Research Article  Open Access
Yuhai Yuan, "Relations between the Spectral Indices and Flux Densities of Eight Blazars", Advances in Astronomy, vol. 2019, Article ID 8041087, 11 pages, 2019. https://doi.org/10.1155/2019/8041087
Relations between the Spectral Indices and Flux Densities of Eight Blazars
Abstract
Relations between the flux densities (F) and spectral indices () can help us analyze the emission process. In this paper, we choose 8 blazars (0235+164, 0430+052, 1156+295, 3C345, 1308+326, 1413+135, 3C454.3, and 1749+096) from the University of Michigan Radio Observatory (UMRAO) database to study the relations between the spectral indices () and flux densities at 14.5GHz (). The main results are the following. (1) There are strong anticorrelations between and , with the correlation coefficient r in the range from to . (2) The distributions show elliptic appearance, which have been fitted by elliptic curves. (3) For most of the sources, the time intervals of elliptic circle () are consistent with the quasiperiodicities calculated by the averaged light curves and spectral variances ().
1. Introduction
Blazars are a subclass of active galactic nuclei (AGNs). They show some extreme properties, such as violent optical variability, core dominance, superluminal motion, and so on [1, 2]. Blazars can be divided into two subclasses, BL Lacerate objects (BL Lacs), and flatspectrum radio quasars (FSRQs). BL Lacs are characterized by featureless optical spectra or weak emission lines [3]. Flat radio spectrum and broad optical emission lines are typical for FSRQs [1]. Generally, the division between the two subclasses is based on the equivalent width (EW) of the optical broad emission lines; BL Lacs show Å [1, 4–6].
The time scale of optical variability is an important quantity which is often used to probe the physical processes in blazars. The time scales are in the range from minutes to years and can be divided into three types: intraday variability (IDV), shortterm variability, and longterm variability.
Usually, the intraday variability and shortterm variability are nonperiodic, but the longterm variability is quasiperiodic. The longterm optical variabilities of blazars have been discussed by many papers [3, 7–18]. The explanation about the longterm variabilities can be the binary blackhole model, the thermal instability model and the perturbation model and so on [14].
For blazars, there are many papers discussing relationship between the spectrum and flux density [16, 19–26]. Dai et al. (2009) [27] presented longterm observations and discussed the correlations between color index and brightness. Beaklini & Abraham (2014) [28] used variability at 7 mm to find evidence of shocks and precession in the jet. Fan et al. (2014) [15] analyzed the relation between Vband flux density () and spectral index () for two nearby quasars and found two different relations below and above mJy. When mJy, , and showed anticorrelation, when mJy, , and showed positive correlation. Yuan & Fan (2015) [29] found that, in 3C273 and 3C446, there was elliptic structure in the distributions between the flux densities and spectral indices. Wierzcholska et al. (2015) [22] analyzed the colormagnitude correlations of 30 blazars and observed the bluerwhenbrighter behaviors. Carnerero et al. (2017) [23] pointed out that the flux densities and spectral variability can be compatible with jet models including at least two emitting regions that can change their orientation with respect to the line of sight. Isler et al. (2017) [24] provided an explanation of the longterm opticalinfrared color variabilities in some blazars and presented a general scheme which can apply to these variabilities.
The spectral indices are associated with the emission properties, and the flux densities demonstrate the optical variability properties. The study about the relations between the spectral indices and flux densities can combine the emission properties and optical variabilities. Generally, for BL Lacs, when the sources become brighter, the spectrum becomes flatter and when the sources become fainter, the spectrum becomes steeper.
This paper is arranged as follows. In Section 2, we calculate the spectral indices; in Section 3, we analyze the relations between the radio spectral index and the 14.5 GHz radio flux density ; in Sections 4 and 5, we give the discussion and conclusions, respectively.
