X-ray Luminosity Functions

Updated 12 November 2025

XLFs are mathematical functions that describe the distribution of X-ray luminosities in populations—from binaries to AGNs—capturing key physical processes and formation histories.
They are constructed using rigorous statistical methods, completeness corrections, and forward modeling to account for survey sensitivity variations and background subtraction.
XLFs inform scaling relations and evolutionary models that predict cosmic X-ray backgrounds, source counts, and the interplay between host galaxy properties and X-ray emissions.

X-ray luminosity functions (XLFs) quantify the distribution of X-ray luminosities within populations of astrophysical sources—ranging from X-ray binaries (XRBs) in galaxies to active galactic nuclei (AGN) and galaxy clusters. The XLF, typically expressed as either a differential (dN/dL) or cumulative (N(>L)) function, encodes fundamental information about formation histories, physical processes (e.g., binary evolution, accretion physics), and the scaling relations that link X-ray emission to host galaxy properties or cosmic structure. XLFs are central to empirical population studies, theoretical modeling, and the calibration of predictive frameworks for the X-ray emission of galaxies and large-scale structure.

1. Mathematical Definitions and Canonical Forms

The XLF formalism is nearly universal. For a population of X-ray sources, the differential luminosity function is: $\phi(L,z) = \frac{dN}{dL\,dV}(L,z)$ where $L$ is X-ray luminosity and $z$ is redshift. For comparisons across redshift or between systems, the commonly adopted forms include:

Single power-law:

$\phi(L) = K\,L^{-\alpha}$

Broken power-law:

$\phi(L) = \begin{cases} K_1\,L^{-\alpha_1} & L < L_b \ K_2\,L^{-\alpha_2} & L > L_b \end{cases}$

where $L_b$ is the break luminosity.

Schechter function (galaxy clusters, AGN populations):

$\phi(L) = \phi^*\,\left(\frac{L}{L^*}\right)^{-\alpha}\exp\left(-\frac{L}{L^*}\right)$

Double power-law (AGN XLF, e.g., LDDE/PLE evolution):

$\phi(L,z) = \frac{A(z)}{\left[\left(\frac{L}{L^*(z)}\right)^{\gamma_1(z)} + \left(\frac{L}{L^*(z)}\right)^{\gamma_2(z)}\right]}$

where all four shape parameters may evolve with $z$ (Aird et al., 2015, Ueda et al., 2014, Alqasim et al., 2022).

These analytic forms are chosen to reflect physical breakpoints (e.g., Eddington limits, transitions between neutron-star and black-hole binaries) and can be extended with exponential cutoffs or higher-order terms capturing spatial, age, or metallicity variations (Lehmer et al., 25 Oct 2024, Wang et al., 2016).

2. Construction, Calibration, and Completeness Corrections

Empirical XLF construction requires careful survey selection, sensitivity modeling, and background subtraction to account for both incompleteness and contamination. In extragalactic deep fields and large-area surveys:

Completeness is modeled as a function of sky position and flux threshold, yielding a sensitivity map $\Omega(S)$ —the area over which a source of flux $S$ is detectable (Huang et al., 8 Nov 2025).
For a survey with variable sensitivity, the binned XLF in bin ( $L_i, z_j$ ) is estimated via the $1/V_\mathrm{max}$ or Page-Carrera methods (Alqasim et al., 2022, Radzom et al., 2022, Binder et al., 2012):

$\phi_\mathrm{bin}(L_i, z_j) = \frac{N_{ij}}{\int \frac{dV}{dz}\, dz\, d\log L}$

where integration bounds are set by effective survey limits.

Forward modeling convolves intrinsic model components (AGNs, LMXBs, HMXBs) with their selection functions (stellar mass, SFR, sky coverage), and fits to observed $dN/dS$ via maximum likelihood or C-statistic minimization (Huang et al., 8 Nov 2025, Lehmer et al., 25 Oct 2024).
Background subtraction uses control regions (e.g., galaxy halo or off-disk fields) assumed to contain only uniform AGN+star populations (Huang et al., 8 Nov 2025), or explicit CXB contributions (Lehmer et al., 2019).

Systematic uncertainties in completeness, identification, and selection functions are rigorously quantified using Monte Carlo simulations, marginalization over model uncertainties, and error propagation in volume and luminosity calculations (Koens et al., 2012).

