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The Astronomical Journal, 133:303–312, 2007 January # 2007. The American Astronomical Society. All rights reserved. Printed in U.S.A.
OPTICAL MICROVARIABILITY IN RADIO-QUIET QUASARS M. T. Carini, J. C. Noble, R. Taylor, and R. Culler Department of Physics and Astronomy, Western Kentucky University, Bowling Green, Kentucky, USA;
[email protected] Received 2006 March 24; accepted 2006 September 24
ABSTRACT We report the results of a study of optical microvariability in radio-quiet and radio-intermediate quasars. Observations were obtained on a total of seven objects: five radio-quiet quasars and two radio-intermediate quasars. No microvariability was detected in either the radio-quiet or radio-intermediate objects in our sample, despite intensive monitoring for several consecutive nights. In one object, PG 1257+346, evidence for interday variability was detected. We examined a sample of 117 radio-quiet objects found in the literature that have been studied for microvariability. This sample is discussed in terms of classification, redshift distribution, R (the ratio of the radio [5 GHz] flux to the optical [4400 8] flux), optical magnitude, luminosity, and observing strategy. Objects with 10 > R > 1 were found to show a higher instance of microvariability than those with R < 1. This suggests that R ¼ 1 is the appropriate cutoff between radioloud and radio-quiet objects. A preference for low-redshift objects to display microvariability more often than higher redshift objects is seen; however, this is explainable via selection effects. We find a dependence in detection probability on observation length similar to that seen in radio-loud objects. Key words: galaxies: photometry — quasars: general Online material: machine-readable table
1. INTRODUCTION
a night. The result was often undersampled light curves, with events defined by so few points (in some cases only one) that they were simply not definitive. Later studies, such as those undertaken by Gopal-Krishna et al. (2003) and Stalin et al. (2004), adopted the strategy advocated by Carini (1990) for blazars that found that three to five consecutive nights of monitoring for a minimum of 4–6 hr were required in order to have a 50% chance of detecting microvariability. While these later studies may have produced more conclusive positive detections of microvariability, they found, as have almost all such studies, that microvariability is a rare phenomenon in radio-quiet quasars, with duty cycles much lower than those observed in radio-loud objects. The lone exception to this was the study of de Diego et al. (1998), who found that microvariability occurred with similar frequency in radio-quiet and core-dominated radio-loud objects. It is worth noting, however, that their variability analysis techniques and definition of radio-quiet differ from all other studies, rendering their results difficult to directly compare with the majority of other studies (Stalin et al. 2004). The possible existence of sources whose radio properties are intermediate between radio-quiet and radio-loud objects was first proposed by Miller et al. (1993) from an analysis of the correlation between the total monochromatic radio luminosity at 5 GHz and the [O iii] k5007 line luminosity from quasars in the PalomarGreen (PG) survey. Falcke et al. (1995) also concluded that such a population exists based on the relationship between the accretion disk luminosity and radio core emission from a broad sample of objects, including those found in the PG survey. Xu et al. (1999) found a clear bimodal distribution in a plot of radio luminosity at 5 GHz as a function of the [O iii] line luminosity, representing distinct populations of radio-loud and radio-quiet objects. In addition, they found that 3% of their sample objects occupied the region between the two main distributions. The possibility that the emission from these intermediate objects could be more jet dominated than the radio-quiet objects, and thus might have a higher probability of exhibiting microvariability, led us to include two of them on the list of radio-quiet quasars we were studying for microvariability.
Radio-quiet quasars have now been almost as extensively studied for the phenomena of microvariability as their radio-loud counterparts. Microvariability (sometimes referred to as intraday variability) is variability occurring on timescales of minutes to hours with amplitudes ranging from a few hundredths to a few tenths of a magnitude. It was conclusively demonstrated to exist in blazars (extremely radio-loud objects) by Miller & Carini (1989) and subsequently shown to be a common property of the blazar class of objects by Carini (1990) and Noble (1994). Jang & Miller (1995, 1997) established its existence in nonblazar radio-loud objects, and it is now considered a general property of radio-loud quasars. The motivation for searching for microvariability in radio-quiet objects is that unlike for radio-loud objects, in which microvariability arises from the relativistic jet, microvariability in radio-quiet objects may arise from processes on the accretion disk itself, and thus can be used to probe the accretion disk (Stalin et al. 2004). The emerging picture is that microvariability does not occur as often in radio-quiet objects as it does in radio-loud sources. However, the notion of what defines an object as radio-quiet is not always uniform among investigators. Different criteria have been employed to construct samples of radio-quiet objects for microvariability studies. In most (but not all) studies, R, the ratio of the radio (5 GHz) flux to the optical (4400 8) (Kellerman et al. 1989), has been used to select objects that are radio-quiet. Initially, the cutoff between radioquiet and radio-loud objects was taken to be R ¼ 10. Many studies have taken an even more conservative approach, looking for samples of ‘‘radio-silent objects,’’ where R < 1. In other studies, if the radio flux at 5 GHz is less than 0.5 mJy (e.g., Gopal-Krishna et al. 1995) the source is presumed to be radio-quiet. Early attempts at detecting microvariability in radio-quiet objects (e.g., Gopal-Krishna et al. 1993a, 1993b, 1995) all pointed to the strong possibility that microvariability was present in radio-quiet objects. However, many of these results were not conclusive. Their observational strategies were usually inadequate in one of two ways: either they only obtained one or two nights of long, intensive monitoring of the sources, or they obtained many nights with sparse sampling within 303
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TABLE 1 Radio-quiet and Radio-intermediate Quasars Observed Object (1)
R.A. (J2000.0) (2)
PHL 938............... PG 1257+346....... PG 1309+355 ...... PG 1444+407....... PG 1700+518 ...... PG 2112+059 ....... II Zw 175 .............