2. The Spectral Indices
Based on the UMRAO (University of Michigan Radio Astronomy Observatory) database (https://dept.astro.lsa.umich.edu/datasets/umrao.php), we collected the radio flux densities at 4.8 GHz, 8 GHz, and 14.5 GHz and used the following method to calculate their averaged flux densities and spectral indices, similarly to Yuan & Fan (2011) [30] and Yuan et al. (2014) [31]. Firstly, at each band (4.8 GHz, 8 GHz, and 14.5 GHz), we average the flux densities within the same bin and obtain N sets of data: , which represent the flux density at each band. Secondly, based on the relation, ( = 4.8 GHz, 8 GHz and 14.5 GHz), we obtain and use linear fit to calculate the spectral indices ().
There are 8 blazars (0235+164, 0430+052, 1156+295, 3C345, 1308+326, 1413+135, 3C454.3, and 1749+096) with known quasiperiodicity and enough values (N50). We use them to build a sample, for which, the spectral indices () and the averaged flux densities within the respective bins are displayed in Figure 1. The upper panels show the averaged flux densities at 4.8 GHz, 8 GHz, and 14.5 GHz, and the lower panels show the spectral indices. The detailed descriptions about have been listed in Table 1, where Col. 1: Name, Col. 2: redshift z, Col. 3: Type, ‘F’: FSRQ, ‘BL’: BL object, Col. 4: number of values, Col. 5: (the minimum ), Col. 6: (the maximum ), and Col. 7: (the averaged ).

3. The Relations between the Spectral Indices and Flux Densities
3.1. Methods
For two variable data sets, , , we use the linear fitting to analyze their correlations, , where k is the slope, is the intersection, is the Student’s t probability, and n is the number of points in the data set. The Pearson’s correlation coefficient r is expressed as [32–34]where is the averaged value of and is the averaged value of .
When x, y show elliptical appearance, we use the following elliptic curve to make the elliptic fit: with a, b, c, d, e, f being free parameters and being normalized.
Considering x as and y as , we make the following calculations.
3.2. The Linear Correlations
We use the linear correlations to analyze the relations between and . The results are shown in Figure 2, where the solid lines indicate the fit between and . The slope k, the intersection , the correlation coefficient r, and the chance probability are listed in Table 2, where Col. 1: Name, Col. 2: the slope , Col. 3: the slope error , Col. 4: the intersection , Col. 5: the intersection error , Col. 6: the correlation coefficient , and Col. 7: the chance probability . Among the eight target sources, values are in the range from to , so and show strong anticorrelations.

3.3. The Elliptic Fitting
For every targeted source, based on time sequence, and the variable trend of spectral indices () dependent on flux densities, we can find the elliptic appearances in the distributions, as seen in Figure 3, where the filled circles, crosses, and open circles stand for the different elliptic appearances. For the whole elliptic appearances, the variable trends are anticlockwise. We use the elliptic curve to make fit, and the results are shown in Figure 3 and listed in Table 3.

In Table 3, Col. 1: Name; Col. 2: sequence number of elliptic circle; Col. 3: , beginning time in Julian Date (JD); Col. 4: , end time of elliptic circle in JD; Col. 5: the time span in yr; Col. 611: the coefficients of elliptic fitting.
4. Discussion
Phenomenon that there are cycle structures in blazar variabilities can help us study the theoretical models in both optical and radio light curves. This method has been used in analysis of the quasiperiodic behavior in some blazars [17, 18, 35, 36]. Villata & Raiteri (1999) [37] pointed out that the quasiperiodic behavior of Mkn501 can be associated with the helical structures of the jet. Pyatunina et al. (2006) [38] presented that the activity of blazars can be characterized by two parameters: the time scale for the activity of the central engine, , and the time scale for the evolution of the jet, . They also proposed that determines an ’activity cycle’ for the source. Pyatunina (2007) [17] defined the activity cycles as the time intervals between the successive ’core’ outbursts. They obtained cycle times of more than 14 yr for 1308+326, about 12 yr for 2223052, and 12.4 yr for 2251+158 (3C 454.3). Our circle durations are 6.6 years and 13.78 years for 1308+326, which are consistent with Pyatunina (2007) [17]. For 2251+158, our results show that 8.93 years is shorter than Pyatunina (2007) [17]. Main reason for this should be that we averaged the light curves with the fixed time intervals and then lead to the less observations.