3. Physical Components, Population Decomposition, and Scaling Relations

The population composition of an XLF model is tightly linked to scaling with physical drivers:

X-ray binaries:
- LMXB XLFs scale with enclosed stellar mass $M_*$ and are parameterized as broken power-laws, reflecting donor-type transitions and globular cluster contributions (Wang et al., 2016, Huang et al., 8 Nov 2025, Lehmer et al., 2019, Binder et al., 2012).
- HMXB XLFs scale with star formation rate (SFR), typically characterized by a power-law slope $\gamma\simeq1.6-1.7$ with a high-L cutoff (Sazonov et al., 2016, Lehmer et al., 2019).
- Population separation is increasingly realized via direct donor mass classification using optical data, yielding distinct XLFs for LMXB, IMXB, and HMXB subpopulations (Hunt et al., 2021).
Active galactic nuclei (AGN):
- AGN XLFs employ double power-law or Schechter functions, with parameters evolving with $z$ according to LDDE, PLE, or hybrid models (Ueda et al., 2014, Aird et al., 2015, Alqasim et al., 2022, Barlow-Hall et al., 2022).
- Absorbed and unabsorbed AGNs require separate XLFs; the absorbed (high- $N_H$ ) population dominates at low $L$ and exhibits a steeper faint-end, higher normalization, and lower break luminosity (Aird et al., 2015).
- Type-1 vs. Type-2 XLFs illuminate obscuration effects and the "Steffen effect"—the increasing dominance of Type-1 AGNs at high luminosity (Pović et al., 2013, Radzom et al., 2022).
Galaxy clusters:
- Cluster XLFs, modeled as evolving Schechter functions, trace the hierarchical assembly and test cosmic structure formation (Koens et al., 2012).
- Negative evolution—declining number density of high-L_X clusters with $z$ —confirms $\Lambda$ CDM predictions.
Low-luminosity Galactic sources:
- ASBs and CVs individually contribute resolved fractions of the Galactic ridge X-ray emission, with new XLFs showing increased normalization and extending to lower luminosities (Warwick, 2014). Their aggregate volume emissivity can account for $\sim$ 80–90% of the GRXE.

4. Spatial, Metallicty, and Evolutionary Effects

XLF shapes, breaks, and normalizations are modulated by spatial position, stellar population age, and metallicity:

Disk vs. halo regions of galaxies exhibit distinct XLFs; in M31, LMXB normalization per $M_*$ ( $\alpha_\mathrm{LMXB}$ ) and XLF break parameters vary across disk, inner arm, ring, and inter-arm regions. Outer disks show a larger fraction of luminous LMXBs: $S_b$ increases and $\beta_2$ flattens outward (Huang et al., 8 Nov 2025).
Metallicity dependence is encapsulated by the empirical framework of (Lehmer et al., 25 Oct 2024): the XLF normalization per mass $A(t, Z)$ declines by 2–3 dex from $10\,\mathrm{Myr}$ to $10\,\mathrm{Gyr}$ , slower at low $Z$ . Bright-end ( $\alpha_3$ ) slopes steepen with both age and metallicity—driven by mass-loss and binary disruption physics.
Age effects: intermediate-age LMXB populations exhibit enhanced luminous XRBs, flattening the bright-end slope ( $\alpha_2$ ) above the canonical break in empirical and synthesis models (Huang et al., 8 Nov 2025, Lehmer et al., 2019).
Globular cluster-specific frequency can boost LMXB numbers by $2-3\times$ in individual galaxies relative to global scaling (Lehmer et al., 2019).

5. Evolutionary Implications and Theoretical Constraints

XLFs are powerful diagnostics of binary evolution, supernova physics, accretion processes, and cosmic SMBH growth:

Normal galaxies: population synthesis models, calibrated on cosmological simulations and galaxy metallicity/SFH tracks, show that the integrated X-ray luminosity density $\rho_X(z)$ peaks near $z\sim2.5$ , tracing the global SFR (Tremmel et al., 2012). Deficits in predicted bright early-type and very bright late-type systems point to missing GC-LMXB and starburst contributions.
High-mass XRBs: synthetic XLFs generated by detailed population synthesis codes (e.g., POSYDON) exhibit intrinsic breaks at $L_b\sim10^{38}$ erg s $^{-1}$ —tracking Eddington limits—and their amplitudes are sensitive to black-hole kick prescriptions, circularization physics at Roche-lobe overflow, and wind-fed disk formation criteria (Misra et al., 2022). Overabundance of bright RLO-BH systems in models can be suppressed by adopting fall-back moderated kicks and physical disk formation criteria.
AGN XLF evolution: parameter-rich models (FDPL, LDDE) find the break luminosity increases with $z$ , a signature of "cosmic downsizing"—the space density of highly-luminous AGNs peaks at $z\sim2$ , with lower-luminosity AGNs peaking later (Alqasim et al., 2022, Ueda et al., 2014, Barlow-Hall et al., 2022). The absorbed fraction $f_\mathrm{abs}(L,z)$ decreases with luminosity and shifts upward with redshift, reflecting the evolving transition between absorbed and unabsorbed populations (Aird et al., 2015).
Cluster XLFs: maximum-likelihood and Bayesian fitting in surveys such as WARPS quantifies negative evolution (A, B < 0) in Schechter parameters for clusters above $L_X \sim 5 \times 10^{43}$ erg s $^{-1}$ , supporting hierarchical structural assembly (Koens et al., 2012).

6. Numerical Results and Benchmark Parameters

Selected normalization, slope, break, and scaling relations from recent, large-sample XLF determinations:

Population	Functional Form / Band	Normalization (K)	Slope(s) (α)	Break / Cutoff	Scaling
LMXBs (global)	Broken PL (0.5–8keV)	33.8–7.3 / $10^{11}\,M_\odot$	α₁=1.28/α₂=2.33	$L_b=1.48–0.7 \times10^{38}$ erg s $^{-1}$	$L_X/M_*$
HMXBs (global)	PL + cutoff (0.5–8keV)	1.96–0.14 / SFR (M $_\odot$ yr $^{-1}$ )	γ=1.65	$L_c=10^{40.7}$ erg s $^{-1}$	$L_X/$ SFR
AGN (FDPL)	Double PL (2–10keV)	$A(z)=10^{-5.13+4.73\zeta-7.10\zeta^2}$	γ₁=0.67 at $z=0$	$L_*(z)=10^{43.5+...}$	—
M31 LMXBs	Broken PL (2–4.5keV)	Disk: 2.4, Center: 9.3, ... (per $10^{10}\,M_{\odot}$ )	β₁=1.17, β₂=2.8–6	$S_b$ varies by region	$L_X/M_*$
Milky Way HMXBs	PL (2–10keV)	54–8	α=0.48–0.19	L $_\mathrm{max}$ snapshot	—

All units and errors as quoted in the respective summary datasets.

7. Interpretation, Utility, and Emerging Extensions

XLFs underpin the interpretation of the cosmic X-ray background, resolve the contributions of different binary and AGN populations, and inform the expected source counts, surface brightness, and luminosity scaling relations across spatial, temporal, and physical parameter space. Integrations over the XLF yield scaling laws such as

$L_X = \alpha_\mathrm{LMXB} M_* + \beta_\mathrm{HMXB}\mathrm{SFR}$

with empirical values of $\log \alpha_\mathrm{LMXB} \sim 29.96$ erg s $^{-1}$ M $_\odot^{-1}$ and $\log \beta_\mathrm{HMXB} \sim 39.30$ erg s $^{-1}$ (M $_\odot$ yr $^{-1}$ ) $^{-1}$ (Lehmer et al., 25 Oct 2024). Physical modeling continues to explore the inclusion of metallicity, star-formation history, dynamical formation channels, and fine-grained population synthesis, moving toward fully predictive frameworks calibrated by empirical XLFs (Lehmer et al., 25 Oct 2024, Lehmer et al., 2019).

Future wide-area, high-resolution surveys will enable refined decomposition and greater dynamical range in XLF measurements, yielding increased sensitivity to physical processes such as rapid fading in intermediate-age LMXB populations (Huang et al., 8 Nov 2025), metallicity-driven HMXB enhancements, and the coevolution of host galaxies and black holes.

This overview synthesizes the state-of-the-art in XLF methodology, parameterization, empirical calibration, and theoretical significance, drawing on large survey analyses, empirical frameworks, and population synthesis studies. The XLF is a keystone for understanding the demographics, scaling relations, and evolutionary history of X-ray emitting source populations in the universe.