01 00 12 59 13 12 14 46 17 01 21 14 22 17
54.1 48.8 17.8 45.9 24.8 52.6 12.2
Decl. (J2000.0) (3) 02 11 34 23 35 15 40 35 51 49 06 07 14 14
37 23 21 05 20 42 21
Z (4)
R (5)
MB (6)
Class (7)
1.95 1.38 0.18 0.267 0.29 0.47 0.066
... ... 0.18 R > 1 displayed microvariability, while 15.9% of the sources with R < 1 displayed microvariability. Furthermore, the sources with 10 > R > 1 comprised 32% of all sources found to exhibit microvariability and 44.4% of the variable sources whose R-value was known. Given that microvariability
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Fig. 4.—Histogram of the number of objects found in redshift bins for the entire sample of objects (open histogram) and objects in which microvariability was detected ( filled histogram). Bin size is 1.0.
Fig. 6.—Histogram of the number of objects found in MB bins for the entire sample of objects (open histogram) and objects in which microvariability was detected ( filled histogram). Bin size is 1.0 mag.
is a common trait among radio-loud sources, we conclude, based on variability arguments alone, that R ¼ 1 is the appropriate dividing line between radio-quiet and radio-loud objects. A two sample K-S test was performed on the absolute magnitude and z distributions of sources with R < 1 and R > 1. In the case of the absolute magnitudes, we found the probability that the two samples arise from different parent populations to be >91%. For the redshifts we found the probability that the two samples arise from different parent populations to be >99%. We also performed a contingency table analysis on the number of objects for R < 1 and R > 1 that did or did not display variability. The results from both 2 and Fisher exact tests showed dependence within the values in the constructed contingency table, indicating that we can be confident in the dichotomy between variability results for sources with R < 1 and R > 1. When combined with the results of the K-S test, we have high confidence in this result. Ho & Peng (2001) argue that one needs to correct for the contribution of the underlying galaxy component before calculating R-values for Seyfert galaxies. From their sample of objects, they find that 75% of Seyfert galaxies would lie in the range 1 < R < 100. While it is beyond the scope of this work to calculate nuclear absolute magnitudes and R-values for the Seyfert galaxies in our sample, we can make a general statement as to what effect this
correction would have on our results. Assuming that 75% of the Seyfert galaxies in our sample currently classified with R < 1 would be reclassified with R > 1, we find that the number of sources with R < 1 that display microvariability rises from 15% to 20%. This change is small and does not affect any of our conclusions.
Fig. 5.—Histogram of the number of objects found in optical magnitude bins for the entire sample of objects (open histogram) and objects in which microvariability was detected ( filled histogram). Bin size is 1.0 mag.