So, studies about the activity cycles are very important for testing the theoretical models, the jet structure, the properties of central engine, and predicting the likely outbursts.
According to the time interval calculated by the elliptic fitting, we can divide the total light curves into different parts, as seen in Figure 4, and then compare the time intervals of elliptic circle with the quasiperiodicities calculated by the averaged light curves and spectral variances.
The most common tool for periodicity analysis of both evenly and unevenly sampled signals is the periodogram method, which is an estimator of the signal energy in the frequency domain [39]. Lomb (1976) [40] introduced a modified form of this method.
We used the Lomb’s periodogram method to calculate the periodicity and used the half width at half maximum (HWHM) to calculate the corresponding error. For every targeted sources, based on the averaged light curves and spectral indices, we calculate the two periodic signals, then place them together, and choose the common signal as the last periodicity, . The period signals are displayed in Figure 5, where the upper panels show the averaged flux density, and the lower panels show the spectral variances. The red noise [41] with the noise levels 80%, 90%, 95%, and 99% is also shown in Figure 5.
For every targeted source, the time interval of elliptic circle (), the periodicities calculated from the averaged light curves, and spectral variances () are listed in Table 4. In order to make comparison, we quoted the quasiperiodicities () calculated by Fan, Liu & Yuan (2007) [14], which were calculated from the whole light curves and listed in Table 4.

We use a linear fitting to compare , , and . The results show that , with r=0.58, p13.3%; , with r = 0.78, p2.1%, as seen in Figure 6, with the black line standing for vs. and the red line standing for vs. . The two fitting results vs. and vs. can be consistent with each other, which show strong correlations between the time internal of elliptic circle and quasiperiodicities not only calculated from the whole light curve , but also calculated from the averaged light curves and spectral variances .
Quasiperiodicity is a hot research topic in radio variability of blazars, but the reason for the periodicity is unclear. There are many models proposed to explain this phenomenon, for example, the binary blackhole model, the thermal instability model, and the perturbation model [14]. Many authors [42–45] apply the model of a moving shock in a relativistic jet with a helical magnetic field to explain the flux density variances. The circles might come from the helical jet produced by the binary black holes.
5. Conclusion
In this paper, we choose eight blazars from the UMRAO data base to calculate the spectral indices. We average the light curves with the fixed interval, calculate the averaged flux densities () and spectral indices (), and then compare the relations between and .
For each source, we obtain strong anticorrelations between and , which can combine the emission properties and radio variabilities.
Based on time sequence, the  distributions showed elliptic appearances and the variable trends are anticlockwise. We use the elliptic curve to make fit, find that the variation directions of elliptic cycle are anticlockwise, and then obtain the cycle durations. Based on the averaged light curves and spectral variances, we calculate the quasiperiodicities and compare them with the cycle durations. The results show that they are consistent with each other, so the elliptic appearances should come from the helical structures of jets or the jet models containing at least two emitting regions.
Data Availability
The text formatting data used to support the findings of this study come from the University of Michigan Radio Observatory (UMRAO), which are public. The main results have been listed in Tables 1–3, which can be used by others.
Conflicts of Interest
The author declares that they have no conflicts of interest.
Acknowledgments
The work is partially supported by the National Natural Science Foundation of China (NSFC U1831119, NSFC 11733001, NSFC U1531245, NSFC 10633010, NSFC 11173009, and NSFC U1431112), Natural Science Foundation of Guangdong Province (2017A030313011), supports for Astrophysics Key Subjects of Guangdong Province and Guangzhou City, and Science and Technology Program of Guangzhou (201707010401). This research has made use of data from the University of Michigan Radio Astronomy Observatory which has been supported by the University of Michigan and the National Science Foundation.
