:14, subject to the Cambridge Core terms of use, available atGalaxy mergers and the main sequence 107the opposite class by the EAGLE trained network. This also has applications for theupcoming large surveys, such as Euclid (Laureijs et al. 2011) and the Large SynopticSurvey Telescope (LSST Science Collaboration et al. 2009). If this technique proves tofunction as well as, or better than, an observationally trained network, it will reduce theneed to first create a sample of galaxies for humans to classify for training a network.The simulation trained network can immediately be applied to the data from the largesurveys and create data sets for science quickly.Similarly, the simulated images can be passed through the observation trained network.This will allow us to check that the simulation data are representative of the observeduniverse. Objects from simulations that are classified as mergers but the observationtrained network classifies as non-mergers can be identified. Understanding why theseobjects are rejected as mergers by observations could provide insight into shortcomingsof the simulations and inform future work simulating our Universe.ReferencesAckermann, S., Schawinski, K., Zhang, C., Weigel, A., & Turp, M. 2018, MNRAS, 479, 415Boquien, M., Burgarella, D., Roehlly, Y., et al. 2018, ArXiv arXiv:1811.03094Brinchmann, J., Charlot, S., White, S. D. M., et al. 2004, MNRAS, 351, 1151Cortijo-Ferrero, C., González Delgado, R. M., Pérez, E., et al. 2017, A&A, 607, A70Darg, D. W., Kaviraj, S., Lintott, C. J., et al. 2010a, MNRAS, 401, 1552Darg, D. W., Kaviraj, S., Lintott, C. J., et al. 2010b, MNRAS, 401, 1043Dieleman, S., Willett, K. W., & Dambre, J. 2015, MNRAS, 450, 1441Driver, S. P., Norberg, P., Baldry, I. K., et al. 2009, Astronomy and Geophysics, 50, 5.12Griffin, M. J., Abergel, A., Abreu, A., et al. 2010, A&A, 518, L3Huertas-Company, M., Gravet, R., Cabrera-Vives, G., et al. 2015, ApJS, 221, 8Hurley, P. D., Oliver, S., Betancourt, M., et al. 2017, MNRAS, 464, 885Knapen, J. H. & Cisternas, M. 2015, ApJ, 807, L16Knapen, J. H., Cisternas, M., & Querejeta, M. 2015, MNRAS, 454, 1742Laureijs, R., Amiaux, J., Arduini, S., et al. 2011, ArXiv arXiv:1110.3193Lintott, C., Schawinski, K., Bamford, S., et al. 2011, MNRAS, 410, 166LSST Science Collaboration, Abell, P. A., Allison, J., et al. 2009, ArXiv arXiv:0912.0201Luo, W., Yang, X., & Zhang, Y. 2014, ApJ, 789, L16Man, A. W. S., Zirm, A. W., & Toft, S. 2016, ApJ, 830, 89Noeske, K. G., Weiner, B. J., Faber, S. M., et al. 2007, ApJ, 660, L43Noll, S., Burgarella, D., Giovannoli, E., et al. 2009, A&A, 507, 1793Pearson, W. J., Wang, L., Hurley, P. D., et al. 2018, A&A, 615, A146Pearson, W. J., Wang, L., van der Tak, F. F. S., et al. 2017, A&A, 603, A102Petrillo, C. E., Tortora, C., Chatterjee, S., et al. 2017, MNRAS, 472, 1129Pozzetti, L., Bolzonella, M., Zucca, E., et al. 2010, A&A, 523, A13Sanders, D. B. & Mirabel, I. F. 1996, ARA&A, 34, 749Schaye, J., Crain, R. A., Bower, R. G., et al. 2015, MNRAS, 446, 521Schweizer, F. 2005, in Astrophysics and Space Science Library, Vol. 329, Starbursts: From 30Doradus to Lyman Break Galaxies, ed. R. de Grijs & R. M. González Delgado, 143Speagle, J. S., Steinhardt, C. L., Capak, P. L., & Silverman, J. D. 2014, ApJS, 214, 15Tomczak, A. R., Quadri, R. F., Tran, K.-V. H., et al. 2016, ApJ, 817, 118York, D. G., Adelman, J., Anderson, Jr., J. E., et al. 2000, AJ, 120, 1579DiscussionFabio Fontanot: What is the fraction of merging galaxies as a function of stellar mass?https://www.cambridge.org/core/terms. https://doi.org/10.1017/S1743921319002187Downloaded from https://www.cambridge.org/core. University of Groningen, on 12 Apr 2021 at 08:34:14, subject to the Cambridge Core terms of use, available at108 W. J. Pearson et al.William J. Pearson: This is not something we have looked at yet but it would beinteresting to see. (A quick look after the talk shows that the merger fraction decreasesas stellar mass increases).Minju Lee: It may be worth to separate the sample into two groups of gas-rich andgas-poor to test if gas-rich mergers cause SB while gas-poor don’t.William J. Pearson: Yes, it would indeed be interesting to split by gas-rich/poor tosee where they lie. I have not done this yet but would be interesting.https://www.cambridge.org/core/terms. https://doi.org/10.1017/S1743921319002187Downloaded from https://www.cambridge.org/core. University of Groningen, on 12 Apr 2021 at 08:34:14, subject to the Cambridge Core terms of use, available at

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Deep Learning for Galaxy Mergers in the Galaxy Main Sequence

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