4.4. Distribution of Apparent Magnitude and MB The fluxes of our sources can be characterized by their optical magnitudes. For this we use either the v- or b-band magnitude, whichever is available. This information is found in Table 3, column (6), with the corresponding band noted in column (7). A histogram of the number of objects in a given optical magnitude bin with a bin size of 1.0 mag is shown in Figure 5. Peaks in the numbers of objects that do and do not display microvariability occur in the 15–17 mag range, consistent with the peak in the overall distribution of sources. The average optical magnitude is 15.85 for the entire sample, the average optical magnitude is 15.87 for sources that do not display microvariability, and the average optical magnitude for sources that display microvariability is 15.78. No dependence of microvariability on optical magnitude is seen. We can characterize the luminosity distribution of our sample using the absolute B magnitudes for our sample objects. The sample ranges in MB from 17.4 to 31.9. We find that the average MB for the sample objects is 26.3, the average MB for objects that have detection of microvariability is 26.0, and the average MB for sources that show evidence of microvariability is 26.4. Figure 6 displays a histogram of the number of objects in a given MB bin, with bin sizes of 1.0 mag . This plot shows a peak in the number of objects that display microvariability in the 25:0 > MB > 26:0 bin, consistent with a peak in the overall distribution of sources. A second peak in the overall source distribution occurs between MB ¼ 28 and 30, with a corresponding (although smaller) peak in microvariability detections. No dependence of microvariability on absolute magnitude is seen. In the case of Seyfert galaxies, the underlying galaxy can make a substantial contribution to the observed flux and the absolute magnitude. Ho & Peng (2001) found an average decrease in absolute magnitude of 4:8 3:31 mag when the effect of the underlying galaxy component was removed for their sample of Seyfert galaxies. However, their sample was confined to objects with MB between 19 and 23; extrapolating the result to the absolute magnitudes of the majority of Seyfert galaxies in this sample is risky. Therefore, all we can say is that the morphology of the host galaxy will,
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when the number of positive detections is scaled by the total number of observations. We have examined the sources with observations within this time bin, and find no preference for particular values of z or MB. However, we note that similar behavior was found by Gupta & Joshi (2005). 5. CONCLUSIONS
Fig. 7.—Histogram of the number of observations obtained in a given observation length bin for the entire sample of objects (open histogram) and objects in which microvariability was detected ( filled histogram). Bin size is 1.0 hr.
in the case of Seyfert galaxies, shift the absolute magnitude distribution in Figure 6 such that fewer objects will be found in the 19 to 24 absolute magnitude range. For the optical magnitude distribution shown in Figure 5, it is more difficult to say, since the correction depends strongly on the strength of the underlying galaxy, which is highly variable from object to object. While the shape of the distribution may change in both instances, this has no significant impact on our overall results. 4.5. Duration of the Observations The key to detecting microvariability lies in a well-conceived observing strategy; in particular, the duration of the observations is key to detection of microvariability. Carini (1990) pointed out that the detection of microvariability in blazars depends strongly on the duration of a given observation. Variability was found in 50% of the blazars in that study in observations of 3 hr or longer duration, and in 80% after 8 hr. One would expect that a similar relationship exists for radio-quiet quasars. Using the objects in this sample we constructed a histogram of the total number of observations in a given observation length bin (Fig. 7, open histogram) and a histogram of the number of detections of microvariability in a given observation length bin (Fig. 7, filled histogram). For all observations, we see that the distribution peaks in the 3–5 hr range, while for the sources with positive detections of microvariability, the distribution peaks in the 6–7 hr range. Microvariability was found in 24% of the sources that fell in the 6–7 hr bin. The dramatic decrease in the 7–8 hr bin is unexpected, and occurs even
The results of a search for microvariability in five radio-quiet quasars and two radio-intermediate quasars are presented. No evidence of microvariability was seen in any of our objects, consistent with the idea that microvariability occurs with much less frequency in radio-quiet than in radio-loud objects. Interday variability was detected in one object, PG 1257+346. The amplitude and timescale of this variation are consistent with what one would expect for a radio-quiet quasar. We compiled a sample of 117 objects from the literature for which attempts at detecting microvariability have been made. Despite claims to the contrary, it is clear that radioquiet objects, defined here as objects with R < 1, display microvariability much less frequently than radio-loud objects. We found that sources in our sample with 10 > R > 1 showed a much higher detection percentage than sources with R < 1 and that the dividing line between radio-loud and radio-quiet objects can be set at R ¼ 1, based on variability arguments. Sources with low z were more likely to display microvariability, although this result appears to be explainable via selection effects, and no dependence on absolute B magnitude or optical flux was detected. We found a similar dependence on observation length, as was previously shown for blazars, in that one is more likely to detect microvariability with continuous observations of durations >6 hr. Finally, a small subset of the objects observed (six) were BAL QSOs. These objects showed a higher percentage of microvariability detections than either the Seyfert galaxies or typical quasars in the sample and deserve further investigation.
We wish to thank Lowell Observatory for generous allocations of observing time for our investigations of the microvariability phenomena in AGNs. We are grateful for the comments of the anonymous referee, which have substantially strengthened this paper. M. T. C. wishes to thank Sergey Marchenko and Keith Andrew for useful discussions and Western Kentucky University (WKU), the Applied Research and Technology Program at WKU, and the Kentucky Space Grant Consortium for supporting various stages of this work. This research has made use of the NASA/IPAC Extragalactic Database, which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration.