References
 C. M. Urry and P. Padovani, “Unified schemes for radioloud active galactic nuclei,” Publications of the Astronomical Society of the Pacific, vol. 107, p. 803, 1995. View at: Google Scholar
 U. MarieHelene, L. Maraschi, and C. M. Urry, “Variability of active galactic nuclei,” Annual Review of Astronomy and Astrophysics, vol. 35, pp. 445–502, 1997. View at: Google Scholar
 M. Stickel, J. W. Fried, and H. Kuhr, “The complete sample of 1 Jy BL Lac objects. II  Observational data,” Astronomy and Astrophysics Supplement Series, vol. 98, pp. 393–442, 1993. View at: Google Scholar
 G. Ghisellini, F. Tavecchio, L. Foschini, and G. Ghirlanda, “The transition between BL Lac objects and flat spectrum radio quasars,” Monthly Notices of the Royal Astronomical Society, vol. 414, no. 3, pp. 2674–2689, 2011. View at: Publisher Site  Google Scholar
 T. Sbarrato, G. Ghisellini, L. Maraschi, and M. Colpi, “The relation between broad lines and γray luminosities in Fermi blazars,” Monthly Notices of the Royal Astronomical Society, vol. 421, no. 2, pp. 1764–1778, 2012. View at: Publisher Site  Google Scholar
 G. Ghisellini and F. Tavecchio, “Fermi/LAT broad emission line blazars,” Monthly Notices of the Royal Astronomical Society, vol. 448, pp. 1060–1077, 2015. View at: Google Scholar
 W. E. Kunkel, “A harmonic analysis of the light variation of 3C273,” The Astronomical Journal, vol. 72, p. 1341, 1967. View at: Publisher Site  Google Scholar
 A. Sillanpaa, S. Haarala, M. J. Valtonen, B. Sundelius, and G. G. Byrd, “OJ 287  Binary pair of supermassive black holes,” The Astrophysical Journal, vol. 325, pp. 628–634, 1988. View at: Publisher Site  Google Scholar
 V. E. Chertoprud, L. I. Gudzenko, and L. M. Ozernoy, “ON the character of optical variability of the quasar 3C 273,” The Astrophysical Journal, vol. 182, p. 53, 1973. View at: Google Scholar
 F. K. Liu, G. Z. Xie, and J. M. Bai, “A historical light curve of ON 231 and its periodic analysis,” Astronomy & Astrophysics, vol. 295, pp. 1–10, 1995. View at: Google Scholar
 J. H. Fan, G. Z. Xie, E. Pecontal, A. Pecontal, and Y. Copin, “Historic light curve and longterm optical variation of BL lacertae 2200+420,” The Astrophysical Journal , vol. 507, no. 1, pp. 173–178, 1998. View at: Publisher Site  Google Scholar
 J. H. Fan and R. G. Lin, “The variability analysis of PKS 2155304,” Astronomy & Astrophysics, vol. 355, pp. 880–884, 2000. View at: Google Scholar
 G. Z. Xie, S. B. Zhou, B. Z. Dai et al., “Photometric monitoring of 12 BL Lacertae objects,” Monthly Notices of the Royal Astronomical Society, vol. 329, no. 4, pp. 689–699, 2002. View at: Publisher Site  Google Scholar
 J. H. Fan, Y. Liu, and Y. H. Yuan, “Radio variability properties for radio sources,” Astronomy & Astrophysics, vol. 462, pp. 547–552, 2007. View at: Google Scholar
 J. H. Fan, O. Kurtanidze, Y. Liu, G. M. Richter, R. Chanishvili, and Y. H. Yuan, “Optical monitoring of two brightest nearby quasars, PHL 1811 and 3C 273,” The Astrophysical Journal Supplement Series, vol. 213, no. 2, p. 26, 2014. View at: Google Scholar
 Y. YuHai, Junhui; F., T. Jun et al., “Optical monitoring of BL Lac object S5 0716+714 and FSRQ 3C 273 from 2000 to 2014,” Astronomy & Astrophysics, vol. 605A, p. 43, 2017. View at: Google Scholar
 T. B. Pyatunina, N. A. Kudryavtseva, D. C. Gabuzda et al., “Frequencydependent time delays for strong outbursts in selected blazars from the Metsähovi and UMRAO monitoring data bases – II,” Monthly Notices of the Royal Astronomical Society, vol. 381, no. 2, pp. 797–808, 2007. View at: Publisher Site  Google Scholar
 C. M. Raiteri, M. Villata, H. D. Aller et al., “Optical and radio variability of the BL Lacertae object AO 0235+16: A possible 56 year periodicity,” Astronomy & Astrophysics, vol. 377, pp. 396–412, 2001. View at: Google Scholar
 R. A. Edelson, J. H. Krolik, and G. F. Pike, “Broadband properties of the CfA Seyfert galaxies. III  Ultraviolet variability,” The Astrophysical Journal, vol. 359, pp. 86–97, 1990. View at: Google Scholar
 D. Trevese and F. Vagnetti, “Quasar spectral slope variability in the optical band,” The Astrophysical Journal, vol. 564, pp. 624–630, 2002. View at: Google Scholar
 F. Vagnetti, D. Trevese, and R. Nesci, “Spectral slope variability of BL Lacertae objects in the optical band,” The Astrophysical Journal, vol. 590, no. 1 I, pp. 123–127, 2003. View at: Publisher Site  Google Scholar
 A. Wierzcholska, M. Ostrowski, A. Stawarz, S. Wagner, and M. Hauser, “Longterm optical monitoring of bright BL Lacertae objects with ATOM: Spectral variability and multiwavelength correlations,” Astronomy & Astrophysics, vol. 573, p. 69, 2015. View at: Google Scholar
 M. I. Carnerero, C. M. Raiteri, M. Villata, J. Acosta Pulido, P. Smith, and V. Larionov, “Dissecting the longterm emission behaviour of the BL Lac object Mrk 421,” Monthly Notices of the Royal Astronomical Society, vol. 472, pp. 3789–3804, 2017. View at: Publisher Site  Google Scholar
 J. C. Isler, C. M. Urry, P. Coppi et al., “A consolidated framework of the color variability in blazars: longterm optical/nearinfrared observations of 3C 279,” The Astrophysical Journal, vol. 844, no. 2, p. 107, 2017. View at: Publisher Site  Google Scholar
 J. A. Stevens and W. K. Gear, “Variations in the broadband spectra of BL Lac objects: millimetre observations of an Xrayselected sample,” Monthly Notices of the Royal Astronomical Society, vol. 307, pp. 403–412, 1999. View at: Google Scholar
 D. Trevese, R. D. Kron, and A. Bunone, “Continuum variability of active galactic nuclei in the opticalultraviolet range,” The Astrophysical Journal, vol. 551, pp. 103–110, 2001. View at: Google Scholar
 B. Z. Dai, X. H. Li, Z. M. Liu et al., “The longterm multiband optical observations and colour index for the quasar 3C 273,” Monthly Notices of the Royal Astronomical Society, vol. 392, no. 3, pp. 1181–1192, 2009. View at: Publisher Site  Google Scholar
 P. P. B. Beaklini and Z. Abraham, “3C 273 variability at 7 mm: evidence of shocks and precession in the jet,” Monthly Notices of the Royal Astronomical Society, vol. 437, pp. 489–496, 2014. View at: Google Scholar
 Y. H. Yuan and J. H. Fan, “The relationship between the spectrum and flux density of 3C273 and 3C446,” Astrophysics and Space Science, vol. 357, p. 123, 2015. View at: Google Scholar
 Y. H. Yuan and J. H. Fan, “Long term periodicity analysis of the spectral index of 2251+158,” Research in Astronomy and Astrophysics, vol. 11, pp. 286–292, 2011. View at: Google Scholar