REFERENCES Anupama, G. C., & Chokshi, A. 1998, ApJ, 494, L147 Falcke, H., Malkan, M. A., & Biermann, P. L. 1995, A&A, 298, 375 Bachev, R., Strigachev, A., & Semkov, E. 2005, MNRAS, 358, 774 Gopal-Krishna, Gupta, A. C., Sagar, R., Wiita, P. J., Chaubey, U. S., & Stalin, Blandford, R. 2000, Philos. Trans. R. Soc. London A, 358, 811 C. S. 2000, MNRAS, 314, 815 Blundell, K. M., & Rawlings, S. 2001, ApJ, 562, L5 Gopal-Krishna, Sagar, R., & Wiita, P. J. 1993a, MNRAS, 262, 963 Carini, M. T. 1990, Ph.D. thesis, Georgia State Univ. ———. 1995, MNRAS, 274, 701 Carini, M. T., Miller, H. R., Noble, J. C., & Goodrich, B. D. 1992, AJ, 104, 15 Gopal-Krishna, Stalin, C. S., Sagar, R., & Wiita, P. J. 2003, ApJ, 586, L25 Carini, M. T., Miller, H. R., Noble, J. C., & Sadun, A. C. 1991, AJ, 101, 1196 Gopal-Krishna, Wiita, P. J., & Altieri, B. 1993b, A&A, 271, 89 Carini, M. T., Noble, J. C., & Miller, H. R. 2003, AJ, 125, 1811 Gupta, A. C., & Joshi, U. C. 2005, A&A, 440, 855 Christiani, S., Trentini, S., La Franca, F., Aretxaga, I., Andreani, P., Vio, R., & Ho, L. C., & Peng, C. Y. 2001, ApJ, 555, 650 Gemmo, A. 1996, A&A, 306, 395 Hook, I. M., McMahon, R. G., Boyle, B. J., & Irwin, M. J. 1994, MNRAS, de Diego, J. A., Dultzin-Hacyan, D., Ramirez, A., & Benitez, E. 1998, ApJ, 268, 305 501, 69 Howell, S. B., & Jacoby, G. H. 1986, PASP, 98, 802 di Clemente, A., Giallongo, E., Natali, G., Trevese, D., & Vagnetti, F. 1996, Jang, M.-W. 2005, Ap&SS, 295, 397 ApJ, 463, 466 Jang, M.-W., & Miller, H. R. 1995, ApJ, 452, 582 Dultzin-Hacyan, D., Schuster, W. J., Parrao, L., Pena, J. H., Peniche, R., Benitez, ———. 1997, AJ, 114, 565 E., & Costero, R. 1992, AJ, 103, 1769 Kellerman, K. I., Sramek, R., Schmidt, M., Shaffer, D. B., & Green, R. 1989, Eggers, D., Schaffer, D. B., & Weistrop, D. 2000, AJ, 119, 460 AJ, 98, 1195
312
CARINI ET AL.
Mangalam, A. V., & Wiita, P. J. 1993, ApJ, 406, 420 Meier, D. L. 2002, NewA Rev., 46, 247 Miller, H. R., & Carini, M. T. 1989, Nature, 337, 627 Miller, P., Rawlings, S., & Saunders, R. 1993, MNRAS, 263, 425 Neugebauer, G., Soifer, B. T., Matthews, K., & Elias, J. H. 1989, AJ, 97, 957 Noble, J. C. 1994, Ph.D. thesis, Georgia State Univ. Petrucci, P. P., Chelli, A., Henri, G., Cruz-Gonza´lez, I., Salas, L., & Mujica, R. 1999, A&A, 342, 687 Pica, A. J., & Smith, A. G. 1983, ApJ, 272, 11 Rabbette, M., McBreen, B., Smith, N., & Steel, S. 1998, A&AS, 129, 445
Romero, G. E., Cellone, S. A., & Combi, J. A. 1999, A&AS, 135, 477 Sagar, R., Gopal-Krishna, & Wiita, P. J. 1996, MNRAS, 281, 1267 Smith, A. G. 1996, in ASP Conf. Ser. 110, Blazar Continuum Variability, ed. H. R. Miller, J. R. Webb, & J. C. Noble (San Francisco: ASP), 3 Stalin, C. S., Gopal-Krishna, Sagar, R., & Wiita, P. J. 2004, MNRAS, 350, 175 Stalin, C. S., Gupta, A. C., Gopal-Krishna, Wiita, P. J., & Sagar, R. 2005, MNRAS, 356, 607 Ve´ron-Cetty, M.-P., & Ve´ron, P. 2003, A&A, 412, 399 Wilson, A. S., & Colbert, E. J. M. 1995, ApJ, 438, 62 Xu, C., Livio, M., & Baum, S. 1999, AJ, 118, 1169