 Y. Yuan, “Relation between radio polarization and spectral index of blazars,” International Journal of Astronomy and Astrophysics, vol. 35, no. 3, pp. 417–421, 2014. View at: Publisher Site  Google Scholar
 W. H. Press, S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery, Numerical Recipes in Fortran, The art of Scientific Computing, Cambridge University Press, 2nd edition, 1994. View at: MathSciNet
 V. Pavlidou, J. L. Richards, W. MaxMoerbeck et al., “Assessing the significance of apparent correlations between radio and gammaray blazar fluxes,” The Astrophysical Journal, vol. 751, no. 2, p. 149, 2012. View at: Publisher Site  Google Scholar
 J. Fan, J. H. Yang, J. Zhang et al., “Beaming effect in fermi blazars,” Publications of the Astronomical Society of Japan, vol. 65, no. 2, p. 25, 2013. View at: Publisher Site  Google Scholar
 M. R. Kidger, “The 11 year period in OJ 287 revisited: is it a true longenduring period?” The Astronomical Journal, vol. 119, no. 5, pp. 2053–2059, 2000. View at: Publisher Site  Google Scholar
 T. Pursimo, L. O. Takalo, A. Sillanpaa et al., “Intensive monitoring of OJ 287,” Astronomy and Astrophysics Supplement Series, vol. 146, pp. 141–155, 2000. View at: Google Scholar
 M. Villata and C. M. Raiteri, “Helical jets in blazars,” Astronomy & Astrophysics, vol. 347, pp. 30–36, 1999. View at: Google Scholar
 T. B. Pyatunina, N. A. Kudryavtseva, D. C. Gabuzda et al., “Frequencydependent timedelays for strong outbursts in selected blazars from the Metsähovi and the University of Michigan Radio Astronomy Observatory monitoring databases – I,” Monthly Notices of the Royal Astronomical Society, vol. 373, no. 4, pp. 1470–1482, 2006. View at: Publisher Site  Google Scholar
 T. J. Deeming, “Fourier analysis with unequallyspaced data,” Astrophysics and Space Science, vol. 36, pp. 137–156, 1975. View at: Google Scholar
 N. R. Lomb, “Leastsquares frequency analysis of unequally spaced data,” Astrophysics and Space Science, vol. 39, no. 2, pp. 447–462, 1976. View at: Publisher Site  Google Scholar
 M. Schulz and M. Mudelsee, “REDFIT: Estimating rednoise spectra directly from unevenly spaced paleoclimatic time series,” Computers & Geosciences, vol. 28, no. 3, pp. 421–426, 2002. View at: Publisher Site  Google Scholar
 W. Webb, M. Malkan, G. Schmidt, and C. Impey, “The wavelength dependence of polarization of active galaxies and quasars,” The Astrophysical Journal, vol. 419, p. 494, 1993. View at: Publisher Site  Google Scholar
 K. S. Cheng, J. H. Fan, and L. Zhang, “Basic properties of gammaray loud blazars,” Astronomy & Astrophysics, vol. 352, pp. 32–38, 1999. View at: Google Scholar
 N. Kawakatu, M. Imanishi, and T. Nagao, “Anticorrelation between the mass of a supermassive black hole and the mass accretion rate in type 1 ultraluminous infrared galaxies and nearby QSOs,” The Astrophysical Journal , vol. 661, no. 2 I, pp. 660–671, 2007. View at: Publisher Site  Google Scholar
 M. F. Gu, Z. Y. Chen, and X. W. Cao, “MBHσ relation in Sloan Digital Sky Survey flatspectrum radio quasars,” Monthly Notices of the Royal Astronomical Society, vol. 397, pp. 1705–1710, 2009. View at: Google Scholar
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Copyright © 2019 Yuhai Yuan. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.